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Quality-of-Experience-Aware computation offloading in MEC-Enabled Blockchain-Based IoT Networks

IEEE INTERNET OF THINGS JOURNAL 2024年第8期11卷 14483-14493页

作者： Hosseinpour, Mahsa Moghaddam, Mohammad Hossein Yaghmaee Ferdowsi Univ Mashhad FUM Dept Comp Engn Mashhad *** Iran

Blockchain technology enables the Internet of Things (IoT) to build trust among various stakeholders. offloading the computation-intensive IoT data processing and blockchain mining computations as tasks to mobile edge computing (MEC) and nearby devices in a device-to-device (D2D) network is a promising solution for resource constraint IoT devices. In this article, to decide on the offloading plan, we consider enhancing the users' Quality of Experience (QoE) parameters reflecting the token-based cost of delay and energy consumption. For this purpose, we formulate the problem as a mixed integer nonlinear bilevel programming (MINLBP), which is NP-hard. Therefore, we propose a suboptimal solution with the help of two-sided matching theory games in two phases: 1) we decide to offload IoT data processing tasks using a many-to-one and 2) make a decision for mining tasks elaborating a many-to-many matching game. We also present algorithms based on the deferred acceptance (DA) mechanism to guarantee the stability of the matching results. The simulation results show that the proposed method can perform better in terms of delay and energy cost compared to the existing offloading approaches. Furthermore, it improves the utility of the users deciding to offload their tasks.

关键词： Internet of Things Task analysis Games Servers Data processing Data mining Delays Blockchain computation offloading Internet of Things (IoT) matching theory mobile edge computing (MEC)

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Deep-Reinforcement-Learning-Based computation offloading and Power Allocation Within Dynamic Platoon Network

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 10500-10512页

作者： Wang, Lei Liang, Hongbin Zhao, Dongmei Southwest Jiaotong Univ Sch Transportat & Logist Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L Canada

With the development of Internet of Vehicles (IoV) technology and the application of artificial intelligence-based algorithms, platoon driving based on connected autonomous vehicles (CAVs) has become one of the effective solutions to reduce environmental pollution and improve traffic safety. However, the connectivity, autonomy, and passenger comfort in platooning vehicles cannot be realized without the support of advanced communication technologies and auxiliary computing. In this work, we research the problem of computation offloading and resource allocation within a platoon network. Considering the comprehensive effects of vehicle mobility, co-channel interference, and multivehicle cooperation, we propose a system optimization model for joint computation offloading and power allocation (COPA). Our objective is to minimize the weighted sum of the system average energy consumption and task data processing delay. In the dynamic platoon network, we design a multiagent deep deterministic policy gradient (DDPG)-based joint COPA scheme, which can learn the temporal correlation of environment states and make more accurate power allocation actions. Moreover, we conduct extensive computer simulations to demonstrate the robustness and effectiveness of the DDPG-based COPA scheme. Numerical results demonstrate that the proposed scheme has a better performance compared with other benchmark schemes.

关键词： Resource management Vehicle dynamics Task analysis Dynamic scheduling Real-time systems Transportation Heuristic algorithms computation offloading deep reinforcement learning (DRL) platoon network power allocation

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Joint Charging Scheduling and computation offloading in EV-Assisted Edge Computing: A Safe DRL Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第9期23卷 8757-8772页

作者： Zhang, Yongchao Hu, Jia Min, Geyong Chen, Xin Georgalas, Nektarios Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Beijing Informat Sci & Technol Univ Comp Sch Beijing 100101 Peoples R China British Telecommun PLC Appl Res Dept London EC1A 7AJ England

Electric Vehicle-assisted Multi-access Edge Computing (EV-MEC) is a promising paradigm where EVs share their computation resources at the network edge to perform intensive computing tasks while charging. In EV-MEC, a fundamental problem is to jointly decide the charging power of EVs and computation task allocation to EVs, for meeting both the diverse charging demands of EVs and stringent performance requirements of heterogeneous tasks. To address this challenge, we propose a new joint charging scheduling and computation offloading scheme (OCEAN) for EV-MEC. Specifically, we formulate a cooperative two-timescale optimization problem to minimize the charging load and its variance subject to the performance requirements of computation tasks. We then decompose this sophisticated optimization problem into two sub-problems: charging scheduling and computation offloading. For the former, we develop a novel safe deep reinforcement learning (DRL) algorithm, and theoretically prove the feasibility of learned charging scheduling policy. For the latter, we reformulate it as an integer non-linear programming problem to derive the optimal offloading decisions. Extensive experimental results demonstrate that OCEAN can achieve similar performances as the optimal strategy and realize up to 24% improvement in charging load variance over three state-of-the-art algorithms while satisfying the charging demands of all EVs.

