A Parallelly Implemented Hybrid Multi-Objective Efficient Persuasion of Coverage and Redundancy Programming Model for Internet of Things in 5G Networks using Hadoop

In 5G networks, the demand for IoT devices is increasing due to their applications. With the development and widespread adoption of 5G networks, the Internet of Things (IoT) coverage issue will collide with the issue of enormous nodes. In this paper, a parallell y implemented Hybridised Mayfly and Rat Swarm Optimizer algorithm utilising Hadoop is proposed for optimising the IoT coverage and node redundancy in IoT with massive nodes, which automatically lengthens the IoT's lifecycle. Initially, parallel operation d ivides the IoT coverage problem involving massive nodes into numerous smaller problems in order to reduce the problem's scope, which are then solved using parallel Hadoop. Using the flight behaviour and mating process of mayflies, we optimise the coverage problem here. Rats' pursuing and attacking behaviours are employed to optimise the redundancy problem. Then, select the non critical nodes from the critical nodes in an optimal manner. Lastly, parallel operation effectively resolves the IoT's coverage issu e through massive nodes by strategically extending the IoT's lifespan. Using the NS2 application, the proposed method is simulated. Computation Time, Energy efficiency, Lifespan, Lifetime, and Remaining Nodes are analysed as performance metrics. The propos ed MOP Hyb MFRS IoT 5GN method achieves lower computation times of 98.38%, 92.34%, and 97.45%, higher lifetime of 89.34%, 83.12%, and 88.96%, and lower remaining time as 91.25%, 79.90%, and 92.88% compared with existing methods such as parallel genetic alg orithm spread the lifespan of internet of things on 5G networks (MPGA IoT 5GN)