Energy-efficient computation offloading using hybrid GA with PSO in internet of robotic things environment

Abstract The Internet of Robotic Things (IoRT) is an integration between autonomous robots and the (IoT) based on smart connectivity. It's critical to have intelligent connectivity excellent communication for IoRT with digital platforms in order maintain real-time engagement efficient consumer power new-generation apps. proposed model will be utilized determine optimal way task offloading devices reducing amount energy consumed environment achieving deadline constraints. approach implemented fog computing reduce overhead edge cloud. To validate efficacy schema, extensive statistical simulation was conducted compared other related works. schema evaluated against Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale (WOA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Grey Wolf (GWO), Salp confirm its effectiveness. After 200 iterations, our found most effective energy, a reduction 22.85%. This followed closely by GA ABC, which achieved reductions 21.5%. ALO, WOA, PSO, GWO were less effective, 19.94%, 17.21%, 16.35%, 11.71%, respectively. current analytical results prove effectiveness suggested consumption optimization strategy. experimental findings demonstrate that reduces requests more effectively than technological advances.