关键词： Task analysis Processor scheduling Optimization Vehicle dynamics Charging stations Oceans Resource management Electric vehicles edge computing charging scheduling computation offloading safe reinforcement learning

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Intelligent Online computation offloading for Wireless-Powered Mobile-Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第17期11卷 28960-28974页

作者： Wang, Yanting Qian, Zhuo He, Lijun Yin, Rui Wu, Celimuge Northwestern Polytech Univ Sch Software Xian 710071 Shaanxi Peoples R China Northwestern Polytech Univ Res & Dev Inst Shenzhen 518057 Guangdong Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310015 Zhejiang Peoples R China Univ Electrocommun Meta Networking Res Ctr Chofu 1828585 Japan

In the Internet of Things (IoT) ecosystem, optimizing processing capabilities of devices through wireless-powered mobile-edge computing (WP-MEC) is crucial. This research addresses the challenge of efficiently scheduling task offloading from devices to an edge server, which is vital for enhancing system performance. Prior studies often overlook the necessity for rapid adaptation to changing wireless conditions, resulting in suboptimal offloading strategies. Our work introduces the intelligent online computation offloading (IOCO) algorithm, leveraging deep neural networks (DNNs) to make informed, real-time offloading decisions based on previous experiences. This approach not only optimizes the allocation of wireless and computing resources but also incorporates novel quantization and sampling methods to improve robustness and adaptability. Simulation results demonstrate that IOCO can achieve near-optimal efficiency swiftly and adapt effectively to significant resource changes, highlighting its practicality in dynamic WP-MEC environments.

关键词： Task analysis Servers Processor scheduling Wireless communication Resource management Heuristic algorithms Robustness computation offloading mobile-edge computing (MEC) wireless power transfer (WPT)

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Joint computation offloading and Service Caching in Mobile Edge-Cloud Computing via Deep Reinforcement Learning

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IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 40331-40344页

作者： Shang, Ce Huang, Youliang Sun, Yan Guizani, Mohsen Beijing Univ Posts & Telecommun Dept Comp Sci Beijing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Machine Learning Dept Abu Dhabi U Arab Emirates

Mobile edge computing (MEC) emerges as a promising paradigm that aims to extend the computing capabilities of mobile devices. This enhancement is achieved by offloading heavy computational tasks to nearby edge servers, where relevant services, such as programs, databases, and libraries, are stored to support the execution of these tasks. Given the restricted storage capacity and computing resources of edge servers, it is crucial to carefully consider which tasks to offload and which services to cache. However, the highly dynamic system and closely coupled decisions between offloading and caching pose considerable challenges to developing an effective strategy. In this article, we investigate both computation offloading and service caching in an end-edge-cloud collaborative system under time-varying wireless channels and task requests. We formulate an optimization problem aimed at minimizing the system cost, including time and energy consumption, and model it further as a Markov decision process (MDP). To address this, we propose a deep reinforcement learning (DRL)-based approach with dynamic action masking to concurrently optimize offloading and caching decisions. This approach renormalizes the valid action probability distribution at every step, enabling the agent to explore within the space of valid action combinations and thereby learn the optimal policy. Extensive experimental results demonstrate that the proposed approach greatly reduces the system cost compared to other representative benchmark schemes.

关键词： Task analysis Servers Optimization Mobile handsets Costs Energy consumption Performance evaluation computation offloading deep reinforcement learning (DRL) end-edge-cloud invalid action mask service caching

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A computation offloading Method for Multi-UAVs Assisted MEC Based on Improved Federated DDPG Algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第12期20卷 14062-14071页

作者： Li, Chunlin Jiang, Kun He, Guangxuan Bing, Fan Luo, Youlong Wuhan Univ Technol Shandong Key Lab Water Resources & Environm Wuhan 430062 Peoples R China Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430062 Peoples R China Guizhou Univ Key Lab Publ Big Data Guiyang 550000 Peoples R China Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan 430063 Peoples R China Shandong Key Lab Water Resources & Environm Jinan 250013 Peoples R China Wuhan Univ Technol Sch Management Wuhan 430063 Peoples R China

computation offloading in UAV-assisted MEC has been an interesting research issue. However, in the temporary hotspot areas with traffic explosion, effective solutions that support user privacy security and higher quality of service (QoS) are still lacking. Compared with the previous works, we first constructed a distributed federated learning-based computation offloading method for user privacy security to maximize the normalized weighted sum of task response delay and user energy consumption. We formulated a computation offloading problem considering the UAV hover position, resource allocation, computation offloading decision, and user transmission power allocation, which is a challenging time-series mixed integer nonlinear programming problem. Then, we proposed an improved federated deep deterministic policy gradient (IF-DDPG) algorithm to address it effectively, which improved the traditional federated DDPG algorithm in empirical pooling and exploring noise. Finally, experiment results show that the IF-DDPG can significantly decrease the task response delay and user energy consumption.

