Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT

The advancements of technology in the field of communication made WSN based IoT attractive and applicable to various areas. It is comprised IoT nodes that work on limited battery supplies. Hence, a high-performance routing protocol is essential for routing in such networks to overcome the energy constraint problems. In this paper, an energy efficient routing algorithm Fractional Gravitational Grey Wolf Optimization (FGGWO) is proposed for multipath data transmission. This work is motivated by the Ant Colony Optimization (ACO) algorithm that discovered multipaths based on clustering technique. The proposed algorithm improves the routing process of ACO in a two stage process. At first, the cluster heads are selected by utilizing the previous work Fractional Gravitational Search Algorithm (FGSA). Secondly, multiple paths are generated from the source to the destination using FGGWO, which modifies Grey Wolf Optimization by integrating FGSA in the algorithm. Objectives, such as, energy, inter and intra-cluster distance, delay and lifetime, considered in the fitness function provide optimal paths for the transmission. The experimental results show that the proposed FGSA + FGGWO algorithm has higher performance regarding energy and alive nodes, in comparison with the existing ABC + ACO, FABC + EACO, and Threshold + ACO techniques. The maximum number of alive nodes and energy estimated in FGSA + FGGWO is 25 and 0.1298 for 50 nodes; and 27 and 0.0876, for 100 nodes.