The Internet of Vehicles (IoV) marks a revolutionary leap in transportation, integrating vehicles with the Internet to enhance convenience, intelligence, and efficiency. As IoV applications continue to expand, a spectrum of challenges and untapped opportunities emerge. Among these, the security of IoV stands out as a critical issue in transportation systems, given its direct impact on road safety. To this end, this survey conducts an in-depth analysis of IoV security challenges and introduces a distinctive approach by systematically categorizing threats into inside-vehicle and outside-vehicle domains, providing a comprehensive understanding of the full spectrum of risks. Meanwhile, we examine defense mechanisms designed to counter these threats and explore proactive strategies to enhance IoV security. In addition, this study explores emerging protection techniques, such as AI-driven intrusion detection, blockchain-based trust management, and 5G-enabled secure routing, demonstrating how these technologies can be effectively integrated to safeguard IoV systems. Furthermore, this survey provides actionable recommendations and forward-looking research directions to guide the development of real-world implementations, standardized procedures, and regulatory frameworks, benefiting stakeholders, policymakers, and researchers. As IoV continues its rapid evolution, these insights offer a comprehensive roadmap to strengthen IoV security and contribute to a safer, more resilient intelligent transportation ecosystem.

Smart city applications require machine learning (ML) capabilities at the edge, for which they require edge devices equipped with specialized processors (e.g., GPUs). However, deploying such high-end edge devices in large numbers is challenging, particularly in regions that face infrastructure and budget constraints (e.g., cities lacking ubiquitous broadband) or extreme weather conditions (e.g., hot temperatures over sustained periods of time). Hence, custom ML frameworks are necessary that can support efficient and safe consolidation of multiple applications with diverse workloads on fewer edge servers. We present IceEdge, an inference serving framework for CPU-GPU based edge computing servers, which isolates colocated applications using existing container mechanisms and provides a deadline- and thermal-aware scheduler for the ML workloads. We implement IceEdge for a Dockerized environment on NVIDIA Jetson Xavier AGX platform. IceEdge outperforms state-of-the-art real-time inference serving frameworks Triton and DeepRT, improving the frame drop rate by up to 15% and reducing the temperature rise by around 12°C compared to Triton inference serving framework.

An optimal preemption framework is proposed to minimize Age of Information (AoI) in single-link systems with stochastic packet arrivals and known packet lengths. The problem is formulated as a Markov Decision Process (MDP) solved through relative value iteration (RVI), demonstrating that packet-length-dependent threshold policies achieve minimal average AoI. For scenarios with unknown traffic statistics, a Deep Q-Network (DQN) algorithm learns these thresholds adaptively through real-time interactions without prior distribution knowledge. Numerical evaluations under exponential and bounded Pareto packet length distributions reveal 15.5 percent average AoI reduction compared to non-preemptive baselines using the RVI method, while the DQN method achieves 14.4 percent improvement with less than 1.1 percent performance gap from the optimal policy. Both methods exhibit threshold-driven decision structures that balance immediate AoI reduction against long-term scheduling efficiency. Comparative analysis reveals the framework’s robustness across stationary and dynamic environments, with the DQN maintaining near-optimal performance through dimension-reduced state representation. These results establish a unified solution for AoI minimization that transitions seamlessly between model-aware and model-agnostic configurations, addressing critical challenges in real-time Internet of Things (IoT) networks and status update systems requiring freshness guarantees.

In congested road environments, the spectrum resources available for Roadside Units (RSUs) are often insufficient to meet the communication needs of a large number of users simultaneously. To address this, Unmanned Aerial Vehicles (UAVs) can be deployed to supplement spectrum resources temporarily. This paper proposes a UAV-assisted Vehicle Edge Computing (VEC) system, integrating UAVs to enhance RSU capabilities in congested scenarios. Traditional spectrum sensing techniques, however, struggle to autonomously monitor vehicular movements and maintain stable spectrum performance. To overcome this, we introduce radar sensing devices into the RSUs to improve perception accuracy and consistency. The integration of radar sensors, while beneficial, creates additional competition for limited system resources. We, therefore, formulate the resource allocation problem considering computation delay, communication rate, and perception data, constrained by spectrum resources, offloading decisions, and time-slot allocations. The problem is modeled as a Markov Decision Process (MDP), and we propose an Improved Multi-Agent Proximal Policy Optimization (IMAPPO) algorithm to optimize resource allocation under these constraints. The experimental results show that compared to baseline algorithms such as A3C, our proposed algorithm reduces the average task processing delay by 15.53%, increases the radar estimation mutual information (MI) by 9.52%, and improves the task completion rate by 4.1%.

The localization of sensor networks involves determining the spatial coordinates of each sensor in a deployed network. This task is accomplished by utilizing noisy distance measurements between neighboring sensors and a limited number of anchor sensors, which are sensors with known approximate locations. In this study, we present a unified maximum-likelihood formulation to address the sensor network localization problem. Our formulation incorporates anchor uncertainty by employing a truncated normal multivariate distribution. This unified approach generalizes previous formulations as it is applicable to various network architectures and noise levels, and various anchor certainty or uncertainty scenarios. Additionally, we introduce a novel algorithm that is simple, fast, fully distributed, and parallel. This algorithm converges to critical points of the non-convex, non-smooth, and constrained unified maximum-likelihood formulation. Notably, the algorithm is suitable for both centralized and distributed network architectures, making it well-suited for very large-scale networks comprising even thousands of sensors.

