Thesis:

Presenting a routing algorithm based on artificial rabbit metaheuristics to optimize the performance of industrial Internet of Things networks

In this study, an optimization algorithm inspired by rabbit behavior, called ARO (Artificial Rabbits Optimization), is proposed to solve the routing problem in Industrial Internet of Things (IIoT) networks. Due to the resource-constrained and critical nature of these networks, designing a reliable path with minimal energy consumption and delay is essential. To evaluate the performance of the proposed algorithm, a simulation environment was implemented in MATLAB, where 30 sensor nodes were randomly distributed in a 100×100 meter area. Key metrics such as energy consumption, transmission delay, link stability, and number of intermediate nodes were analyzed. ARO was compared with reference algorithms such as PSO, ACO, as well as newer approaches like HRL-TSCH and GWO. Results from 10 independent simulation runs show that although ARO consumed more energy than some algorithms, it consistently provided more stable routes, acceptable delay, full node coverage, and a more balanced performance. This makes it a reliable option for industrial applications in dynamic IIoT environments.

Keywords: Keywords: IIoT, Optimal Routing, Artificial Rabbits Optimization, Link Stability, Energy Consumption