Automated design of fuzzy rule base using ant colony optimization for improving the performance 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. The sensor nodes have severe constraints, such as: limited power supply, low network bandwidth, short wireless communication range, and limited CPU processing and memory storage. Communication in WSN consumes more energy than sensing and processing performed by the network nodes. Therefore, as the sensor nodes are battery-powered and recharging or replacing batteries, in most cases, is infeasible, maximizing the benefits of limited resources in WSNs have become one relevant and challenging issue. The WSN routing protocols must have autoconfiguration 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 lowest energy level among the nodes that form the 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 the packet loss rate, the necessary time to send a specific number of messages to the sink node and the lifetime of the first sensor node, which is defined as the period that the first sensor node die due to the battery depletion.