The Internet of Things (IoT) renders swarm robotics possible, which makes tasks like surveillance, farming, and disaster response more efficient and flexible. The limited energy and processing power on board make things very difficult, especially since coordination requires a lot of communication. The goal of this paper is to lower the cost of communication in robotic swarms so that they last longer and make better use of resources. We offer a unified optimization framework that combines adaptive communication protocols with heuristic offloading algorithms. The main goal is to reduce communication energy, with computational cost being a secondary concern. The system changes the power levels and transmission rates based on how well the network is working. It uses a heuristic based on PSO to find the best balance between processing data locally and sending it to the cloud. Numerous evaluations on embedded platforms (NVIDIA Jetson Nano and TX2) demonstrate that the proposed method significantly conserves energy, reducing communication related energy consumption by approximately 25–35 percent relative to static schemes, while also mitigating CPU load fluctuations. Our results show that putting communication optimization first greatly improves swarm energy efficiency without hurting coordination performance. • Validate energy-efficient IoT operation through detailed simulations. • Analyze communication energy factors in robotic IoT swarms. • Relate computation and communication costs in swarm scheduling. • Propose a heuristic for joint workload and communication optimization. • Implement adaptive wireless protocols on embedded hardware.
Ijaz et al. (Sat,) studied this question.