Sensor Network Tomography: monitoring wireless sensor networks
ACM SIGCOMM Computer Communication Review
A two-tier data dissemination model for large-scale wireless sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
Loss inference in wireless sensor networks based on data aggregation
Proceedings of the 3rd international symposium on Information processing in sensor networks
Measurement and monitoring in wireless sensor networks
Measurement and monitoring in wireless sensor networks
Contour maps: monitoring and diagnosis in sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Signal Processing
Multicast topology inference from measured end-to-end loss
IEEE Transactions on Information Theory
Using Bayesian network on network tomography
Computer Communications
A factor graph approach to link loss monitoring in wireless sensor networks
IEEE Journal on Selected Areas in Communications
A sensor network performance inference algorithm based on passive measurement
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
BC_EM: a link loss inference algorithm for wireless sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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The internal link performance inference has become an increasingly important issue in operating and evaluating a sensor network. Since it is usually impractical to directly monitor each node or link in the wireless sensor network, we consider the problem of inferring the internal link loss characteristics from passive end-to-end measurement in this paper. Specifically, the link loss performance inference based on the data aggregation is considered. Under the assumptions that the link losses are mutually independent, we formulate the problem of link loss estimation as a Bayesian inference problem and propose a Markov Chain Monte Carlo algorithm to solve it. Through the simulation, we can safely reach the conclusion that the internal link loss rate can be inferred accurately, comparable to the sampled internal link loss rate, and the simulation also shows that the proposed algorithm scales well according to the sensor network size.