Energy-Efficient UAV Scheduling and Probabilistic Task Offloading for Digital Twin-Empowered Consumer Electronics Industry

The digital twin (DT)-powered consumer electronics industry has been proposed to create virtual representations of the physical products, manufacturing processes, and manufacturing systems, and to enable manufacturers to truly promote the development of smart manufacturing of consumer electronics. This gives rise to the DT-empowered consumer electronics industry, which relies on massive data from Industrial Internet of Things (IIoT) devices to update a DT of an entire manufacturing system consisting of different manufacturing processes of consumer electronics. To this end, we employ unmanned aerial vehicles (UAVs) to collect data reports of the IIoT devices while providing aerial computing for them when necessary. We investigate energy-efficient UAV scheduling and probabilistic task offloading for the DT-empowered consumer electronics industry. More specifically, the UAV scheduling problem is formulated to properly dispatch the UAVs to different mission areas to minimize the total energy consumption of all UAVs. After that, we utilize a Stackelberg game approach to study the task offloading and service pricing when a UAV provides offloading services for the IIoT devices under demand uncertainty. Numerical results show that the proposed schemes show superiority to the baseline schemes in reducing the total energy consumption of the UAVs and increasing the economic benefits of the UAV-enabled offloading services.