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A Trust-Based computation offloading Framework in Mobile Cloud-Edge Computing Networks

IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 5370-5385页

作者： Wang, Jianhui Li, Zhetao Liu, Haolin Qiu, Tie Luo, Hongbin Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Xiangtan Univ Sch Comp Sci Xiangtan 411105 Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Cloud service centers (CSCs) can purchase edge computation resources to improve service quality in mobile cloud-edge computing networks. However, edge servers (ESs) are owned by different entities, and dishonest entities may launch computational forgery attacks, i.e., the ES falsely reports its idle computation resources to win more tasks for increased revenue. Most existing approaches ignore the threat of dishonest ESs. To address the challenges, we design a Trust-based computation offloading (TCO) framework. First, we construct the problem for minimizing the difference between the CSC's cost and the expected revenue (DCER), which is a mixed-integer nonlinear programming problem. Second, we develop a trust-based computation offloading method that quickly finds a good solution by decomposing the problem. Finally, a two-tier trust evaluation method was proposed to obtain accurate trust values. Experimental results indicate that TCO's comprehensive performance surpasses the benchmarks and significantly enhances computation offloading reliability with a lower performance loss. Notably, tasks are preferentially offloaded to honest ESs to ensure their revenue and promote ESs' honesty under the TCO framework. Additionally, compared with no trust mechanisms, TCO reduces the service timeout count in an interval by 34.37% - 73.80% with a performance loss of only 1.42% - 4.10%.

关键词： Forgery Mobile computing Edge computing Servers Reliability Optimization Resource management Energy consumption Costs Throughput computation offloading computational forgery attacks mobile cloud-edge computing networks trust evaluation

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Resource-aware and computation offloading based on space-air-ground-sea integrated network

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-21页

作者： Xu, Yanli Xu, Jingxin Shanghai Maritime Univ Shanghai 201306 Peoples R China

The space-air-ground-sea integrated network (SAGSIN) integrates diverse heterogeneous networks, which makes the pervasive communication and computing service of maritime users possible. In SAGSIN, communication nodes are scarce and computing resources are tight. Tasks of users can be offloaded to the network edge, such as at surface ship, buoy and drone. However, some traditional task offloading decisions become inapplicable as users move or the topology of networks changes. Therefore, in order to ensure the continued effectiveness of the task offloading strategy in dynamic ocean scenarios, we need to design an efficient offloading method to improve the overall network performance. To address this issue, we propose a resource-aware deep reinforcement learning method to optimize task offloading decisions. In this scheme, the available resources of mobile users are perceived in real time, and efficient computation offloading and task migration decisions are made based on the results. Simulation results validate the effectiveness of the proposed algorithm. In addition, the simulation results show that the proposed algorithm can effectively reduce the system delay and energy consumption as the number of tasks increases, and reduce task migration.

关键词： Mobile edge computing computation offloading Task migration Deep reinforcement learning

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Mobility-Aware computation offloading in Satellite Edge Computing Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9135-9149页

作者： Zhou, Jian Yang, Qi Zhao, Lu Dai, Haipeng Xiao, Fu Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites' mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem of Mobility-aware computation offloading (MCO) in the SECN via specifying the effect of LEO satellites' high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we design MCO-A, a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance of MCO-A is evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.

关键词： Low earth orbit satellites Satellites Satellite broadcasting Energy consumption Task analysis Edge computing Game theory ADMM computation offloading edge computing mobility analysis satellite network

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Edge computation offloading With Content Caching in 6G-Enabled IoV

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第3期25卷 2733-2747页

作者： Zhou, Xuanhong Bilal, Muhammad Dou, Ruihan Rodrigues, Joel J. P. C. Zhao, Qingzhan Dai, Jianguo Xu, Xiaolong Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Hankuk Univ Foreign Studies Dept Comp Engn Yongin 17035 Gyeonggi Do South Korea Univ Waterloo Fac Math Waterloo ON N2L 3G1 Canada China Univ Petr East China Coll Comp Sci & Technol Qingdao 266555 Peoples R China Inst Telecomunicacoes P-6201001 Covilha Portugal Shihezi Univ Coll Informat Sci & Technol Geospatial Informat Engn Res Ctr Xinjiang Prod & Construct Corps Shihezi 832003 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Enviro Nanjing 210044 Peoples R China

Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a computation offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

关键词： 6G mobile communication Delays Task analysis Servers Edge computing Vehicle dynamics Internet of Vehicles 6G caching edge computing computation offloading reinforcement learning

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MADRLOM: A computation offloading mechanism for software-defined cloud-edge computing power network

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COMPUTER NETWORKS 2024年 245卷

作者： Guo, Yinzhi Xu, Xiaolong Xiao, Fu Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloudedge computing network and shows excellent adaptability to environmental changes.

