Optimizing Wireless Mesh Networks for IoT Using Hybrid Genetic-PSO Algorithm

WMNs are considered as one of the primary enabling technologies for growth and development of IoT systems because of their feature of providing scalable and robust connections in supporting the fast-evolving IoT applications. However, the challenge of efficiently and effectively addressing these issues while enhancing energy efficiency, low latency and high throughput of these network increases as the numbers of connected devices rises. This research work therefore presents a genetic-PSO algorithm for the optimization of WMNs for IoT paradigms. The hybrid of GA and PSO is used to attempt GA capability of exploration with PSO ability to converge when solving multi-objective optimization problems in WMNs; energy, latency and throughput. As a performance comparison metric, we have compared our Hybrid Genetic-PSO algorithm with the original optimization methods for real-world data by the deployment of a smart city IoT and simulated data of NS-3 network simulator. The study shows that the use of the proposed algorithm has a positive effect on energy consumption by cutting it by 20.8%, latency by reducing its by 20.8% and at the same time increasing through put by 28%. Some of these improvements were also tested and confirmed on live networks proving the usefulness of the algorithm in real world application. The Hybrid Genetic-PSO algorithm is a robust optimization technique for WMNs providing assurance that WMNs needs to meet IoT requirements for applications such as real-time control and low-energy data transmission.