Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks

Supriyo Chatterjea, Tim Nieberg, Yang Zhang, Paul Havinga

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)

    Abstract

    Wireless sensor networks (WSNs) are often densely deployed for environmental monitoring applications. Collecting raw data from these networks presents problems, e.g. batteries drain out rapidly due to large amounts of data transmission and poor quality of data results from dropped packets due to limited bandwidth of present day sensor nodes. We present a novel solution to alleviate this problem. Using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation, thus reducing the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.
    Original languageEnglish
    Title of host publicationDistributed Computing in Sensor Systems
    Subtitle of host publicationThird IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings
    EditorsJames Aspnes, Christian Scheideler, Anish Arora, Samuel Madden
    Place of PublicationBerlin
    PublisherSpringer
    Pages368-385
    Number of pages18
    ISBN (Electronic)978-3-540-73090-3
    ISBN (Print)978-3-540-73089-7
    DOIs
    Publication statusPublished - Jun 2007
    Event3rd IEEE Conference on Distributed Computing in Sensor Systems, DCOSS 2007 - Santa Fe, United States
    Duration: 18 Jun 200720 Jun 2007
    Conference number: 3
    http://www.dcoss.org/dcoss07/

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume4549
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd IEEE Conference on Distributed Computing in Sensor Systems, DCOSS 2007
    Abbreviated titleDCOSS 2007
    CountryUnited States
    CitySanta Fe
    Period18/06/0720/06/07
    Internet address

    Fingerprint

    Scheduling algorithms
    Sensor nodes
    Wireless sensor networks
    Data acquisition
    Agglomeration
    Topology
    Parallel algorithms
    Data communication systems
    Energy conservation
    Bandwidth
    Monitoring

    Keywords

    • CAES-PS: Pervasive Systems
    • Data aggregation
    • Distributed scheduling
    • Sensor networks

    Cite this

    Chatterjea, S., Nieberg, T., Zhang, Y., & Havinga, P. (2007). Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks. In J. Aspnes, C. Scheideler, A. Arora, & S. Madden (Eds.), Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings (pp. 368-385). (Lecture Notes in Computer Science; Vol. 4549). Berlin: Springer. https://doi.org/10.1007/978-3-540-73090-3_25
    Chatterjea, Supriyo ; Nieberg, Tim ; Zhang, Yang ; Havinga, Paul. / Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks. Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings. editor / James Aspnes ; Christian Scheideler ; Anish Arora ; Samuel Madden. Berlin : Springer, 2007. pp. 368-385 (Lecture Notes in Computer Science).
    @inproceedings{1dd4cd3f61cc428aa70a7a54c2e9cdbc,
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    abstract = "Wireless sensor networks (WSNs) are often densely deployed for environmental monitoring applications. Collecting raw data from these networks presents problems, e.g. batteries drain out rapidly due to large amounts of data transmission and poor quality of data results from dropped packets due to limited bandwidth of present day sensor nodes. We present a novel solution to alleviate this problem. Using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation, thus reducing the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80{\%} compared to raw data collection.",
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    author = "Supriyo Chatterjea and Tim Nieberg and Yang Zhang and Paul Havinga",
    note = "Please do not refer to the paper on the Springer website as there are some major printing errors in the figures. Please download the paper directly from e-Prints instead.",
    year = "2007",
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    doi = "10.1007/978-3-540-73090-3_25",
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    Chatterjea, S, Nieberg, T, Zhang, Y & Havinga, P 2007, Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks. in J Aspnes, C Scheideler, A Arora & S Madden (eds), Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings. Lecture Notes in Computer Science, vol. 4549, Springer, Berlin, pp. 368-385, 3rd IEEE Conference on Distributed Computing in Sensor Systems, DCOSS 2007, Santa Fe, United States, 18/06/07. https://doi.org/10.1007/978-3-540-73090-3_25

    Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks. / Chatterjea, Supriyo; Nieberg, Tim; Zhang, Yang; Havinga, Paul.

    Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings. ed. / James Aspnes; Christian Scheideler; Anish Arora; Samuel Madden. Berlin : Springer, 2007. p. 368-385 (Lecture Notes in Computer Science; Vol. 4549).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

    T1 - Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks

    AU - Chatterjea, Supriyo

    AU - Nieberg, Tim

    AU - Zhang, Yang

    AU - Havinga, Paul

    N1 - Please do not refer to the paper on the Springer website as there are some major printing errors in the figures. Please download the paper directly from e-Prints instead.

    PY - 2007/6

    Y1 - 2007/6

    N2 - Wireless sensor networks (WSNs) are often densely deployed for environmental monitoring applications. Collecting raw data from these networks presents problems, e.g. batteries drain out rapidly due to large amounts of data transmission and poor quality of data results from dropped packets due to limited bandwidth of present day sensor nodes. We present a novel solution to alleviate this problem. Using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation, thus reducing the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.

    AB - Wireless sensor networks (WSNs) are often densely deployed for environmental monitoring applications. Collecting raw data from these networks presents problems, e.g. batteries drain out rapidly due to large amounts of data transmission and poor quality of data results from dropped packets due to limited bandwidth of present day sensor nodes. We present a novel solution to alleviate this problem. Using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation, thus reducing the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.

    KW - CAES-PS: Pervasive Systems

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    KW - Sensor networks

    U2 - 10.1007/978-3-540-73090-3_25

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    M3 - Conference contribution

    SN - 978-3-540-73089-7

    T3 - Lecture Notes in Computer Science

    SP - 368

    EP - 385

    BT - Distributed Computing in Sensor Systems

    A2 - Aspnes, James

    A2 - Scheideler, Christian

    A2 - Arora, Anish

    A2 - Madden, Samuel

    PB - Springer

    CY - Berlin

    ER -

    Chatterjea S, Nieberg T, Zhang Y, Havinga P. Energy-Efficient Data Acquisition using a Distributed and Self-organizing Scheduling Algorithm for Wireless Sensor Networks. In Aspnes J, Scheideler C, Arora A, Madden S, editors, Distributed Computing in Sensor Systems: Third IEEE International Conference, DCOSS 2007, Santa Fe, NM, USA, June 18-20, 2007. Proceedings. Berlin: Springer. 2007. p. 368-385. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-540-73090-3_25