Abstract

With the development of the Internet of things (IoT), the energy consumption as well as latency and durability of the networks have become major issues that must be resizing in complicated networks. In order to solve these challenges, the current paper develops the Multi-Fractional Jaya Puzzle Optimization (FJPO) model to address the problems. The model for the optimization of resource allocation in IoT networks utilizes fractional calculus together with Jaya Puzzle Optimization algorithm and Deep Residual Networks (DRNs). A critical look is taken at limitations that are inherent in POP techniques like Particle Swarm Optimization Genetic Algorithm and Simulated Annealing, Ant Colony, Optimization among others basing on energy efficiency, latency and scalability. Simulation outcomes show that the proposed FJPO model decreases energy consumption of mobile nodes by 10-15 %, communication delay at least by 20 % and increases network lifetime more than on 10%. This is done through the dynamic choice of routing paths and Cluster Heads with the ability to consider several objectives at once. Based on these characteristics, the presented FJPO model can be considered as a promising solution for IoT networks, especially for those, located in the areas with strict limitations to the number of resources available.Keywords: Internet of Things (IoT), congestion-aware routing, Fractional Jaya Puzzle Optimization (FJPO), Cluster Head (CH) selection, Deep Residual Network (DRN), energy consumption, delay, network coverage, optimization algorithm, Fractional Calculus.