Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms

Abstract

Connectivity and different coverage types including target, area, and barrier coverages are among the most critical challenges of wireless sensor networks (WSN). This paper investigates node deployment for the challenges of target coverage, area coverage, and connectivity over randomly distributed homogeneous and heterogeneous WSNs, and suggests a new method in both centralized and distributed modes which (i) covers all targets by the required number of sensors, (ii) provides maximum area coverage and connectivity over the network, and (iii) manages sensors' movement from the initial locations to the final ones. Specifically, in the case of target coverage, a new analytical deployment method using the steepest descend (SD) algorithm with Armijo and Wolf rules is proposed instead of evolutionary methods. The results demonstrate this method outperforms over Genetic algorithm (GA) in managing sensors' movement towards targets, guaranteeing 100% coverage in targets, and providing a significant reduction- more than 40% in the worst condition-in algorithm complexity. However, when area coverage and connectivity are also considered, a new hybrid deployment method, which first uses GA to extract the optimal sensors' coordinates via analyzing these challenges simultaneously and then employs SD algorithm to move sensors towards these locations, is suggested. Numerical results confirm the complexity of this two-step method is approximately identical to the GA method of existing research; however, it provides sensors' trajectory and more accuracy for network coverage and connectivity.