A hybrid approach based on genetic fuzzy systems for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are composed of sensor nodes in order to detect and transmit features from the physical environment. Generally, the sensor nodes transmit informations to a special node, called sink. The use of an unique sink represents a bottleneck in a network, especially for applications in real time. In this sense, some researches have directed studies to the use of multiple sinks. The approach proposed by this paper presents the application of Genetic Fuzzy System (GFS) for the selection of routes in WNSs, in order to make the communication between multiple sensor nodes and multiple sink nodes. Fuzzy Inference System of Mamdani are used to determine the most appropriate sink node through consideration of some characteristics of the sensors network, such as energy and number of hops. Genetic Algorithms are employed to obtain the optimal adjustment of Mamdani's fuzzy inference system parameters. By applying GAs, we intend to achieve both a fuzzy database and a fuzzy rules base to maximize performance of the application of Mamdani's inference system in the selection of routes in Wireless Sensor Networks. The proposed route selection was applied by means of computer simulations to demonstrate the feasibility of the approach implemented. The results obtained through simulations demonstrated a sensor network with a longer lifetime, through the choice of the adequate sink used for sending packets through the network in order to find the best routes.