Thanks to the advantages of zero carbon dioxide emissions and low operation cost, the number of on-road electric vehicles (EVs) is expected to keep increasing. They usually get charged through charging stations powered by either the grid or renewable plants. Due to the potential difference in electricity price between the grid and the renewable plants, an EV may purchase electricity at charging stations powered by renewable plants, and then discharge the surplus energy in the battery to the grid, to gain profits and enhance the overall renewable energy utilization. In this work, we aim to optimize the route selection and charging/discharging scheduling to improve the overall economic profits of EVs, taking into account the constraints, including the time-varying energy supply caused by the intermittent generation of renewable energy, the limited number of charging piles in a charging station, and the traveling delay tolerance of EVs. Firstly, a time-expanded vehicle-to-grid graph is designed to model the objective and related constraints. Then, we apply an AI-based A* algorithm to find K-shortest paths for each EV. Finally, a joint routing selection and charging/discharging algorithm, namely, K-Shortest-Paths-Joint-Routing-Scheduling (KSP-JRS), is proposed to minimize the total cost of EVs by maximizing their revenue from energy discharging under time constraints. The proposed approach is evaluated using the real traffic map around Santa Clara, California. The simulation, with different numbers of testing EVs, shows the feasibility and superiority of the proposed algorithm.

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

Unlike a traditional centralized and producer-controlled power grid, the smart grid is a more complicated distributed power system consisted of many resources and applications. In smart grid, huge amounts of data generated by edge devices are collected by different parties. To achieve high operation efficiency, it is important to enable the data sharing and cooperative computation among different parties. How to protect the security and privacy of the utility data and the identities of their owners has become a major concern. There have been some studies on this issue. However, most of these works failed to consider the privacy protection in the dual sides of the data owner and receiver. In this article, we propose BPM4SG, a blockchain-based dual-side privacy-preserving multiparty computation (MPC) scheme for edge-enabled smart grid. In BPM4SG, the data segmentation method is adopted to ensure the security of MPC (e.g., summation) in edge nodes. The consortium blockchain and smart contract are used to further increase the system security and avoid the dependency on trusted third parties. Additionally, a data obfuscation method based on the ring signatures and a new one-time address scheme are proposed to protect the privacy of both the data owner and data receiver. The analysis shows that BPM4SG can meet the security and privacy requirements of smart grid. The experimental evaluation results demonstrate that our scheme has a better performance compared with other popular schemes.

Although vehicles are the most commonly used means of transportation in people's daily life, their computational power and memory resources are often ignored. The new paradigm, Internet of Vehicles, has turned on-road vehicles into intelligent service providers, making full use of on-board processing and storage capabilities. With the advent of 6G technology, e.g., millimeter wave and terahertz, fast and massive data transfers become possible between moving objects. In this paper, we consider a scenario where vehicles act as mobile edge caching nodes and form a local cloud, to provide popular contents for nearby users via 6G communication. To maximize the efficiency of content allocation under the limitation of short communication range, we first predict the driving trajectories of vehicles with a Markov chain model and estimate the popularity level of contents in each urban area. Next, we design a scoring system based on the above predictions to compare users' satisfaction and computational efficiency of different algorithms. Finally, we find a subset of vehicles with appropriate content to serve the demand in the area. The simulation experiments demonstrate that the proposed scheme outperforms the conventional methods by providing a cost-efficient content allocation solution with lower delay.

Electric vehicles are considered the alternative to fossil fuel-powered vehicles to address issues of greenhouse gas emissions in the transportation system. Moreover, by taking the advantage of potential price differences, electric vehicles can purchase electricity from renewable charging stations and sell the remaining energy back to the grid for profit via Vehicle-to-Grid (V2G). In this paper, we aim to maximize the profit of EV coalition under constraints such as the energy capacity of EVs, the limited number of charging piles at charging stations, and the accessibility of traffic routes. The new advances of 6G technology can facilitate distributed intelligent cooperation between vehicles, due to the high availability, high reliability, and high throughput. In this paper, since centralized coordination is costly, we first use the time expanded graph to describe various constraints, and then the graph algorithm is adopted to model the problem. The cooperative game is introduced to solve the problem of vehicle path planning. In this regard, an algorithm to make charging/discharging decisions for EVs is proposed. Finally, the feasibility and superiority of the proposed method is evaluated using synthetic data derived from a real traffic map.