High-definition (HD) maps generated from autonomous vehicle (AV) sensor data are essential for enabling high levels of driving automation. However, offloading large volumes of raw sensory data to edge servers in dense vehicular ad hoc networks (VANETs) introduces significant latency due to network congestion and packet collisions. Existing solutions primarily focus on dynamically adjusting the minimum contention window (CWmin), while additional MAC-layer parameters — including the maximum contention window (CWmax) and interframe space number (IFSn) — remain largely underexplored. To address this, we propose a cross-layer multi-agent reinforcement learning (MARL) framework that jointly optimises CWmin–CWmax, IFSn, and transmission waiting time within IEEE 802.11p-compliant bounds. The proposed multi-task mixed-dependence (MTMD) framework decomposes the optimisation problem into specialised subtasks handled by selectively coupled agents, balancing coordination and scalability while avoiding the overhead of fully symmetric MARL or centralised hierarchical controllers. A lightweight orchestration layer coordinates agent interaction with the simulation environment via secure message exchange. Evaluated against standard EDCA and representative RL baselines, MTMD achieves latency reductions of 31%, 49%, 87.3%, and 64% for Voice, Video, HD Map, and Best-Effort traffic, respectively, confirming the effectiveness of structured multi-parameter optimisation for latency-critical vehicular applications.

Efficient service function chain (SFC) placement is critical for optimizing network service delivery in dynamic cross-domain networks (CDNs), especially under resource-constrained and heterogeneous environments. However, existing approaches face fundamental limitations in achieving effective multi-objective optimization, particularly in balancing latency minimization with efficient resource utilization. These challenges are further compounded by the inability to capture future resource dynamics and limited visibility across multiple domains. To address these challenges, we propose a novel multi-objective framework for SFC placement that jointly considers latency and resource utilization. The framework integrates Transformer-based prediction with linear programming (LP) to explicitly model future deployability, enabling proactive and globally informed placement decisions. In addition, a dynamic modeling mechanism is developed using domain-aware detection and graph autoencoders (GAEs) to capture evolving network topologies and cross-domain structural dependencies. A Pareto-based optimization strategy is further employed to systematically balance latency and resource efficiency across heterogeneous domains and varying workload conditions. Extensive experiments across multiple network scales and diverse SFC configurations demonstrate that the proposed framework achieves a superior trade-off between latency and deployment capability, while improving scalability, robustness, and long-term resource efficiency in dynamic and large-scale CDN environments.

The prediction of network latency with partial measurements is of importance for ever-increasing personal devices to ensure their Quality of Service (QoS). However, the current matrix-factorization-based efforts, as a promising paradigm, for network latency prediction have failed to intelligently exploit inherent factors hidden in networks to accurately infer the unknown network latency. Furthermore, it is more complicated to execute extensive network measurements on pervasive personal devices due to unstable communication environments. To alleviate these problems, in this paper, a novel accurate network latency prediction (DALP) solution via Deep Reinforcement Learning (DRL) is proposed for personal devices under cost-aware sampling. Specifically, we first alternately implement cost-aware latency measurement based on temporal correlation, and model it as a network latency matrix, in which unmeasured and missing elements need to be inferred. In order to achieve accurate prediction performance, the DRL-based Matrix Factorization with Double Weights (DWMF) is designed to exploit the potential network factors and multiple rules of matrix factorization, which can be alternatively executed, to minimize the prediction errors. Furthermore, an angle-loss-based reward strategy is designed to enhance the quality of model training. Simulation results on real-world datasets illustrate that DALP outperforms the previous approaches with quicker convergence and lower prediction errors.

WiFi Indoor localization plays a crucial role in an emerging application domain for tracking indoor people, however, the serious issue is that the WiFi signals from access points (APs) vary greatly over time and the deployment structure of APs may be changed, for example, some APs are replaced or removed over time, which cause localization accuracy reduced. To solve this problem, this paper presents WiLoc, a Long-term WiFi localization with Lightweight Siamese Neural Network. This method introduces a Siamese neural encoder-based framework to learn the similarity between three inputs, where the Siamese network only consists of three linear layers without any convolutional layer or transformer. The triplet loss function is utilized to supervise the training of the feature encoder. Then, the encodings from this encoder are input to K-Nearest Neighbors (KNN) to predict the user’s positions. Extensive experiments on the UJI dataset, show the proposed WiLoc can effectively relieve the degradation of localization accuracy over time compared to the state-of-the-art algorithms, the degradation is reduced from 51% to 12.1%, and the average localization error is 2.06 m.

The adoption of High-Definition (HD) mapping applications represents a critical step towards achieving Level-5 autonomous driving, revolutionizing road safety and paving the way for unprecedented advancements in transportation technology. However, HD mapping imposes significant computational demands in processing the raw data generated by autonomous vehicle sensors. To mitigate this issue, researchers have opted to offload the data reducing the processing time. Unfortunately, the current de-facto standard IEEE802.11p in Vehicular Ad-hoc Network (VANET) does not provide the best latency or throughput for applications with low latency and heavy data transfer requirements. This is because of the fixed Contention Window (CW). To address this problem, solutions have been developed to dynamically allocate the CW nowadays with the help of Machine Learning (ML) paradigms. Nonetheless, these solutions do not include a strategy to dynamically allocate an optimal CW per service type. Instead, they focus on sharing the wireless channel fairly. In this paper, we have developed a cross-layer Reinforcement Learning (RL) algorithm between the application and Medium Access Control (MAC) layer that allocates CW per service type. Results showed improvement with a different gap in the latency Cumulative Distribution Function (CDF) of 181%, 120%, 107%, and 119% for Voice, Video, HD Map, and Best-effort respectively compared to other different approaches.

