关键词： Mobile edge computing  

摘要： With the rapid advancement of devices requiring intensive computation—such as Internet of Things (IoT) devices, smart sensors, and wearable technology—the computational demands on individual platforms with limited resources have escalated, necessitating the offloading of the generated tasks by the devices to edge. These tasks are often real-time with strict response time requirements. Among these devices, autonomous vehicles present unique challenges due to their critical need for timely and accurate processing to ensure passenger safety. Selecting suitable servers in a heterogeneous mobile edge computing (MEC) architecture is vital to optimizing real-time task processing rates for such applications. To address this, we present an algorithmic solution to improve the allocation of heterogeneous servers to real-time tasks, aiming to maximize the number of processed tasks. By analyzing task and server characteristics in the MEC architecture, we develop the suitability-based adaptive resource selection (SARS) algorithm, which evaluates server suitability based on factors like time constraints and server capabilities. Additionally, we introduce the proactive on-demand resource allocation (PORA) algorithm, which strategically reserves computational resources to ensure availability for critical real-time tasks. We compare the proposed algorithms with several classical and state-of-the-art algorithms. Computational results demonstrate that our approach outperforms existing algorithms, processes more tasks, and effectively prioritizes urgent tasks, particularly in autonomous driving applications. Copyright © 2024, The Authors. All rights reserved.

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： Autonomous aerial vehicles   Energy consumption   Trajectory   Optimization   Wireless communication   Array signal processing   Resource management   NOMA   Quality of service   Games   Unmanned aerial vehicle (UAV)   reconfigurable intelligent surface (RIS)   mobile edge computing   energy consumption   Stackelberg game  

摘要： Unmanned Aerial Vehicles (UAVs) can be employed as short-term aerial base stations or as access points for User Equipments (UEs) to communicate with other UEs effectively. However, communication links may be obstructed by buildings, leading to poor data transfer performance and significant energy consumption. Deploying Reconfigurable Intelligent Surfaces (RIS) as part of the UAV-assisted communication system proves to be an effective means to avoid building obstructions and enhance wireless information quality. However, the complexity of communication relationships in multi-UAV systems with RIS-aided communication poses a significant challenge in energy reduction. Therefore, this study investigates a new RIS-aided multi-UAV communication framework for edge computing systems. The system aims to meet the quality-of-service (QoS) for UEs while minimizing the total energy consumption. To optimize the total energy consumption of RIS-aided multi-UAV communication, the impact of communication between multiple UAVs and differences between UE clusters on that system's performance is also considered. We introduce a Stackelberg game to deal with the communication relationship between multiple UAVs and design a K-means-based clustering algorithm to segment UEs periodically. A model-free deep reinforcement learning algorithm grounded in maximum entropy is proposed to jointly optimize UAV trajectory design, phase shift control, and power allocation to reduce energy consumption further. Experimental results indicate that the system proposed performs favorably concerning both energy consumption and throughput.

关键词： Mobile edge computing  

摘要： Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this paper, we explore the potential of edge computing for real-time classification of leaf diseases using thermal imaging. We present a thermal image dataset for plant disease classification and evaluate deep learning models, including InceptionV3, MobileNetV1, MobileNetV2, and VGG- 16, on resource-constrained devices like the Raspberry Pi 4B. Using pruning and quantization-aware training, these models achieve inference times up to 1.48x faster on Edge TPU Max for VGG16, and up to 2.13x faster with precision reduction on Intel NCS2 for MobileNetV1, compared to high-end GPUs like the RTX 3090, while maintaining state-of-the-art accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Cloud computing   Mobile edge computing   Service placement   Service allocation   Optimization  

摘要： Mobile edge computing (MEC) is a new networking pattern that deploys resources layer by layer to yield high-quality services. The MEC reduces the response time, energy consumption, process time, and bandwidth usage while enhancing privacy, security, and communications in real-life applications. Determining the most optimal resources from the edge, fog, and cloud layers for the requested services by the users under different quality-of-service constraints is one of the most critical challenges in MEC networks, known as the service placement problem (SPP). An optimal service placement algorithm can increase the benefits of service providers and users' satisfaction. Consequently, a novel metaheuristic-based resource allocation algorithm named ERSGWO in presented in the current paper. In the ERSGWO, the reptile search algorithm (RSA) and gray wolf optimizer (GWO) are hybridized through an innovative complementary strategy. The RSA is enhanced in the ERSGWO by increasing the number of leaders. The outcome hybrid algorithm is then discretized using an intermediate phase. Likewise, a neighborhood search procedure with two new movement strategies is introduced in the ERSGWO to augment exploration and exploitation capabilities. Besides, the local departure ability is enriched through a mechanism that detects and reinitializes the trapped search agents in local optimums. Eventually, 12 MEC environments with different specifications are designed, and the ERSGWO is applied to them. The results are compared with the ARO, AOA, FOX, GWO, MRFO, RSA, SCA, STOA, TSA, APO, and EHO algorithms numerically and visually. The experimental results reveal that the ERSGWO outperformed its competitors in 97.73% of test cases.

