Sensing task assignment via sensor selection for maximum target coverage in WSNs

In this paper, we consider the problem of assigning the sensing task to cover maximum number of targets while minimizing the energy consumption of the sensing operation. To this end, we define the sensing task as an optimization problem of adjusting the sensing range parameter jointly with selection of nodes in a target coverage mission. We derive an energy consumption model for the sensing operation and propose a distributed greedy-based heuristic. Each node extracts a priority value based on its utility function, which is related to the distances of targets from that node. Nodes with less priority reduce their sensing range before their neighbors and optimal adjustment of sensing range of active nodes is done via a dual-based algorithm. We further extend the algorithm for scenarios with dynamic movements of targets and for localization of possible targets between neighboring nodes. Comparison of simulation results with three other methods, shows an average reduction of 30% and a maximum of 38% in the sensing energy consumption by reducing the overlaps at an average of 56%. As a result of adjusting the sensing ranges and reduced overlaps, energy consumption is distributed more uniformly in the network, which consequences in increasing the network lifetime by 26%.