The COVID-19 global pandemic emerged in the 2019 causing so far a massive global health disruption with fatalities and huge economy impact, enforcing most if not governments to a global lockdown. Besides the battle on the front-line, governments and the industry also massively the deployment of information and communication to track and curb the spread of the virus. On front-line of these efforts, have been the so-called Mobile Tracing Applications (MCTA). These refer to mobile apps exploit the rich ecosystem of mobile sensors (e.g., location, as well as social networks to facilitate the process identification of persons who may have been previously into with a covid infected person and subsequent collection of information about these contacts. Although MCTA can theory help governments fight the rapid spread of diseases COVID-19, there are important privacy considerations and claim that these technologies will put in place a massive surveillance infrastructure that will survive even when vaccine for the COVID-19 disease has been found. This panel to discuss the major challenges and open topics surrounding The panelists are expected to bring wealth of experience vision from the academic governmental and industrial sector answer a set of challenging questions that are currently open public debate as well as the global benefits one can expect when fighting the COVID-19 spread.
The goal of this session is to discuss the ongoing climate crisis and the role of EBDT/ICDT in this crisis. It will consist of a short introductory presentation by the chairs followed by an open discussion about the carbon footprint of the EBDT/ICDT conference, and the research opportunities that can help address the crisis.
Big data architectures have transformed the way enterprises collect, store and analyze massive amounts of spatio-temporal data from the mobile workforce. In this talk, I will present operators and systems for querying and managing such Mobile Big Data (MBD), that address challenges of performance, privacy, utility and network efficiency. I will start out by overviewing Anyplace, our open-source Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in prefetching and privacy-aware data processing of Indoor MBD (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit, which is an award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to its k Nearest Neighbors (kNN) as they move in space. I will particularly focus on Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing operator to Rayzit. I will then overview Spate, which is a novel spatio-temporal data processing and analytic system for telecommunication data (e.g., CDR and MR), which makes compression and decaying a first-class citizen. Particularly, I will overview a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Finally, I will be concluding with an outlook to our current and future research agenda.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. In this talk, I will present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work. More: https://anyplace.cs.ucy.ac.cy.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. In this talk, I will present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
A Telecommunication company (Telco) is tradition- ally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. The ability to perform analytics on the generated big data within a tolerable elapsed time and share it with key Smart City Enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future Smart Cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce, mobility patterns and emergency response. The ability to perform analytics on the generated big data within a tolerable elapsed time and share it with key Smart City Enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future Smart Cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.
Geographic Information Systems (GIS) have enabled a vast range of applications in outdoor spaces, but these systems are bound to accurate localization technologies that are not available inside buildings where people carry 90% of their activities. Additionally, GIS don 't address the unique characteristics of complex indoor environments off-the-shelf. At the same time, we witness the uptake of a new class of Indoor Information Systems (IIS), which store indoor spatial models along with sensor signals (e.g., wireless, light and magnetic) used to localize users. Such IIS might be considered as specialized GIS applications that are tailored to the unique challenges pertinent to indoor spaces, namely new indoor data management operators, new indexes, new data privacy schemes, built-in data-driven localization algorithms, models to crowdsource IIS data and these might even use NoSQL architectures. This panel will explore how the academia and industry are tackling the future challenges that rise in the scope of IIS. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.
Big data architectures have transformed the way enterprises collect, store and analyze massive amounts of spatio-temporal data from the mobile workforce. In this talk, I will present operators and systems for querying and managing such Mobile Big Data (MBD), that address challenges of performance, privacy, utility and network efficiency. I will start out by overviewing Anyplace, our Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in prefetching and privacy-aware data processing of Indoor MBD (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit, which is an award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to its k Nearest Neighbors (kNN) as they move in space. I will particularly focus on Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing operator to Rayzit. I will then overview Spate, which is a novel spatio-temporal data processing and analytic system for telecommunication data (e.g., CDR and MR), which makes compression and decaying a first-class citizen. Finally, I will be concluding with an outlook to our current and future research agenda.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
The pervasiveness of smartphones is leading to the uptake of a new class of Internet-based Indoor Navigation (IIN) services, which might soon diminish the need of Satellite-based localization technologies in urban environments. These services rely on geo-location databases that store spatial models along with wireless, light and magnetic signals used to localize users and provide better power efficiency and wider coverage than predominant approaches. In this talk I will overview the research behind the building blocks of the Anyplace IIN, an open, modular, extensible and scalable navigation architecture that exploits crowdsourced Wi-Fi data to develop a novel navigation service that won several international research awards for its utility and accuracy (i.e., less than 2 meters). Our MIT-licenced open-source software stack has to this date been used by hundreds of researchers and practitioners around the globe, with the public Anyplace service reaching over 80,000 real user interactions. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will conclude with a summary of other recent research work.
A wide spectrum of Internet-scale mobile applications, ranging from social networking, gaming and entertainment to emergency response and crisis management, all require efficient and scalable All k Nearest Neighbor (AkNN) computations over millions of moving objects every few seconds to be operational. Most traditional techniques for computing AkNN queries are centralized, lacking both scalability and efficiency. Only recently, distributed techniques for shared-nothing cloud infrastructures have been proposed to achieve scalability for large datasets. These batch-oriented algorithms are sub-optimal due to inefficient data space partitioning and data replication among processing units. In this talk I will present Spitfire, a distributed algorithm that provides a scalable and high performance AkNN processing framework. The proposed algorithm deploys a fast load-balanced partitioning scheme along with an efficient replication-set selection algorithm, to provide fast main-memory computations of the exact AkNN results in a batch-oriented manner. I will also overview Rayzit, an experimental and open-source mobile AkNN service we established and operate that reached over 45,000 real user interactions to this date.
The advances of Internet-of-Things (IoT) technology in recent years is leading to the uptake of a new class of Internet-based navigation services, which might soon diminish the need of Satellite-based technologies in urban environments. These services rely on geolocation databases that store spatial models along with wireless, light and magnetic signals used to localize users. Developing IoT-based navigation services creates a new spectrum of information management challenges ranging from crowdsourcing indoor models, acquiring and fusing big-data velocity signals, localization algorithms, location privacy of custodians and others. In this talk, I will overview the current landscape of academic and industrial such services using a multi-dimensional taxonomy of emerging topics in this domain, including location, crowdsourcing, privacy and modeling. I present the dimensions of our taxonomy through the lens of an open, modular, extensible and scalable navigation architecture, coined Anyplace, concluding with open challenges.
Spatial big data architectures have transformed the way enterprises collect, store and analyze massive amounts of velocity data that features a spatial extend. In this talk, I will summarize an array of research problems and applications that our laboratory has investigated or aims to investigate in this scope. I will start out by overviewing Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house Internet-based Indoor Navigation service that won several international research awards for its accuracy and utility. Particularly, I will discuss the challenges in processing and visualizing indoor big data (e.g., Wi-Fi and magnetic signals). My talk will be succeeded by a summary of Rayzit (https://rayzit.com/), which is our award-winning location-based crowd messaging service that allows a mobile crowd to instantly connect to their k Nearest Neighbors (kNN) as they move in space. I will particularly summarize Spitfire, which is a distributed algorithm that provides a scalable and high-performance All k Nearest Neighbor processing framework to Rayzit. I will then overview Spate, which is a novel spatio-temporal time machine for telecommunication data (e.g., CDR, NMS, PCHR). Spate supports a range of visual analytic cues (heatmaps, POI clusters, etc.) that enables a Telco operator to quickly compare abstract models to real velocity data but also to monitor and replay network traces using a declarative SQL language that breaks down to SPARK jobs. I will finally also introduce GreenCharge, which is a big data GIS architecture to guide electric vehicles to green energy excess. GreenCharge is expected to maximize self-consumption of electricity by prosumers, contributing in that way to the stability of power grids and a sustainable future. GreenCharge is anticipated to be pilot at the University of Cyprus, a self-sufficient 17GWh/annum producer of solar electricity.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks. In the third part of this talk, I will focus on an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc).
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which provide the location of a user upon request using structures called RadioMaps (RMs). In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc).
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks.
Research entails a deep systematic investigation of sources in order to reach new, previously unknown conclusions. In Computer Science, these conclusions are often in the form of algorithmic developments or in the form of software artifacts tackling unconventional, real-life challenges. In both cases, the conclusions have to be publishable at high caliber journals, conferences or competitions, such that the credibility of the developments is externalized and recognized by the wider research community. While the above type of research is a fundamental attribute of postgraduate studies, undergraduate students are often not part of this process for a variety of reasons, including: i) lack of advanced domain knowledge (usually taught at the post-graduate level); ii) lack of mathematical or laboratory skills (e.g., proving a theorem or managing a complex software stack); and iii) lack of writing and presentation skills (i.e., the dissertation is written at the end of their studies). During the last decade, I have applied a wide range of empirical methods to engage undergraduate students in research as soon as they are admitted to the University. Particularly, students have been encouraged through a variety of methods to familiarize themselves with research through our courses and become active contributors to our quest for academic excellence. The applied methods have had some very promising results, particularly: i) several summer research internships since 2012 (for 1st year BSc students); ii) several research interviews available on Youtube since 2014 (for 2nd year BSc students); iii) 5 distinctions in the Intl. ACM SIGMOD programming competition since 2010 (for 3rd year BSc students); iv) 21 journal and conference proceedings since 2007 (for 4th year BSc students); and v) 3 international awards for innovative research systems since 2007 (again for 3rd and 4th year students). In the scope of my case study, I will detail the applied methodology for each different type of activitity and also summarize the lessons learnt. I claim that the provided achievements have helped our undergraduate students to reach their personal goals and our Data Management Systems Laboratory (DMSL) to meet its research agenda. Particularly, the above engagement allowed 6 students to obtain fellowships for graduate studies (e.g., Fulbright, Oxford), 5 students to advance to PhD programs, while all of them have moved on with MSc studies in the US, UK, Canada and Switzerland.
Wi-Fi (or WLAN) based indoor navigation applications mobiles rely on cloud-based services (s) that take of a user’s (u) localization task using structures called (RMs). It is imperative for u to have a stable WiFi in order to either continuously receive location from s or to download RMs a priori for offline navigation. networks however, suffer from intermittent connectivity due poor network planning that results in sparse deployment access points and effectively areas where Wi-Fi coverage be guaranteed. This inherently affects the localization and therefore the navigation experience of users. In paper, we propose an innovative framework for accurate fast indoor localization over an intermittently connected WiFi coined Prefetching Localization (PreLoc). In Preloc, prioritize the download of RM records based on knowledge from historic traces of other users inside the same Instead of downloading the complete RM from s to we propose a Probabilistic Group Selection (PGS) strategy, identifies RM records that have a higher probability of necessary to a user moving inside a target area. We have our framework using a real prototype developed in as well as realistic Wi-Fi traces we collected at the of Cyprus. Our experimental study reveals that PreLoc PGS and conventional fingerprint-based indoor positioning can yield accuracy that is as good as using the same with a complete RM even under scenarios of weak Wi-Fi coverage.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on Internet-based Indoor Navigation (IIN) services, which can provide the location of a user upon request. Unfortunately, those technologies are both inaccurate and additionally raise important location privacy concerns, as the IIN can know where the user is at all times. this talk, I will start out by overviewing the building blocks of Anyplace (https://anyplace.cs.ucy.ac.cy/), our in-house IIN services that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IIN service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will be succeeded by a summary of Rayzit which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
This advanced seminar presents the fundamental mobile data management concepts behind the realization of innovative indoor information services that deal with all aspects of handling indoor data as a valuable resource, including data modeling, data acquisition, query processing, privacy and energy consumption. The goal is to provide an overview of the emerging field of indoor data management with a particular emphasis on mobile systems. We tackle the topic from a wide range of perspectives: fundamentals, definitions, current state, academic & industrial perspective, reality & visionary scenarios as well as future challenges. The seminar captures the big picture, such that interested researchers and practitioners can expand their study by following the references. Our presentation will be carried out through the lens of an experimental Indoor Information System we developed at the University of Cyprus, coined Anyplace, which has obtained three international awards and was ranked the second most accurate indoor localization technology by Microsoft Research at IEEE/ACM IPSN '14.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on cloud-based Indoor Positioning Services (IPS), which can provide the location of a user upon request but those are both inaccurate and additionally raise important location privacy concerns, as the IPS can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace, our in-house IPS that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IPS, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests both of which are shown to be resilient to a variety of privacy attacks.
