A distributed monitoring mechanism for wireless sensor networks
WiSE '02 Proceedings of the 1st ACM workshop on Wireless security
Taming the underlying challenges of reliable multihop routing in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Loss inference in wireless sensor networks based on data aggregation
Proceedings of the 3rd international symposium on Information processing in sensor networks
Sympathy for the sensor network debugger
Proceedings of the 3rd international conference on Embedded networked sensor systems
Models and solutions for radio irregularity in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
An analysis of unreliability and asymmetry in low-power wireless links
ACM Transactions on Sensor Networks (TOSN)
Optimal and near-optimal test sequencing algorithms with realistic test models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Network Tomography of Binary Network Performance Characteristics
IEEE Transactions on Information Theory
The use of end-to-end multicast measurements for characterizing internal network behavior
IEEE Communications Magazine
A factor graph approach to link loss monitoring in wireless sensor networks
IEEE Journal on Selected Areas in Communications
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Faulty components in a network need to be localized and repaired to sustain the health of the network. In this paper, we propose a novel approach that carefully combines active and passive measurements to localize faults in wireless sensor networks. More specifically, we formulate a problem of optimal sequential testing guided by end-to-end data. This problem determines an optimal testing sequence of network components based on end-to-end data in sensor networks to minimize testing cost. We prove that this problem is NP-hard and propose a greedy algorithm to solve it. Extensive simulation shows that in most settings our algorithm only requires testing a very small set of network components to localize and repair all faults in the network. Our approach is superior to using active and passive measurements in isolation. It also outperforms the state-of-the-art approaches that localize and repair all faults in a network.