关键词： task offloading   deep reinforcement learning   mobile edge computing  

摘要： With the proliferation of intelligent applications, mobile devices are increasingly handling computation-intensive tasks but often struggle with limited computing power and energy resources. Mobile Edge Computing (MEC) offers a solution by enabling these devices to offload computation-intensive tasks to resource-rich edge servers, thus reducing processing latency and energy consumption. However, existing task-offloading strategies often neglect critical security concerns. In this paper, we propose a security-aware task-offloading framework that utilizes Deep Reinforcement Learning (DRL) to solve these challenges. Our framework is designed to minimize the latency of task accomplishment and energy consumption while ensuring data security. We model system utility as a Markov Decision Process (MDP) and design a Proximal Policy Optimization (PPO)-based algorithm to derive optimal offloading strategies. Experimental results demonstrate that the proposed algorithm outperforms traditional methods regarding task execution latency and energy consumption.

关键词： Mobile edge computing   successive convex approximation   unmanned aerial vehicle   resource management   Mobile edge computing   successive convex approximation   unmanned aerial vehicle   resource management  

摘要： Unmanned aerial vehicles (UAVs) have been widely utilized to expand wireless network coverage and provide computation service for Internet-of-Things (IoT) devices in signal-blocked or shadowed environments. In this paper, we propose a novel multi-UAV-enabled mobile edge computing (MEC) architecture in which multiple UAVs provide both communication and computation services for IoT devices that cannot directly access the ground edge clouds. To achieve min-max fairness of energy consumption among UAVs, we minimize the maximal energy consumption among UAVs by jointly optimizing computation offloading decisions, communication and computation resource allocation, UAV positions, and task splitting decisions, while meeting the delay requirement of all tasks. The required optimization is a large-scale mixed-integer non-linear program that is generally intractable. To solve this problem, we propose an efficient iterative algorithm based on the successive convex approximation (SCA). The simulation results show that the proposed scheme outperforms various baseline schemes in processing computation-intensive and latency-critical tasks.

关键词： federated learning   reverse auction   mobile edge computing  

摘要： Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients' volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

关键词： Mobile edge computing   edge server planning   mobility management   graph neural network   learning-based framework   Mobile edge computing   edge server planning   mobility management   graph neural network   learning-based framework  

摘要： Mobile service continuity is essential important to ensure that user sessions and services will survive user mobility. The 5G enhances its mobility management by providing the flexibility and offering three types of Session and Service Continuity (SSC) modes to address various service continuity requirements. Multi-access edge computing (MEC) is a type of widely adopted network architecture that delivers network services from the boundary of the mobile network by provisioning a set of edge servers. Determining an optimum planning of MEC edge servers, which involves determining edge servers appropriate geographical positions and their serving areas, is a precondition for more efficient service provisioning and better usage of network resources. In this work, we investigate the MEC servers planning problem by considering the management cost for maintaining MEC service continuity. The problem is formulated as a graph partitioning problem to partition the RAN graph with minimum SSC management costs and balanced MEC servers workloads. Then, we adapt a generalizable approximate Graph Partitioning framework which leverages on Graph Neural Network (GNN) to embed the RAN network spacial feature and on Multilayer Perceptron (MLP) for graph partitioning. Based on the framework, we propose a MEC server planning algorithm named MECP-GAP. Finally, we evaluate MECP-GAP with extensive simulations and real network data. Comparing to several baselines, MECP-GAP achieves better performance with lower running time.

关键词： Unmanned aerial vehicle   mobile edge computing   trajectory planning   offloading scheduling   multi-objective reinforcement learning  

摘要： Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations or single-objective reinforcement learning (RL) cannot reduce the backlog of task bits and simultaneously improve energy efficiency in highly dynamic network environments, where the design problem amounts to a sequential decision-making problem. In order to address the aforementioned problems, as well as the curses of dimensionality introduced by the growing number of terrestrial terrestrial users, this paper proposes a distributed multi-objective (MO) dynamic trajectory planning and offloading scheduling scheme, integrated with MORL and the kernel method. The design of n-step return is also applied to average fluctuations in the backlog. Numerical results reveal that the n-step return can benefit the proposed kernel-based approach, achieving significant improvement in the long-term average backlog performance, compared to the conventional 1-step return design. Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time.

关键词： Mobile edge computing   multiple edge service providers   task offloading   resource expansion  

摘要： As a potential computing solution for fast-growing mobile and IoT applications, edge computing has been developed rapidly. However, due to the relatively limited resources of each edge node, it is difficult for edge nodes to provide quality-guaranteed services to dynamic and massive computation tasks individually. To address this challenge, this paper proposes a two-stage resource expansion and sharing scheme, named SESAME, to enable resource sharing among the edge nodes within/across multiple edge service providers (ESPs), to improve the overall efficiency of the edge computing system. To facilitate the operation and reduce complexity, the resource management scheme has both long-term and short-term decision periods. During the long-term period, an optimal conservative estimation-based resource expansion and pricing strategy has been designed to ensure the system stability and the interests of ESPs. During the short-term period, a resource-sharing strategy considering the internal and external behaviors of ESPs has been proposed to reduce resource-sharing costs while fully utilizing internal resources. In such a way, the resources from different ESPs can collaborate efficiently. Extensive experiments on real datasets show that our algorithm can effectively reduce ESP costs and improve system stability.

