An Improved Genetic Algorithm for Scheduling Sensor Nodes in WSN

Abstract

The extended lifetime of Wireless Sensor Networks (WSN) is an attractive goal for various types of research. This can be achieved if not all sensor nodes in the network consume their energy; this is because the energy consumption of every sensor node is a vital resource for a WSN. The scheduling techniques have recently enticed the interest of the researchers’ community, as they provide the ability to adjust a set of nodes in sleep mode instead of activating all sensor nodes. However, we consider the sensors that were selected to be in sleep mode, which will not affect network coverage for any target or full connectivity. In this paper, the genetic algorithm has been used to build efficient scheduling for the sensor nodes, which were based on multiple objectives in the fitness function, and we have proposed improved mutation and crossover operations. Then, we evaluate our approaches in a target tracking application compared with previous GA approaches. Our simulations show that we have an optimal chromosome that contains a minimum number of active sensor nodes for scheduling within 3-5 iterations.