The tremendous advancement of Internet-of-things (IoT) has proliferated the interaction between the physical and cyber worlds. Consumer electronics, as the first tier in the physical world, would play a crucial role to enable ubiquitous information exchange. The adoption of IoT technologies will inevitably enhance the maturity of consumer products and services, proliferating the market of consumer electronics. Nonetheless, the ever-growing number of uncoordinated connections and unregulated integrations of technologies would pose various challenges and threats, resulting in vulnerability to consumer electronics. Thus, there is an urgent need for defining the trusted infrastructure and research directions for consumer electronics. In this paper, the next-generation (next-gen) consumer electronics would be defined for the first time. Also, the state-of-the-art architecture of next-gen consumer electronics would be presented, in compliance with the emerging IoT standard IEEE 2668. To address the underlying challenges in next-gen consumer electronics, this paper will classify the essential research topics for consumer electronics and present future research opportunities, such as the exploitation of complex network analysis, cybersecurity measures, etc., to foster the development of consumer electronics in a reliable, efficient, safe, and secure manner.

This paper provides an overview of the current state-of-the-art digital twin and digital thread technology in industrial operations. Both are transformational technologies that have the advantage of improving the efficiency of current design and manufacturing. Digital twin is an important element of the Industry 4.0 digitalization process; however, the huge amount of data that are generated and collected by a digital twin offer challenges in handling, processing and storage. The paper aims to report on the development of a new framework that combines the digital twin and digital thread for better data management in order to drive innovation, improve the production process and performance and ensure continuity and traceability of information. The digital twin/thread framework incorporates behavior simulation and physical control components, in which these two components rely on the connectivity between the twin and thread for information flow and exchange to drive innovation. The twin/thread framework encompasses specifications that include organizational architecture layout, security, user access, databases and hardware and software requirements. It is envisaged that the framework will be applicable to enhancing the optimization of operational processes and traceability of information in the physical world, especially in an Industry Shipyard 4.0.

Wireless sensor networks utilize large numbers of wireless sensor nodes to collect information from their sensing terrain. Wireless sensor nodes are battery-powered devices. Energy saving is always crucial to the lifetime of a wireless sensor network. Recently, many algorithms are proposed to tackle the energy saving problem in wireless sensor networks. In these algorithms, however, data collection efficiency is usually compromised in return for gaining longer network lifetime. There are strong needs to develop wireless sensor networks algorithms with optimization priorities biased to aspects besides energy saving. In this paper, a delay-aware data collection network structure for wireless sensor networks is proposed. The objective of the proposed network structure is to minimize delays in the data collection processes of wireless sensor networks. Two network formation algorithms are designed to construct the proposed network structure in a centralized and a decentralized approach. Performances of the proposed network structure are evaluated using computer simulations. Simulation results show that, when comparing with other common network structures in wireless sensor networks, the proposed network structure is able to shorten the delays in the data collection process significantly.

Motions of mobile robots need to be optimized to minimize their energy consumption to ensure long periods of continuous operations. Shortest paths do not always guarantee the minimum energy consumption of mobile robots. Moreover, they are not always feasible due to climbing constraints of mobile robots, especially on steep terrains. We utilize a heuristic search algorithm to find energy-optimal paths on hilly terrains using an established energy-cost model for mobile robots. The terrains are represented using grid-based elevation maps. Similar to A*-like heuristic search algorithms, the energy-cost of traversing through a given location of the map depends on a heuristic energy-cost estimation from that particular location to the goal. Using zigzag-like path patterns, the proposed heuristic function can estimate heuristic energy-costs on steep terrains that cannot be estimated using traditional methods. We proved that the proposed heuristic energy-cost function is both admissible and consistent. Therefore, the proposed path planner can always find feasible energy-optimal paths on any given terrain without node revisits, provided that such paths exist. Results of tests on real-world terrain models presented in this paper demonstrate the promising computational performance of the proposed path planner in finding energy-efficient paths.

After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.