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IEEE International Conference on Communications (ICC)

作者： Zhang, Rongrong Zhou, Chen Capital Normal Univ Coll Informat Engn Beijing Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

The rapid development of Wireless Body Area Networks (WBANs) has brought revolutionary changes to the healthcare system. However, due to the shortcomings of the sink node with limited energy resource and computing capability, it is difficult to handle all computation tasks effectively and timely. The emergence of Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) may provide a potential and efficient solution. Therefore, we devote this paper to developing a computation task offloading scheme based on MCC and MEC for WBANs. Technically, we first propose a three-tier system model with one Remote Cloud Server (RCS), several Mobile Edge Servers (MESs) and multiple WBAN users. Then, an optimization problem with the objective to minimize the total cost in terms of the energy consumption and the delay is formulated. In order to solve the problem, we next investigate a computation task offloading Scheme based on Differential Evolution algorithm, called CTOS-DE. The simulation results demonstrate that our proposed CTOS-DE scheme can provide a best computation task offloading decision in terms of the total cost and load balancing.

关键词： computation task offloading Mobile cloud computing Mobile edge computing Wireless body area networks

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A Survey of computation offloading With task Types

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 8313-8333页

作者： Zhang, Siqi Yi, Na Ma, Yi Univ Surrey Inst Commun Syst Guildford GU2 7XH Surrey England

computation task offloading plays a crucial role in facilitating computation-intensive applications and edge intelligence, particularly in response to the explosive growth of massive data generation. Various enabling techniques, wireless technologies and mechanisms have already been proposed for task offloading, primarily aimed at improving the quality of services (QoS) for users. While there exists an extensive body of literature on this topic, exploring computation offloading from the standpoint of task types has been relatively underrepresented. This motivates our survey, which seeks to classify the state-of-the-art (SoTA) from the task type point-of-view. To achieve this, a thorough literature review is conducted to reveal the SoTA from various aspects, including architecture, objective, offloading strategy, and task types, with the consideration of task generation. It has been observed that task types are associated with data and have an impact on the offloading process, including elements like resource allocation and task assignment. Building upon this insight, computation offloading is categorized into two groups based on task types: static task-based offloading and dynamic task-based offloading. Finally, a prospective view of the challenges and opportunities in the field of future computation offloading is presented.

关键词： task analysis Resource management Vehicle dynamics Surveys Optimization computational efficiency Robot sensing systems computation task offloading task type energy static task dynamic task offloading strategy

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Onboard Edge Computing: Optimizing Resource Allocation and offloading in Mobile Scenarios

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 345-361页

作者： Chen, Zijian Zhang, Hong Wang, Miao Wang, Liqiang Zhang, Lei Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Hebei Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

The rapid development of the Internet of Things (IoT) has propelled mobile edge computing (MEC) into the forefront of both academia and industry. Nevertheless, the surge in urban activities driven by economic development is putting a strain on infrastructure like transportation and utilities. Increased demand for computing tasks and server failures from natural disasters can severely strain MEC in a specific region due to its reliance on static edge servers. To address these challenges, we introduce an MEC framework called onboard edge computing (OBEC), which explores onboard servers to provide computational offloading services in mobile scenarios. To determine the end devices that each onboard server will serve, we propose the concept of "hunger value" to accurately measure the resource idleness of an onboard server. We also implement a Genetic Optimization-based Hunting-Predation Algorithm, an onboard server as a predator and a service end device as a prey, to minimize the overall hunger value of the whole system. Taking into account the power consumption of onboard servers and the satisfaction of end devices, we introduce a Stackelberg game to allow each onboard server to select the optimal serviced end devices and efficiently provide the required resources. Since this Stackelberg game lacks an analytical solution, we employ gradient descent to calculate the optimal offloading and resource allocation strategy. Finally, we conduct simulation experiments to demonstrate the superiority of the proposed OBEC framework over other state-of-art methods across various scenarios, underscoring its potential to foster synergistic interactions between servers and end devices.

