Optimizing the Performance of Rule-Based Fuzzy Routing Algorithms in Wireless Sensor Networks

Effective data routing is one of the crucial themes for energy-efficient communication in wireless sensor networks (WSN). In the WSN research domain, fuzzy approaches are in most cases superior to well-defined methodologies, especially where boundaries between clusters are unclear. For this reason, a significant number of studies have recently proposed fuzzy-based solutions for the problems encountered in WSNs. Rule-based fuzzy systems are part of these widespread fuzzy-based solutions that often involve some field experts for identification and derivation of fuzzy rules as well as fuzzy membership functions; thus, a considerable amount of time is devoted to the realization of the final system. Nevertheless, it is almost impossible or not feasible to realize a fuzzy system with an optimality property. In this study, we utilize the modified clonal selection algorithm (CLONALG-M) to improve the performance of rule-based fuzzy routing algorithms. Although previous studies have been devoted to fuzzy optimization in general, to the best of our knowledge, improving the efficiency of rule-based fuzzy routing algorithms has not yet been considered. For this reason, CLONALG-M is applied to determine the approximate form of the output membership functions that improve the overall performance of fuzzy routing algorithms, whose rule base and shapes of membership functions are initially known. Experimental analysis and evaluations of the approach used in this study are performed on selected fuzzy rule-based routing algorithms and the obtained results verify that our approach performs and scales well to improve fuzzy routing performance.