Probabilistic inference of lossy links using end-to-end data in sensor networks
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
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
Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework
Journal of Visual Communication and Image Representation
Loss tomography in wireless sensor network using Gibbs sampling
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
Belief propagation, Dykstra's algorithm, and iterated information projections
IEEE Transactions on Information Theory
Large scale probabilistic available bandwidth estimation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Full length article: Neighbor discovery in wireless networks: A multiuser-detection approach
Physical Communication
Routing topology inference for wireless sensor networks
ACM SIGCOMM Computer Communication Review
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The highly stochastic nature of wireless environments makes it desirable to monitor link loss rates in wireless sensor networks. In a wireless sensor network, link loss monitoring is particularly supported by the data aggregation communication paradigm of network traffic: the data collecting node can infer link loss rates on all links in the network by exploiting whether packets from various sensors are received, and there is no need to actively inject probing packets for inference purposes. In this paper, we present a low complexity algorithmic framework for link loss monitoring based on the recent modeling and computational methodology of factor graphs. The proposed algorithm iteratively updates the estimates of link losses upon receiving (or detecting the loss of) recently sent packets by the sensors. The algorithm exhibits good performance and scalability, and can be easily adapted to different statistical models of networking scenarios. In particular, due to its low complexity, the algorithm is particularly suitable as a long-term monitoring facility.