This paper investigates the fee scheduling problem of electric vehicles (EVs) at the micro-grid scale. This problem contains a set of charging stations controlled by a central aggregator. One of the main stakeholders is the operator of the charging stations, who is motivated to minimize the cost incurred by the charging stations, while the other major stakeholders are vehicle owners who are mostly interested in user convenience, as they want their EVs to be fully charged as soon as possible. A bi-objective optimization problem is formulated to jointly optimize two factors that correspond to these stakeholders. An online centralized scheduling algorithm is proposed and proven to provide a Pareto-optimal solution. Moreover, a novel lowcomplexity distributed algorithm is proposed to reduce both the transmission data rate and the computation complexity in the system. The algorithms are evaluated through simulation, and results reveal that the charging time in the proposed method is 30% less than that of the compared methods proposed in the literature. The data transmitted by the distributed algorithm is 33.25% lower than that of a centralized one. While the performance difference between the centralized and distributed algorithms is only 2%, the computation time shows a significant reduction.

Malicious attacks in the power system can eventually result in a large-scale cascade failure if not attended on time. These attacks, which are traditionally classified into physical and cyber attacks, can be avoided by using the latest and advanced detection mechanisms. However, a new threat called cyber-physical attacks which jointly target both the physical and cyber layers of the system to interfere the operations of the power grid is more malicious as compared with the traditional attacks. In this paper, we propose a new cyber-physical attack strategy where the transmission line is first physically disconnected, and then the line-outage event is masked, such that the control center is misled into detecting as an obvious line outage at a different position in the local area of the power system. Therefore, the topology information in the control center is interfered by our attack. We also propose a novel procedure for selecting vulnerable lines, and analyze the observability of our proposed framework. Our proposed method can effectively and continuously deceive the control center into detecting fake line-outage positions, and thereby increase the chance of cascade failure because the attention is given to the fake outage. The simulation results validate the efficiency of our proposed attack strategy.

Douyin, internationally known as TikTok, has become one of the most successful short-video platforms. To maintain its popularity, Douyin has to provide better Quality of Experience (QoE) to its growing user base. Understanding the characteristics of Douyin videos is thus critical to its service improvement and system design. In this paper, we present an initial study on the fundamental characteristics of Douyin videos based on a dataset of over 260 thousand short videos collected across three months. The characteristics of Douyin videos are found to be significantly different from traditional online videos, ranging from video bitrate, size, to popularity. In particular, the distributions of the bitrate and size of videos follow Weibull distribution. We further observe that the most popular Douyin videos follow Zifp's law on video popularity, but rest of the videos do not. We also investigate the correlation between popularity metrics used for Douyin videos. It is found that the correlation between the number of views and the number of likes are strong, while other correlations are relatively low. Finally, by using a case study, we demonstrate that the above findings can provide important guidance on designing an efficient edge caching system.

Wind power is a clean and widely deployed alternative to reducing our dependence on fossil fuel power generation. Under this trend, more turbines will be installed in wind farms. However, the inspection of the turbines in an offshore wind farm is a challenging task because of the harsh environment (e.g., rough sea, strong wind, and so on) that leads to high risk for workers who need to work at considerable height. Also, inspecting increasing number of turbines requires long man hours. In this regard, unmanned aerial vehicles (UAVs) can play an important role for automated inspection of the turbines for the operator, thus reducing the inspection time, man hours, and correspondingly the risk for the workers. In this case, the optimal number of UAVs enough to inspect all turbines in the wind farm is a crucial parameter. In addition, finding the optimal path for the UAVs' routes for inspection is also important and is equally challenging. In this paper, we formulate a placement optimization problem to minimize the number of UAVs in the wind farm and a routing optimization problem to minimize the inspection time. Wind has an impact on the flying range and the flying speed of UAVs, which is taken into account for both problems. The formulated problems are NP-hard. We therefore design heuristic algorithms to find solutions to both problems, and then analyze the complexity of the proposed algorithms. The data of the Walney wind farm are then utilized to evaluate the performance of the proposed algorithms. Simulation results clearly show that the proposed methods can obtain the optimal routing path for UAVs during the inspection.

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