An evolutionary routing protocol for load balancing and QoS enhancement in IoT enabled heterogeneous WSNs

Abstract

Energy awareness is a key concern of recent advances in the Internet of Things (IoT) enabled wireless sensor networks (WSNs), and many optimization approaches to reduce energy consumption have been proposed. The most widely used routing technique for achieving energy efficiency in WSNs is the clustering hierarchy. Data transmission over long distances has a negative impact on network efficiency in terms of stability period, network lifetime, and QoS due to the selection of inadequate Cluster Heads (CHs). This paper describes a new Evolutionary Gateway-based Load-Balanced Routing (E-GLBR) algorithm for efficiently selecting appropriate CHs. The proposed algorithm is based on the genetic algorithm optimization method with a new fitness function that takes into account four major parameters to achieve the following goals: improving the CH selection process, reducing energy transmission range, increasing network stability and lifetime, and improving WSN coverage. A comparison simulation with the most recent related methods in MATLAB simulator is performed to evaluate the performance of our proposed algorithm. The simulation findings demonstrate that applying the developed evolutionary approach reduces the network’s energy consumption rate and increases the wireless network throughput. In various network scenarios, our suggested approach surpasses all other examined methods, extending the network coverage and prolonging the stability periods of Evolutionary Routing Protocol (ERP), Energy-Efficient Weighted Clustering (EEWC), Distance Incorporated Modified Stable Election Protocol (D-MSEP), and Energy dependent cluster formation (EDCF) by 55%, 43%, 26%, and 12% respectively.