关键词： Servers Resource management Games Edge computing Internet of Things Autonomous aerial vehicles computational modeling computation task offloading genetic algorithm (GA) mobile edge computing (MEC) stackelberg game vehicular edge computing (VEC)

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AIEC-RSC: AI and Edge Collaboration Empowered Reliable Service Computing for High-Speed Mobile Businesses

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第1期17卷 224-236页

作者： Xu, Siya Chi, Jingye Wang, Shi Guo, Shaoyong Qiu, Xuesong Meng, Luoming Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the rapid development of high-speed assistant driving and smart inspections, the edge network is required to provide quick and reliable service to avoid large service response delays and frequent re-transmissions caused by interruption. However, the reasonable service component caching, efficient edge collaboration and reliable cross-domain computation offloading are still key problems to be solved. Thus, we consider an AI and mobile edge computing (MEC) integrated service framework, which is highly reliable for high-speed mobile businesses, and we divide the service process into component caching phase and task offloading phase. In the first phase, we novelly define the edge collaborative service domain (ECSD) which allows multiple edge nodes to collaboratively share resources from a global perspective and design a user behavior aware service component pre-caching method to increase resource utilization. In the second phase, based on the formed ECSDs and cached service components, we present an AI-empowered cross-domain computation task offloading mechanism including task partition and backup to enhance the reliable service capability of edge networks. Simulation results verify that the proposed mechanism can jointly optimize the allocation of caching, computation, and communication resources, while improving the service response speed and resource utility of edge networks.

关键词： High-speed mobile business mobile edge computing service component caching computation task offloading

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Delay, Energy, and Outage Considerations in GenAI-Enhanced MEC-NOMA-Enabled Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Saleem, Muhammad Asim Zhou, Shijie Fengli, Zhang Ahmad, Tanveer Nigar, Nahida Hadi, Muhammad Usman Shabaz, Mohammad Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus East Delta Univ Dept Comp Sci Chittagong 4209 Bangladesh Ulster Univ Sch Engn Belfast BT15 1AP North Ireland Model Inst Engn & Technol Jammu 181122 Jammu And Kashm India

In recent years, vehicular networks have experienced substantial growth, transforming the landscape of the transportation industry. In particular, the issue of computing task offloading in vehicular networks is critically important. The two key considerations are offloading task delay and energy consumption. Thus, a multiple access (MA) scheme is necessary in vehicular networks to facilitate more efficient computation task offloading. Non-orthogonal multiple access (NOMA) is recognized as a promising candidate for multiple access in fifth-generation (5G) and beyond networks due to its potential to improve overall spectrum efficiency through superposition coding. Moreover, mobile edge computing (MEC) can minimize computation task delay and energy consumption by bringing computational resources closer to vehicles. Therefore, this paper examines the potential of employing mobile edge computing (MEC) with non-orthogonal multiple access (NOMA) in vehicular networks to reduce overall computation offloading task delay and energy consumption. Specifically, computation models for task delay and energy consumption are presented. Additionally, considering universal frequency reuse, we analyze interference-limited scenarios and allow interference from the transmissions of other vehicles and roadside units. The optimization problems for delay and energy are formulated based on the described models. Due to the non-convex nature of the optimization problem, they are solved numerically using the Python programming language. Furthermore, analytical expressions for outage probability are provided to evaluate the typical vehicle's outage performance. Simulation results demonstrate the delay and energy performance of the MEC-NOMA-enabled vehicular networks compared to their OMA-based counterparts. The results indicate that MEC-NOMA achieves lower delay, reduced energy consumption, and improved outage performance compared to OMA-based vehicular networks.

关键词： Delays NOMA Energy consumption Optimization Interference computational modeling Servers Probability Power system reliability Real-time systems MEC vehicular networks computation task offloading interference outage probability

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Path Planning for the Dynamic UAV-Aided Wireless Systems Using Monte Carlo Tree Search

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第6期71卷 6716-6721页

作者： Qian, Yuwen Sheng, Kexin Ma, Chuan Li, Jun Ding, Ming Hassan, Mahbub Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 210094 Peoples R China CSIRO Data61 Sydney NSW 2015 Australia Univ New South Wales UNSW Sch Comp Sci & Engn Sydney NSW 2052 Australia

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

关键词： task analysis Trajectory Autonomous aerial vehicles Energy consumption Wireless communication Vehicle dynamics Monte Carlo methods Unmanned aerial vehicle path planning Monte Carlo tree search throughput maximization computation task offloading

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offloading computational tasks for MIMO-NOMA in mobile edge computing utilizing a hybrid Pufferfish and Osprey optimization algorithm

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AIN SHAMS ENGINEERING JOURNAL 2024年第12期15卷

作者： Midhulasri, J. Ravikumar, C. V. Vellore Inst Technol Sch Elect Engn Vellore 632014 India

