Elements of information theory
Elements of information theory
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Distributed Algorithms
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Enforcing Consensus While Monitoring the Environment in Wireless Sensor Networks
IEEE Transactions on Signal Processing - Part II
Consensus-based Page's test in sensor networks
Signal Processing
Distributed sensor failure detection in sensor networks
Signal Processing
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Consensus in sensor networks is a procedure to corroborate the local measurements of the sensors with those of the surrounding nodes, and leads to a final agreement about a common value that, in detection applications, represents the decision statistic. As the amount of collected data increases, the convergence toward the final statistic is ruled by suitable scaling laws, and the question arises if the asymptotic (large sample) properties of a detection statistic are retained when this statistic is approximated via consensus algorithms. We investigate the asymptotic properties of running consensus detectors both under the Neyman-Pearson paradigm (fixed number of data) and in the sequential case. An appropriate asymptotic framework is developed, and exact theoretical results are provided, showing the asymptotic optimality of the running consensus detector. In addition, numerical experiments are performed to address nonasymptotic scenarios.