Distributed data gathering in multi-sink sensor networks with correlated sources

Abstract

In this paper, we propose an effective distributed algorithm to solve the minimum energy data gathering (MEDG) problem in sensor networks with multiple sinks. The problem objective is to find a rate allocation on the sensor nodes and a transmission structure on the network graph, such that the data collected by the sink nodes can reproduce the field of observation, and the total energy consumed by the sensor nodes is minimized. We formulate the problem as a linear optimization problem. The formulation exploits data correlation among the sensor nodes and considers the effect of wireless channel interference. We apply Lagrangian dualization technique on this formulation to obtain a subgradient algorithm for computing the optimal solution. The subgradient algorithm is asynchronous and amenable to fully distributed implementations, which corresponds to the decentralized nature of sensor networks. © IFIP International Federation for Information Processing 2006.