One of the promising technologies to minimize computing delay and task offloading is Mobile Edge Computing (MEC), which enables mobile devices to offload high computational works. Non-Orthogonal Multiple Access (NOMA) is also regarded as one of the significant approaches for improving the spectrum efficacy, whereas the Massive Multiple-Input Multiple-Output (MIMO) helps to assist the enormous amount of candidates for continuous offloading. These two mechanisms can support the offloading and then enhance the MEC framework's performance. Nevertheless, the incorporation of portable cloud computing might result in substantial delays in offloading or congestion in network traffic due to the restricted capacity of the main network infrastructure, particularly when a considerable number of users simultaneously seek computational offloading. This emphasizes the necessity for sophisticated network management and improvements to infrastructure in order to satisfy the increasing demands of mobile edge computing. This work presents a highly effective MIMO-NOMA technique in MEC to increase the process of task offloading. The objective of this work is to decrease the overall transmission and computing delay under the MEC computing capability and the transmit power of the user. With the support of this pairing mechanism for Massive MIMO-NOMA, the lower channel gain candidates and the greater channel gain users can offload their information to the MEC. The Hybrid PufferFish Osprey Optimization (HPFOO) algorithm is utilized to optimize the parameters in the model. The HPFOO is a combination of the Pufferfish Optimization Algorithm (POA) and Osprey Optimization Algorithm (OOA) with the goal of reducing both transmission and computing delay. The performance investigation of the NOMA-MIMO-based MEC device is carried out by comparing its overall latency and total data throughput with those of the current system configurations.

关键词： computation task offloading MIMO-NOMA Mobile edge computing Hybrid PufferFish Osprey Optimization Throughput

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Distributed task Replication for Vehicular Edge Computing: Performance Analysis and Learning-Based Algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第2期20卷 1138-1151页

作者： Sun, Yuxuan Zhou, Sheng Niu, Zhisheng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In a vehicular edge computing (VEC) system, vehicles can share their surplus computation resources to provide cloud computing services. The highly dynamic environment of the vehicular network makes it challenging to guarantee the task offloading delay. To this end, we introduce task replication to the VEC system, where the replicas of a task are offloaded to multiple vehicles at the same time, and the task is completed upon the first response among replicas. First, the impact of the number of task replicas on the offloading delay is characterized, and the optimal number of task replicas is approximated in closed-form. Based on the analytical result, we design a learning-based task replication algorithm (LTRA) with combinatorial multi-armed bandit theory, which works in a distributed manner and can automatically adapt itself to the dynamics of the VEC system. A realistic traffic scenario is used to evaluate the delay performance of the proposed algorithm. Results show that, under our simulation settings, LTRA with an optimized number of task replicas can reduce the average offloading delay by over 30% compared to the benchmark without task replication, and at the same time can improve the task completion ratio from 97% to 99.6%.

关键词： task analysis Delays Heuristic algorithms Wireless communication Vehicle dynamics Edge computing Cloud computing Vehicular edge computing computation task offloading task replication online learning combinatorial multi-armed bandit

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Joint Optimization of Path Planning and Resource Allocation in Mobile Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第9期19卷 2129-2144页

作者： Liu, Yu Li, Yong Niu, Yong Jin, Depeng Tsinghua Univ Dept Elect Engn Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Beijing Jiaotong Univ Beijing Engn Res Ctr High Speed Railway Broadband State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity L-step lookahead branch scheme (referred to as L-step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed L-step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

关键词： task analysis Resource management Edge computing Path planning Optimization Processor scheduling Mobile computing Mobile edge computing vehicles computation task offloading path planning piecewise linear approximation

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task offloading and resource allocation for UAV-assisted mobile edge computing with imperfect channel estimation over Rician fading channels

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-19页

作者： Wang, Guangying Yu, Xiangbin Xu, Fangcheng Cai, Jiali Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn 29 Jiangjun Blvd Nanjing 211100 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China

In this paper, we develop task offloading and resource allocation scheme for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with channel estimation errors over Rician fading channels. The objective is to maximize the system utility with constrained network stability, transmit power, and data arrival rate. We consider a general multi-user UAV-assisted MEC system based on frequency division multiple access (FDMA), and we assume that the computation tasks are split into separate tasks and offloaded to the server for computing. We study stochastic computational resource management based on the Lyapunov optimization algorithm. The optimal transmit power and bandwidth allocation for computation offloading are obtained alternately, and the optimal computation task admission at each time slot and the optimal value of the auxiliary variable are derived. Simulation results verify the effectiveness of the proposed scheme in the paper and evaluate the influence of various parameters to the system performance.

关键词： Unmanned aerial vehicle Mobile edge computing Channel estimation error computation task offloading Resource allocation

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