Eavesdropping-Based Gossip Algorithms for Distributed Consensus in Wireless Sensor Networks


During this letter, we present an eavesdropping-based mostly gossip algorithm (EBGA). In the novel algorithm, when a node unicasts its values to a randomly selected neighboring node, all different nodes, which eavesdrop these values, simultaneously update their state values. By exploiting the broadcast nature of wireless communications, this novel algorithm has similar performance to broadcast gossip algorithms. Although broadcast gossip algorithms have the fastest rate of convergence among all gossip algorithms, they either converge to a random price rather than the typical consensus, or would like out-degree information accessible for each node to guarantee convergence to the typical consensus. Utilizing non-negative matrix theory and ergodicity coefficient, we have a tendency to have proved that this novel algorithm can converge to the typical consensus without any assumption which is troublesome to be realized in real networks.

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