Ant Colony Optimization Based Routing Strategies for Internet of Things

Abstract

: In this paper, two meta-heuristic approaches have been optimized using SI (Swarm Intelligence) optimization technique. The method comprises computation of shortest path of both, static and dynamic IoT (Internet of Things) network using “widely used algorithms” among researchers, namely, ‘Breadth first search’ and ‘Dijkstra algorithm.’ Further, the determined route has been optimized using ant colony optimization. The issue of route selection to reach the destination, as well as parameters such as network energy, departed node count, residual energy of IoT nodes, and critical points of IoT nodes have been explored using proposed smart routing techniques. The two unique routing approaches have been simulated with rigorous iteration run, i=2000. The proposed methods, ‘Ant colony optimization-Breadth first computation-Minkowski-Static’ (ABMS) technique and ‘Ant colony optimization-Breadth first computation-Minkowski-Dynamic’ (ABMD) technique have been simulated. After comparing efficiency between both techniques, ABMS method outperforms the ABMD routing technique for IoT network. A significant energy savings has been reported, extending network lifetime of a static IoT network scenario. With the implementation of both techniques, the comparison between dynamic and static results closely.