In vehicular networks enhanced by unmanned aerial vehicles (UAVs), vehicle state information is efficiently collected, and traffic safety is assured. UAVs, serving as aerial base stations, enable vehicle network access and provide edge computing services in the absence of roadside units (RSUs). This study explores a multi-UAV-assisted vehicular network, where multiple UAVs collaboratively offer services to vehicles. The goal is to minimize task completion time by optimizing trajectory planning, spectrum resource allocation, and dynamic data offloading. An enhanced multi-agent deep deterministic policy gradient (MADDPG) algorithm is introduced to address the optimization challenge in cooperative multi-UAV scenarios. Within this framework, each UAV, acting as an agent, devises strategies for movement, data offloading, and resource allocation based on the current states of vehicles and fellow UAVs. The simulation results reveal that the proposed algorithm improves task completion efficiency and ensures vehicle quality of service (QoS) over existing benchmarks.

The resource allocation problem in Multi-access Edge Computing (MEC) has been widely studied to maximize its operation efficiency under limited resource constraint. However, the existing literatures overlooked the setup cost and the associated dynamic operations. In this work, we consider server configuration and overload in the multi-server scenario where servers are switched on/off depending on the network environment. A novel pricing mechanism maximizing the utility of base station (BS) monitoring multiple servers is proposed, which jointly optimizes the setup cost and server load. We aim to maximize the BS utility under one-day task requests, and divide the time into off-peak and peak periods based on task requests. In the off-peak period, we flexibly switch on/off servers for BS to reduce setup costs. In the peak period, to avoid overloading, we introduce crowdsourcing where servers as agents purchase idle resources from private users (PUs) for mobile users (MUs) and minimize MUs’ cost by a contract-based knapsack algorithm. Lastly, a pricing mechanism is proposed to solve the BS utility maximization problem with an exploratory Upper Confidence Bound (UCB)-based algorithm adjusting server prices dynamically. Simulation results show that the proposed algorithm is superior to others in minimizing MUs cost and maximizing BS utility.

In the vehicular edge networks (VEN), task migration is complicated by issues like vehicle movement, diverse resource allocation, and integrating sensing with communication technologies. This paper presents a task migration strategy to optimize task flow under limited resources in PMN-assisted VEN. Vehicles can send public and private tasks to roadside units (RSUs), constrained by bandwidth, computational power, and storage space. Public tasks aim at data collection for road transportation management, while private tasks cover a spectrum of services from work to entertainment. To address the limitations imposed by resource scarcity and meet the demands of task migration, we have developed a dual-layer graph neural network (GNN) that leverages vehicle mobility patterns. In particular, the first layer of GNN acquires vehicle information and the latest surrounding information, and sends it to the nearby RSU. Considering the variety of tasks and multi-dimensional resource constraints, the second GNN layer forecasts RSU resource availability and vehicular trajectories. Subsequently, a task-based maximum flow algorithm (T-MFA) is proposed to refine task migration paths and resource allocation strategies to maximize task flow. Simulation experiments validate the efficacy of the proposed algorithm, demonstrating its capability to achieve optimal task migration by accommodating differences in tasks, resources, and capacities.

Integrating data security with resource management enhances security, efficiency, and reliability of blockchain-enabled mobile edge computing (MEC) systems. However, challenges such as secure data storage, timely task execution, and limited parallelism introduce complexities in task offloading decisions and resource allocation strategies. To address these challenges, the task latency minimization problem in blockchain-enabled MEC networks is formulated as an NP-hard optimization problem. The model incorporates constraints on parallelism, partial task offloading, bandwidth and computation resource allocation among mobile users (MUs) and edge servers (ESs). To enhance the reliability and transparency of data storage, a dual-blockchain framework is proposed, consisting of multiple MU blockchains and a dedicated ES blockchain. To tackle the NP-hard problem, the original optimization problem is decomposed into multiple sub-problems, facilitating parameter decoupling. An alternating optimization algorithm is employed to refine task offloading decisions and resource allocation of MUs and ESs with limited parallelism. The ESs update their strategies iteratively based on feedback mechanisms. Additionally, a task prioritization formulation is developed to enhance scalability, considering sub-level task importance, urgency, and first-level task classification. Extensive simulation experiments demonstrate that the proposed algorithm achieves lower task latency compared to existing methods across varying network sizes, offloading schemes, and parallelism constraints. By optimizing the parallel processing of tasks, the waiting latency of this algorithm is reduced on average by 35. 35%, 57. 16% and 35. 35% compared to other methods, respectively.

The emergence of deploying Deep neural network (DNN) services on edge servers has spurred research into efficiently provisioning inference services. However, previous studies have neglected to consider the implications of different types of DNN and varying quality of service (QoS) requirements on QoS violation rates. In this article, we propose a novel framework, named Coinf, for scheduling heterogeneous DNN inference tasks on edge servers. Coinf has the following four advantages to effectively handle attribute analysis, performance balancing, parallel execution, and model accuracy: (1) It enables efficient profiling of domain-specific attributes of various DNN tasks during the offline stage, achieved by constructing a regression model to predict the end-to-end latency of each task. (2) By utilizing the predicted execution time, Coinf achieves a commendable balance among inference latency, system throughput, and QoS violation rate. (3) It employs emerging deep reinforcement learning (DRL) to aggregate individual DNN tasks into batches, enabling concurrent parallel execution. (4) Coinf preserves the accuracies of the provided DNN models by not modifying them. Numerical experiments are constructed to validate the reliability and efficiency of Coinf in handling heterogeneous inference tasks.

The Grey Wolf Optimizer (GWO) is a popular metaheuristic, yet it often suffers from premature convergence and rapid diversity loss in complex, high-dimensional, or highly constrained optimization problems. This paper introduces HAGWO, a novel Hierarchical Adversarial Grey Wolf Optimizer that addresses these limitations through three synergistic enhancements: dynamic hierarchical population stratification, adaptive Levy flight perturbation, and hierarchical adversarial-like position updating. These mechanisms enable adaptive balancing of global exploration and local exploitation while preserving population diversity throughout the search process. Extensive experiments on the CEC 2017 bound-constrained benchmark suite across 30D, 50D, and 100D dimensions demonstrate that HAGWO achieves superior overall performance among eight state-of-the-art algorithms, with statistically significant advantages confirmed by Friedman mean ranks and Wilcoxon signed-rank tests. When adapted to the strongly NP-hard three-dimensional bin packing problem with identical bins (3D-SBSBPP), HAGWO delivers highly competitive results, outperforming the well-established BRKGA and most other metaheuristics while closely approaching the original GWO in solution quality and exhibiting exceptional run-to-run stability. By rigorously evaluating HAGWO across both high-dimensional continuous benchmarks and a practical constrained combinatorial application, this study validates the effectiveness of its hierarchical adversarial-like framework and provides valuable insights into algorithm design and transferability across different problem domains.