The explosive number of smartphones with ever growing computing and sensing capabilities have brought a paradigm shift to many traditional domains of the computing field. In this talk, I will present three (3) ongoing testbeds and applications we are developing in-house in the space of smartphone and human-centric systems: In will start out with SmartLab (smartlab.cs.ucy.ac.cy), a Mobile Infrastructure-as-a-Service cloud we have developed and deployed at the University of Cyprus. In SmartLab, an intuitive web-based interface supplies a variety of complex mobile management utilities that provide fine-grained and low-level control over real smartphones, e.g., usage of networking, storage and sensors as well as automated mockup executions. We present our research experiences from using SmartLab in different research settings as well as our envisioned future scenarios for urban-scale deployment, federation issues and security studies. I will continue with Anyplace (anyplace.cs.ucy.ac.cy), our in-house Indoor Positioning Service that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. I will conclude with Rayzit (rayzit.com) which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on cloud-based Indoor Positioning Services (IPS), which can provide the location of a user upon request but those are both inaccurate and additionally raise important location privacy concerns, as the IPS can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace, our in-house IPS that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IPS, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will be succeeded by a summary of related research efforts, namely SmartLab, which is a novel in-house programming cluster of smartphones that we use in our experimental studies; and Rayzit which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.
The explosive number of smartphones with ever growing computing and sensing capabilities have brought a paradigm shift to many traditional domains of the computing field. Smartphone users nowadays gain access to unprecedented possibilities, knowledge and power due to a diverse landscape of applications. Developers on the other hand are challenged with a fragmented mobile landscape that is extremely dynamic to changes in both hardware and software. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this talk, we present the abstractions comprising SmartLab, a Mobile Infrastructure as a Service cloud we have developed and deployed at the University of Cyprus. In SmartLab, an intuitive web-based interface supplies a variety of complex mobile management utilities that provide fine-grained and low-level control over real smartphones, e.g., usage of networking, storage and sensors as well as automated mockup executions. We present our research experiences from using SmartLab in different research settings as well as our envisioned future scenarios for urban-scale deployment, federation issues and security studies. My talk will be succeeded by a summary of related mobile data management research efforts, namely Anyplace, which is our in-house indoor localization and navigation service; and Rayzit which is our award-winning location-based crowd messaging service.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. Multi-sensing capabilities and multi-modal connectivity means of smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. Unfortunately, numerous new challenges ranging from big data stream volume and velocity, to location privacy and energy consumption as well as device diversity issues among other, arise in this new context. In this talk, I will summarize mobile crowdsourcing challenges and applications related to Microblogging Urban Sensing and Indoor Information Systems.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. Multi-sensing capabilities and multi-modal connectivity means of smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. Unfortunately, numerous new challenges ranging from big data stream volume and velocity, to location privacy and energy consumption as well as device diversity issues among other, arise in this new context. this talk, I will start out by a discussion on how primitive crowdsourcing challenges emerge and evolve into urban data collection scenarios. I will present these new challenges through the lens of some in-house systems we’ve developed over the last few years, particularly: i) a smartphone programming cluster coined SmartLab, which provides means for general-purpose urban-scale sensing scenarios and smart cities; ii) an indoor information system coined Anyplace, which addresses big-data, privacy and energy consumption for building WiFi Radiomaps of indoor places; and iii) a location-based crowd messaging service coined Rayzit which addresses big data velocity with parallel algorithms and distributed nosql databases.
One of the popular research trends at present focuses on the use of sensor data generated/collected by consumer mobile devices to infer the ‘urban state’. There are a fairly large number of research initiatives that view such a citizen-centric distributed and mobile sensing platform as one of the most promising ways to gather data about various aspects of cities, such as environmental parameters & pollution levels, traffic congestion, popularity of events at various public spaces, etc. there are many skeptics who doubt that this “decentralized, bottom-up” approach can be an effective & commercially viable approach in the long run, due to open challenges in many aspects, such as resource limitations, privacy data quality and incentives. This panel will explore how academia and industry are tackling these challenges and debate on what types of applications are likely to be sustainable under this crowd-sourced paradigm.
Crowdsourcing refers to a distributed problem-solving model in which a crowd of undefined size is engaged to solve a complex problem through an open call. This novel problem-solving model found its way into numerous applications on the web for voting, fund-raising, micro-works and wisdom-of-the-crowd scenarios. On the other hand, the shift of desktop users to mobile platforms in the post-PC era, along with the unique multi-sensing capabilities of modern mobile devices are expected to eventually unfold the full potential of Crowdsourcing. This is true, as smartphones offer a great platform for extending and diversifying web-based crowdsourcing applications to a larger contributing crowd, making contribution easier and omnipresent. This advanced seminar presents the fundamental concepts behind crowdsourcing and its applications to mobile data management. In the first part of the seminar, we will overview the crowdsourcing landscape from a variety of perspectives, with a particular emphasis on the latest data management trends. In the second and more extended part of the seminar, we will focus on an in-depth coverage of emerging mobile crowdsourcing architectures and systems, through a multi-dimensional taxonomy that will address location, sensing, power, performance, big-data and privacy among others. Furthermore, we will overview a number of in-house crowdsourcing prototypes we have developed and deployed over the last few years. The seminar concludes with challenges opportunities and new directions in the field.
Big data refers to data sets whose size and structure strains the ability of commonly used relational DBMSs to capture, manage, and process the data within a tolerable elapsed time. Big data sizes commonly range from a few dozen terabytes to many petabytes in a single database and their underlying data model might be anything from structured (relational or tabular) to semi-structured (XML or JSON) or even unstructured (Web text and log files). Big data architectures are highly parallel and distributed in order to cope with the inherent I/O and CPU limitations. Such systems typically perform on mid-scale private clouds, offering higher privacy, to large-scale public clouds, both exposing operational and analytic functionality stand-alone or as-a-Service. This talk aims to overview the current big-data management landscape, the underlying technologies and their provenance, the latest NoSQL and NewSQL trends, possible applications of big-data management systems for online and offline processing of sensor data, text data, social data and medical data in enterprise environments. The talk will also overview ongoing big-data research and teaching activities at the University of Cyprus.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. In this talk, I will present a collection of ongoing testbeds and applications we are developing in-house for this new era of ubiquituous smartphone computing. In particular, I will be presenting SmartLab, which an innovative open programming cloud of 40+ Android devices deployed at our Department over the last three years. SmartLab is the first open Smartphone IaaS (Infrastructure-as-a-Service) cloud that enables fine-grained, low-level interactions over static or moving smartphones via an intuitive web-based interface. Such a testbed provides means for general-purpose urban-scale sensing scenarios and personal gadget management. This talk will also briefly cover other smartphone sensing testbeds and applications we 've developed: a localization engine for fine-grained positioning without GPS, a trajectory comparinson framework with privacy gurranttees, a P2P smartphone searching framework and a neighborhood sensing framework.
Smartphones have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of smartphones connected to the Internet are nowadays proposed for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and many other domains, prompting undeniably the post-PC era. In this talk, I will present distributed architectures for querying and managing such sensor data by taking into account energy, data disclosure and networking aspects. I will particularly focus on SmartTrace, a powerful query processing framework for finding similar smartphone trajectories without disclosing the traces of participating users. I will also present SmartLab, a first-of-a-kind programmable cloud of 40+ smartphones deployed at our department enabling a new line of systems-oriented research on smartphones. Finally, I will also overview other related smartphone data management frameworks we 've developed for peer-to-peer search, crowdsourcing and indoor positioning, concluding with an outlook to our future research agenda.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. The uptake of applications in this domain, is currently severely hampered by the fact that these devices have: i) a limited energy budget (i.e., smartphone devices still operate on batteries), ii) limited connectivity (i.e., not all regions offer unlimited Internet connectivity at the same cost); and iii) high privacy constraints (i.e., these devices might reveal the identity and habits of their custodians.) In this talk, I will present a collection of data management techniques that deal with Smartphone Networks. In particular, I will start out with SmartTrace, a powerful framework for finding similar trajectories in a smartphone network without disclosing the traces of the participating users. SmartTrace relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query-processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers quickly and efficiently. I will then introduce SmartOpt, a multi-objective query optimizer that enables efficient content searches in smartphone networks. I will also introduce Proximity, a spatial neighborhood computation framework for smartphone networks. My talk will be succeeded by the presentation of SmartNet, our in-house programming cloud for smartphone networks
Smartphone computational platforms equipped with multitude of sensors and capable of generating vast amounts of data (geo-location, audio, video, etc.) On the other hand, these devices operate on a strict energy budget, thus have a limited lifetime on a single charge. Consequently, we need to identify new energy-aware algorithms and techniques to provide innovative, feature-rich applications and services. In this white paper, we start out by providing recent trends in Smartphone technology and Smartphone networks. Our description is succeeded by an anatomy of the energy costs associated with data processing in a Smartphone Network. We conclude with prominent research directions in energy-aware data management for Smartphone networks.
Smartphone devices have emerged as powerful computational platforms equipped with multitude of sensors that are capable of generating vast amounts of data (geo-location, audio, video, etc.) Collections of such devices connected to the Internet yield Smartphone Networks, which can be utilized for opportunistic and participatory sensing applications in intelligent transportation systems, social networking applications, city planning and others. The uptake of applications in this domain, is currently severely hampered by the fact that these devices have: i) a limited energy budget (i.e., smartphone devices still operate on batteries), ii) limited connectivity (i.e., not all regions offer unlimited Internet connectivity at the same cost); and iii) high privacy constraints (i.e., these devices might reveal the identity and habits of their custodians). In this talk I will present SmartTrace, a powerful framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, coined SmartTrace, quickly answers queries of the form: “Report the users that move more similar to Q, where Q is some query trace.” SmartTrace relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query-processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. We assess our propositions with realistic and real workloads from Microsoft Research Asia and other sources. Our study reveals that SmartTrace computes the desired results with 74% less energy consumption and 13% faster than its centralized and decentralized counterparts. My talk will be succeeded by a summary of related research efforts, namely SmartNet, an innovative programming cloud for smartphone networks; and SmartOpt, a multi-objective query optimizer that enables efficient content searches in smartphone networks.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, sensors are typically organized in structures coined Query Routing Trees (QRTs). Our study reveals that predominant data acquisition systems construct QRTs in ad-hoc manners leading to a significant waste of energy. In this talk I will present MicroPulse+, a framework for minimizing the consumption of energy during data collection in Sensor Networks. MicroPulse+ eliminates a variety of data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse+ introduces: i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data collection activity and on identifying the bottlenecks using an in-network execution of the critical path method; and ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. The talk will conclude with an outlook into current and future research work.
In this talk I will present a family of algorithms for Top-k ranking of query results in a distributed environment. A Top-K query focuses on the subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of Top-K query processing algorithms for centralized and middleware systems. I will then highlight the limitations of these algorithms and focus on three novel algorithms we developed designated for networked environments (i.e., Peer-to-Peer Networks, Wireless Sensor Networks and Smartphone Networks). I will also present evaluation studies of these algorithms on: i) a Wireless Sensor Network testbed of 54 sensor devices; ii) a Peer-to-Peer testbed of 1000 peers deployed on 75 linux workstations; and iii) A smartphone network deployment on Android-based smartphone devices. The talk will conclude with an overview of related research problems that I am currently working on and an outlook to future work.
Panel Chair: Anastasia Ailamaki. Other Panelists: Marios Dikaiakos, Evangelia Pitoura, Peter Triantafillou and Akrivi Vlachou
In this talk I present the fundamental concepts behind distributed Top-K query processing algorithms. A Top-K query focuses on the subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized and middleware systems. I will then highlight the limitations of these algorithms and focus on two novel algorithms we developed designated for networked environments (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will also present evaluation studies conducted on: i) a Peer-to-Peer testbed of 1000 peers deployed on 75 workstations; ii) a Wireless Sensor Network testbed of 54 sensor devices and iii) A Smartphone Network, deployed on a number of Android-based smartphone devices. The talk will conclude with an overview of related research problems that I am currently working on and an outlook to future applications of the presented ideas.
