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ABSTRACT
In this paper we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale vwireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sands or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting discovery and query in large scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for discovery in large scale geometric networks are demonstrated. We also raise the issue of query coverage in unreliable networks and investigate how redundancy can improve the coverage via both theoretical analysis and simulation. Last, we study adaptive strategies for the case where the frequencies of query and events are unknown a priori and time-varying.
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CITED BY 18
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R. C. Chakinala , A. Kumarasubramanian , K. A. Laing , R. Manokaran , C. Pandu Rangan , R. Rajaraman, Playing push vs pull: models and algorithms for disseminating dynamic data in networks, Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures, July 30-August 02, 2006, Cambridge, Massachusetts, USA
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Jie Gao , Leonidas Guibas , Nikola Milosavljevic , John Hershberger, Sparse data aggregation in sensor networks, Proceedings of the 6th international conference on Information processing in sensor networks, April 25-27, 2007, Cambridge, Massachusetts, USA
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