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arXiv 2024年

作者： Ahmed, Sohaib Khalid, Muhammad Hassan Hamza, Muhammad Farhat, Danyal FAST School of Computing FAST NUCES Lahore Lahore Pakistan

mobile edge computing (MEC) has emerged as a solution to the high latency and suboptimal Quality of Experience (QoE) associated with mobile Cloud computing (MCC). By processing data near the source, MEC reduces the need to send information to distant data centers, resulting in faster response times and lower latency. This paper explores the differences between MEC and traditional cloud computing, emphasizing architecture, data flow, and resource allocation. Key technologies like Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are discussed for their role in achieving scalability and flexibility. Additionally, security and privacy challenges are addressed, underscoring the need for robust frameworks. We conclude with an examination of various edge computing applications and suggest future research directions to enhance the effectiveness and adoption of MEC in the evolving technological landscape. © 2024, CC BY.

关键词： mobile edge computing

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mobile edge computing Aided Integrated Sensing and Communication With Short-Packet Transmissions

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 7759-7774页

作者： Huang, Ning Dou, Chenglong Wu, Yuan Qian, Liping Lin, Bin Zhou, Haibo Shen, Xuemin Univ Macau State Key Lab Internet Things Smart City Taipa Macau Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China Zhuhai UM Sci & Technol Res Inst Zhuhai 519031 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Dalian Maritime Univ Dept Commun Engn Dalian 116026 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

关键词： Sensors Radar Reliability Interference Performance evaluation Wireless communication Resource management mobile edge computing integrated sensing and communication short-packet transmissions

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mobile edge computing Model for Dynamically Processes Tasks Through Multi-UAV Cooperation

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第9期34卷

作者： Byeon, Haewon Gupta, Sachin Keshta, Ismail Soni, Mukesh Khan, Muhammad Attique Ben Brahim, Ghassen Al Hammadi, Dina Abdulaziz Korea Univ Technol & Educ Dept Future Technol Workers Care & Digital Hlth Lab Cheonan South Korea Maharaja Agrasen Inst Technol Dept CSE Delhi India Al Maarefa Univ Riyadh Coll Appl Sci Comp Sci & Informat Syst Dept Riyadh Saudi Arabia Dr DY Patil Vidyapeeth Dr DY Patil Sch Sci & Technol Hyderabad India Lovely Profess Univ Div Res & Dev Phagwara India Prince Mohammad Bin Fahd Univ Coll Comp Engn & Sci Dept AI Al Khobar Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia

With the advancement of communication technology, mobile edge computing (MEC) is considered a key technology for handling computation-intensive and latency-sensitive tasks. However, in scenarios such as disaster response and emergency rescue, edge servers cannot be quickly deployed to provide task processing services. The impressive mobility and simplicity of deployment of unmanned aerial vehicles (UAVs) have garnered a lot of attention for their potential to help MEC. However, allocating resources is made more difficult by the fact that UAVs have limited energy and computational capabilities. A suggested resource-efficient technique for multi-UAV collaboration (LUAVs-Cor) aims to tackle this issue. Tasks are dynamically processed by multi-UAV collaboration in this technique. Using a search for the ideal task combination, it finds task transmission mechanisms to make the most of UAVs' idle processing capabilities. Additionally, by estimating UAV processing capabilities, task quantity, and processing status, it simplifies the process of deploying UAVs to lower power usage. We observed that the LUAVs-Cor method boosts service capacity by around 6.8% and decreases total UAV energy consumption by 10.3% through comprehensive simulated trials. More customers may be served at reduced energy costs with the suggested LUAVs-Cor method, which involves minimum cooperation expenses for UAVs.

关键词： UAV mobile edge computing dynamically processes computing resources communication technology

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mobile-edge computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1845-1853页

作者： Liu, Weiqi Arif Hossain, Mohammad Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computing reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computing resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： mobile edge computing

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mobile edge computing Aided Cell-Free Massive MIMO Networks

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IEEE TRANSACTIONS ON mobile computing 2024年第2期23卷 1246-1261页

作者： Femenias, Guillem Riera-Palou, Felip Univ Balearic Isl UIB Mobile Commun Grp Palma De Mallorca 07122 Illes Balears Spain

A mobile edge computing (MEC) aided cell-free massive MIMO network is investigated in this paper that aims at optimizing the task offloading process from the wireless devices to the MEC server. Partial computation offloading strategies are designed that aim at optimizing either the aggregated latency or the energy consumption by considering the finite computational capability of the MEC server and assuming that the tasks are subject to a maximum latency constraint. The proposed optimization approach considers the joint optimization of both the per-task offloading ratio and the corresponding computational resources allocated to the tasks at the MEC server. This optimization is performed by taking into account the computing capabilities of both the wireless devices and the MEC server and both the UL and DL spectral efficiencies provided by the cell-free massive MIMO network. The proposed optimization problems are shown to be convex and, inspired by the well-known waterfilling algorithm, optimal solutions based on low-complexity iterative algorithms are devised. Extensive numerical results reveal the potential of the proposed MEC-enabled cell-free massive MIMO network.

关键词： Task analysis Massive MIMO Servers Optimization Wireless communication Energy consumption Resource management Cell-free massive MIMO mobile edge computing task offloading optimization

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mobile edge computing offloading scheme based on improved multi-objective immune cloning algorithm

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WIRELESS NETWORKS 2023年第4期29卷 1737-1750页

作者： Zhu, Si-feng Cai, Jiang-hao Sun, En-lin Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China

In the mobile edge computing scenario, a large number of computing tasks are offloaded to the edge computing server, which may cause the tasks not to be processed on time. In this paper, the response time of the computing tasks, the energy consumption of the mobile terminal device and the load balance of the server were regarded as three optimization objectives, a multi-objective optimization model were set up, and an offloading decision scheme based on multi-objective optimization immune algorithm was proposed. A large number of comparative experiments are done to verify the effectiveness of proposed scheme. Experimental results show that the proposed scheme can make the whole server system achieve a better load balancing state while meeting the requirements of response time and energy consumption.

