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Issue No. 06 - Nov.-Dec. (2012 vol. 9)
ISSN: 1545-5971
pp: 785-797
Yan Sun , Beijing University of Posts and Telecommunications, Beijing
Hong Luo , Beijing University of Posts and Telecommunications, Beijing
Sajal K. Das , The University of Texas at Arlington. Arlington
For wireless multimedia sensor networks (WMSNs) deployed in noisy and unattended environments, it is necessary to establish a comprehensive framework that protects the accuracy of the gathered multimedia information. In this paper, we jointly consider data aggregation, information trust, and fault tolerance to enhance the correctness and trustworthiness of collected information. Based on the multilayer aggregation architecture of WMSNs, we design a trust-based framework for data aggregation with fault tolerance with a goal to reduce the impact of erroneous data and provide measurable trustworthiness for aggregated results. By extracting statistical characteristics from different sources and extending Josang's trust model, we propose how to compute self-data trust opinion, peer node trust opinion, and peer data trust opinion. According to the trust transfer and trust combination rules designed in our framework, we derive the trust opinion of the sink node on the final aggregated result. In particular, this framework can evaluate both discrete data and continuous media streams in WMSNs through a uniform mechanism. Results obtained from both simulation study and experiments on a real WMSN testbed demonstrate the validity and efficiency of our framework, which can significantly improve the quality of multimedia information as well as more precisely evaluate the trustworthiness of collected information.
Peer to peer computing, Fault tolerance, Fault tolerant systems, Wireless sensor networks, Monitoring, fault tolerance, Wireless multimedia sensor networks, trust, data aggregation

S. K. Das, H. Luo and Y. Sun, "A Trust-Based Framework for Fault-Tolerant Data Aggregation in Wireless Multimedia Sensor Networks," in IEEE Transactions on Dependable and Secure Computing, vol. 9, no. , pp. 785-797, 2012.
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