In this presentation I will present a powerful and distributed spatio-temporal query processing framework, coined HUB-K. Our framework can be utilized to promptly answer queries of the form: ' 'Report the objects (i.e., trajectories) that follow a similar spatio-temporal motion to Q, where Q is some query trajectory. ' ' HUB-k, relies on an in-situ data storage model, where spatio-temporal data remains on the smartphone that generated the given data, as well a state-of-the-art top-k query processing algorithms, which exploit distributed trajectory similarity measures in order to identify the correct answers promptly. We present preliminary design choices, an outline of our preliminary implementation and an outlook to future challenges.
The widespread deployment of mobile phones along with the massive production of sensors for every aspect of modern life provides evidence that Computer Science research and education will evolve dramatically over the next few years. The boundaries of Mobile Devices and Sensor Devices are nowadays blurring as the former devices are already equipped with a multitude of sensing capabilities, including GPS (which enables the derivation of geospatial coordinates), accelerometers (which enable the derivation of orientation, vibration and shock) and an exciting set of other sensors (e.g., proximity sensors, ambient light sensors, while more traditional sensors such as temperature, acoustic, magnetometers and others will be integrated in these devices very soon). That creates the notion of Mobile Sensor Devices that will become even more ubiquitous than their predecessor 'smart-phone ' devices. In this talk, I will provide an overview and definitions of Mobile-Sensor-Network (MSN) related platforms and applications. In particular, I will show how applications in environmental monitoring, body sensor networks, vehicular sensor networks and intelligent transportation systems have brought a dramatic shift on how spatio-temporal data is nowadays generated. I will then outline some semantic challenges that arise in this context including: vastness, uncertainty, data integration, query processing and privacy. I will also address some more general challenges that currently hinder the evolution and uptake of semantic MSNs.
In this talk I will present the fundamental concepts of distributed Top-K query processing algorithms. A Top-K query focuses on a subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized DBMS systems. I will then highlight the limitations of these algorithms and focus on the Threshold Join Algorithm (TJA), our distributed top-k query processing algorithm designated for distributed computing networks (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will finally present an evaluation study conducted with our middleware system deployed over a network of 1000 peers on 75 workstations.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will present MicroHash, an external memory index structure that is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. MicroHash exploits the asymmetric read/write characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of energy and storage media lifetime constraints.
In this talk I will present the fundamental concepts of distributed Top-K query processing algorithms. A Top-K query focuses on a subset of most relevant answers for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the quality of the answer set such that the user is not overwhelmed with irrelevant results. I will start out by providing an overview of state-of-the-art Top-K query processing algorithms for centralized DBMS systems. I will then highlight the limitations of these algorithms and focus on the Threshold Join Algorithm (TJA), our distributed top-k query processing algorithm designated for distributed computing networks (i.e., Wireless Sensor Networks, Peer-to-Peer Networks, Vehicular Networks, etc.) I will finally present an evaluation study conducted with our middleware system deployed over a network of 1000 peers on 75 workstations.
Wireless Sensor Networks create an innovative technology that enables users to monitor and study the physical world at an extremely high resolution. Query processing in such ad-hoc environments is a challenging task due to the complexities imposed by the inherent energy and communication constraints. To this end, the research community has proposed to take into account user-defined parameters in order to derive the K most relevant (or Top-K) answers quickly and efficiently. A Top-K query returns the subset of most relevant answers, in place of all answers, for two reasons: i) to minimize the cost metric that is associated with the retrieval of all answers; and ii) to improve the recall and the precision of the answer set, such that the user is not overwhelmed with irrelevant results. This tutorial presents the fundamental concepts behind distributed Top-K query processing and the adaptations of these algorithms to distributed and wireless sensor networks. It additionally provides a gentle overview of rudimentary and advanced techniques covering a significant body of research in this domain. The tutorial will start out with an overview of the most influential centralized and middleware Top-K query processing algorithms and then proceed with an elaborate description of distributed Top-K ranking algorithms for One-time Top-K Queries, Continuous Top-K Queries and Approximate Top-K Queries. Finally, it will provide an outlook to compelling future applications that can be constructed on the foundation of these algorithms. Although the tutorial is specifically geared towards Wireless Sensor Networks, many of the presented ideas find extensions in other mobile environments such as Adhoc Networks, Vehicular Networks and the Mobile Web.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will present MicroHash, an external memory index structure that is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. MicroHash exploits the asymmetric read/write characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of energy and storage media lifetime constraints.
The emerging Peer-to-Peer (P2P) model has become a very powerful and attractive paradigm for developing Internet-scale services for sharing resources, including files and documents. The distributed nature of these systems, where nodes are typically located across different networks and domains, inherently hinders the efficient retrieval of information. In this talk I will present techniques to perform content-based search over data repositories that are geographically scattered over peers of different networks. Data repositories in this context contain documents of text, audio, video or other semi-structured data and the task is to locate a certain set of keywords or multimedia features. We present the components of the pFusion architecture, an open source system that builds on work in unstructured P2P systems and topologically-aware overlay construction techniques. Our empirical results using datasets from AKAMAI, NLANR and TREC, show that the architecture we propose is both efficient and practical. In this talk I will also overview other related research activities in Grid, P2P and Sensor systems that we are currently involved in.
Emerging applications in Sensor and Peer-to-Peer networks make the concept of data integration without centralization nowadays more meaningful than ever. In these environments, data is generated continuously and potentially automatically across geographically diverse locations. Organizing data in centralized repositories is becoming prohibitively expensive and in many occasions impractical. Storing data in-situ however, complicates query processing because data relations are fragmented over a number of remote sites. Furthermore, accessing these fragmented relations is only feasible by traversing a network of other nodes. This makes the execution of a query an even more complex task. We claim that in many occasions it might more beneficial to find the K highest ranked (or Top-K) answers, for some user defined parameter K, if this can minimize the query execution cost. In this talk, I will present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
The Internet of Things (IoT) revolution has massively introduced sensor-rich tracking devices to an ever growing of smart spaces (e.g., factories, hospitals, and ships). problem that remains unsolved over the years is the problem for IoT, given that Satellite-based solutions inaccurate in indoor spaces where human activity takes place of the time. In this paper, we introduce a novel opensource architecture for IoT localization, coined Anyplace 4.0 IoT which exploits signal fingerprinting to organize under same roof a wide range of different localization technologies Wi-Fi, BLE, Cellular, UWB, Computer Vision). We present technical requirements of A4IoT inspired by the Alstom SA factory, operating worldwide in rail transport markets. comprises a crowdsourcing architecture where deployers collect and organize fingerprint signals inside smart spaces a designated localization service running on the Edge (from to Datacenter). The service incorporates timeseries for tracking targets and deployers can provide accurate localization accuracy (≈ 2 m) on a variety of platforms Android, Linux, Mac, Windows Robot OS) but also A4IoT through Web 2.0 endpoints to their software
Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates that state- of-the-art methods.
Large document collections can be hard to explore if the user presents her information need in a limited set of keywords. Ambiguous intents arising out of these short queries often result in long-winded query sessions and many query reformulations. To alleviate this problem, in this work, we propose the novel concept of semantic aspects (e.g., ⟨michael- phelps athen
There is a wealth of schema-free tables on the Web, valuable information about quantities on sales and costs, footprint of cars, health data and more. Table can only be properly interpreted in conjunction with the context that surrounds the tables. This paper introduces quantity alignment problem: bidirectional linking between mentions of quantities and the corresponding table cells, order to support advanced content summarization and faster between explanations in text and details in tables. We the BriQ system for computing such alignments. BriQ designed to cope with the specific challenges of approximate aggregated quantities and calculated quantities in text are common but cannot be directly matched in table cells. judiciously combine feature-based classification with joint by random walks over candidate alignment graphs. with a large collection of tables from the Common Crawl project demonstrate the viability of our methods.
In this demonstration paper, we present an integrated signal management studio, coined Fingerprint Studio (FMS), which provides a spatio-temporal to: (i) manage the collection of location-dependent readings (i.e., fingerprints) in indoor environments; (ii) the localization accuracy based on the collected fingerprints; (iii) assess Wi-Fi coverage and data rates. The will present the components comprising FMS, CSM (Crowd Signal Map), ACCES (Accuracy Estimation) WS (Wi-Fi Surveying), through a compelling map-based analytic interface implemented on top of our open-source navigation service, coined Anyplace. We will present FMS two modes: (i) Online Mode, where attendees will be able to and analyze real fingerprints at the conference venue; and Offline Mode, where attendees will be able to interact with of University campus in Cyprus a Hotel in the and an Expo in S. Korea.
In this demonstration paper, we present the TBDDP which relies on existing Machine Learning (ML) to abstract Telco Big Data (TBD) into compact models can be stored and queried when necessary. Our proposed operator has the following two conceptual phases: (i) an offline phase, it utilizes a LSTM-based hierarchical ML to learn a tree of models (coined TBD-DP tree) over and space; (ii) in an online phase, it uses the TBD-DP tree recover data within a certain accuracy. Our framework also visual and declarative interfaces for a variety of telcospecific exploration tasks. We demonstrate the efficiency of proposed operator using SPATE, which is a novel TBD visual architecture we have developed. Our demo will enable to interactively explore synthetic antenna signal traces, will provide, in both visual and SQL mode. In both cases the of the propositions will be quantitatively conveyed the attendees through dedicated dashboards
In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which doesn’t exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup we measure the efficiency of the proposed operator using a ∼10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.
Recent studies in urban navigation have revealed new demands diversity, safety, happiness, serendipity) for the navigation that are critical to providing useful recommendations to This exposes the need to design next-generation navigation that accommodate these newly emerging aspects. In paper, we present a prototype system, namely, EPUI (an Experimental of Urban Informatics), which provides a testbed exploring and evaluating venues and route recommendation that balance between different objectives (i.e., demands) the newly discovered ones. In addition, EPUI incorporates modularized design enabling researchers to upload their algorithms and compare them to well-known algorithms using performance metrics. Its user interface makes it easily by both end-user and experienced researchers.
A telecommunication company (telco) is traditionally only perceived the entity that provides telecommunication services, such as and data communication access to users. However, the backbone infrastructure of such entities spanning densely urban and widely rural areas, provides nowadays a unique to collect immense amounts of mobility data that can valuable insights for road tra c management and avoidance. this paper we outline the components of the Tra c-TBD Telco Big Data) architecture, which aims to become an innovative tra c analytic and prediction system with the following i) provide micro-level tra c modeling and prediction goes beyond the current state provided by Internet-based enterprises utilizing crowdsourcing; ii) retain the location boundaries of users inside their mobile network operator, avoid the risks of exposing location data to third-party mobile and iii) be available with minimal costs and using infrastructure (i.e., cell towers and TBD data streams are available inside a telco). Road tra c understanding, management analytics can minimize the number of road accidents, fuel and energy consumption, avoid unexpected delays, to a macroscopic spatio-temporal understanding of traf- in cities but also to “smart” societies through applications in city public transportation logistics and eet management for startups and governmental bodies.
With the proliferation of mobile computing and the ability to collect detailed data for the urban environment a number of systems that aim at providing Points of Interest (POIs) and tour recommendations have appeared. The overwhelming majority of these systems aims at providing an optimal recom- mendation, where optimality refers to objectives of minimizing the distance to be covered or maximizing the quality of the POIs recommended. A major problem is that by focusing on the optimization of these objectives, there remains little room to the user for serendipity. Urban and social scientists have identified serendipity, i.e., the ability to come across unexpected places, as a feature that makes a city livable. In this work, we introduce a prototype of an experimental platform for evaluating venue recommendation algorithms by providing informative tour recommendations based on the suggested venues. Our prototype system integrates the notion of serendipity in urban navigation at both the venue as well as the route recommendation level without compromising the quality and diversity of the recommended POIs. In addition our system allows the user to upload their own algorithms and explore their performance as compared to many well-known algorithms.
The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel frame- work for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds demonstrates that the navigability score closely matches the accuracy variations users experience.
In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fin- gerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone; and (ii) Reflection Mode where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap.
