With the widespread application of artificial intelligence (AI) in Internet of Vehicles (IoV), particularly in autonomous driving and intelligent traffic management. IoV is facing tremendous pressure for large amounts of data transmission and real-time perception. As a key technology of 6G, integrated sensing and communication (ISAC) is expected to alleviate this pressure by improving spectrum utilization and reducing transmission latency. In addition, when obstacles exist in IoV, communication performance is seriously affected, which is detrimental to driving safety. To address this issue, Reconfigurable Intelligent Surfaces (RIS) as a relay is a feasible solution. Therefore, we construct a RIS-assisted ISAC IoV scenario, and further consider adding dynamic obstacles. Our optimization problem aims to enhance overall performance by maximizing a weighted combination of communication rate and sensing accuracy through the optimization of beamforming and RIS phase shifts. The problem has temporal characteristics and is a Markov Decision Process (MDP). To this end, we use the Long Short-Term Memory-Deep Deterministic Policy Gradient (LSTM-DDPG) algorithm to solve the problem. The results from the simulation illustrate the efficacy of the proposed algorithm in handling dynamic and complex blockage scenarios. Additionally, introducing RIS in blockage scenarios significantly increases the communication rate by up to 58.33%.

Mobile Crowdsensing (MCS) is a promising paradigm where embedded sensor are exploited for collecting and sharing environmental data. In IoV, participating vehicles sense the environment, collect data and transmit the data to the edge server for processing to provide real-time services. However, real-time services not only pose energy challenges for massive data transmission and analysis, but also demand dynamic multi-timeslot optimisation for MCS systems. Additionally, sensing data often require continuous updates to prevent the provision of obsolete services. To address this, we introduce the concept of Urgency of Information (UoI) to characterize the freshness of sensing data. Diverging from the linear growth trend of Age of Information (AoI), UoI enables dynamic adjustment of the decay rate of data freshness based on traffic complexity. In this paper, we propose a dynamic multi-timeslot MCS system for IoV, under constraints of UoI, which intelligently leverages the spatial correlation of perception to update data and minimizes network energy consumption while ensuring compliance with UoI constraints. Then we propose a Joint Data Collection and Resource Allocation (JDCRA) algorithm to obtain the solution based on convex optimization. To the best of our knowledge, this is the first work to jointly optimize multi-road data collection and resource allocation, taking into account UoI metrics. We evaluate JDCRA by experiments on SUMO in real-world scenarios. Experimental results show that JDCRA outperforms state-of-the-art methods in terms of energy consumption and UoI violation probability, and obtains solutions that consumes only 7.43% more energy than the optimal solution in polynomial complexity.

The Vehicular Fog Computing (VFC) provides low-latency computing service to support emerging intelligent transportation applications in Internet of Vehicles (IoV). Multi-node cooperative VFC can utilize Connected and Autonomous Vehicles (CAVs) to implement cooperative intelligence. Due to the mobility of vehicles, service migration is necessary when task offloading service providers change. This paper proposes an Asynchronous Task Offloading Scheme (ATO-S) that allows each CAV to choose an independent optimization period and provides robust task offloading services under unknown vehicle mobility probability distribution. To the best of our knowledge, this is the first work to investigate asynchronous and robust multi-node cooperative task offloading in dynamic VFC-enhanced IoV scenarios. Furthermore, we formulate the long-term energy consumption minimization problem of VFC and transfer it into each time slot problem by Lyapunov optimization. Then we design Asynchronous Task Offloading Algorithm (ATO-A) to jointly optimizing CAVs matching, communication and computation resource allocation, and transmission power based on multiple mathematical techniques and hybrid heuristic algorithm. Extensive simulations based on real-world traffic scenario are conducted by varying multiple crucial parameters. Simulation results demonstrate the energy efficiency and task queue stability achieved by ATO-A, and service robustness achieved by ATO-S, in comparison with benchmark solutions.

Artificial Intelligence Internet of Things (AIoT) is an emerging technology within the Internet of Things (IoT), bringing an increasing demand for intelligent task offloading. Multi-node cooperative Vehicular Fog Computing (VFC) offers efficient and low-latency data processing to meet this requirement. However, due to the limited resource of fog nodes and latency-sensitive and computing-intensive task requirements, how to efficiently improve Quality of Service (QoS) and reduce energy consumption is an important issue in VFC. In this paper, we propose QECLO, a novel QoS-aware task offloading method. Unlike most previous studies, our goal is to improve QoS while minimizing energy consumption thus avoiding the overallocation of resource and high energy consumption caused by the one-sided pursuit of high QoS for few high-priority tasks. We solve the computation resource allocation subproblem by convex optimization and then optimize the communication resource and power allocation by an improved heuristic algorithm based on Decision Tree (DT). Moreover, we evaluate proposed approach through traffic scenario simulations. The experimental results show that our proposed approach outperforms existing methods in terms of energy consumption and latency.

In the rapidly evolving landscape of wireless communication technology, Integrated Communication and Sensing (ISAC) has emerged as a focal point of research owing to its efficient utilization of hardware resources. Unmanned Aerial Vehicles (UAVs), serving as an auxiliary tool in this domain, extend the applicability of ISAC significantly due to their high mobility, cost-effectiveness, and portability. This study delves into an innovative UAV-assisted ISAC dynamic patrolling model. In this paradigm, UAVs equipped with ISAC capabilities function not only as mobile aerial base stations providing communication services but also as radar and computational units for transmitting sensory information. To cater to the requisites of communication and sensing tasks, while mitigating the impact of UAV mobility, a Mix-ISAC frame structure is proposed, ensuring continuous communication services. Furthermore, this research introduces a method for jointly optimizing UAV trajectory and power allocation, aimed at augmenting the energy efficiency of UAVs. Considering the dynamic nature of user and UAV positioning, a Deep Reinforcement Learning(DRL)-based dynamic trajectory optimization and power allocation (DTPA)algorithm is proposed. To address the inefficiency of traditional non-uniform sampling strategies in training actor networks in continuous action spaces, a multi-tiered prioritized sampling strategy is incorporated, ensuring the effectiveness of the training process. Numerical experiments validate the convergence of the proposed algorithm and, through comparative analysis, demonstrate its superior performance.

In the field of road extraction, an dominant network is D-LinkNet which won the first place in DeepGlobe 2018 challenge. Although D-LinkNet creatively proposed D-block with progressively enlarged dilated convolution and proved its efficiency, the$\mathrm{N}\times \mathrm{N}$square kernel it used still has limitations for road extraction. Road in aerial imagery usually has narrow-and-long shape, and the direction is randomly distributed. Therefore, not only large receptive field but also anisotropic long-range contextual information should be considered. Based on this intuition, we integrate a new pooling strategy named strip pooling which uses a long but narrow kernel i.e.$1\times \mathrm{N}$or$\mathrm{N}\times 1$into D-LinkNet. With strip pooling module(SPM) and mixed pooling module(MPM) designed based on strip pooling, two modifications are made to D-LinkNet: 1) We insert SPM into Res-block of the original encoder ResNet34 and name it Res-SPM-block. 2) Inspired by MPM, we connect strip pooling in parallel with D-block and name it SPD-block. We name the upgraded D-LinkNet as SPD-LinkNet. Experimental results on DeepGlobe 2018 dataset prove that SPD-LinkNet outperforms original D-LinkNet in accuracy while maintaining nearly the same inference speed.