Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 59, Issue 8, Pages 3992-4001Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2010.2062547
Keywords
Algorithm/protocol design; data aggregation; sensor networks; statistical information extraction
Categories
Funding
- National Natural Science Foundation of China [60803115, 60873127]
- Fundamental Research Funds for the Central Universities [M2009022]
- Youth Chenguang Project of Wuhan City [201050231080]
- CHUTIAN Scholar Project of Hubei Province
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
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Wireless sensor networks (WSNs) have a broad range of applications, such as battlefield surveillance, environmental monitoring, and disaster relief. These networks usually have stringent constraints on the system resources, making data-extraction and aggregation techniques critically important. However, accurate data extraction and aggregation is difficult, due to significant variations in sensor readings and frequent link and node failures. To address these challenges, we propose data-aggregation techniques based on statistical information extraction that capture the effects of aggregation over different scales. We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation-maximization (EM) algorithm. We demonstrate that the proposed techniques not only greatly reduce the communication cost but also retain valuable statistical information that is otherwise lost in many existing data-aggregation approaches for sensor networks. Moreover, simulation results show that the proposed techniques are robust against link and node failures and perform consistently well in broad scenarios with various network configurations.
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