In this demonstration paper, we present SPATE, innovative telco big data exploration framework whose are two-fold: (i) minimizing the storage space needed incrementally retain data over time; and (ii) minimizing the time for spatiotemporal data exploration queries over data. Our framework deploys lossless data compression ingest streams of telco big data in the most compact manner full resolution for data exploration tasks. We augment storage structures with decaying principles that lead to progressive loss of detail as information gets older. Our also includes visual and declarative interfaces for a of telco-specific data exploration tasks. We demonstrate in two modes: (i) Visual Mode, where attendees will able to interactively explore synthetic telco traces we will and (ii) SQL Mode where attendees can submit custom queries based on a provided schema.
In the realm of smart cities, telecommunication companies (telcos) are expected to play a protagonistic role as these can capture a variety of natural phenomena on an ongoing basis, e.g., traffic in a city, mobility patterns for emergency response or city planning. The key challenges for telcos in this era is to ingest in the most compact manner huge amounts of network logs, perform big data exploration and analytics on the generated data within a tolerable elapsed time. This paper introduces SPATE, an innovative telco big data exploration framework whose objectives are two-fold: (i) minimizing the storage space needed to incrementally retain data over time; and (ii) minimizing the response time for spatiotemporal data exploration queries over recent data. The storage layer of our framework uses lossless data compression to ingest recent streams of telco big data in the most compact manner retaining full resolution for data exploration tasks. The indexing layer of our system then takes care of the progressive loss of detail in information, coined decaying, as data ages with time. The exploration layer provides visual means to explore the generated spatio-temporal information space. We measure the efficiency of the proposed framework using a 5GB anonymized real telco network trace and a variety of telco-specific tasks, such as OLAP and OLTP querying, privacy-aware data sharing, multivariate statistics clustering and regression. We show that out framework can achieve comparable response times to the state-of-the-art using an order of magnitude less storage space.
Wi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user’s (u) localization task using structures called RadioMaps (RMs). It is imperative for u to have a stable WiFi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity dueto poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected WiFi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM even under scenarios of weak Wi-Fi coverage.
In this paper we present a comprehensive architecture to carry out experimental repeatability studies on clusters of smartphones. Our architecture is founded on SmartLab (https://smartlab.cs.ucy.ac.cy/), our in-house architecture for managing real and virtual smartphones via an intuitive Web user interface. Our presented architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner as well as executing mockup experiments by “feeding” the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our testbed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.
People do most of their activities, business, commerce, entertainment and socializing indoors. As all of these are increasingly aided by online services and indoor spaces are becoming bigger and more complex, there is a growing need for cost-effective indoor localization, mapping, navigation and information services. In this paper, we present a complete Indoor Information Service, coined Anyplace (https://anyplace.cs.ucy.ac.cy/), which has an open, modular, extensible and scalable architecture, making it ideal for a wide range of applications. Our service features three highly desirable properties, namely crowdsourcing, scalability and accuracy. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowd-sensed data, filter incorrect user contributions and exploit Wi-Fi data from heterogeneous mobile devices. Moreover, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. Finally, our service relies on the abundance of sensory data on smartphones (e.g. Wi-Fi signal strength and inertial measurements) to deliver reliable indoor geolocation information that received several international awards.
The smartphone revolution has introduced a new era of social networks where users communicate over anonymous messaging platforms to exchange opinions, ideas and even carry out commerce. These platforms enable individuals to establish social interactions between strangers based on a common interest or attribute. In this paper we present Rayzit, a novel anonymous crowd messaging architecture, which utilizes the location of each user to connect them instantly to their k Nearest Neighbors (kNN) as they move in space. Contrary to the very large body of location-based social networks that suffer from bootstrapping issues our architecture enables a user to always interact with the geographically closest possible users around. We establish this communication using a fast computation of an All kNN query that generates a dynamic global social graph every few seconds. We present motivating application scenarios and the detailed backend architecture that allows Rayzit to scale. We have collected and analyzed data from the interactions of thousands of active users and confirm our claims.
In this demonstration paper, we present the Anyplace system that relies on the abundance of sensory data on smartphones (e.g., WiFi signal strength and inertial mea- surements) to deliver reliable indoor geolocation information. Our system features two highly desirable properties, namely crowdsourcing and scalability. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowdsensed data, filter incorrect user contributions and exploit WiFi data from heterogeneous mobile devices. More- over Anyplace follows a big-data architecture for efficient and scalable storage and retrieval of localization and mapping data.
In this demonstration paper we present SmartLab, an architecture for managing a cluster of both Android Real De- vices (ARDs) and Android Virtual Devices (AVDs) via an intuitive web-based interface. Our architecture consists of several exciting components for re-programming and instrumenting smartphones to perform application testing and data gathering in a facile manner as well as executing mockup experiments by “feeding” the devices with GPS/sensor readings. We will particularly demonstrate the various components of our architecture that encompasses smartphone sensor data collected by mobile users and organized in our distributed NoSQL document store. The given datasets can then be replayed on our testbed comprising of real and virtual smartphones accessible to developers through our Web 2.0 user interface. We present the applicability of our architecture through various mockup experiments over different application scenarios.
The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. In this paper, we make three major contributions: First, we propose a comprehensive architecture, coined SmartLab, for managing a cluster of both real and virtual smartphones that are either wired to a private cloud or connected over a wireless link. Second, we propose and describe a number of Android management optimizations (e.g., command pipelining, screen-capturing, file management), which can be useful to the community for building similar functionality into their systems. Third we conduct extensive experiments and microbenchmarks to support our design choices providing qualitative evidence on the expected performance of each module comprising our architecture. This paper also overviews experiences of using SmartLab in a research-oriented setting and also ongoing and future development efforts.
In this paper we present our Crowdsourced Linked Open Data Architecture (CLODA), a first attempt to combine crowdsourcing, localization and location-based services to gener- ate, collect, validate and relate real-world, geo-spatial and multi- dimensional information using smartphones and other mobile devices. CLODA focuses on the construction of URI addressable, interlinked and semi-structured data following the Linked-Open Data (LOD) paradigm. The validity of the constructed data is then contributed by a participating crowd. We present our prototype implementation on top of Google Maps and a blend of in-house technologies, particularly our indoor positioning framework, coined Airplace, our trajectory similarity framework, coined SmartTrace, our neighborhood detection framework, coined Proximity and our smartphone testing platform coined SmartLab.
Big data refers to data sets whose size and structure strains the ability of commonly used relational DBMSs to capture, manage, and process the data within a tolerable elapsed time. Big data sizes commonly range from a few dozen terabytes to many petabytes in a single database and their underlying data model might be anything from structured (relational or tabular) to semi-structured (XML or JSON) or even unstructured (Web text and log files). Big data architectures are highly parallel and distributed in order to cope with the inherent I/O and CPU limitations. Such systems typically perform on mid-scale private clouds, offering higher privacy, to large-scale public clouds, both exposing operational and analytic functionality stand-alone or as-a-Service. This talk aims to overview the current big-data management landscape, the underlying technologies and their provenance, the latest NoSQL and NewSQL trends, possible applications of big-data management systems for online and offline processing of sensor data, text data, social data and medical data in enterprise environments. The talk will also overview ongoing big-data research and teaching activities at the University of Cyprus.
Consider a centralized query operator that identifies to every smartphone user its k geographically nearest neighbors at all times, a query we coin Continuous All k-Nearest Neighbor (CAkNN). Such an operator could be utilized to enhance public emergency services, allowing users to send SOS beacons out to the closest rescuers, allowing gamers and social networking users to establish ad-hoc overlay communication infrastructures, in order to carry out complex interactions. In this paper, we study the problem of efficiently processing a CAkNN query in a cellular or WiFi network, both of which are ubiquitous. We introduce an algorithm, coined Proximity, which answers CAkNN queries in O(n(k+λ)) time, where n denotes the number of users and λ a network-specific parameter (λ << n). Proximity does not require any additional infrastructure or specialized hardware and its efficiency is mainly attributed to a smart search space sharing technique we introduce. Its implementation is based on a novel data structure, coined k+heap, which achieves constant O(1) look-up time and logarithmic O(log(k*λ)) insertion/update time. Proximity, being parameter-free, performs efficiently in the face of high mobility and skewed distribution of users (e.g., the service works equally well in downtown, suburban, or rural areas). We have evaluated Proximity using mobility traces from two sources and concluded that our approach performs at least one order of magnitude faster than adapted existing work.
The majority of smartphone localization systems useAssistedGPS for fine-grained localization in outdoor spaces or WiFibased RSS (Received Signal Strength) technologies for coarsegrain positioning in indoor and outdoor spaces. The former consumes precious energy from mobile devices, is strictly affected by the environment (e.g., cloudy day, forests, etc.) and does not work in indoor spaces. The latter collects RSS from WiFi beams within a user’s vicinity and transfers an RSS vector to the server for localization, in which the position of the user is disclosed possibly violating users’ privacy. In this paper, we present BloomMap, an innovative and efficient algorithm that conducts a localization process without unveiling the user’s location to the localization service, minimizing the energy consumption of the mobile unit and also minimizing the network traffic by not transferring large positioning structures to the client (i.e., known as radiomap). Our framework is designed for planet-scale RSS localization scenarios, which are expected to emerge in the near-future. In particular, a user may localize itself using a subset of a vast data repository of RSS signals that is updated in real time by smartphone wardrivers. Our preliminary evaluation shows that our propositions can localize a device without unveiling its location in approximately 80% less time, energy and network resources than competitive approaches. We also describe our WiFi-based prototype system developed on the Android OS.
In this paper we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, coined SmartTrace, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: 'Report the users that move more similar to Q, where Q is some query trace '. SmartTrace, relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and data-disclosure reasons. SmartTrace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. We assess our ideas with realistic and real workloads from Microsoft Research Asia and other sources. Our study reveals that SmartTrace computes the desired results with 74% less energy consumption and 13% faster than its centralized and decentralized counterparts. Our experimental results also confirm our analytical study.
The bulk of social network applications for smartphones (e.g., Twitter, Facebook, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smartphone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined SmartOpt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner 's smartphones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft 's Geolife project and social patterns derived by DBLP. Our study reveals that SmartOpt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, sensors are typically organized in structures coined Query Routing Trees (QRTs). Our study reveals that predominant data acquisition systems construct QRTs in ad-hoc manners leading to a significant waste of energy. In this talk I will present MicroPulse+, a framework for minimizing the consumption of energy during data collection in Sensor Networks. MicroPulse+ eliminates a variety of data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse+ introduces: i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data collection activity and on identifying the bottlenecks using an in-network execution of the critical path method; and ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. The talk will conclude with an outlook into current and future research work.
We present a novel distributed algorithm (MHS) that constructs a query routing tree that minimizes collisions during query execution. It was shown in previous work that minimizing collisions during query execution saves significant amount of energy[1]. In the same paper it is shown that balancing the node degrees of a query routing tree significantly reduces collisions during query execution. We address the inefficiencies of the previously proposed algorithm and propose a simpler, purely distributed, parameter-free, cheaper and more efficient algorithm. Our resulting query trees are optimally balanced, guarantee minimum collisions and minimum latency for query execution and allow for opportunistic in-network processing. MHS poses the minimum possible communication overhead to the network and is parameter-free as opposed to previously proposed algorithms. Our proposed algorithm can be used for acquiring data from the nodes of any distributed systems where the main objective is to minimize the communication cost.
In long-term deployments of Wireless Sensor Networks, it is often more efficient to store sensor readings locally at each device and transmit those readings to the user only when requested (i.e., in response to a user query). Many of the techniques that collect information from a sensor network require that the data is sorted on some attribute (e.g., range queries, top-k queries, join queries, etc.) Yet, the underlying storage medium of these devices (i.e., Flash media) presents some unique characteristics which renders traditional disk-based sorting algorithms inefficient in this context. In this paper we devise the FSort algorithm, an efficient external sorting algorithm for flash-based sensor devices with a small memory footprint. FSort minimizes the expensive write/delete operations of flash memory minimizing in that way the consumption of energy. In particular, FSort uses a top-down replacement selection algorithm in order to produce sorted runs on flash media in a log-based manner. Sorted runs are then recursively merged in order to yield the sorted result. Our experimentation with real traces from Intel Research Berkeley show that FSort greatly outperforms the traditional External Mergesort Algorithm both in regards to time and energy consumption. We found similar advantages in regards to the wearability constraints of flash media.
