Localization of WSN Using Fuzzy Inference System with Optimized Membership Function by Bat Algorithm

Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system using the data of received signal strength in real indoor condition. The author has realized the algorithm on Zigbee platform and the experimental comparison on other different centroid localization algorithms indicates that Mamdani fuzzy inference adopting the membership function optimized by bat algorithm renders smaller mean localization errors.