A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor Networks

When wireless sensors are randomly deployed in natural environments such as ecological monitoring, military and disaster the initial position of is generally formed through deployment methods air-drop, then, second carried out existing optimization methods, but these will still lead to serious coverage holes. In order solve this problem, paper proposes an algorithm improve rate for sensor networks based on improved metaheuristic algorithm. The model was firstly established, network problem transformed into a high-dimensional multimodal function problem. Secondly, global searching ability range enhanced by reverse expansion populations. Finally, firefly principle introduced reduce local binding force sparrows avoid population search process. experimental results showed that compared with ALO, GWO, BES, RK, SSA algorithms, EFSSA better than other algorithms benchmark tests, especially test function. tests different monitoring ranges number nodes, higher algorithms. result can tell effectively enhance deployment.