This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in mobile environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter, in order to minimize energy consumption, while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the nodes laying on the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a low communication complexity. For storage and fault-tolerance we devise the Data Replication Algorithm (DRA), a voting-based replication scheme that enables the exact retrieval of events from the network in cases of failures. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates. In particular, we found that when failures are less than 60\% failure then we can recover over 80\% of generated events exactly.
Continuous queries in Wireless Sensor Networks (WSNs) are founded on the premise of Query Routing Tree structures (denoted as T ), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor the physical world at an extremely high fidelity. Research in this area has to this day primarily focused on the trade-off between local computation and communication in order to minimize the transfer of data over the fundamentally expensive wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy in Wireless Sensor Networks as the communication channel is only accessed for answering on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each sensor has to be complemented by efficient access methods that will speed up the execution of queries when required. In this talk I will provide an overview of recent developments in Wireless Sensor Network Technology and highlight some important data indexing and searching challenges that arise in this context. In particular, I will present MicroHash which is an external memory index structure that is tailored to the distinct characteristics of flash memory, the most prevalent type of non-volatile memory used in sensor systems.
ICGrid (Intensive Care Grid) is a distributed platform that enables the seamless integration, correlation and retrieval of clinically interesting episodes across Intensive Care Units, which is currently under development by our group. Such a task requires huge processing and data storage capabilities, which are common attributes of Grid infrastructures. ICGrid is based on a hybrid architecture that combines i) a heterogeneous set of monitors that sense the inpatients and ii) Grid technology that enables the storage, processing and information sharing task between Intensive Care Units.
The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is to make these systems \em dependable. In this presentation we present \em FailRank a nove
This paper assumes a set of $n$ mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter in order to minimize energy consumption while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a message complexity of O(p + n), where p denotes the number of nodes on the perimeter and $n$ the overall number of nodes. For storage and replication we devise a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles and minimum bounding cuboids. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates.
In this talk I will introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wildlife monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions. In this talk, I will also present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is to make these systems \em dependable. In this paper we present \em FailRank a nove
In this paper we present MicroPulse, a novel framework for adapting the waking window of a sensing device S based on the data workload incurred by a query Q. Assuming a typical tree-based aggregation scenario, the waking window is defined as the time interval t during which S enables its transceiver in order to collect the results from its children. Minimizing the length of t enables S to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. Our method is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the Critical Path Method. We show through trace-driven experimentation with a real dataset that MicroPulse can reduce the energy cost of the waking window by three orders of magnitude.
In this paper we introduce MINT (Materialized In-Network Top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V ' (\subseteq V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively-defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.
In this talk, I will present techniques to efficiently answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric, such as the utilization of the communication medium, which is associated with the retrieval of the desired answer set. I will provide an overview of state-of-the-art algorithms that solve the Top-K problem in a centralized setting and show why these are not applicable to the distributed case. I will then focus on the Threshold Join Algorithm (TJA), which is a novel solution for executing Top-K queries in a distributed environment. I will also present results from our performance study with a real middleware testbed deployed over a network of 75 workstations.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor and understand the physical world at an extremely high fidelity. Research to this day has primarily focused on the trade-off between local computation and communication, in order to offset the expensive transfer of data over the fundamentally unreliable wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy, as we access the communication channel to answer on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each node has to be complemented by efficient index structures that will enable access to data when required. In this talk we present MicroHash, an external memory index structure which is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. Our index structure exploits the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of a low energy budget.
Intensive Care Units (ICUs) at hospitals utilize cutting edge technology in order to acquire the physiological state of inpatients, which are in a critical (life-threatening) physiological state, at an extremely high fidelity. In particular, ICUs utilize a very large number of monitoring and sensing devices that are continuously attached on inpatients in order to uncover the physiological state of the inpatients. Such measurements can then be utilized for i) education, ii) early diagnosis and iii) for defining early warning systems that identify when a human life is jeopardy. A problem with the current setting is that individual ICUs are limited to the locally acquired measurements. As a result, the number of clinically 'interesting ' episodes available to doctors is also very limited. ICGrid (Intensive Care Grid) is a distributed platform that enables the seamless integration, correlation and retrieval of clinically interesting episodes across Intensive Care Units, which is currently under development by our group. Such a task requires huge processing and data storage capabilities, which are common attributes of Grid infrastructures. ICGrid is based on a hybrid architecture that combines i) a heterogeneous set of monitors that sense the inpatients and ii) Grid technology that enables the storage, processing and information sharing task between Intensive Care Units. Our demonstration aims at presenting the first part of the hybrid architecture of ICGrid (i.e. the acquisition of real signals from inpatients and their storage on the Grid). Our demonstration platform operates on a standalone laptop. In a real setting, this software is able to extract the physiological parameters from monitoring devices installed at ICUs.
Wireless Sensor Networks offer a non-intrusive and non-disruptive technology that enables users to monitor and understand the physical world at an extremely high fidelity. Research to this day has primarily focused on the trade-off between local computation and communication, in order to offset the expensive transfer of data over the fundamentally unreliable wireless link. On the contrary, we focus on the challenges of storing sensor readings locally at each node. This In-Situ storage paradigm offers a novel perspective for conserving energy, as we access the communication channel to answer on-demand queries rather than for percolating each and every event to a centralized database. Storing large quantities of data locally at each node has to be complemented by efficient index structures that will enable access to data when required. In this talk we present MicroHash, an external memory index structure which is tailored to the distinct characteristics of the most prevalent type of non-volatile memory used in sensor systems, namely flash memory. Our index structure exploits the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of a low energy budget.
Modern Sensor and Peer-to-Peer data management systems have to cope with data that is generated automatically and continuously across distributed and potentially geographically diverse locations. Organizing data in centralized repositories is becoming increasingly expensive and in many occasions impractical. Additionally, users are usually only interested in finding the highest ranked answers to their queries rather that the complete range of answers. In this talk, I will present efficient techniques to answer Top-K queries in a distributed environment. A Top-K query returns the K highest ranked answers to a user defined similarity function. At the same time it also minimizes some cost metric which is associated with the retrieval of the desired answer set. My talk focuses on the Threshold Join Algorithm (TJA), which is a novel distributed Top-K query processing algorithm that combines local similarity scores available at each computing site. I will also present the LB-K and UBLB-K algorithms which utilize lower and upper bounds, when exact scores are not available. An extensive experimental evaluation with our distributed middleware testbed reveals that the proposed methods are orders of magnitudes more efficient than their competitors.
We initiate a study on the effect of the network topology on the performance of Peer-to-Peer (P2P) information retrieval systems. The emerging P2P model has become a very powerful and attractive paradigm for developing Internet-scale systems for sharing resources, including files, or documents. We show that the performance of Information Retrieval algorithms can be significantly improved through the use of fully distributed topologically aware overlay network construction techniques. Our empirical results, using the Peerware middleware infrastructure, show that the approach we propose is both efficient and practical.
One important problem in peer-to-peer (P2P) networks is searching and retrieving the correct information. However, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. We propose two mechanisms for information retrieval in pure peer-to-peer networks. The fir, the modified Breadth-First-Search (BFS) mechanism, is an extension of the current Gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. The second, the Intelligent Search mechanism, uses the past behavior of the P2P network to further improve the scalability of the search procedure. In this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. The algorithm is entirely distributed, and therefore scales well with the size of the network. We implemented our mechanisms as middleware platforms. To show the advantages of our mechanisms we present experimental results using the middleware implementation.
Οι διαδικτυακές αρχιτεκτονικές IIN-SOA (Internet-based Indoor Navigation Service-Oriented Architectures) οργανώνουν σήματα προερχόμενα από συσκευές τύπου IoT (Διαδίκτυο των Πραγμάτων) σε ένα ευρύ φάσμα καινοτόμων εφαρμογών για εσωτερικούς χώρους, όπου οι άνθρωποι αφιερώνουν το 80-90% του χρόνου τους. Σε αυτή την εργασία, μελετάμε το πρόβλημα της προ-φόρτωσης (ή συσσώρευσης) των σημαντικότερων IoT δεδομένων από μια IIN-SOA σε μια κινητή συσκευή, χωρίς να γνωρίζουμε τον προορισμό του χρήστη κατά την διάρκεια της πλοήγησης. Το προτεινόμενο μας πλαίσιο, με ονομασία Grap (Graph Prefetching - Προ-φόρτωση Γράφου), αναλύει δομικά τις τοπολογίες των κτηρίων για να εντοπίσει τις σημαντικότερες περιοχές τους οι οποίες και γίνονται εικονικοί στόχοι για τον ευρυστικό αλγόριθμο αναζήτησης τον οποίο έχουμε αναπτύξει. Δοκιμάσαμε το πλαίσιο Grap σε σύνολα δεδομένων που έχουν συλλεχθεί από μια πραγματική IIN-SOA και βρήκαμε ότι παρουσιάζει εξαιρετική ακρίβεια.
Σε αυτή την εργασία, παρουσιάζουμε έναν καινοτόμο τελεστή αποσύνθεσης μεγάλων τηλεπικοινωνιακών δεδομένων (Telco Big Data, TBD), ο οποίος κάνει χρήση της τεχνικής Επίρρησης Δεδομένων (Data Postdiction, DP) και ονομάζεται TBD-DP. Σε αντίθεση με την πρόβλεψη δεδομένων, η οποία στοχεύει στην εξεύρεση μιας μελλοντικής τιμής πλειάδας, ο διατυπωμένος όρος Eπίρρηση Δεδομένων στοχεύει στην ανάκαμψη μιας προηγούμενης τιμής πλειάδας, η οποία δεν υπάρχει πλέον εφόσον έχει διαγραφεί για να απελευθερωθεί ο σχετικός χώρος που καταλαμβάνεται στη δευτερεύουσα μνήμη. Ο TBD-DP στηρίζεται σε υφιστάμενους αλγόριθμους μηχανικής μάθησης για να γενικευθούν τα TBD σε συμπαγή μοντέλα τα οποία μπορούν να αποθηκευτούν και να αναζητηθούν όταν αυτό είναι απαραίτητο. Ο προτεινόμενος TBD-DP τελεστής μας έχει τις εξής δύο πιο κάτω εννοιολογικές φάσεις: (i) σε μια εκτός λειτουργίας φάση, χρησιμοποιεί έναν ιεραρχικό αλγόριθμο μηχανικής μάθησης βασισμένο σε LSTM, που προσπαθεί να μάθει ένα δέντρο μοντέλων (TBD-DP δέντρο) βασισμένο στον χρόνο και χώρο, (ii) σε μια άμεσης απόκρισης φάση, χρησιμοποιεί το δέντρο TBD-DP για να ανακτήσει δεδομένα με μια ορισμένη ακρίβεια. Στην πειραματική μας μελέτη στο Tensorflow μέσω HDFS μελετήσαμε την αποτελεσματικότητα του προτεινόμενου τελεστή χρησιμοποιώντας περίπου 10GB ανώνυμα TBD δεδομένα. Τα πειραματικά αποτελέσματά μας είναι εξαιρετικά ενθαρρυντικά καθώς δείχνουν ότι ο TBD-DP εξοικονομεί μια τάξη μεγέθους χώρο αποθήκευσης ενώ διατηρεί υψηλή ακρίβεια στα ανακτηθέντα δεδομένα.
