Hybrid Optimization-Based Multi-Path Routing for Dynamic Cluster-Based MANET

Abstract

Mobile Ad-hoc Networks (MANETs) have emerging applications in real-time with lots of research challenges. Specifically, the dynamic nature of the mobile nodes limits the performance of routing in MANET. The existing routing algorithms, such as AODV, DSR, and DSDV, lack performance due to an ineffectual route discovery procedure. When it comes to large-scale applications such as air pollution monitoring, routing becomes more complex and consumes more energy for route selection. This research work aims to increase data delivery while minimizing energy consumption for air pollution monitoring applications. To achieve this, we have proposed a novel Hybrid Optimization methodology for MANETs. First, we partitioned the network into multiple dynamic clusters by using Dual Constraint Clustering (DCC) approach that works upon Mobility Metric (MoM) and Hop Count (HC). In each cluster, the Cluster Head (CH) is selected by Type-II fuzzy approach. Then, routing is performed by Hybrid Cellular Automata and African Buffalo Optimization (HCA2BO) algorithm. The proposed optimization algorithm considers multiple metrics to select an optimum route. The extensive analysis in the ns-3 simulation tool shows enhanced performance in network lifetime, energy consumption, and delay. Also, an air pollution monitoring application is demonstrated in the proposed work.