MULTI-OBJECTIVE WHALE OPTIMIZATION ALGORITHM USING FRACTIONAL CALCULUS FOR GREEN ROUTING IN INTERNET OF THINGS
In today’s world, the Internet of Things (IoT) performs a crucial character. It is going to integrate almost all things such as household devices to daily usable things with the internet slowly. Everyone including academicians and industry people looks at Internet of Things as a huge opportunity. The things that will be connected to the internet will have unique identifiers for presenting their uniqueness across the network. Things will generate huge amount of data which will be further need to be routed via a better route to the expected destination.
This paper discusses a multi-objective fractional Whale optimization algorithm which is proposed for finding the suitable cluster heads in the randomly deployed network for routing the data in energy efficient manner in Internet of Things network. The intention behind this protocol is to extend the life span of the energy constrained IoT devices so that they can survive for longer time in the network and the network owner will get maximum output from the network for the deployed application. This algorithm selects cluster heads by using the fitness function which is built on several parameters such as lifetime of link, distance and the latency of communication between two nodes.
The simulation and analysis of the functionality of the algorithm is done using MATLAB. Further, the outcome of this algorithm is compared with the similar algorithms like Multi-Objective Fractional Gravitational Search Algorithm (MOFGSA), Multi Particle Swarm Immune cooperation algorithm (MPSICA) and Multi-Objective Fractional Artificial Bee colony algorithm (MOFABC). It shows that the proposed algorithm outperforms the existing algorithms.