Key points are not available for this paper at this time.
The computationally intensive tasks generated by AIoTD face challenges due to its limitations in battery power and computing capabilities. These devices typically operate in resource-constrained environments where energy efficiency (EE), computational efficiency, and real-time responsiveness are critical factors. To address these challenges, MEC is a promising solution as it allows tasks to be transferred from IoT devices to MEC for processing. By utilizing UAVs in MEC systems, computing and storage resources are brought closer to end devices, particularly in remote areas. This study investigates the TO issue within a AIoT-enabled UAV-assisted MEC system empowered by DT technologies. The DT concept is employed to provide environmental information and facilitate data exchange for agents linked to AIoT devices. The TO problem is structured to optimize EE and distribute workloads effectively among edge servers. Subsequently, the problem is reformulated as a MDP and an energy-efficient TO strategy, termed FedRL-EETO (FedRL based), is proposed. Moreover, a DT-enabled FedRL-EETO approach is implemented to enhance privacy protection and train decentralized RA strategies for communication networks, further improving network efficiency. Experimental evaluations are conducted to represents the benefits of the FedRL-EETO algorithm in enhancing EE and workload distribution, demonstrating its superior performance in comparison to existing method.
Consul et al. (Fri,) studied this question.