FOLD: Fog-dew infrastructure-aided optimal workload distribution for cloud robotic operations

Abstract

In our fast-paced, technology-driven world, multi-robot systems have emerged as crucial solutions to tackle contemporary challenges, from industrial automation to disaster response, especially where the scope of human interventions is significantly constrained. In such scenarios, a notable number of event-driven operations trigger robots to perform a substantial amount of tasks. Nonetheless, completion of the tasks proves challenging due to the limited computational capabilities inherent to many robotic systems. Although cloud computing solutions can be integrated to address these limitations by distributing the workload to clouds, ensuring optimized performance remains a formidable challenge due to the communication bottlenecks encountered by the robots. Moreover, the presence of robots’ energy constraints and stringent real-time service requirements further exacerbate this workload distribution problem. In response to the aforementioned challenges, this paper introduces a fog-dew-enabled robotic system designed to mitigate latency and energy consumption while orchestrating crucial workload distribution decisions among robots. The execution of decision-making tasks is conceptualized as a multi-objective optimization problem. Due to the NP-hardness of the multi-objective optimization, we propose an innovative solution based on a meta-heuristic Binary Particle Swarm Optimization algorithm. Through rigorous experimentation conducted via simulation within the iFogSim2 simulator, our results demonstrate that the proposed algorithm surpasses the current state-of-the-art in simultaneously optimizing latency and energy consumption.