关键词： computation offloading deep deterministic policy gradient (DDPG) federated learning (FL) mobile edge computing (MEC) unmanned aerial vehicle (UAV)

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COPSA: a computation offloading strategy based on PPO algorithm and self-attention mechanism in MEC-empowered smart factories

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 1-20页

作者： Chen, Yining Peng, Kai Ling, Chengfang Huaqiao Univ Coll Engn Quanzhou Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

With the dawn of Industry 5.0 upon us, the smart factory emerges as a pivotal element, playing a crucial role in the realm of intelligent manufacturing. Meanwhile, mobile edge computing is proposed to alleviate the computational burden presented by substantial workloads in smart factories. Nonetheless, it is very challenging to effectively incorporate edge computing resources to improve the efficiency of resource deployment in smart factories. Accordingly, we devise a novel approach based on Proximal Policy Optimization algorithm with the Self-Attention Mechanism to implement computing resource allocation in MEC-Empowered Smart Factories. More specifically, the self-attention mechanism is incorporated to enable dynamic focus on state information, accelerates convergence and facilitates global control. A great number of experiments conducted on both simulated and real datasets have verified the superiority of our proposed approach compared to the state-of-the-art baselines.

关键词： Smart factory computation offloading Proximal policy optimization Attention mechanism

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An improved DDPG-based privacy sensitive level protection computation offloading method in mobile edge computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 159卷 522-532页

作者： Zhang, Hongxia Cao, Luyao Kumar, Neeraj Zhang, Jianyong Zhang, Peiying Wang, Jian China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Thapar Univ Dept Comp Sci & Engn Patiala 147004 India Beijing Jiaotong Univ Inst Lightwave Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab All Opt Network & Adv Telecommun Network E Beijing 100044 Peoples R China China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist Educ Jinan 250014 Peoples R China

With the development of fifth -generation mobile communication technology and artificial intelligence, resource -intensive applications on mobile terminal devices process tasks by offloading them to servers. However, the presence of untrustworthy edge servers poses a threat to user terminal (UT) privacy. Therefore, this paper proposes an efficient computation offloading architecture based on personalized privacy protection to address the privacy leakage problem during computation offloading. In this architecture, we design a personalized privacy -sensitive level calculation method to achieve hierarchical protection of personal information. This method calculates based on users' demands for four indicators: integrity, confidentiality, availability, and real-time performance of tasks at different times and locations. An improved deep deterministic policy gradient (DDPG) method is proposed to resolve the optimization solution. The method solves the overestimation problem in the original DDPG algorithm and utilizes an optimal behavior selection mechanism with privacypreserving constraints. Simulation experiments show that the proposed scheme increases the weighted sum of latency and energy consumption by an average of 29.32%, while effectively protecting user privacy.

关键词： Sensitive information Privacy protection computation offloading Mobile edge computing Deep reinforcement learning

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Mobility-Aware computation offloading for Hierarchical Mobile Edge Computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第3期21卷 3372-3384页

作者： Shokouhi, Mohammad Hossein Hadi, Mohammad Pakravan, Mohammad Reza Sharif Univ Technol Dept Elect Engn Tehran *** Iran

Mobile edge computing (MEC) is a promising technology that aims to reduce the total latency of user equipment (UE) by deploying computation resources at the edge of mobile networks. UE mobility is a challenging factor that causes the traditional MEC architecture to suffer from several issues, such as decreased efficiency and frequent service interruptions. One popular method to manage UE mobility is virtual machine (VM) migration, which requires high bandwidth and causes undesirable latency, rendering it impractical for real-time tasks with stringent latency requirements. This paper proposes a hierarchical architecture for MEC networks that facilitates mobility management and mitigates the need for VM migration. In order to utilize this architecture efficiently, a Markov chain-based predictive strategy is introduced to predict UE mobility. Afterward, an optimization problem is formulated to make the optimal long-term offloading decisions for UEs such that their expected cost is minimized subject to latency commitments and resource consumption constraints. Simulation results demonstrate that the proposed scheme reduces the cost of high-mobility UEs by up to 25% compared to traditional schemes. Furthermore, the measures of movement direction predictability and offloading decision popularity are introduced that provide insights into the behavior of the proposed and counterpart schemes.

关键词： Servers Cloud computing Task analysis Computer architecture Edge computing Optimization Costs computation offloading edge computing mobile edge computing mobility management resource allocation

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A review of optimization methods for computation offloading in edge computing networks

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Digital Communications and Networks 2023年第2期9卷 450-461页

作者： Kuanishbay Sadatdiynov Laizhong Cui Lei Zhang Joshua Zhexue Huang Salman Salloum Mohammad Sultan Mahmud College of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China

Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational *** Computing is an emerging computation paradigm that is employed to conquer this *** can bring computation power closer to the end devices to reduce their computation latency and energy ***,this paradigm increases the computational ability of SMDs by collaboration with edge *** is achieved by computation offloading from the mobile devices to the edge nodes or ***,not all applications benefit from computation offloading,which is only suitable for certain types of *** properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading ***,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing *** this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and *** each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed *** discuss a few research problems that are still *** purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

关键词： Edge computing computation offloading Latency and energy consumption minimization

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