关键词： Servers   Delays   Costs   Quality of service   Heuristic algorithms   Optimization   Cloud computing   5G mobile communication   Resource management   Prediction algorithms   Mobile edge computing   batch learning   multi-armed bandits  

摘要： With the development of the fifth generation (5G) technology, Mobile Edge Computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within the MEC system could save more time on data transmission, in which the tasks can be executed on the edge side. Multi-armed bandits (MAB) are powerful tools that help users offload tasks to their best servers in the MEC system. However, as the system scale grows, the traditional MAB algorithms are weak and the channel condition deteriorates rapidly, which will lead to an increasing time cost of edge tasks offloading and computing. Therefore, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) to solve such a problem. BMSE contains two sub-algorithms, BMSE in User Level (BMSE-UL) and BMSE in System Level (BMSE-SL). BMSE-UL is applied among users for the initial stage which can help them discard servers with obvious bad performance. BMSE-SL is applied in the whole MEC system which can guide users offloading tasks collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness through extensive experiments.

关键词： Training   Servers   Computational modeling   Accuracy   Data models   Convergence   Protocols   Uncertainty   Decision making   Costs   Federated learning   mobile edge computing   semi-asynchronous   timeGAN  

摘要： Federated learning (FL) has gained substantial attention as a promising solution to the need for client privacy in mobile edge computing (MEC). However, FL suffers from instability of accuracy because of the invalid clients who become stragglers caused by frequent fluctuation of available resources in MEC. To tackle this challenge, most of the frameworks of asynchronous FL allow the parameter server (PS) to schedule clients reasonably. This centralized decision-making paradigm makes it difficult to select all potential clients because client resources in MEC vary frequently. As another category of solutions, the semi-asynchronous FL frameworks follow the selection-after-training paradigm, which allows all potential clients to participate in FL but leads to a large waste of computation and communication resources. In this paper, we propose HiveFL, a new semi-asynchronous FL framework in which clients can proactively evaluate the changes in their resources, to improve global accuracy and reduce system overhead (i.e., the waste of resources induced by ineffective clients). HiveFL has the following notable properties. Firstly, HiveFL allows clients to perceive their resource status. Then, self-determining clients autonomously determine by themselves whether to participate in FL training according to the evaluation of their resource status. Secondly, comparing the experimental results with other baselines, HiveFL improves the average global test accuracy and the effective update ratio by 5.20%-22.21% and 20.3%-88.6%, respectively. Finally, HiveFL can reduce the average computation cost (measured by FLOPs) by 31.04%-81.45%. In addition, to address the problem brought by limited client resource status data, we adopt the time-series Generative Adversarial Networks (TimeGAN) method to provide more client data while training FL models. We prove the effectiveness of introducing the GAN-generated data in our experiments.

关键词： VANET   Mobile edge computing   Variable beacon power   Routing algorithms   Q-learning  

摘要： In traditional Vehicle-to-Vehicle-to-Infrastructure (V2V2I) routing algorithms, the beacon power of vehicles remains fixed, lacking adaptability to dynamic adjustments based on events or on-demand operational modes, leading to poor dynamic adaptability of vehicles and significant energy wastage. To enhance the flexibility of beacon power adjustment for vehicles in dynamic environments, while considering practical factors such as the signal coverage status, selfishness, and motion characteristics of routing vehicles, a self-adaptive V2V2I routing algorithm named Adaptive Beacon Power Routing algorithm(ABPR) is proposed. In the algorithm, in order to enhance the stability of the vehicle's connection to RSU, vehicles first collect neighbor beacons and analyze the current signal coverage status of the vehicle. Subsequently, they adaptively adjust the beacon power of the vehicle, and then analyze available neighbor vehicle nodes through improved Q-learning, searching for the optimal neighbor node and selecting it as the next-hop route. Simulation experiments conducted on the Cologne dataset demonstrate that compared to RSAR and GPSR algorithms, the ABPR algorithm achieves an average improvement of 15% in residual energy consumption, 13.79% in selfish vehicle count, 38.58% in routing hop count, and 51.37% in routing link lifetime proportion under +/- 30% standard beacon power conditions. This algorithm enhances the flexibility of beacon power adjustment in VANET environments while optimizing the overall data offloading link quality.

