Optimal Radius for Enhanced Lifetime in IoT using Hybridization of Rider and Grey Wolf Optimization

Abstract : Connecting all devices through the Internet is now practical via the Internet of Things (IoT). IoT is characterized by using smart and self-configuring objects that can interact with each other via global network infrastructure. Clustering is the promising technique that effectively works on the enhancement of network lifetime. This paper intends to introduce a new clustering technique, where the selection of cluster head is done by a new hybrid algorithm termed Over taker Assisted Wolf Update (OA-WU), which hybrids the concept of Rider Optimization Algorithm (ROA) and Grey Wolf Optimization algorithm (GWO). This cluster head selection has been dealt with certain constraints like (i) Energy (ii) Distance and (iii) Cluster Radius. The proposed OA-WU performance is compared with the traditional methods with respect to alive node analysis, cost function analysis and energy analysis. The results demonstrate that the proposed OA-WU algorithm adequately improves the energy conservation and convergence rate in a minuscule period.

 EXISTING SYSTEM :

 ? The protocols which are existing now have rigid boundaries. So it is mandatory to build a routing protocol that can incorporate all types of heterogeneity in it. ? In this algorithm tends to divide the existing data into meta data and sends it to neighboring nodes to avoid redundant data transmission. ? There are various challenges exist in MANET, for example every mobile node has limited range of communication, power supply is limited and because of node movements, there exists chances of link breakage. ? The service discovery is exist in application layer and the protocols are Bonjour, UPnP and Alljoyn.

 DISADVANTAGE :

 ? It is mixed because there are two different approaches that are involved to generate a final optimal solution of the test benchmark and real life problems. ? Exploitation is the convergence ability to the most excellent solution of the problem near a good optimal solution and exploration is the capability of an algorithm to locate whole parts of a problem search space. ? Under this research, the Whale Optimizer Algorithm is used for the exploration phase as it uses logarithmic spiral problems, so it covers broader areas in uncertain search spaces. ? The average computational time of the successful runs and the average number of problem evaluations of successful runs, are applied to estimate the cost of the standard problem.

 PROPOSED SYSTEM :

 • Extensive simulation is carried out using MATLAB 2019a and the performance of the proposed T2FL-PSO is compared with recent similar algorithms, namely, PSO-C and PSO-WZ. • The simulation is carried out and by comparison, the proposed work increases the lifetime 8%–10% than LEACH. • The proposed MOTCO algorithm improves the network lifetime by 10% compared with LEACH, PSO, ABC and FABC. • To overcome these issues, the authors proposed a krill herd (KH) optimization algorithm to select the best CH in the network. • The proposed TTDFP protocol is compared with similar cluster-based routing protocols.

 ADVANTAGE :

 ? Several algorithms have also been developed to improve the convergence performance of GWO that includes parallelized GWO, a hybrid version of GWO with PSO and binary GWO. ? The common goal of these algorithms is to improve quality of solutions, stability and convergence performance. ? The position and convergence performance of the grey wolf (alpha) is improved using position update equations of SCA. ? The performance of the new hybrid algorithm has been tested on several standard test functions and performance of the algorithm has been compared with different metaheuristics. ? This variant has been developed for the purpose of improving the exploration and exploitation performance of the basic GWO algorithm.