Σε αυτή την επίδειξη λογισμικού παρουσιάζουμε τον τελεστή ΤΒD-DP (Telco Big Data – Data Postdiction), ο οποίος βασίζεται σε υφιστάμενους αλγόριθμους μηχανικής μάθησης για να γενικευθούν τα TBD σε συμπαγή μοντέλα τα οποία μπορούν να αποθηκευτούν και να αναζητηθούν όταν αυτό είναι απαραίτητο. Ο προτεινόμενος TBD-DP τελεστής μας έχει τις εξής δύο πιο κάτω εννοιολογικές φάσεις: (i) σε μια εκτός λειτουργίας φάση, χρησιμοποιεί έναν ιεραρχικό αλγόριθμο μηχανικής μάθησης βασισμένο σε LSTM, που προσπαθεί να μάθει ένα δέντρο μοντέλων (TBD-DP δέντρο) βασισμένο στον χρόνο και χώρο, (ii) σε μια άμεσης απόκρισης φάση, χρησιμοποιεί το δέντρο TBD-DP για να ανακτήσει δεδομένα με μια ορισμένη ακρίβεια. H επίδειξη μας περιλαμβάνει τις γραφικές και δηλωτικές διεπαφές για ποικίλες επερωτήσεις διερεύνησης τηλεπικοινωνιακών δεδομένων. Δείχνουμε την αποτελεσματικότητα του προτεινόμενου τελεστή χρησιμοποιώντας το SPATE, το οποίο είναι μια καινοτόμα οπτική αναλυτική αρχιτεκτονική για TBD που αναπτύξαμε. Η επίδειξη θα πραγματοποιηθεί μέσω δύο λειτουργιών: (i) Οπτική λειτουργία, όπου οι συμμετέχοντες θα μπορούν να διερευνήσουν διαδραστικά συνθετικά δεδομένα τα οποία θα παρέχουμε, και (ii) SQL λειτουργία, όπου οι συμμετέχοντες θα μπορούν να υποβάλουν επερωτήσεις SQL βάσει ενός παρεχόμενου σχήματος. Και στις δύο περιπτώσεις η απόδοση των προτεινομένων τεχνικών θα παρουσιαστεί ποσοτικά στους συμμετέχοντες μέσω μιας γραφικής διεπαφής χάρτη.
Σε αυτή την επίδειξη λογισμικού παρουσιάζουμε ένα ολοκληρωμένο περιβάλλον διαχείρισης σημάτων Wi-Fi σε εσωτερικούς χώρους, με ονομασία Fingerprint Management Studio (FMS), το οποίο παρέχει μια ενοποιημένη διαπροσωπεία για: (i) διαχείριση της συλλογής μετρήσεων από αισθητήρες και ειδικότερα των Ψηφιακών Αποτυπωμάτων Wi-Fi, (ii) εκτίμηση της ακρίβειας εντοπισμού θέσης, βάσει των συλλεγόμενων ψηφιακών αποτυπωμάτων, και (iii) αξιολόγηση της κάλυψης και των ρυθμών μετάδοσης δεδομένων της υποδομής Wi-Fi. Η επίδειξη θα παρουσιάσει τα συστατικά του FMS, ειδικότερα το CSM (Crowd Signal Map), το ACCES (Ακρίβεια Εκτίμησης) και το WS (Wi-Fi Surveying), μέσα από μια διαδραστική και διερευνητική διεπαφή που βασίζεται σε χάρτη και υλοποιείται πάνω από την ανοικτή μας υπηρεσία πλοήγησης σε εσωτερικούς χώρους με ονομασία Anyplace. Θα παρουσιάσουμε το FMS με δύο τρόπους: (i) λειτουργία με σύνδεση, όπου οι συμμετέχοντες θα μπορούν να συλλέγουν και να αναλύουν πραγματικά ψηφιακά αποτυπώματα στο χώρο του Συμποσίου, και (ii) λειτουργία χωρίς σύνδεση, όπου οι συμμετέχοντες θα μπορούν να αλληλοεπιδράσουν με μετρήσεις που έχουν συλλεχθεί στο παρελθόν σε μια Πανεπιστημιούπουλη στην Κύπρο ένα ξενοδοχείο στις ΗΠΑ και μια έκθεση στη Ν. Κορέα.
Η ανάγκη για υπηρεσίες γεωτοποθέτησης σε εσωτερικούς χώρους έχει οδηγήσει στην ανάπτυξη τεχνικών που δημιουργούν έναν Χάρτη Ψηφιακών Αποτυπωμάτων (ΧΨΑ) των σημάτων από αισθητήρες (π.χ., μαγνητικό πεδίο, Wi-Fi, Bluetooth) σε καθορισμένα σημεία μέσα σε έναν εσωτερικό χώρο και στη συνέχεια χρησιμοποιούν τον ΧΨΑ ως αναφορά για μεταγενέστερες γεωτοποθετήσεις. Σε αυτή την προσέγγιση είναι σημαντική η αξιολόγηση της ποιότητας του ΧΨΑ πριν την εγκατάσταση του συστήματος, με τέτοιο τρόπο ώστε να μην εξαρτάται από την προέλευση των δεδομένων αλλά και να παρέχει πληροφορίες σχετικά με την ποιότητα γεωτοποθέτησης στο προσωπικό που είναι επιφορτισμένο με την εγκατάσταση του συστήματος. Παρόλο που οι αλγόριθμοι γεωτοποθέτησης με ΧΨΑ παρέχουν συνήθως εκτιμήσεις για την ακρίβεια κατά τη διάρκεια της λειτουργίας του συστήματος (π.χ., απεικονίζουν την αβεβαιότητα γύρω από την τοποθεσία ενός χρήστη με έναν κύκλο ή έλλειψη αβεβαιότητας), δεν παρέχουν καμία πληροφορία σχετικά με την αναμενόμενη ακρίβεια πριν την παροχή της υπηρεσίας γεωτοποθέτησης. Σε αυτό το άρθρο, αναπτύσσουμε μία καινοτόμα μεθοδολογία για την αξιολόγηση της ποιότητας τυχαίων ΧΨΑ, με την ονομασία ACCES. Η μεθοδολογία μας αποτελείται από μία γενική μέθοδο παρεμβολής δεδομένων με χρήση Γκαουσιανών Διεργασιών (ΓΔ), από την οποία εξάγεται μία βαθμολογία πλοήγησης για κάθε σημείο χρησιμοποιώντας το κάτω όριο Cramer-Rao. Η προσέγγισή μας είναι ανεξάρτητη από το υποκείμενο φυσικό μοντέλο που διέπει τα ψηφιακά αποτυπώματα σημάτων. Η εκτενής πειραματική αποτίμηση με ΧΨΑ μαγνητικών σημάτων, αντιπαραβάλοντας την εμπειρική ακρίβεια γεωτοποθέτησης με τα θεωρητικά κάτω όρια αποδεικνύει ότι η βαθμολογία πλοήγησης αντικατοπτρίζει τις μεταβολές στην ακρίβεια της γεωτοποθέτησης που βιώνουν οι χρήστες.
Σε αυτή την επίδειξη λογισμικού παρουσιάζουμε το ACCES, ένα καινοτόμο πλαίσιο το οποίο επιτρέπει την αποτίμηση της ποιότητας τυχαίων Χάρτων Ψηφιακών Αποτυπωμάτων (ΧΨΑ) και τη μη πραγματικoύ χρόνου εκτίμηση της ακρίβειας για γεωτοποθέτηση με τα ΧΨΑ. Το πλαίσιο μας αξιολογεί αποτυπώματα με τέτοιο τρόπο ώστε να μην εξαρτάται από την προέλευση των δεδομένων σε οποιοδήποτε σημείο. Η μεθοδολογία μας αποτελείται από μία γενική μέθοδο παρεμβολής δεδομένων με χρήση Γκαουσιανών Διεργασιών (ΓΔ), από την οποία εξάγεται μία βαθμολογία πλοήγησης για κάθε σημείο χρησιμοποιώντας το κάτω όριο Cramer-Rao. Η προσέγγισή μας είναι ανεξάρτητη από το υποκείμενο φυσικό μοντέλο που διέπει τα ψηφιακά αποτυπώματα σημάτων. Η παρουσίαση του λογισμικού μας περιλαμβάνει μια αυτόνομη έκδοση της δημοφιλούς και ανοικτού πηγαίου κώδικα υπηρεσίας εσωτερικής γεωπλοήγησης με όνομα Anyplace, στην οποία έχουν προστεθεί τα λογισμικά συστατικά του ACCES. Στο συμπόσιο παρουσιάζουμε το ACCES με δυο τρόπους: (i) Φάση Συλλογής, όπου οι συμμετέχοντες θα μπορούν να υποβοηθήσουν στη συλλογή αποτυπωμάτων του χώρου με τη χρήση ενός κινητού Android; και (ii) Φάση Προβολής όπου οι συμμετέχοντες μπορούν να παρατηρήσουν τόσο τις μετρήσεις που συλλέχθηκαν όσο και τις αντίστοιχες εκτιμήσεις ακρίβειας ACCESS με τη μορφή θερμόμετρου επικάλυψης πάνω από μια Διαδικτυακή διεπαφή Ιστού.
Σε αυτή την επίδειξη λογισμικού, παρουσιάζουμε το SPATE, ένα καινοτόμο πλαίσιο διερεύνησης μεγάλων τηλεπικοινωνιακών δεδομένων, το οποίο έχει τους ακόλουθους δυο στόχους: (i) την ελαχιστοποίηση του αποθηκευτικού χώρου που απαιτείται για την σταδιακή διατήρηση των δεδομένων με την πάροδο του χρόνου; και (ii) την ελαχιστοποίηση του χρόνου απόκρισης σε χωρο-χρονικές διερευνητικές επερωτήσεις. Το πλαίσιο μας χρησιμοποιεί μη-απωλεστική συμπίεση για να απορροφήσει πρόσφατες ροές μεγάλων τηλεπικοινωνιακών δεδομένων με τον πιο συμπαγή τρόπο, διατηρώντας πλήρη ανάλυση στις διεργασίες ανάλυσης των δεδομένων. Οι δομές αποθήκευσης στο SPATE επαυξάνονται με έννοιες προοδευτικής απώλειας της λεπτομέρειας στα δεδομένα, δημιουργώντας την έννοια της φθοράς των δεδομένων. Το πλαίσιο μας περιλαμβάνει επίσης οπτικές και δηλωτικές διεπαφές για μια πλειάδα διαφορετικών διερευνητικών διεργασιών. Στη παρούσα επίδειξη λογισμικού παρουσιάζουμε το SPATE με δυο τρόπους: (i) Οπτική Διερεύνηση, όπου οι συμμετέχοντες θα μπορούν να διερευνήσουν συνθετικά ίχνη τηλεπικοινωνιακών δεδομένων τα οποία θα παρέχουμε μέσω μιας Διαδικτυακής διεπαφής με χάρτες; και (ii) SQL Διερεύνηση όπου οι συμμετέχοντες μπορούν να υποβάλλουν προσαρμοσμένες ερωτήσεις SQL βάσει ενός παρεχόμενου σχεσιακού σχήματος δεδομένων.
Στη εποχή των έξυπνων πόλεων οι τηλεπικοινωνιακές εταιρείες (telcos) αναμένεται να διαδραματίσουν ένα πρωτεύοντα ρόλο καθώς αυτές μπορούν να συλλάβουν μια ποικιλία φυσικών φαινομένων σε συνεχή βάση, π.χ. οδική κυκλοφορία σε μια πόλη, μοτίβα κινητικότητας για αντιμετώπιση καταστάσεων έκτακτης ανάγκης ή για τη βελτιστοποίηση των πολεοδομικών σχεδιασμών. Οι βασικές προκλήσεις για τους telcos σε αυτή την εποχή είναι να καταναλώνουν με τον πιο συμπαγή τρόπο τεράστιες ποσότητες αρχείων καταγραφής δικτύου και να υποστηρίξουν με αυτά τα συστήματα λήψης αποφάσεων. Στο παρών άρθρο παρουσιάζουμε το SPATE, ένα πρωτοποριακό πλαίσιο διερευνητικής ανάλυσης μεγάλων τηλεπικοινωνιακών δεδομένων σε αποδεκτά χρονικά πλαίσια, το οποίο έχει τους ακόλουθους δυο στόχους: (i) την ελαχιστοποίηση του αποθηκευτικού χώρου που απαιτείται για την σταδιακή διατήρηση των δεδομένων με την πάροδο του χρόνου; και (ii) την ελαχιστοποίηση του χρόνου απόκρισης σε χωρο-χρονικές διερευνητικές επερωτήσεις. Το επίπεδο αποθήκευσης του πλαισίου μας χρησιμοποιεί μη-απωλεστική συμπίεση για να απορροφήσει πρόσφατες ροές μεγάλων τηλεπικοινωνιακών δεδομένων με τον πιο συμπαγή τρόπο, διατηρώντας πλήρη ανάλυση για τις διεργασίες ανάλυσης των δεδομένων. Το επίπεδο ευρετηρίασης του συστήματός μας στη συνέχεια φροντίζει για την προοδευτική απώλεια της λεπτομέρειας στα δεδομένα, εισάγοντας την έννοια της φθοράς των δεδομένων. Το επίπεδο διερεύνησης παρέχει οπτικά μέσα για τη κατανόηση του παραγόμενου χωρο-χρονικού χώρου πληροφοριών. Στη πειραματική μας αποτίμησή, μετράμε την αποτελεσματικότητα του προτεινόμενου πλαισίου χρησιμοποιώντας ένα 5GB ανώνυμο κατάστιχο τηλεπικοινωνιακών δεδομένων με μια ποικιλία επερωτήσεων, όπως επερωτήσεις OLAP και OLTP, ανωνυμοποίηση δεδομένων με εγγυήσεις, πολυδιάστατη στατιστική ανάλυση ομαδοποίηση δεδομένων και ανάλυση παλινδρόμησης. Μέσω της μελέτης μας δείχνουμε ότι το πλαίσιο μας μπορεί να επιτύχει συγκρίσιμους χρόνους απόκρισης με σχετικές τεχνικές καταλαμβάνοντας ωστόσο μια τάξη μεγέθους λιγότερο χώρο αποθήκευσης.
