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.

Offshore wind energy has attracted worldwide attention and investments in the last decade due to the stability and abundance of wind resources. As one of the main components of this, internal array cables have a great impact on the levelised cost of energy of offshore wind farms, and thus their connection layout is a matter of concern. In this paper, a classical mathematical problem—the traveling salesman problem, which belongs to the field of graph theory—is applied to solve the offshore wind farm cable connection layout optimization problem. Both the capital investment on cables, cable laying, and the cost of power losses associated with array cables are considered in the proposed model. A modified bat algorithm is presented to resolve the problem. Furthermore, a cable crossing detection method is also adopted to avoid obtaining crossed cable connection layouts. The effectiveness was verified through a case study.

Reconfigurable Wireless Sensor Network (RWSN) schedules a set of devices with reconfigurable wireless interface to accomplish different data collection plans in a cost-effective way. AI technologies are applied to optimize decision making for high-level network reconfiguration. Besides, AI based data mining tools are exploited by third parities to extract useful information underlying raw data. This leads to the emergence of AI enabled RWSN. We further study a data trading market to provide the data-centric environment for large-scale applications of AI enabled RWSN. A network operator employs the devices to gather environmental data, and sells the collected data to interested third parities as data consumers. After that, two-level optimizations are performed to ensure efficient data trading. In data collection, a contract based incentive mechanism is presented for the network operator to stimulate the devices and simultaneously achieve the contractor's goal subject to feasible constraints. In data selling, a non-cooperative game is formulated among multiple data consumers. They balance the data demand since the final data price is correlated with the total data demand. Nash equilibrium is analyzed and solved under different scenarios. Finally, numerical results are provided to demonstrate the effectiveness of our scheme.