5th International ICST Conference on COMmunication System softWAre and middlewaRE

Research Article

Efficient Support for Multi-Resolution Queries in Global Sensor Networks

  • @INPROCEEDINGS{10.1145/2016551.2016562,
        author={Andreas Benzing and Boris Koldehofe and Kurt Rothermel},
        title={Efficient Support for Multi-Resolution Queries in Global Sensor Networks},
        proceedings={5th International ICST Conference on COMmunication System softWAre and middlewaRE},
        publisher={ACM},
        proceedings_a={COMSWARE},
        year={2012},
        month={3},
        keywords={dsps global sensor network indexing query processing},
        doi={10.1145/2016551.2016562}
    }
    
  • Andreas Benzing
    Boris Koldehofe
    Kurt Rothermel
    Year: 2012
    Efficient Support for Multi-Resolution Queries in Global Sensor Networks
    COMSWARE
    ACM
    DOI: 10.1145/2016551.2016562
Andreas Benzing1,*, Boris Koldehofe1, Kurt Rothermel1
  • 1: Distributed Systems Group, Universität Stuttgart
*Contact email: andreas.benzing@ipvs.uni-stuttgart.de

Abstract

Stream processing has evolved as a paradigm for efficiently sharing and integrating a massive amount of data into applications. However, the integration of globally dispersed sensor data imposes challenges in the effective utilization of the IT infrastructure that forms the global sensor network. Especially simulations require the integration of sensor streams at widely differing spatial and temporal resolutions. For current stream processing solutions it is necessary to generate a separate data stream for each requested resolution. Therefore, these systems suffer from high redundancy in data streams, wasting a significant amount of bandwidth and limiting their scalability.

This paper presents a new approach to scalable distributed stream processing of data which stems from globally dispersed sensor networks. The approach supports applications in establishing continuous queries for sensor data at different resolutions and ensures efficient bandwidth usage of the data distribution network. Unlike existing work in the context of video stream processing which provides multiple resolutions by establishing separate channels for each resolution, this paper presents a stream processing system that can efficiently split/combine data streams in order to decrease/increase their resolution without loss in data precision. In addition the system provides mechanisms for load balancing of sensor data streams that allow efficient utilization of the bandwidth of the global sensor network.