Improving Energy Consumption Using Fuzzy-GA Clustering and ACO Routing in WSN

With the progression of the sensors network and it’s was widely used in many fields, wireless sensor network (WSN) emerges as a new topic in the world. These networks which deployed often in remote locations has important constraints affected to it, such as energy and memory limitation of the nodes. Many studies proposed to enhance the network’s performance in WSNs. Fuzzy logic (FL) used to prolong the lifetime of the network. In this article fuzzy logic has been designed to form the clusters and select the clusters head. In traditional fuzzy, the rules and memberships of FL are manually tuning to get to the best operation, this makes leak to the system and consumed more time. Because GA is a powerful and largely applicable stochastic search technique, it can be implemented to multi-objective function and it easy to exploit previous or alternate solutions. Therefore, a fuzzy-based Genetic algorithm was proposed to enhance the execution of the re-composition of these clusters in WSNs. GA was employed to generate optimum fuzzy rules and tuning the output value of fuzzy logic’s memberships. Moreover, the ant colony algorithm (ACO) proposed to route the information in the shortest path between the cluster heads to the base station (BS). All these tasks can be considered as optimization or search processes within large solution spaces to get the best results. The contribution of this work was considering short routing and less loading balance in such networks. The results show that a lifetime of the network is higher than other methods and load balance has good results.