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dc.contributor.authorTang, Bin
dc.contributor.authorJaggi, Neeraj
dc.contributor.authorWu, Haijie
dc.contributor.authorKurkal, Rohini
dc.date.accessioned2013-06-24T16:14:25Z
dc.date.available2013-06-24T16:14:25Z
dc.date.issued2013-06-24
dc.identifier.citationTang, Bin; Jaggi, Neeraj; Wu, Haijie; Kurkal, Rohini. 2013. Association for Computing Machinery. ACM Transactions on Sensor Networks, v.9:no.2:articl1 no.11
dc.identifier.issn1550-4859
dc.identifier.urihttp://dx.doi.org/10.1145/2422966.2422968
dc.identifier.urihttp://hdl.handle.net/10057/5747
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractWe 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.en_US
dc.description.sponsorshipNSF: under grants CNS-1116849 and EPS-0903806.en_US
dc.language.isoen_USen_US
dc.publisherASSOC COMPUTING MACHINERY
dc.subjectAlgorithmsen_US
dc.subjectDesignen_US
dc.subjectPerformanceen_US
dc.subjectData redistributionen_US
dc.subjectEnergy efficiencyen_US
dc.subjectSensor networksen_US
dc.subjectAlgorithmen_US
dc.subjectImplementationen_US
dc.subjectApproximationen_US
dc.titleEnergy-efficient data redistribution in sensor networksen_US
dc.typeArticleen_US


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