An intelligent energy efficient optimized approach to control the traffic flow in Software-Defined IoT networks

Abstract

In modern society numerous digital devices play a very significant role in day-to-day life. Digital devices are well connected and easily accessible through multiple sensors and Internet of Things (IoT) devices. Due to the rapid growth of digital devices, large amount of data traffics are being generated, which induces network congestion. To deal with large amount of data traffic a programmable Software Defined IoT (SD-IoT) infrastructure is utilized. For efficient and sustainable network, the data must be transmitted through optimal path in such a way to as to minimize energy consumption. Here, the network is partitioned into clusters to find an optimal path. Finding an optimal path from a set of possible paths is an NP-complete problem. To solve this problem, we propose to find a set of optimal border nodes of each cluster with other clusters in the network, so as to reduce the number of possible paths between clusters. The set of optimal border nodes will be selected in such a way so that they have maximum energy and minimum distances. This paper proposes an intelligent approach to find the set of optimal border nodes using Lion Swarm Optimization algorithm (LSOA). Once a set of optimal border nodes are obtained, an optimal path can be generated using a routing mechanism. The performance of the proposed work is analyzed in terms of packet delivery ratio, average latency, network lifetime and energy consumptions. The results show that the border nodes selected using LSOA finds better routes as compared to the border nodes selected using other state-of-the-art metaheuristics algorithm thereby, increases suitability of the network by energy conservation.