关键词： eavesdropping   massive multiple input multiple output   mobile edge computing   partial offloading   secure computation efficiency  

摘要： In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

关键词： Mobile edge computing   SAC   communication security   A-Star   UAV  

摘要： In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain *** attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or ***,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping ***,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also *** solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)*** algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading ***,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering *** approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy *** simulation results demonstrate that,compared to other algorithms,our approach achieves superior system ***,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary *** for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in div

关键词： Convolutional Neural Network   Deep Learning   Energy optimization   Internet of Things   Mobile Edge Computing  

摘要： The importance of the Internet of Things (IoT) devices is rising in this digital age. However, the high energy consumption of these devices limits theirlong-term functioning and efficiency. Task offloading technique in IoT is often used to address energy optimization. Conventional task offloading strategies lead to shorter lifespansand inefficient use of devices. Energy-aware task offloading is critical in IoT edge networks within the Mobile Edge Computing (MEC) environments to enhance resource utilization and reduce latency. This paper proposes an optimized Convolutional Neural Network (CNN) for efficient task offloading. The method efficiently predicts optimal offloading decisions using a comprehensive dataset that includes network characteristics, user behavior and resource utilization features. The optimized CNN architecture achieved notable performance with 91.075% accuracy, 91.82% precision, 91.07% recall, and 90.74% F1score. These results showed that the model ensures efficient resource allocation and extends the operational life of IoT devices. The key findings highlight the potential of Deep Learning (DL)models in contributing to the energy-aware task offloading fieldin real-time adaptive decision making within the MEC environments. © 2024 Seventh Sense Research Group.

关键词： Intelligent reconfigurable surface   Mobile edge computing   Unmanned aerial vehicle   Non-orthogonal multiple access  

摘要： Intelligent reconfigurable surface (IRS) is an emerging technology for the enhancement of spectrum and energy efficiency. We propose a novel IRS-and-unmanned aerial vehicle (UAV)-Assisted mobile edge computing (MEC) framework, where a MEC server installing on an UAV to facilitate task calculations by mobile users (MUs), and an IRS modulates channels between MUs and the UAV. Non-orthogonal multiple access (NOMA) is employed for further improving system-wide spectral efficiency. There are needs for joint optimization of multiple parameters, e.g., the task partition parameters and the transmit power of all MUs, the reflection coefficient matrix of the IRS and the movement trajectory of the UAV, and such needs raises the challenge of minimizing the long-term total energy consumption of all MUs while satisfying required transmission rate and task completion delay. We divide optimization tasks into two sub-problems and propose specific solutions respectively, i.e., relevant decisions about the UAV and MUs to be solved by deep reinforcement learning (DRL);and the reflection coefficient matrix of the IRS to be solved by block coordinate descent (BCD). A series of experiments have verified the effectiveness of the proposed communication techniques and optimization algorithms. Simulation results demonstrate that (1) NOMA-IRS technique achieves better energy efficacy compared to the cases where random IRS or no IRS is deployed and the conventional orthogonal multiple access (OMA) technique with IRS. (2) our proposed deep deterministic policy gradient (DDPG)-BCD algorithm outperforms other four benchmark algorithms in solving the complex and dynamic optimization problem.

关键词： Task analysis   Resource management   Servers   Uncertainty   Optimization   Dynamic scheduling   Heuristic algorithms   Chance-constrained program   mobile edge computing   online resource management   probabilistic deadline guarantee   uncertain demands  

摘要： With the proliferation of resource-hungry applications, a common paradigm to quickly respond to the ever-increasing demands of tasks is mobile edge computing (MEC), which deploys resources on the premise of grasping task information (including time and quantity of task release, etc). Actually, task generation is dynamic in many practical scenarios, and it is unrealistic for MEC servers to acquire a task's demand for computing resources beforehand until task execution is completed. It is also challenging to ensure all tasks can be processed before their deadlines. In this paper, we characterize task generation as a dynamic process, implying that MEC servers only obtain the task information in the current time slot. Also, we assume the task's demand for computing resources is uncertain with only knowledge of its mean and variance. To eliminate this uncertainty, we leverage the chance-constrained program (CCP) method, which also offers a probabilistic guarantee to the task deadline. Moreover, we formulate a long-term time-averaged task processing power minimization problem, which is transformed into a series of per-slot deterministic optimization problems utilizing the Lyapunov algorithm. Given that the transformed problem is a mixed-integer non-linear programming (MINLP) problem, a joint optimization algorithm considering UE access, power control, and resource allocation (JUPR) is proposed to decouple the MINLP problem into three sub-problems. Finally, simulation results demonstrate the JUPR algorithm can consume 22% less power per task than the average resource allocation algorithm and 12% less power per task than the random resource allocation algorithm.