Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, owing to their flexible mobilities and fast deployment. However, UAVs typically have very limited battery and computational capacities, which makes them harder to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue of the battery and computation resource limitation with a fog computing based UAV system. Specifically, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To avoid the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

With the rapid development of marine networks, there have been growing demands for computation-intensive and delay-sensitive marine applications and services. However, the limited underwater energy supply and the acoustic channels result in the low efficiency for computing tasks and high transmission delay. In this paper, we investigate the unmanned aerial vehicles (UAVs)-assisted multi-access computation offloading in marine networks, with the objective of minimizing the energy consumption of ocean devices. Specifically, for the underwater segment, we consider the scenario that multiple underwater sensor nodes (USNs) covered by the unmanned surface vehicle (USV) upload their sensing data via non-orthogonal multiple access (NOMA) for improving the channel utilization. For the radio frequency segment, we consider the scenario that multiple UAVs hovering in the air act as the aerial edge servers for providing computing services, in which the USV offloads the workloads to UAVs via frequency division multiple access (FDMA) for avoiding their co-channel interference, while taking into account that a malicious node overhears the USV's offloading transmission. To improve the computation offloading efficiency, we formulate an optimization problem for USNs and USV to minimize the total energy consumption by jointly optimizing the USN's uploading time, USV's computation offloading, USV's offloading time, and the secrecy provisioning. Despite the non-convexity of the formulated joint optimization problem, we exploit a layered structure to decompose the problem, and propose efficient algorithms to obtain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms. Numerical results demonstrate that our algorithms can significantly reduce the energy consumption in comparison with the benchmark schemes.

The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. The studies provide a solid research foundation but result in complicated models with limited scalability. Meanwhile, emerging machine learning techniques bring promising prospects, yet exhibit suboptimal performance with insufficient data. Additionally, existing studies often overlook several critical areas such as overcoming data scarcity, security and privacy concerns, managing the inherent stochasticity of demand data, selecting forecasting methods for a specific feature, and developing standardized performance metrics. Considering the impact of the research topic, EV charging demand forecasting demands careful study. In this paper, we present a comprehensive survey of EV charging demand forecasting, focusing on both probabilistic and learning algorithms. First, we introduce the general procedure of EV charging demand forecasting, encompassing data sources, data pre-processing, and the key EV features. We then provide a taxonomy of existing EV charging demand forecasting techniques, followed by a critical analysis and comparative study of state-of-the-art research. Finally, we discuss open issues, which offer useful insights and future direction for various stakeholders.

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

Empowered by crowdsensing technology, unmanned aerial vehicles (UAVs) equipped with rich sensors can perform sensing tasks in extreme scenarios. However, due to the selfishness and distrust of UAVs, some malicious UAVs with insufficient resources may provide false sensing data to the task publishers. In addition, the centralized sensing platform is vulnerable to various attacks (e.g., data tampering attacks and single point of failure) in traditional crowdsensing, resulting in degraded data quality and severe security issues. To cope with these problems, we propose a blockchain-based crowdsensing framework with reputation incentive (BCFR) for UAV-assisted mobile crowdsensing. Specifically, a reputation-based incentive scheme is first proposed to relate the behavior of each UAV with its reputation, and then to choose UAVs with high reputation to perform sensing tasks, thereby improving the security of sensing data sharing. Afterwards, we design a blockchain-based secure data transmission scheme to securely record data transactions of UAVs. Furthermore, UAVs may be reluctant to perform compute-intensive mining tasks due to their limited computing capabilities. Therefore, a two-stage Stackelberg game is introduced to motivate UAVs to participate in the block creation process. In a more realistic scenario, where participants are bounded rational, prospect theory (PT) is utilized to capture the potential subject perceptions of UAVs. Finally, simulation results and security analysis demonstrate that the proposed BCFR scheme can effectively improve the probability of successful mining and ensure the security of data sharing.

Plug-in electric vehicle (PEV) commercialization propels an extensive charging station deployment in power distribution system to satisfy fast growing PEV charging demands. Considering the power distribution, some stations deployed at limited capacity feeders may undergo power overload at peak hours due to time-varying traffic and PEV demands. The potential power overload could lead to severe transformer degradation or even black-out on the aged power infrastructure. To avoid power overload without excessive expenditure on the infrastructure upgrade, proper energy compensation at limited capacity station is highly effective. In this paper, we investigate an energy compensation problem based on utility-owned mobile vehicular electric storage (MVES), aiming to mitigate the overload issues among a group of charging stations (GCS). First, a Markov Chain based energy capacity model is developed to estimate the energy statuses among GCS and a graph theory based GCS transportation model is developed to facilitate on-road MVES allocation. Then, a two-tier energy compensation framework is introduced to efficiently schedule MVESs to minimize the scheduling cost. Simulations are conducted based on real traffic data on California highway collected by California department of transportation, and the results validate the effectiveness of the introduced framework.