Improving the Network Lifetime and Performance of Wireless Sensor Networks for IoT Applications Based on Fuzzy Logic

Abstract

Wireless sensor network (WSN) is one of the key enablers for Internet of Things (IoT) applications such as smart homes, intelligent manufacturing, agriculture, healthcare monitoring among others. Small sensors are deployed in a specific environment to sense and acquire the vital data and transmit to Base Station (BS). Due to resource constraints of the sensors and the need for long lifetime, energy consumption is a challenging issue that directly affects the network lifetime and performance of the IoT applications. In this paper, we present a novel intelligent clustering technique utilizing a computational intelligence technique, namely fuzzy logic, to efficiently improve the network lifetime and performance. In particular, we propose a load balance clustering algorithm (LBCA) that performs load balancing on the selection of cluster head (CH) among all sensors, based on a priority queue, using a fuzzy inference system, to minimize and distribute the energy consumption. In addition, we propose a scheduling algorithm based on TDMA for reducing unnecessary intra-cluster communication that leads to a prolonged lifetime and enhanced performance. Simulations are conducted to evaluate the performance of the proposed fuzzy logic based clustering technique, taking into account the network lifetime in terms of First Node Dead, Half Nodes Dead and End Node Dead, and the network performance in terms of packets sent to BS. Based on the simulation results, the proposed clustering technique has shown significant benefits compared to other conventional solutions, revealing the proficient network lifetime and performance provided by the proposed fuzzy logic based clustering technique.