The scale of electric buses is experiencing rapid growth in the progress of electrification for public transport. While in many cities traditional charging mode is still adapted, which is highly dependent on charging piles deployed at fixed charging stations. However, due to the limited charging station areas and insufficient power supply, vehicles would suffer from long detours and queuing time, which would burden the sustainable operation of electric buses. To address these issues, integrating solar panels as on-board photovoltaic equipment to provide en-route power supply for electric buses has become a potential feasible solution to ease such pressures. Considering the impact of time varying light intensity and demand density, a continuous approximation model was constructed on the grid network, considering passengers' time costs, agency costs, and charging facilities costs and a mixed heuristic was designed to solve the model. Take a region in Panjin city, China as an example, the results indicate that: (1) On-board photovoltaic power supply can significantly improve the service level of electric buses without increasing system costs, and alleviate the dependence of electric buses on charging stations; (2) Increasing light intensity and coverage of solar panels have positive implications for reducing the number of charging piles and queuing time at stations. When light intensity exceeds 6.4×1041x, the charging queue time will be reduced by 97.32% with 100% coverage compared to 20% coverage.

With the emergence of parallel computing systems and distributed time-sensitive applications, it is urgent to provide statistical guarantees for age of information (AoI) in wireless cyber-physical systems (WCPS) with diverse parallelisms. However, most of the existing research on AoI have tended to focus on serial transmission, and the AoI performance of multi-stage parallel systems remains unclear. To help address these research gaps, in this work, we set out to investigate the age of event (AoE) violation probability in a three-stage WCPS with diverse parallelisms such as fork-join and split-merge. We analyze both transient and steady-state characteristics of AoE violation probability (AoEVP). Using these characteristics, we transform the AoEVP minimization problem into an equivalent minimization problem. Moreover, we develop a queuing model to capture the queue dynamics under the max-plus theory of stochastic network calculus (SNC) approach. Based on the max-plus model, we derive a closed-form Chernoff upper bound for the equivalent problem by applying the union bound and the Chernoff inequality. Furthermore, we characterize the service process for different parallelisms applicable to each stage. By solving the Chernoff upper bound with the service moment generation functions (MGFs), we obtain heuristic update period solutions for minimizing the AoEVP of three-stage WCPS. Simulation results validate our analysis and demonstrate that our heuristic update period solutions are near optimal for minimizing the AoEVP of three-stage WCPS with diverse parallelisms. Through the theoretical framework, we further provide insights into how such networks can be designed for better age performance.

Given the network and the time-triggered flow requests of a Time Sensitive Network (TSN), configuring the gate control lists (GCL) of IEEE 802.1Qbv for the ports of each node can be formed as a Job Shop Scheduling Problem, which is NP-hard. At present, most of the existing heuristic solutions for such problems consider scenarios where all given traffic flows can be scheduled. In order to solve the undetermined flow scheduling problem in scenarios no matter whether the flows can be scheduled or not, we propose to maximize the remaining time in conjunction with optimizing the network utilization instead of only minimizing the flowspan. Though the new problem is still NP-hard, it is a unified framework capable of covering general scenarios. On the basis of the new framework, we propose a novel Mixed initial population Genetic Algorithm (MGA) to solve the problem. Extensive simulation evaluation shows that MGA performs better and faster in different network scenarios while other methods prevails only in specific scenarios. This feature makes the method attractive in realistic TSN scheduling applications for in most cases it is hard for users to properly classifying the problem.