关键词： Task analysis   Heuristic algorithms   Vehicle dynamics   Adaptation models   Space vehicles   Real-time systems   Mobile computing   Digital twin   generative adversarial network   mobile edge computing   swarm intelligence   task offloading   vehicular edge computing  

摘要： Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, Adaptive Swarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRediction In VEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveD genetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.

关键词： Mobile edge computing   Computation offloading   Load balancing   Cooperative topology   Multi-agent deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a new computing paradigm that has shown great potential. How to extract the cooperative topological relationship between MEC servers to realize jointly computing is the key problem to solve the bottleneck of MEC computational capability. In previous studies, multi-MEC servers are regarded as unit computing nodes with the same cooperation relationship to jointly schedule offloading tasks, without considering the hierarchical and clustered topology of the server collaborative work. As a result, in the scenario of unbalanced distribution of computing resources, it is difficult to obtain the optimal joint scheduling strategy for offloading tasks according to the cooperation relationship and resource differences among MEC servers. Therefore, this paper considers introducing the topological relationship of group cooperation among multi-MEC servers to optimize the joint scheduling strategy, and proposes a Multi-Agent Hierarchical Graph Attention Soft Actor-Critic algorithm (MHSAC). Firstly, based on the differences in their own resources and the demands of the tasks they undertake, MEC servers are divided into series clusters. Then, a Hierarchical Graph Attention Network (HGAT) is used to model each agent to extract the physical communication topology information of the MEC server and the group topology information of multi-edge cooperation. The multi-agent soft Actor-Critic algorithm is used to obtain the offloading scheduling decision of multi-edge cooperation. Experiments show that the MHSAC algorithm that considering the topological relationship of multi-edge group cooperation can optimize load distribution under low latency and resource-limited requirements, achieving optimal load balancing values and task drop rates.

关键词： Mobile edge computing  

摘要： Handling long tail corner cases is a major challenge faced by autonomous vehicles (AVs). While large language models (LLMs) hold great potentials to handle the corner cases with excellent generalization and explanation capabilities and received increasing research interest on application to autonomous driving, there are still technical barriers to be tackled, such as strict model performance and huge computing resource requirements of LLMs, which are difficult to be met locally at AVs. In this paper, we investigate a new approach of applying remote or edge LLMs to support autonomous driving. With this approach connected autonomous vehicles (CAVs) send driving assistance requests to the LLMs. LLMs deployed at the edge of the networks or remote clouds process the requests and generate driving assistance instructions for the CAVs. A key issue for such LLM assisted driving system is the assessment of LLMs on their understanding of driving theory and skills, ensuring they are qualified to undertake safety critical driving assistance tasks for CAVs. As there is no published work on assessing LLM of driving theory and skills, we design and run driving theory tests for several proprietary LLM models (OpenAI GPT models, Baidu Ernie and Ali QWen) and open-source LLM models (Tsinghua MiniCPM-2B and MiniCPM-Llama3-V2.5) with more than 500 multiple-choices theory test questions. These questions are close to the official UK driving theory test ones. Model accuracy, cost and processing latency are measured from the experiments. Experiment results show that while model GPT-4 passes the test with improved domain knowledge and Ernie has an accuracy of 85% (just below the 86% passing threshold), other LLM models including GPT-3.5 fail the test. For the test questions with images, the multimodal model GPT4-o has an excellent accuracy result of 96%, and the MiniCPM-Llama3-V2.5 achieves an accuracy of 76%. While GPT-4 holds stronger potential for CAV driving assistance applications, the

关键词： Task analysis   Training   Multitasking   Costs   Computational modeling   Data models   Resource management   Artificial intelligence (AI)   federated learning (FL)   mobile edge computing   multitask learning (MTL)  

摘要： The wide applications of artificial intelligence (AI) on massive Internet of Things or smartphones raises significant concerns about privacy, heterogeneity, and resource efficiency. Correspondingly, federated learning (FL) emerges as an effective way to enable AI over massively distributed nodes without uploading the raw data. Conventional works mostly focus on learning a single unified model for one solitary task. Multitask learning (MTL) outperforms single-task learning by training multiple models concurrently, leading to reduced model sizes and increased flexibility. However, existing FL efforts often face challenges in efficiently managing MTL scenarios, particularly with the presence of stragglers, without incurring prohibitive computation and communication *** this article, inspired by the natural cloud-base station (BS)-terminal hierarchy of cellular networks, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the task heterogeneity corresponding to different nonindependent and identically distributed (IID) training data sets. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization subproblems within BSs. Therefore, it enables solving different tasks within a BS and aggregating the multitask result in the cloud without uploading the raw data. Furthermore, compared with existing methods that reduce resource costs by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for adjusting the iteration number on local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.