关键词： Servers   Wireless communication   Computational modeling   Artificial neural networks   Resource management   Energy consumption   Wireless sensor networks   Semantic communication   Performance evaluation   Optimization   Mobile edge computing   computation offloading   deep neural network   semantic communication  

摘要： In this paper, we focus on computation-aware offloading for executing deep neural network (DNN) inference tasks in a mobile edge computing (MEC) system. To cope with the challenges of insufficient wireless resources during task offloading, we resort to semantic communications (SCs), through which the users can offload the compressed task data to the edge server for remote execution. Specifically, we establish the relationship between the compression ratio and computation ratio for different DNN tasks. To achieve energy-efficient offloading, we formulate an optimization problem to minimize the energy consumption of all users by jointly optimizing the compression ratio, computation allocation, uploading time, and DNN layer selection. We first consider a special case with the preconfigured time scheduling and derive closed-form solutions to computation allocation and offloading time, which yield a threshold-based structure determined by users' channel conditions and local computation consumption. Inspired by the characteristics of these optimal solutions, a general low-complexity iterative algorithm is then designed to solve the original non-convex problem. Simulation results demonstrate that our proposed SC-based computation-aware offloading scheme can substantially reduce users' energy consumption compared to the conventional offloading and full offloading, especially with scarce wireless resources.

关键词： Mobile edge computing   Dynamic pricing   Deep reinforcement learning   Computation offloading  

摘要： The research on Mobile Edge Computing (MEC) has attracted considerable attention in recent years. However, previous studies often confined themselves to exploring the issue of task resource scheduling from a single perspective of mobile terminals or edge servers, or failed to take into account the growth of users, thus unable to face complex and diverse practical scenarios and effectively address various challenges in task offloading. In view of this, this paper adopts a more comprehensive and in-depth perspective, considering not only the user's performance benefits and the service provider's economic benefits, but also conducting a thorough analysis of the potential continuous growth of user numbers in actual scenarios. To effectively address the rapid increase in user numbers within large-scale networks, we employ a decentralized approach where each user independently makes decisions regarding their own offloading strategies, rather than relying on centralized decision-making by the server. Based on this, we propose an innovative edge computing task offloading scheme that is particularly suitable for large-scale network environments. Specifically, we combine the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and innovatively propose a decentralized task offloading scheme called DSMECO-DP. In this scheme, considering the dynamic nature of networks and user volatility, the server aims to maximize profit through coarse time scale dynamic pricing, while the mobile terminal reduces its own costs by bidding on a narrow time scale considering multiple objectives such as time delay, energy consumption, and payment cost. Simulation results demonstrate the effectiveness of the TD3 algorithm compared to other reinforcement learning algorithms, as well as the effectiveness of the dynamic pricing mechanism. Furthermore, the processing time of this algorithm is significantly reduced compared to other centralized algorithms.

关键词： Mobile edge computing   Multi-agent deep reinforcement learning   Computation offloading  

摘要： Mobile Edge Computing (MEC) has emerged as an effective paradigm for reducing latency and enhancing computational efficiency. However, the rapid proliferation of edge servers and user devices has significantly increased the complexity of task processing and resource management. Traditional task offloading approaches often rely on centralized decision-making, resulting in high computational complexity and time costs. To address these challenges, this paper introduces a dynamic collaborative framework involving multiple users and edge servers. We formulate the problem of resource allocation and task offloading as a multi-objective Markov Decision Process (MDP) with a mixed action space. To solve this, we propose a novel algorithm called Multi-Agent Mobile Edge Computing (MA-MEC), which leverages multi-agent reinforcement learning. In MA-MEC, each mobile edge server (MES) operates as an independent learning agent. Through centralized training and decentralized execution, these agents collaborate to develop efficient task offloading strategies in complex and dynamic edge environments. Simulation results demonstrate the effectiveness of our approach. MES agents learn to execute tasks more efficiently, increasing the number of processed tasks by 12.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, while task offloading rates rise by 17%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and time costs are reduced by 53%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepa

