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2006
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Scalable and Robust Aggregation Techniques for Extracting Statistical Information in Sensor Networks

10 years 4 months ago
Scalable and Robust Aggregation Techniques for Extracting Statistical Information in Sensor Networks
Wireless sensor networks have stringent constraints on system resources and data aggregation techniques are critically important. However, accurate data aggregation is difficult due to the variation of sensor readings and due to the frequent communication failures. To address these difficulties, we propose a scalable and robust data aggregation algorithm. The novelty of our work includes two aspects. First, our algorithm exploits the mixture model and the Expectation Maximization (EM) algorithm for parameter estimation. Hence, it captures the effects of aggregation over different scales while keeping the communication cost low. Second, our algorithm exploits loss-tolerant multi-path routing schemes. Hence, it obtains accurate statistical information even in the presence of high link and node failure rates. We demonstrate that our techniques reduce communication cost while retaining the precious statistical information otherwise neglected by other aggregation techniques. Our evaluati...
Hongbo Jiang, Shudong Jin
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICDCS
Authors Hongbo Jiang, Shudong Jin
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