关键词： mobile edge computing Offloading decision Response time Energy consumption Load balance Multi-objective optimization immune algorithm

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mobile edge computing (MEC)-Enabled UAV Placement and Computation Efficiency Maximization in Disaster Scenario

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第10期72卷 13406-13416页

作者： Shah, Zaiba Javed, Umer Naeem, Muhammad Zeadally, Sherali Ejaz, Waleed COMSATS Univ Islamabad Dept Elect & Comp Engn Wah Campus Wah Cantt 45550 Pakistan Univ Kentucky Coll Commun & Informat Lexington KY 40506 USA Lake Univ Barrie Hub Dept Elect Engn Barrie ON L4M 3X9 Canada

Unmanned aerial vehicles (UAVs) are considered for disaster management to provide better coverage or help first responders provide advanced services. Furthermore, UAVs combined with mobile edge computing (MEC) capabilities, have recently emerged as a support for cloud computing by deploying UAV-assisted MEC at the network edge to reduce the latency and load of cloud computing data centers. We investigate the problem of maximizing the utility function by jointly optimizing the computing power, user associations, performance, duration, and location of user equipments (UEs) in UAV-assisted MEC networks in a disaster scenario. We considered several constraints including delay, quality of service, and coverage. The optimization problem is a mixed-integer non-linear programming problem. We propose a multi-stage offloading algorithm based on a learning algorithm and an interior-point method to obtain a viable solution. The simulation results obtained demonstrate the effectiveness of our proposed algorithm compared to existing schemes.

关键词： Computational capacity disaster management mobile edge computing offloading unmanned aerial vehicles

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mobile edge computing Architecture Challenges, Applications, and Future Directions

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INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE computing 2023年第2期15卷 1-23页

作者： Sree, B. Teja Varma, G. P. S. Indukurib, Hemalatha Koneru Lakshmaiah Educ Fdn Vaddeswaram Andhra Pradesh India SRKR Engn Coll Bhimavaram Andhra Pradesh India

In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing.

关键词： energy consumption fifth -generation latency mobile edge computing Multimedia tasks network selection service placement surveillance video conferencing traffic monitoring) latency -critical (automated vehicles robotic

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Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer

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Digital Communications and Networks 2024年第6期10卷 1874-1880页

作者： Dawei Wang Xuanrui Li Menghan Wu Yixin He Yi Lou Yu Pang Yi Lu School of Electronics and Information Northwestern Polytechnical UniversityXi'an 710072China College of Information Science and Engineering Jiaxing UniversityJiaxing 314001China Jiaxing Key Laboratory of Smart Transportations Jiaxing UniversityJiaxing 314001China College of Information Science and Engineering Harbin Institute of Technology(Weihai)Weihai 264209China Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology School of Optoelectronic EngineeringChongqing University of Posts and TelecommunicationsChongqing 400065China

In this paper,we study an Intelligent Reflecting Surface(IRS)assisted mobile edge computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices(WDs)and the Access Point(AP).Specifically,in the proposed scheme,the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted ***,powered by the harvested energy,all WDs securely offload their computation tasks through the two links in the time division multiple access *** determine the local and offloading computational bits,we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy *** cope with this non-convex optimization problem,we adopt semidefinite relaxations,singular value decomposition techniques,and Lagrange dual ***,we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence *** numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.

关键词： Intelligent reflecting surface mobile edge computing Power transfer Information security

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Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in mobile edge computing Network

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 210-222页

作者： Jia, Kuan Yang, Dingcheng Wang, Yapeng Shui, Tianyun Liu, Chenji Nanchang Univ Informat Engn Sch Nanchang 330031 Peoples R China Macao Polytech Univ Appl Sci Sch Macau 999078 Peoples R China China Mobile Commun Grp Jiangxi Co Ltd Nanchang 330029 Peoples R China

This paper considers a patrol inspection scenario where multiple autonomous aerial vehicles (AAVs) are adopted to traverse multiple predetermined cruise points for data collection. The AAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This paper proposes a balanced task assignment strategy among patrol AAVs and an energy-efficient trajectory design method. Through jointly optimizing the cruise point assignment, communication scheduling, computational allocation, and AAV trajectory, a novel solution can be obtained to balance the multiple AAVs' task completion time and minimize the total energy consumption. Firstly, we propose a novel clustering method that considers geometry topology, communication rate, and offload volume;it can determine each AAV's cruise points and balance the AAVs' patrol task. Secondly, a hybrid Time-Energy traveling salesman problem is formulated to analyze the cruise point traversal sequence, and the energy-efficient AAV trajectory can be designed by adopting the successive convex approximation (SCA) technique and block coordinate descent (BCD) scheme. The numerical results demonstrate that the proposed balanced task assignment strategy can efficiently balance the multiple AAVs' tasks. Moreover, the min-max task completion time and total energy consumption performance of the proposed solution outperform that of the current conventional approach.

关键词： Autonomous aerial vehicles Inspection Energy consumption Resource management Energy efficiency Base stations Real-time systems Trajectory optimization Topology Multi-access edge computing Cellular-connected uavs patrol inspection task assignment energy consumption mobile edge computing

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