Recent studies show that inaudible attacks pose a non-negligible security risk to smart speakers. While several countermeasures have been proposed to detect the occurrence of the inaudible attack passively, accurately locating the attack source in 3D free space remains an unresolved challenge. Arrow is designed to bridge this gap by attempting to detect the occurrence of inaudible attacks and determine their localization simultaneously. Instead of relying on dedicated hardware components, Arrow is implemented with the microphone array widely deployed on COTS (Commercial Off-The-Shelf) smart speakers. Throughout the spatial information captured by the microphone array, Arrow establishes a spatial mapping model and derives orientation-related features to pinpoint the location of the attack source. Furthermore, to improve the robustness against co-channel interference, Arrow adopt carefully-modulated ultrasonic waveforms to achieve noise-robust attack detection. Through the above technical mechanism, Arrow can significantly improve the security level of voice assistants on smart speakers with nearly zero deployment cost. We implement a prototype of Arrow and conduct a comprehensive performance evaluation. The results show Arrow can achieve 2.5 ○ and 7 ○ error in DoA estimation for horizontal and vertical angles, respectively.

This paper proposes a general solution for point set matching that effectively addresses the challenges of low-overlap, sparse, or featureless point set matching (LSFPM). Unlike previous methods that mainly rely on feature or neighborhood similarity that often fail under such difficult conditions, this work proposes a Geometry-based Point Matching (GPM) method. GPM first introduces two geometric concepts: the “Structural Element” (SE) and the “Superstructural Element” (SSE), both of which are constructed based on local geometric structures. The SSE is an enhanced version of the SE. A descriptor for the SE, called the SE Descriptor (SED), is designed to encode the SE and facilitate an efficient geometry-based similarity metric. We demonstrate that the cosine similarity of SEDs is invariant to scale and rotation. Subsequently, a SE Matching Maximization (SEMM) problem is formulated to identify a size-penalized SSE set that maximizes the sum of similarities. This problem is efficiently solved using the proposed SEMM-MCMC (Markov Chain Monte Carlo) algorithm. The matched SSEs then vote on corresponding point matches, generating high-confidence one-to-one matches, low-confidence one-to-one matches and one-to-many matches. Finally, the InferMatch algorithm is proposed to jointly assess low-confidence one-to-one point set matching while simultaneously distinguishing one-to-many point set matching. The GPM approach can also complement other feature-based and motion-based methods. It has been extensively validated on both synthetic and real datasets, demonstrating its versatility in addressing various point set matching problems, and is not limited to the LSFPM problem. Extensive experiments on diverse datasets, including SPair-71k, UAVDT, VisDrone2021-MOT, and SparseMatch, further demonstrate the robustness and versatility of GPM. The proposed approach significantly improves matching performance under challenging conditions and effectively addresses key limitations of existing point set matching methods.

Online Building Load Forecasting (BLF) is a scheme that designs a model update strategy to continuously update the deployed machine learning (ML)-based BLF model, ensuring its adaptation to changes in data distribution. Numerous online BLF schemes have been proposed recently. However, updates may prove ineffective, leading to a degradation in accuracy or even performance inferior to that of models without updates. A primary cause of this issue is the inadequate preparation of the data used for model updates (referred to as the updating set ). Most existing online BLF schemes rely on historical data for model updates, which may fail to capture the characteristics of future data distributions. Preparing a suitable updating set is a challenging and often ad hoc task. In this paper, we propose the use of automated data augmentation (AutoDA) , a reinforcement learning-based data augmentation (DA) framework, to automatically search for optimal DA policies for generating synthetic data. We introduce AugPlug and demonstrate its capability to generate effective updating sets. A distinctive advantage of its plug-and-play compatibility enables seamless integration into various BLF schemes. Extensive experiments conducted on four published online BLF schemes, involving hundreds of buildings, demonstrate that AugPlug improves the performance of online BLF by 29.37%.

In the realm of underwater wireless communication, vast oceanic expanses often demand large-scale deployment of Underwater Wireless Sensor Networks (UWSNs). UWSNs rely on acoustic communication channels, presenting distinct challenges like prolonged propagation delays, restricted bandwidth, and dynamic topologies. Furthermore, the far-reaching and multi-path nature of acoustic signals results in significant hidden terminal problems and ubiquitous interference between neighboring nodes. Therefore, an efficient medium access control (MAC) protocol is crucial for optimizing UWSN performance. This article proposes IC-MAC, a MAC protocol tailored for UWSNs to avoid collisions and improve network performance. IC-MAC employs distributed clustering to group sensor nodes and the cluster head degree is defined for each node, which is a coefficient that accentuates nodes characterized by a higher incidence of collision associations. To identify interfering nodes and construct an interference-free graph, an interference identification algorithm is proposed. In addition, a heuristic graph coloring technique, guided by particle swarm optimization, allocates time slots efficiently to achieve collision-free transmission scheduling and enhanced spatial reuse. Simulations demonstrate the effectiveness of the IC-MAC protocol in enhancing throughput, reducing delay, and improving packet delivery ratio and energy efficiency. This is achieved through efficient spatial resource utilization and robust management of collisions and interference, specifically tailored for underwater acoustic channels, outperforming existing MAC protocols.