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Challenges in Sensor Networks

Challenges in Sensor Networks. Karl Aberer School of Computer and Communication Science, EPFL National Competence Centre in Mobile Information and Communication Systems (NCCR-MICS). Wireless sensor networks, e.g. SensorScope Published over the Web, e.g. SensorMap

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Challenges in Sensor Networks

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  1. Challenges in Sensor Networks Karl Aberer School of Computer and Communication Science, EPFL National Competence Centre in Mobile Information and Communication Systems (NCCR-MICS)

  2. Wireless sensor networks, e.g. SensorScope Published over the Web, e.g. SensorMap Many data formats and schemas e.g. NASA Global Change Master Directorycontains 36000+ datasets on env. research The usual challenge: data integration Possible solutions (usual and less usual suspects) Standardization, e.g. SensorML Manual data integration, e.g. SkyServer Peer-to-peer data integration Community based, e.g. Flickr Distributed, collaborative reasoning on metadata and schema mappings Emergent semantics From proprietary sites to portals Sensor Data Publishing

  3. Assume we want to integrate sensor data from a variety of scientific experiments, research groups, disciplines, across spatial and temporal scales A big opportunity: common semantic grounding Measurements relate to shared physical phenomena Spatial and temporal correspondences Rich correspondences and redundancies at data level facilitates integration A big challenge: different interpretations of the data Sensor data rarely viewed in raw form: model-based views Data cleaning, calibration, interpolation, physical models Different views depending on available data, knowledge, purpose A new source of heterogeneity! Semantic Integration of Sensor Data

  4. Model-based Integration Problem Model-based View M1 M2 Classical semantic integration problem: D1.t = D2.temperature? D1 D2 Two millennia of mean surface temperatures according to different reconstructions, each smoothed on a decadal scale. The unsmoothed, annual value for 2004 is also plotted for reference. Sensor networks (e.g. different regions) Model-based Integration of Sensor Data

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