Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
On Detection of Malicious Users Using Group Testing Techniques
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Weighted superimposed codes and constrained integer compressed sensing
IEEE Transactions on Information Theory
Asymptotic optimality of running consensus in testing binary hypotheses
IEEE Transactions on Signal Processing
Efficiently decodable non-adaptive group testing
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Optimal Distributed Detection in Clustered Wireless Sensor Networks
IEEE Transactions on Signal Processing - Part II
Group Testing With Random Pools: Optimal Two-Stage Algorithms
IEEE Transactions on Information Theory
Group Testing With Probabilistic Tests: Theory, Design and Application
IEEE Transactions on Information Theory
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We 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.