关键词： Optimization   Heuristic algorithms   Resource management   Processor scheduling   Dynamic scheduling   System performance   Transforms   Routing   Multi-access edge computing   Iterative methods   Mobile edge computing   service caching and workload scheduling   Lyapunov optimization   cloud load  

摘要： Joint service caching and workload scheduling plays an important role in ultra-dense mobile edge computing (MEC) networks to satisfy the stringent requirements of latency-critical services by leveraging the aggregated edge resources (e.g., storage and computing resources) located near the users. However, most of the existing methods incorporating popularity-based and/or size-aware caching strategies fail to match the resource demands of user requests with the heterogeneous resources of edge nodes, leading to heavier cloud load and higher latency. It becomes even worse when user requests exhibit dynamic variations over time. To address these issues, we propose an online approach for resource-efficient joint service caching and workload scheduling in ultra-dense MEC networks, called CoShare. The core idea is to fully utilize the heterogeneous resources of the edge layer to further reduce the cloud load and thus the service latency. First, we formulate the joint service caching and workload scheduling problem as a mixed integer nonlinear programming problem with the goal of minimizing the cloud load. Then, an online algorithm is developed to transform this optimization problem into a series of per-slot sub-problems by leveraging Lyapunov optimization. Next, to solve these sub-problems, we design a cacheability-based alternating iterative algorithm utilizing Gibbs sampling, in which the cacheability indicator considers both service resource demands and service popularity. Finally, simulation results show that CoShare can effectively exploit available edge resources to achieve lower cloud loads compared to other strategies.

关键词： Mobile edge computing  

摘要： Video analytics demand substantial computing resources, posing significant challenges in computing resource-constrained environment. In this paper, to achieve high accuracy with acceptable computational workload, we propose a cost-effective regions of interest (RoIs) extraction and adaptive inference scheme based on the informative encoding metadata. Specifically, to achieve efficient RoI-based analytics, we explore motion vectors from encoding metadata to identify RoIs in non-reference frames through morphological opening operation. Furthermore, considering the content variation of RoIs, which calls for inference by models with distinct size, we measure RoI complexity based on the bitrate allocation information from encoding metadata. Finally, we design an algorithm that prioritizes scheduling RoIs to models of the appropriate complexity, balancing accuracy and latency. Extensive experimental results show that our proposed scheme reduces latency by nearly 40% and improves 2.2% on average in accuracy, outperforming the latest benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： The advent of edge intelligence and escalating concerns for data privacy protection have sparked a surge of interest in device-cloud collaborative computing. Large-scale device deployments to validate prototype solutions are often prohibitively expensive and practically challenging, resulting in a pronounced demand for simulation tools that can emulate real-world scenarios. However, existing simulators predominantly rely solely on high-performance servers to emulate edge computing devices, overlooking (1) the discrepancies between virtual computing units and actual heterogeneous computing devices and (2) the simulation of device behaviors in real-world environments. In this paper, we propose a high-fidelity device simulation platform, called SimDC, which uses a hybrid heterogeneous resource and integrates high-performance servers and physical mobile phones. Utilizing this platform, developers can simulate numerous devices for functional testing cost-effectively and capture precise operational responses from varied real devices. To simulate real behaviors of heterogeneous devices, we offer a configurable device behavior traffic controller that dispatches results on devices to the cloud using a user-defined operation strategy. Comprehensive experiments on the public dataset show the effectiveness of our simulation platform and its great potential for application. The code is available at https://***/opas-lab/olearning-sim. © 2025, CC BY.

关键词： Mobile edge computing  

摘要： Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper introduces a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores two tiers of collaboration. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet latency requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to address multi-access issues of bandwidth and computation resources for multiple vehicles. A two-step method is proposed to solve the subchannel allocation problem. Detailed experiments are conducted to demonstrate the effectiveness of the proposed method and assess the impact of different parameters on system energy consumption. Copyright © 2025, The Authors. All rights reserved.