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Pervasive computing provides an exciting paradigm for supporting anywhere anytime services, and is built on the tremendous advances made in a broad spectrum of technologies including wireless communication, wireless and sensor networking, mobile and distributed computing, as well as signal and information processing. Pervasive computing enables computers to interact with the real world in a ubiquitous and natural manner. Quality of service (QoS), related to transmission delay, bandwidth, or packet loss, has been studied in various building blocks in pervasive computing, e.g., different QoS mechanisms are presented for wireless or wired networks; the notion of computational QoS is used for parallel processing. The emerging pervasive computing paradigm, however, is application-driven and mission-critical and the existing QoS notions to do not really match. Quality of Information (QoI) or Information Quality (IQ) of sensor-originated information relates to the fitness of the information for a sensor-enabled application. Harnessing and optimizing QoI of information derived from sensor networks will be key to bringing together information acquisition and processing systems that support the on-demand information needs of a broad spectrum of smart, sensor-enabled applications such as remote real-time habitat monitoring, utility grid monitoring, environmental control, supply-chain management, health care, machinery control, intelligent highways, military intelligence, reconnaissance and surveillance (ISR), border control, and hazardous material monitoring, just to mention a few.
The proliferation of smartphone has also enabled the possibility to retrieve data also by users on the move. This data collection paradigm is often called crowdsensing, or crowdsourcing, and builds upon the willingness of users to share data together, which eventually gets aggregated to provide novel services to the community.
Although fascinating, and potentially disruptive, this paradigm inherently carries a set of technical challenges, at various levels and which should be studied by different research communities. At first, to make the data granularity spread enough, the crowd should be sufficiently large. This means that the application which runs on the users’ device has to be optimized, and should not interfere with the normal activity the users want to perform. This raises the challenge of having smart interfaces which communicate with the user only when necessary, along with the battery efficiency, which plays a crucial role being these devices almost always battery powered. Another technical challenge comes from the heterogeneous data aggregation, as data can be in many different shapes, formats, and labeled in different languages. Hence, automatically linking data that comes from different platforms becomes challenging, and again clustering techniques, supervised and unsupervised machine learning algorithms have to be developed to perform such task efficiently.
Achieving the desired “pervasiveness” of mobile applications, which in turns enable to retrieve data for the community, and the assessment of the QoI itself is key.
The objective of this workshop (which is unique venue in its scope for the pervasive community) is to provide a forum to exchange ideas, present results, share experience, and enhance collaborations among researchers, professionals, and application developers in various aspects of QoI, QoE, QoS for pervasive computing and crowdsensing in network contexts including wireless, mobile and sensor networks.

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Topics of interest addressing the challenging joint aspects of QoI and QoE include:
Joint QoI- & QoS-driven system design and architectural principlesNetwork services (time sync, QoS) for target/event detection, localization, tracking and classificationQoI-aware wireless sensor networkingEnergy-efficient data fusion, sensor fault analysis, sensor data cleansingfor task mapping and schedulingCoordinated QoS for cross-layer, cross-application, and cross-node integration (including QoI-QoS integration)Query optimization for event processing in pervasive environmentsData and query models for QoI-aware event processingAdaptive QoI and QoS under dynamic environmentsTrust, security, privacy, and data provenance issues in QoI and QoSQoI characterization, representation, performance metrics, and evaluationQoI and QoS for emerging pervasive computing applicationsModels of semantics and context in QoI-aware applicationsMarket-based mechanisms to influence QoIQuality of Experience (QoE) issues for pervasive applicationsValue of information (VoI) and quality of action for sensor/actuator networksPrototype test-bed design, implementation, and field trialsEnergy efficiency in crowdsensed services and applicationsProtocols enhancement for crowdsensed servicesSocial Internet of thingsBig data semanticData science for crowdsensed servicesOpportunistic crowdsensed servicesRewarding mechanism for crowdsensed servicesCrowdsensed testbeds and platformsFog computing for IoTHeterogeneous data aggregationNLP techniques for crowdsensed servicesMachine learning techniques for data aggregationMachine learning techniques for data classificationPrivacy for crowdsensed dataUser behavior classification from public dataUser activity recognitionUser profiling

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Important Date
  • Conference Date

    Mar 19



    Mar 22