DDA-COV: A Distributed Dual-based Algorithm for Sensing Range Adjustment in WSNs

In this paper, we have modeled the problem of adjusting the sensing range parameter for the application of multiple target coverage in wireless sensor networks (WSNs) through the Network Utility Maximization (NUM) framework. Our problem consists of maximizing the logarithm of sensing range of each sensor node as an approximation to the number of covered targets subject to energy constraint and reduction in the number of common covered targets. We assume the placement of sensors is known and the sensing range of each node is a continuous parameter. We used the dual decomposition technique to derive a distributed price-based algorithm, DDA-COV, for each node to adjust its sensing range during iterations in which the targets are static. Simulation results show the convergence of sensing ranges and utility function towards optimal values in few iterations with near optimal energy consumption of nodes.