HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network

Abstract

In IoT-based WSN applications, energy is a vital parameter since limited residual energy of a node often leads to packet loss, diminished network lifetime, and end-to-end delay. IoT nodes are often resource-constrained, with limited computational and storage capabilities, communication range, and energy-saving potential. To offer an improved Quality of Service (QoS) guarantee, the network lifetime and energy consumption of the WSN-based IoT applications need to be improved. QoS is an important parameter in time-sensitive IoT applications. Hence this paper introduces a novel QoS improvement framework for the WSN-IoT networks, and the objective of this work is twofold. In the first phase, we utilize an Aquila optimizer (AO) for an efficient and reliable cluster head selection which is based on different factors such as node level, distance to the sink node, energy level, etc. The AO algorithm enhances the lifetime of the WSN network by electing the QoS-aware relay node as the cluster head. In the next phase, the Hybrid Fuzzy Levy Flight Particle swarm optimization algorithm (HFLPSO) is presented to enhance the QoS of the network even further by identifying a set of optimal paths to route the packets and measure the reliability of each path via different metrics (packet loss rate, residual energy, end-to-end delay, link stability, and the number of hops). The simulation experiments were conducted using different metrics such as End to End delay, Packet loss rate, and Packet delivery ratio to prove that the proposed model is capable of improving the QoS of the WSN-IoT networks when compared to the different state-of-art methodologies. When compared to the existing techniques, the proposed model offers a 10% improvement in the packet loss rate and a 17% improvement in the packet delivery rate.