Wireless sensor network simulation by artificial intelligent

Wireless sensor network (WSN) consisting of a large number of sensors are valuable for gathering data in a variety of environments. It is a great challenging aim to design an energy efficient network since the sensors operate on battery of limited power. Energy-efficiency is one of the major concern in wireless sensor (WSN) is since it impacts the network lifetime along with all targets can be covered. In the target area after the random deployment of sensors, the problem of finding the largest number of disjoint sets of sensors, with every set being able to completely cover the target area, is nondeterministic polynomial-complete. Finding the maximum number of sensor covers can be solved via transformation to the Disjoint Set Covers (DSC) problem, which has been proved to be NP-complete. This paper proposes a genetic algorithm to solve the DSC problem. Maximizing the lifetime of a sensor network by scheduling operations combining a genetic algorithm of sensors is an effective way to construct energy efficient wireless sensor networks. This paper proposes a hybrid approach of combining a genetic algorithm with schedule transition operations. The algorithm adopts a forward encoding scheme for chromosomes in the population and uses some effective genetic and sensor schedule transition operations.