• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Electrical Engineering and Computer Science
    • EECS Faculty Scholarship
    • EECS Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Energy-efficient data redistribution in sensor networks

    Date
    2013-06-24
    Author
    Tang, Bin
    Jaggi, Neeraj
    Wu, Haijie
    Kurkal, Rohini
    Metadata
    Show full item record
    Citation
    Tang, Bin; Jaggi, Neeraj; Wu, Haijie; Kurkal, Rohini. 2013. Association for Computing Machinery. ACM Transactions on Sensor Networks, v.9:no.2:articl1 no.11
    Abstract
    We address the energy-efficient data redistribution problem in data-intensive sensor networks (DISNs). In a DISN, a large volume of data gets generated, which is first stored in the network and is later collected for further analysis when the next uploading opportunity arises. The key concern in DISNs is to be able to redistribute the data from data-generating nodes into the network under limited storage and energy constraints at the sensor nodes. We formulate the data redistribution problem where the objective is to minimize the total energy consumption during this process while guaranteeing full utilization of the distributed storage capacity in the DISNs. We show that the problem is APX-hard for arbitrary data sizes; therefore, a polynomial time approximation algorithm is unlikely. For unit data sizes, we show that the problem is equivalent to the minimum cost flow problem, which can be solved optimally. However, the optimal solution's centralized nature makes it unsuitable for large-scale distributed sensor networks. Thus, we design a distributed algorithm for the data redistribution problem which performs very close to the optimal, and compare its performance with various intuitive heuristics. The distributed algorithm relies on potential function-based computations, incurs limited message and computational overhead at both the sensor nodes and data generator nodes, and is easily implementable in a distributed manner. We analytically study the convergence and performance of the proposed algorithm and demonstrate its near-optimal performance and scalability under various network scenarios. In addition, we implement the distributed algorithm in TinyOS, evaluate it using TOSSIM simulator, and show that it outperforms EnviroStore, the only existing scheme for data redistribution in sensor networks, in both solution quality and message overhead. Finally, we extend the proposed algorithm to avoid disproportionate energy consumption at different sensor nodes without compromising the solution quality.
    Description
    Click on the DOI link to access the article (may not be free).
    URI
    http://dx.doi.org/10.1145/2422966.2422968
    http://hdl.handle.net/10057/5747
    Collections
    • EECS Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2021  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV