The availability of conventional mobile edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchainenabled vehicular mobile edge computing (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the Alternating Direction Method of Multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53 and energy consumption by 43.43 schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.

Flying Ad-Hoc Networks (FANETs) composed of Unmanned Aerial Vehicles (UAVs) offer innovative solutions in various fields. However, their dynamic nature and sparse topology pose significant challenges for connectivity and routing efficiency. To address these issues, we propose the Adaptive Virtual Tunnel Routing protocol (AVTR) with eliminating boundary effects for FANETs. AVTR is a location-based, on-demand protocol that introduces a hop-by-hop virtual relay tunnel (HH-VRT) to confine forwarding within a limited set of UAVs. This approach reduces unnecessary transmissions and overhead. Additionally, AVTR incorporates a novel eliminating boundary effect factor (Q) to minimize path deviation and a new link quality factor (LQ) to evaluate link stability. By considering residual energy, LQ, and hop count, AVTR optimally selects the next forwarding node, enhancing routing efficiency and connectivity. Simulation results demonstrate that AVTR outperforms existing EARVRT, Pipe, IHCR, IoDMix routing protocols across seven critical metrics, including end-to-end delay and routing overhead, validating its effectiveness in improving FANET performance.

UAV object detection is crucial in open water search and rescue missions. Due to varying perspectives and altitudes of UAV images, the apparent size of objects varies significantly. Challenges such as insufficient feature representation and background confusion make open water object detection particularly difficult. Currently, deep learning-based detection methods rely on convolution to extract features at a fixed spatial scale. This limited receptive field leads to insufficient feature representation, causing false detections and missed detections, which severely impact detection accuracy. This paper proposes an efficient, feature-enhanced real-time detection network based on transformer architecture, called OWRT-DETR, to address the challenges of diverse UAV image detection in open water. To the best of our knowledge, a Transformer-based detection network has not yet been explored for open water UAV images. OWRT-DETR incorporates a cross-scale feature pyramid module (CFPIM), multi scale sensing fusion (MSSF), and small object enhancement module (SOEM). These modules enhance cross-scale interaction, cross-channel spatial global association, and local perception of the network, while avoiding increased complexity, improving weak feature representation of small targets, and suppressing easily confused backgrounds. Three public datasets are used to validate the effectiveness of OWRT-DETR. OWRT-DETR achieves an averaged precision (AP) of 51.5%, 45.9%, 50.6% on the SeaDronesSee, Aerial Dataset of Floating Objects, and Aerialbus Ship datasets, exceeding the performance of several state-of-the-art models. To ensure efficiency and reduce computational resources, OWRT-DETR is optimized by reconstructing the backbone network using PConv and Rep, resulting in Light-OWRT-DETR. Compared with OWRT-DETR, Light-OWRT-DETR is faster, uses fewer parameters, requires less computational power, and achieves higher accuracy. The code will be available at https://github.com/mshauima/OWRT-DETR.

The increasing popularity of electric passenger vehicles (EPVs) has significant implications for transportation networks and power grids. We aim to tackle the routing and charging dispatching problem for EPVs while considering charging station (CS) power limits. We formulate the problem using a clustered rolling framework and introduce an energy criterion to determine EPV availability for shuttle services. The EPV charging dispatching is modeled as a constraint programming problem under CS power limits, with a fixed charging rate assumed at the start of charging. The RCLBD algorithm, based on logic-based benders decomposition, effectively handles binary and continuous variables. The EPV routing model serves as the master problem, while the charging model acts as the sub-problem. Simulation experiments demonstrate the RCLBD algorithm’s performance and efficiency. The algorithm successfully provides efficient pickup and delivery services, minimizing waiting times for customers. It ensures successful routing and charging solutions for all arriving EPVs. The electricity cost of our proposed RCLBD algorithm is 2.13%; 10.68% lower than that of MIP and MIPC method when the number of EPVs is 750. Our proposed routing and charging algorithm shows good performance and efficiency, addressing the challenges posed by the increasing popularity of EPVs.

With the increasing use of electric vehicles (EVs) and the development of emerging transportation network services, autonomous EVs (AEVs) may play an important role in the future of transportation. AEVs can automatically plan their route, park in the charging station, and support the vehicle-to-grid (V2G) services. However, V2G services may influence user dissatisfaction due to the task delays. There is a tradeoff between the optimization of electricity cost and user dissatisfaction. In this article, we formulate the problem to minimize the electricity cost of AEVs and the degradation cost of the charging station batteries with the constraint of V2G services and user dissatisfaction, which is a nonconvex problem and is difficult to solve. To solve the nonconvex optimization problem, we design a suboptimal charging algorithm with some constraints (SCAC) based on the Lyapunov optimization technique to find a tradeoff between the total cost and user dissatisfaction. This algorithm cannot find the optimal solution but can give a selection criterion. Furthermore, in order to get a global charging schedule, we use the criterion from the SCAC algorithm as a priori knowledge to design the charging scheduling reinforcement-learning-based (CSRL) algorithm, which is more efficient than the reinforcement learning (RL) method without any particular criterion. We do simulations by using day-ahead price and practical profiles of AEVs to evaluate the proposed algorithms. The numerical results show that the CSRL algorithm has a better performance 5.12% than the SCAC algorithm and both algorithms are 12.66% and 17.14% better than the benchmark algorithm which is the shortest path (SP)-based algorithm. The CSRL algorithm has more efficiency ε(1 - Pr(Λ(t) = 0)) than the SCAC algorithm, where Pr(Λ(t) = 0) is a selection criterion calculated from the SCAC algorithm.