A Novel Enhanced Coverage Optimization Algorithm for Effectively Solving Energy Optimization Problem in WSN

In Wireless Sensor Networks (WSN), Efficient-Energy Coverage (EEC) is one of the important issues for considering the (WSNs) implementation. In this study, we have developed the new algorithm ECO (Enhanced Coverage Optimization) for solving the EEC problem effectively. The proposed algorithm uses three types of major work for effectively solving the problem. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI) and the other is used to form a sensor set that has as many senses as an ant has selected the number of active sensors by using the former pheromone. This study also introduces one technique that leads to a more realistic approach to solving the EEC problem that is to utilize the probabilistic sensor detection model. The main goal of ECO is Efficient Coverage on target area with minimum energy consumption and increased network's lifetime.