One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires constant updates. This implies that latency and throughput requirements must be guaranteed by the wireless system. To achieve this, adjustable contention windows (CW) allocation strategies in the standard IEEE802.11p have been explored by numerous researchers. Nevertheless, their implementations demand alterations to the existing standard which is not always desirable. To address this issue, we proposed a Q- Learning algorithm that operates at the application layer. Moreover, it could be deployed in any wireless network thereby mitigating the compatibility issues. The solution has demonstrated a better network performance with relatively fewer optimization requirements as compared to the Deep Q Network (DQN) and Actor-Critic algorithms. The same is observed while evaluating the model in a multi-agent setup showing higher performance compared to the single-agent setup.

In order to locate the position of intelligent connected vehicles (ICVs) robustly in urban intersection scenarios, a novel compound positioning method based on the Dempster-Shafer evidence theory and the improved multi-layer unscented Kalman filter (DS-UKF) is proposed in this paper. The Euclidean distance between the heterogeneous data is used to optimize the Dempster-Shafer (D-S) evidence theory, so that the position of ICVs and the distance between ICVs and roadside positioning platform with higher confidence can be obtained. Meanwhile, considering the high complication of urban intersections, the unscented Kalman filter (UKF) algorithm is improved based on the covariance of error sequences so that the influences caused by observation noise anomalies can be reduced. To further improve the accuracy of positional prediction, the position of roadside positioning platform is extended dimensionally into the state vector of ICVs to observe the distance between ICVs and roadside positioning platform. Then the particle filter (PF) theory is introduced to integrate the upper-layer with the lower-layer UKF to reduce the positioning errors brought by signal interferences. Besides, an adaptive factor is adopted to connect D-S theory with the improved multi-layer UKF (IUKF) to dynamically update the weights of output values from sensors. Furthermore, a communication mechanism between the ICVs platform and the roadside positioning platform is established in this project with considering the reliability and low latency of vehicle to everything (V2X) communications. Finally, the feasibility and superiority of the proposed method is validated by experiments with three ICVs at an urban intersection. The experimental results have shown that the proposed method can locate the position of ICVs rapidly and stably in urban intersections and has better positioning performance compared to the current state-of-the-art algorithms.

With the unprecedented scalability issues rising in vehicular edge computing (VEC), we argue in this paper that the scalability, along with the remarkable growth of demands for offloading, should be integrated into the modelling for effective offloading decision-making strategies requested by a large number of vehicles. A two-stage game-theory model can depict offloading decision-making strategies by considering both the revenue of network operators and the cost of VEC users. However, heuristic processes of solving such models show significant limitations in terms of high computational complexity and energy consumption due to the changing VEC environment. Therefore, our objective in this study is to solve the game-theory model efficiently and achieve scalable offloading for the changing VEC environment. We first develop a two-stage game-theory model for the offloading decision-making strategy for VEC, by which an operator’s revenue, energy consumption and latency are considered. Then a neural network (NN) model is designed to learn the predicted behaviours of the established game-theory model for offloading decisions in a more efficient manner. After that, a feature-based transfer learning algorithm is proposed for scalable offloading optimization under unseen VEC environments. Experimental results show that the proposed NN can significantly improve the efficiency of solving the game theory model, and the developed transfer learning approach can effectively achieve the scalability of offloading decisions in a changing VEC environment. The results demonstrate that the accuracy of the proposed transfer learning approach is 37% higher than that of several state-of-the-art algorithms, and the runtime halves.

Vehicular platoon control can effectively achieve group consensus, improve vehicular running safety and increase road capacity. However, some constraints exist in practical situations due to the limitations of traffic environment in time-varying metrics (time-delay, packet-dropout or interruption) in wireless communication systems. In this work, a distributed model predictive control (MPC) algorithm is proposed for connected vehicle platoon with a focus on switching communication topologies and control strategy under abnormal communications. Firstly, the predecessor-leader following is selected as the basic communication topology, by which the switching communication topology and the desired vehicle spacing policy are established. Secondly, the platoon control algorithm of connected vehicles is established and a set of constraints is analyzed. Thirdly, the${\mathcal{ L}}_{2} $-norm string stability criterion and the asymptotic stability criterion are considered within the proposed MPC. Finally, a co-simulation platform for connected vehicle platoon is developed based on Prescan/Matlab/V2X communication simulator. In addition, the platoon control algorithm is tested in three traffic scenarios including normal communication, leading vehicle with abnormal communication and following vehicle with abnormal communication. The experiments demonstrate that the communication topologies in different communication environments can be switched well in real time through the proposed platoon control algorithm. In addition, the string stability, the consistency of vehicle spacing, speed and acceleration are proven to be guaranteed simultaneously.

Offloading decisions in mobile edge computing have been extended with multiple objectives, such as revenue maximization, energy conservation and latency reduction. Revenues of network/service operators, as the realistic and ultimate goal at intensive competitive markets, have not been thoroughly studied under a pricing scheme in combination with offloading decisions, especially with the aims of reducing and restricting energy consumption and latency. To bridge this important gap, this paper studies the revenue maximization of network operators through a pricing scheme in mobile edge computing, by explicitly formulating energy consumption and latency into the offloading strategy. A two-stage game-theory framework based on the Stackelberg game is established, through which the optimal price for both the network operator and the customer can be reached. The offloading data size can be dynamically adjusted according to the agreed price. The existence of equilibrium in the Stackelberg game is proved, and experiments are conducted to verify the effectiveness of our proposed model.