Ένα ευρύ φάσμα κινητών εφαρμογών Διαδικτυακής κλίμακας, από εφαρμογές κοινωνικής δικτύωσης και παιχνίδια μέχρι εφαρμογές αντιμετώπισης και διαχείρισης καταστάσεων έκτακτης ανάγκης, απαιτούν έναν αποτελεσματικό και κλιμακώσιμο τρόπο υπολογισμού επερωτήσεων k Πλησιέστερων Γειτόνων Όλων των Αντικειμένων (All k Nearest Neighbors - AkNN) πάνω από εκατομμύρια κινούμενα αντικείμενα κάθε λίγα δευτερόλεπτα. Οι πλείστες γνωστές τεχνικές για τον υπολογισμό AkNN ερωτημάτων δεν είναι κατανεμημένες και συνεπώς δεν μπορούν να εφαρμοστούν αποδοτικά σε μεγάλη κλίμακα και γρήγορες ροές δεδομένων. Μόνο πρόσφατα προτάθηκαν κατανεμημένες τεχνικές βασιζόμενες σε υποδομές νέφους για την επίτευξη κλιμάκωσης σε μεγάλα σύνολα δεδομένων, οι οποίες ωστόσο υστερούν σε απόδοση διότι δεν επιτυγχάνουν το βέλτιστο διαχωρισμό του χώρου δεδομένων και τη βέλτιστη αντιγραφή δεδομένων μεταξύ των μονάδων επεξεργασίας. αυτό το άρθρο παρουσιάζουμε τον Spitfire, έναν κατανεμημένο αλγόριθμο που παρέχει ένα επεκτάσιμο και υψηλής απόδοσης πλαίσιο επεξεργασίας AkNN ερωτημάτων σε κατανεμημένη κύρια μνήμη. Ο προτεινόμενος αλγόριθμος χρησιμοποιεί ένα αποδοτικό πλαίσιο διαχωρισμού των δεδομένων και ένα πλαίσιο κλωνοποίησης δεδομένων, τα οποία επιτυγχάνουν ισορροπημένα φορτία επεξεργασίας μεταξύ των υπολογιστικών μονάδων. Στην αναλυτική και πειραματική μας αξιολόγηση δείχνουμε πως η απόδοση κλαδέματος του αλγορίθμου Spitfire διαδραματίζει κεντρικό ρόλο στη μείωση του φόρτου επικοινωνίας και του χρόνου ανταπόκρισης ως και μια τάξη μεγέθους σε σύγκριση με τρεις άλλους κατανεμημένους αλγόριθμους αιχμής που επιλύουν AkNN επερωτήσεις σε κατανεμημένη κύρια μνήμη.
Τα συστήματα εντοπισμού θέσης σε εσωτερικούς χώρους έχουν λάβει πρόσφατα ιδιαίτερη προσοχή, κυρίως επειδή το GPS είναι ενεργοβόρο και εφόσον αυτό δεν είναι διαθέσιμο σε εσωτερικούς χώρους. Τα υποσυστήματα γεωτοποθέτησης των έξυπνων κινητών συσκευών βασίζονται πλέον σε Διαδικτυακές υπηρεσίες παροχής θέσης. Αυτό ωστόσο αναδεικνύει το πρόβλημα της ιδιωτικότητας θέσης, εφόσον οι πάροχοι θέσης μπορούν να γνωρίζουν πλέον τη θέση των χρηστών ανά πάσα στιγμή. αυτό το άρθρο παρουσιάζουμε ένα καινοτόμο αλγόριθμο για την προστασία της ιδιωτικότητας θέσης των χρηστών από υπηρεσίες εντοπισμού χωρίς να εμποδίζουμε την παροχή ενημερώσεων θέσης σε συνεχή βάση. Ο προτεινόμενος αλγόριθμος, με το όνομα Temporal Vector Map (TVM), επιτρέπει στους χρήστες να προσδιορίσουν τη θέση τους με ακρίβεια χρησιμοποιώντας ένα εξειδικευμένο φίλτρο Bloom, το οποίο παρέχει k-ανωνυμία (kAB), και μια τεχνική δημιουργίας καμουφλαρισμένων αιτήσεων εντοπισμού θέσης, με το όνομα bestNeighbors. Το προτεινόμενο πλαίσιο έχει αξιολογηθεί χρησιμοποιώντας ένα πραγματικό πρωτότυπο, το οποίο αναπτύχθηκε στο Android και Hadoop HBase μαζί με αρκετά GBs από ρεαλιστικά ίχνη Wi-Fi. Η αναλυτική αξιολόγηση και η πειραματική μας μελέτη αποκαλύπτουν ότι το TVM δεν είναι ευάλωτο σε επιθέσεις που παραδοσιακά παραβιάζουν την προστασία k-ανωνυμίας και δείχνουν ότι το TVM μπορεί να προσφέρει ακρίβεια γεωτοποθέτησης με περίπου τέσσερις τάξεις μεγέθους λιγότερη ενέργεια και αριθμό μηνυμάτων από ανταγωνιστικές προσεγγίσεις.
Οι σύγχρονες υπηρεσίες πλοήγησης σε εσωτερικούς χώρους εξαρτώνται από υπηρεσίες νέφους, οι οποίες εντοπίζουν τη θέση του χρήστη χρησιμοποιώντας δομές Ραδιοχαρτών (RM), οι οποίες καταγράφουν την ένταση ισχύος σημείων πρόσβασης Wi-Fi σε διάφορα σημεία στο χώρο. Σε αυτές τις υπηρεσίες, είναι σημαντικό να υπάρχει σταθερή σύνδεση του χρήστη στο δίκτυο Wi-Fi, έτσι ώστε να μπορεί ο χρήστης να βρίσκει συνεχώς την θέση του από την υπηρεσία ή για να κατεβάσει o χρήστης ολόκληρο το RM εκ των προτέρων βρίσκοντας τη θέση του αυτόνομα. Τα δίκτυα Wi-Fi, ωστόσο, υποφέρουν από διακοπτόμενη συνδεσιμότητα λόγω ελλιπούς σχεδιασμού των δικτύων ή για οικονομικούς παράγοντες. Αυτό επηρεάζει άμεσα την ακρίβεια προσδιορισμού της θέσης του χρήστη και ως εκ τούτου την εμπειρία πλοήγησης του στο χώρο. αυτό το άρθρο παρουσιάζουμε ένα καινοτόμο πλαίσιο, με την επωνυμία PreLoc, για γρήγορο και ακριβή προσδιορισμό της θέσης των χρηστών σε εσωτερικούς χώρους με υποδομή δικτύων Wi-Fi διακοπτόμενης συνδεσιμότητας. Στο PreLoc, γίνεται μια ευφυής διάταξη των εγγραφών του RM βασιζόμενοι σε γνώση που ανακτήθηκε από ιστορικές πλοηγήσεις χρηστών στο ίδιο κτίριο. Αντί να ανακτηθεί ολόκληρο το RM από την υπηρεσία στον χρήστη, προτείνουμε μια στρατηγική με το όνομα PGS, η οποία προσδιορίζει τις εγγραφές του RM που έχουν την μεγαλύτερη πιθανότητα να είναι αναγκαίες στον χρήστη καθώς πλοηγείται. Αξιολογήσαμε το πλαίσιο μας χρησιμοποιώντας ένα πραγματικό πρότυπο που αναπτύχθηκε σε Android, αλλά και ρεαλιστικά ίχνη Wi-Fi που συλλέχθηκαν στο Πανεπιστήμιο Κύπρου. Η πειραματική μας μελέτη αποκαλύπτει ότι το πλαίσιο PreLoc, με χρήση της στρατηγικής PGS και συμβατικών αλγόριθμων εντοπισμού θέσης σε εσωτερικούς χώρους, μπορεί να αποφέρει παρόμοια ακρίβεια με αυτή που επιτυγχάνεται εάν χρησιμοποιούσαμε ολόκληρο το RM ακόμη και σε σενάρια ασθενούς κάλυψης του χώρου από τα σημεία πρόσβασης Wi-Fi.
Panel Co-Chairs: Yannis Ioannidis and Panagiotis Karras. Other Panelists: Alexandros Labrinidis, Peter Triantafillou and Herodotos Herodotou.
Μία άκρως επιθυμητή λειτουργία των κινητών συσκευών είναι η συνεχής παροχή των Κ γεωγραφικά πλησιέστερων γειτόνων. Αυτή η λειτουργία ονομάζεται συνεχής επερώτηση Κ-πλησιέστερων γειτόνων για όλους (CAkNN). Μια τέτοια λειτουργία θα ενίσχυε τις δημόσιες υπηρεσίες έκτακτης ανάγκης, επιτρέποντας στον χρήστη να στείλει σήματα SOS στους πλησιέστερους χρήστες, ή εφαρμογές παιγνίων, επιτρέποντας στο χρήστη να αλληλεπιδράσει με τους γύρω του παίκτες. Στην παρούσα εργασία, μελετάμε το πρόβλημα της αποτελεσματικής επεξεργασίας μιας CAkNN επερώτησης σε ασύρματα δίκτυα έξυπνων κινητών συσκευών. Παρουσιάζουμε τον αλγόριθμος μας, Proximity, ο οποίος απαντά μια επερώτηση CAkNN σε χρόνο O(n(Κ+λ)), όπου το n δηλώνει τον αριθμό των χρηστών και το λ την χωρητικότητα ενός δικτυακού σημείου σύνδεσης (λ << n). Ο Proximity δεν κάνει χρήση πρόσθετων υποδομών ή εξειδικευμένου υλικού και η αποτελεσματικότητα του επιτυγχάνεται κυρίως λόγω της έξυπνης κοινής χρήσης του πεδίου αναζήτησης. Η υλοποίηση βασίζεται σε μια νέα δομή δεδομένων με όνομα K+-σωρός, η οποία επιτυγχάνει σταθερό χρόνο O(1) πρόσβασης και λογαριθμικό χρόνο O(log(Κ*λ)) εισαγωγής και ενημέρωσης. Ο Proximity, δεν χρειάζεται παραμέτρους και είναι αποτελεσματικός σε περιβάλλοντα υψηλής κινητικότητας και ασύμμετρης κατανομής χρηστών (π.χ., η υπηρεσία λειτουργεί εξίσου καλά στο κέντρο μιας πόλης, στα προάστια και σε αγροτικές περιοχές). Έχουμε αξιολογήσει τον Proximity χρησιμοποιώντας τροχιές από δύο διαφορετικές πηγές και συμπεραίνουμε ότι η προσέγγισή μας είναι τουλάχιστον μία τάξη μεγέθους γρηγορότερη σε σύγκριση με προσαρμοσμένες υφιστάμενες τεχνικές.
