Energy-efficient decentralized event detection in large-scale wireless sensor networks

This paper addresses the problem of decentralized event detection in large-scale wireless sensor networks (WSNs). Compared with centralized or hierarchical solutions, decentralized algorithms are superior in terms of scalability and robustness. However, traditional decentralized optimization tools, such as consensus optimization, entail intensive information exchange of high-dimensional decision vectors and multipliers. This paper exploits the phenomenon of limited influence, namely, the influence of one event only affects its neighboring area. For this scenario, we let each sensor make decisions for its local area rather than for the entire network, and individual decisions seek to collaboratively reach the global optimum through iterative local communications at low network costs. An optimal solution based on the alternating direction method of multipliers (ADMM) is developed. To further reduce the network communication load, we also propose a heuristic decentralized linear programming (DLP) algorithm, which is shown to be efficient via simulations.