An approach based on fuzzy inference system and ant colony optimization for improving the performance of routing protocols in Wireless Sensor Networks

The Wireless Sensor Networks (WSNs) are composed of small sensor nodes capable of sensing (collecting), processing and transmitting data related to some phenomenon in the environment. However, the sensor nodes have severe constraints, such as: low network bandwidth, short wireless communication range, and limited CPU processing capacity, memory storage and power supply. Therefore, maximizing the benefits of limited resources in WSNs have become one relevant and challenging issue. One of the most relevant problem is related with the energy consumption during data transmission, since, sensor nodes are battery-powered and recharging or replacing batteries, in most cases, is infeasible. Communication in WSN consumes more energy than sensing and processing performed by the network nodes. The strategy proposed in this paper, to reduce the energy consumption, consists in optimizing the operations of routing protocols. The WSN routing protocols must have self configuration features in order to find out which is the best route for communication, thus increasing delivery assurance and decreasing the energy consumption between nodes that comprise the network. This paper presents a proposal for estimating the quality of routes using fuzzy systems to assist the Directed Diffusion routing protocol. The fuzzy system is used to estimate the degree of the route quality, based on the number of hops and the energy level of the nodes that compose a route. An Ant Colony Optimization (ACO) algorithm is used to adjust, in an automatic way, the rule base of the fuzzy system in order to improve the classification strategy of routes, hence increasing the energy efficiency of the network. The simulations showed that the proposal is effective from the point of view of three metrics: packet loss rate, message delay to the sink node and time of death of the first sensor node.