Distributed sensor failure detection in sensor networks

Abstract

a b s t r a c tWe investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from the neighbor measurements. We build on the sparse nature of the binary sensor failure signals to propose a novel distributed detection algorithm based on gossip mechanisms and on Group Testing (GT), where the latter has been used so far in centralized detection problems. The new distributed GT algorithm estimates the set of scattered defective sensors with a low complexity distance decoder from a small number of linearly independent binary messages exchanged by the sensors. We first consider networks with one defective sensor and determine the minimal number of linearly independent messages needed for its detection with high probability. We then extend our study to the multiple defective sensors detection by modifying appropriately the message exchange protocol and the decoding procedure. We show that, for small and medium sized networks, the number of messages required for successful detection is actually smaller than the minimal number computed theoretically. Finally, simulations demonstrate that the proposed method outperforms methods based on random walks in terms of both detection performance and convergence rate.

Distributed group testing detection in sensor networks

“…Errors occur in cases when the number of column flips in the probabilistic tests matrix generation is higher than ǫ or when its disjunct property is violated. Finally, we show in [6] that, if the number of collected messages is proportional to O(K log(S)/p 3 ), the disjunct property holds for any fixed test matrix with high probability.…”

“…The complete proof of this proposition is available in [6]. In short, we bound the error causing events by employing a Chernoff bound analysis and show that the probability of decoding failure per cluster is small.…”

“…In RW, each node along the path stores the values of all previously "visited" nodes. More details about algorithms are given in [6]. Fig.…”

“…In spite of that, the messages generated within clusters that contain more than one defective sensor may still cause error occurrence. Erroneous messages are generated when a fraction of defective sensors in a cluster participates actively in tests, but this has a negligible probability [6].…”

Learning the Interference Graph of a Wireless Network

“…For both versions of the problem we present easy-to-implement graph estimation algorithms. arXiv:1208.0562v2 [cs.IT] 16 Dec 2016…”