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With the rapid development of Industrial Internet of Things (IIoT), time-sensitive networking (TSN) with deterministic and real-time features has gained broad interest. However, most existing research focuses on the network scheduling with fixed task placement and computing resource allocation, restricting the scheduling space of coupled task-network. To tackle this coupling problem, an efficient task-network scheduling (ETNS) scheme is proposed in this article for TSN. A task-conflict metric (TCM) is established to quantify the competition degree of scheduling resources. For increasing the overall scheduling space, a TCM-aware prescheduling method is proposed by optimizing task placement and routing paths to reduce the potential conflicts between tasks. Integrated with the prescheduling method, we design a TCM-aware parallel group-scheduling algorithm by reducing the conflicts between task groups to enhance schedulability and scalability. Experiments show that our ETNS scheme significantly improves the schedulability and scalability performances compared with the existing scheduling approaches. The larger the number of tasks, the higher the performance improvement.

In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRTX, which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time.

Cyber-physical power system (CPPS) stability is severely affected by the communication network performance due to cyber-physical interdependence. Thus, uncertain cyber-physical coordinated attacks (CPCA) pose more significant threats to CPPS than physical attacks. However, the defense strategies in recent works assume real-time and reliable transmission under an ideal cyber-attack, leading to inaccurate cyber-physical interdependence modeling. Therefore, the strategies are ineffective against uncertain CPCA. This paper proposes a hierarchical CPPS network architecture that integrates time-sensitive networking (TSN) to improve resilience against uncertain CPCA. To model the uncertain CPCA, a coordinated N-K ambiguity set is proposed considering all possible link failures on both cyber and physical layers. For the cyber layer, we propose a joint routing-scheduling model of TSN-based network to support deterministic and reliable data transmission, which improves the accuracy of cyber-physical interdependence. A distributionally robust coordinated defense (DRCD) strategy integrated with cyber-physical interdependence is then proposed to mitigate the priority-based load shedding considering the uncertainties of CPCA. The formulated DRCD strategy is linearized and transformed into a tri-level optimization problem which is efficiently solved by C-CG algorithm. Case studies indicate that the DRCD strategy has superiorities in improving the resilience of CPPS compared with other conventional models.