Παρουσίαση Λογισμικού | Μία άκρως επιθυμητή λειτουργία των κινητών συσκευών είναι η συνεχής παροχή των Κ γεωγραφικά πλησιέστερων γειτόνων. Αυτή η λειτουργία ονομάζεται συνεχής επερώτηση Κ-πλησιέστερων γειτόνων για όλους (CAkNN). Μια τέτοια λειτουργία θα ενίσχυε τις δημόσιες υπηρεσίες έκτακτης ανάγκης, επιτρέποντας στον χρήστη να στείλει σήματα SOS στους πλησιέστερους χρήστες, ή εφαρμογές παιγνίων, επιτρέποντας στο χρήστη να αλληλεπιδράσει με τους γύρω του παίκτες. Στην παρούσα εργασία, μελετάμε το πρόβλημα της αποτελεσματικής επεξεργασίας μιας CAkNN επερώτησης σε ασύρματα δίκτυα έξυπνων κινητών συσκευών. Παρουσιάζουμε τον αλγόριθμος μας, Proximity, ο οποίος απαντά μια επερώτηση CAkNN σε χρόνο O(n(Κ+λ)), όπου το n δηλώνει τον αριθμό των χρηστών και το λ την χωρητικότητα ενός δικτυακού σημείου σύνδεσης (λ << n). Ο Proximity δεν κάνει χρήση πρόσθετων υποδομών ή εξειδικευμένου υλικού και η αποτελεσματικότητα του επιτυγχάνεται κυρίως λόγω της έξυπνης κοινής χρήσης του πεδίου αναζήτησης. Η υλοποίηση βασίζεται σε μια νέα δομή δεδομένων με όνομα K+-σωρός, η οποία επιτυγχάνει σταθερό χρόνο O(1) πρόσβασης και λογαριθμικό χρόνο O(log(Κ*λ)) εισαγωγής και ενημέρωσης. Ο Proximity, δεν χρειάζεται παραμέτρους και είναι αποτελεσματικός σε περιβάλλοντα υψηλής κινητικότητας και ασύμμετρης κατανομής χρηστών (π.χ., η υπηρεσία λειτουργεί εξίσου καλά στο κέντρο μιας πόλης, στα προάστια και σε αγροτικές περιοχές). Έχουμε αξιολογήσει τον Proximity χρησιμοποιώντας τροχιές από δύο διαφορετικές πηγές και συμπεραίνουμε ότι η προσέγγισή μας είναι τουλάχιστον μία τάξη μεγέθους γρηγορότερη σε σύγκριση με προσαρμοσμένες υφιστάμενες τεχνικές.
Η πλειοψηφία των κοινωνικών δικτύων (π.χ., Twitter, Facebook, Foursquare, κτλ.) στηρίζεται σε κεντρικοποιημένες αρχιτεκτονικές για το διαμοιρασμό δεδομένων ή τη διεξαγωγή αναζητήσεων. Τέτοια δίκτυα χρησιμοποιούνται από εκατομμύρια άτομα εν κίνηση, μέσω επεκτάσεων που προσφέρονται για έξυπνες κινητές συσκευές. Από την άλλη πλευρά, οι αρχιτεκτονικές πίσω από αυτές τις υπηρεσίες υποφέρουν από προβλήματα ιδιωτικότητας δεδομένων των χρηστών τους (π.χ., αποκάλυψη προσωπικών τοπικών δεδομένων ενός χρήστη με μία κεντρική υπηρεσία) όπως επίσης προβλήματα επίδοσης (π.χ., κατανάλωση ενέργειας ή δικτυακών πόρων κατά τη διάρκεια της ανάρτησης των δεδομένων). Σε αυτό το άρθρο, παρουσιάζουμε ένα καινοτόμο πλαίσιο για την αποδοτική αναζήτηση αντικειμένων (π.χ., εικόνες, βίντεο, κτλ.) σε ένα κατανεμημένο κοινωνικό δίκτυο έξυπνων κινητών συσκευών. Το πλαίσιο μας, με ονομασία SmartOpt, στηρίζεται σε ένα τοπικό μοντέλο αποθήκευσης δεδομένων όπου οι αναζητήσεις διεξάγονται πάνω από μία καινοτόμα δομή αναζήτησης με ονομασία Δέντρο Δρομολόγησης Επερωτήσεων με Πολλαπλούς Στόχους. Η δομή μας βελτιστοποιεί πολλαπλούς συγκρουόμενους στόχους χρησιμοποιώντας ένα Εξελικτικό Αλγόριθμό Πολλαπλών Στόχων βασισμένο στην Αποσύνθεση, ο οποίος υπολογίζει ένα σύνολο από λύσεις ψηλής ποιότητας ελαχιστοποιώντας την κατανάλωση ενέργειας, την καθυστέρηση αναζήτησης και μεγιστοποιώντας τον αριθμό των σχετικών απαντήσεων που επιστρέφονται σε μία επερώτηση. Οι ιδέες μας αξιολογούνται με χρήση δεδομένων κίνησης ατόμων τα οποία καταγράφονται από τη Microsoft, όπως επίσης συσχετίσεις μεταξύ ατόμων, τα οποία καταγράφονται από τη υπηρεσία βιβλιογραφικών αναφορών DBLP. Η πειραματική μας μελέτη αποκαλύπτει ότι το πλαίσιο SmartOpt μπορεί να επιτύχει σημαντική αύξηση των σχετικών ανακτήσεων (της τάξης του 95%) μειώνοντας παράλληλα τον απαιτούμενο χρόνο και την απαιτούμενη ενέργεια (κατά μία με δύο τάξεις μεγέθους αντίστοιχα) σε σχέση με άλλες προσεγγίσεις.
Σε αυτό το άρθρο παρουσιάζουμε ένα καινοτόμο κατανεμημένο πλαίσιο αναζήτησης όμοιων τροχιών σε δίκτυα έξυπνων κινητών συσκευών, χωρίς να αποκαλύπτουμε τις τροχιές των χρηστών που συμμετέχουν στον υπολογισμό. Το πλαίσιο μας, με όνομα SmartTrace, κάνει χρήση ευκαιριακής και συμμετοχικής αίσθησης για να απαντήσει γρήγορα επερωτήσεις της μορφής: «Βρες τους χρήστες οι οποίοι κινήθηκαν όμοια με το Q, όπου Q είναι η αναζητούμενη τροχιά». Το SmartTrace στηρίζεται σε ένα επί-τόπου μοντέλο αποθήκευσης όπου γεωγραφικά δεδομένα καταγράφονται τοπικά πάνω στις έξυπνες κινητές συσκευές για λόγους απόδοσης αλλά και για λόγους μη-αποκάλυψης των δεδομένων στον επεξεργαστή επερωτήσεων. Το SmartTrace εισάγει επίσης ένα αποδοτικό αλγόριθμο επεξεργασίας κορυφαίων-κ επερωτήσεων ο οποίος εκμεταλλεύεται κατανεμημένες μετρικές σύγκρισης τραχιών οι οποίες είναι ανεκτικές σε χωρικά και χρονικά σφάλματα. Οι ιδέες μας αποτιμούνται με πραγματικά φόρτοι εργασίας προερχόμενα από την Microsoft και άλλες πηγές. Η μελέτη μας καταδεικνύει ότι το SmartTrace υπολογίζει τα αναμενόμενα αποτελέσματα με 74% λιγότερη ενέργεια και 13% γρηγορότερα από τις αντίστοιχες κεντρικοποιημένες και αποκεντρικοποιημένες υλοποιήσεις. Η πειραματική μας μελέτη επιβεβαιώνει επίσης την αναλυτική μας μελέτη.
Σε αυτό το άρθρο παρουσιάζουμε ένα καινοτόμο κατανεμημένο πλαίσιο αναζήτησης όμοιων τροχιών σε δίκτυα έξυπνων κινητών συσκευών, χωρίς να αποκαλύπτουμε τις τροχιές των χρηστών που συμμετέχουν στον υπολογισμό. Το πλαίσιο μας, με όνομα SmartTrace, κάνει χρήση ευκαιριακής και συμμετοχικής αίσθησης για να απαντήσει γρήγορα επερωτήσεις της μορφής: «Βρες τους χρήστες οι οποίοι κινήθηκαν όμοια με το Q, όπου Q είναι η αναζητούμενη τροχιά». Το SmartTrace στηρίζεται σε ένα επί-τόπου μοντέλο αποθήκευσης όπου γεωγραφικά δεδομένα καταγράφονται τοπικά πάνω στις έξυπνες κινητές συσκευές για λόγους απόδοσης αλλά και για λόγους μη-αποκάλυψης των δεδομένων στον επεξεργαστή επερωτήσεων. Το SmartTrace εισάγει επίσης ένα αποδοτικό αλγόριθμο επεξεργασίας κορυφαίων-κ επερωτήσεων ο οποίος εκμεταλλεύεται κατανεμημένες μετρικές σύγκρισης τραχιών οι οποίες είναι ανεκτικές σε χωρικά και χρονικά σφάλματα. Οι ιδέες μας αποτιμούνται με πραγματικά φόρτοι εργασίας προερχόμενα από την Microsoft και άλλες πηγές. Η μελέτη μας καταδεικνύει ότι το SmartTrace υπολογίζει τα αναμενόμενα αποτελέσματα με 74% λιγότερη ενέργεια και 13% γρηγορότερα από τις αντίστοιχες κεντρικοποιημένες και αποκεντρικοποιημένες υλοποιήσεις. Η πειραματική μας μελέτη επιβεβαιώνει επίσης την αναλυτική μας μελέτη.
Σε αυτό το άρθρο παρουσιάζουμε έναν κατανεμημένο αλγόριθμο για την δημιουργία ενός ισοζυγισμένου δένδρου επικοινωνίας που αποσκοπεί στην συλλογή δεδομένων από ένα ασύρματο δίκτυο αισθητήρων. Ο αλγόριθμος αυτός έχει ελάχιστο κόστος εκτέλεσης και το απορρέον δένδρο επικοινωνίας έχει σχεδόν βέλτιστη ισορροπία. Κατά την συλλογή δεδομένων κάθε σύγκρουση μεταξύ πακέτων προκαλεί την επαναποστολή τους. Η ίση κατανομή των βαθμών μεταξύ των κόμβων στο δένδρο επικοινωνίας έχει ως αποτέλεσμα την ελαχιστοποίηση των συγκρούσεων αυτών και συνεπώς την εξοικονόμηση ενέργειας και την αύξηση του χρόνου ζωής του ασύρματου δικτύου αισθητήρων. Συγκρίνουμε τον αλγόριθμό μας με έναν υπάρχον αλγόριθμο και έναν κεντρικό αλγόριθμο. Τα αποτελέσματα δείχνουν ότι ο αλγόριθμός μας υπερέχει του ανταγωνισμού για την πλειοψηφία των δικτυακών τοπολογιών και επιτυγχάνει σχεδόν βέλτιστη ισορροπία στο δέντρο. Επίσης, έχει το ελάχιστο δυνατό κόστος εκτέλεσης συντελώντας έτσι ακόμα περισσότερο στην εξοικονόμηση ενέργειας στο δίκτυο.
Continuous queries in wireless sensor networks are established on the premise of a routing tree that provides each sensor with a path over which answers can be transmitted to the query processor. The number of tuples received by S in a given epoch e denotes the workload of S. Since the exact workload of a node is not known ahead of time, a node has to enable its transceiver for a sub-optimal amount of time in order to collect the results from its children. We found that this leads to an enormous waste of energy in predominant data acquisition frameworks such as TAG and Cougar. We found that these structures are sub-optimality constructed in predominant data acquisition systems leading to an enormous waste of energy. In this paper we present MicroPulse, a workload-aware optimization algorithm for query routing trees in wireless sensor networks. Our algorithm is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method. A node S utilizes this information in order to locally derive the time instance during which it should wake up, the interval during which it should deliver its workload and the workload increase tolerance of its parent node. We additionally provide an elaborate description of energy-conscious algorithms for disseminating and maintaining the critical path cost in a distributed manner. Our trace-driven experimentation with real sensor traces from Intel Research Berkeley shows that MicroPulse can reduce the data acquisition costs by many orders.
In this paper we introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wildlife monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.
In this paper we introduce MINT (Materialized In-Network Top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V ' (\subseteq V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively-defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.