Improved Memetic Algorithm for Energy Efficient Sensor Scheduling with Adjustable Sensing Range

In this paper, a novel improved memetic algorithm is proposed for maximizing the sensor covers from the randomly deployed sensors in a hostile environment. This is achieved by optimizing the sensing range of the sensors by reducing the redundant target coverage and partitioning the set of all sensors into several subsets or sensor covers in such a way that each sensor cover monitors the entire targets. Further, sensor covers are activated one after another for maximizing the lifetime of a sensor network. The proposed algorithm identifies the maximum number of sensor covers by selecting the best sensors and adjusts the required sensing range. Simulation results of various problem instances proves that network lifetime of improved memetic algorithm is 1.1662 times higher than the existing memetic algorithm and 1.6848 times higher than the existing genetic algorithm.