关键词： computation offloading Deep reinforcement learning Software -defined networking Computing power network

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Energy-Efficient computation offloading Based on Multiagent Deep Reinforcement Learning for Industrial Internet of Things Systems

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IEEE INTERNET OF THINGS JOURNAL 2024年第7期11卷 12228-12239页

作者： Chouikhi, Samira Esseghir, Moez Merghem-Boulahia, Leila Univ Technol Troyes Comp Sci & Digital Soc Lab F-10300 Troyes France

The term Industrial Internet of Things (IIoT) was created to describe a specific area of the Internet of Things (IoT) that integrates information and communication technologies (ICTs) like cloud/edge computing, wireless sensor/actuator networks, and connected objects to enable and accelerate the development of Industry 4.0. IIoT applications (e.g., smart manufacturing, remote control of industrial machinery, and critical system monitoring) have various levels of criticality and Quality-of-Service (QoS) requirements. However, the characteristics of data collected by interconnected devices complicate the task of guaranteeing the QoS requirements in terms of latency and reliability in addition to the huge amount of energy consumption. As a potential solution, edge computing offers additional powerful resources in the proximity of the IIoT devices. Hence, the required QoS can be achieved by offloading computation-intensive tasks to edge servers. Moreover, the offloading process needs to be optimized to take full advantage. Unlikely, conventional optimization methods are very complex to be applied in the IIoT context. To overcome this issue, we propose a computation offloading approach based on deep reinforcement learning (DRL) to minimize long-term energy consumption and maximize the number of tasks completed before their tolerant deadlines. We introduce a system with multiple agents to deal with the increasing dimension of the action space, where each IIoT device is represented by its own DRL model. The goal of the model is to maximize a flexible and long-term reward. In addition, the DRL models are trained in the cloud and make decisions online in the edge servers, allowing quick decision making by avoiding iterative online optimization procedures. The performance of the proposed approach is evaluated through simulation. The proposal shows promising results compared to other approaches.

关键词： Task analysis Industrial Internet of Things Energy consumption Quality of service Servers Deep learning Reinforcement learning computation offloading deep reinforcement learning (DRL) edge computing Industrial Internet of Things (IIoT)

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RVEAPE: An Approach to computation offloading for Connected Autonomous Vehicles

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第3期21卷 2412-2424页

作者： Su, Jian Pan, Jinguo Ruan, Xiukai Zhang, Xuedong Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Jiangsu Peoples R China Wenzhou Univ Inst Intelligent Lock Wenzhou 325035 Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233000 Peoples R China

With the development of information technology, a variety of mobile devices with large computing requirements and delay constraints are growing rapidly, such as Connected Autonomous Vehicles (CAVs). In addition to the energy consumption required for vehicle driving, the CAV also needs to support the normal operation of sensors and computing platforms, which brings huge energy consumption and affects the endurance of vehicles. In this paper, a Mobile Edge Computing (MEC) system is considered that includes three types of network nodes: connected Autonomous Vehicles, Roadside Units (RSUs), and a Base Station (BS). Considering the task duration of the CAV, tasks on the CAVs are partitioned into three sections, which are executed at CAVs, RSUs, and BS, respectively. It is challenging to correlate resource-limited CAVs with other types of network nodes with high performance and implement partial computation offloading between them to minimize the total energy consumption of CAVs and RSUs under the delay constraint of CAVs. Based on the classical orthogonal-frequency-division multiple-access (OFDMA), a joint computing and communication cooperation offloading protocol is proposed to minimize the total energy consumption of all CAVs and RSUs under delay constraints and is described as a constrained Mixed-Integer Nonlinear Program (MINLP) problem. To address this problem, a Reference Vector-Guided Evolutionary Algorithm for Multi-Objective Optimization based on Prior Experience (RVEAPE) is designed in this paper. RVEAPE jointly optimizes the task division ratio variables and resource allocation variables (computation resources, communication resources). Simulations demonstrate that RVEAPE significantly outperforms the GA, NSGA-II, and NSGA-II algorithms in terms of the total energy consumption of CAVs and RSUs in the whole system consisting of CAVs, RSUs, and a BS. Note to Practitioners-This paper is motivated by the use of multi-objective optimization algorithms to improve t

关键词： Mobile edge computing connected autonomous vehicle joint computation and communication cooperation computation offloading resource allocation

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MCOTM: Mobility-aware computation offloading and task migration for in industrial IoT

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 151卷 232-241页

作者： Qin, Wei Chen, Haiming Wang, Lei Xia, Yinshui Nascita, Alfredo Pescape, Antonio Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Zhejiang Peoples R China Zhejiang Key Lab Mobile Network Applicat Technol Ningbo 315211 Zhejiang Peoples R China Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

Mobility-aware devices are crucial components of Industrial Internet of Things (IIoT). However, they face limitations in terms of battery capacity and computation power, which restrict their ability to provide services requiring broad bandwidth and strong computation power for computation-intensive tasks. While offloading can strengthen device computation power, ineffective offloading decisions result from device mobility and limited adaptability to changes in environmental resources, or are not applicable to the current mobile edge computing (MEC) environment. In this paper, we address these challenges by proposing a mobility-aware computation offloading and task migration approach (MCOTM) based on trajectory and resource prediction to address this issue of mobility offloading, which minimizes task turnaround time and system energy consumption. Simultaneously, our approach enhances the decision agent continuously to decrease task migration rates. MCOTM uses Lagrange interpolation equations to determine the trajectory of mobile devices, and Long Short-Term Memory (LSTM) to track the time-varying resources characteristics in IIoT. These prediction results will be used to assist Deep Deterministic Policy Gradient (DDPG) for making online computation offloading, task migration and resource allocation decisions. Experimental results show that the proposed MCOTM effectively reduces task turnaround time by at least 42% and system energy consumption by 10% while maintaining a low task migration rate of around 50%, even with an increasing number of tasks.

关键词： IIoT MEC computation offloading Task migration Deep reinforcement learning

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DDPG-based optimal task placement strategy for computation offloading in green mobile edge networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第4期17卷 2435-2449页

作者： Lu, Kun Xu, Guorui Zhang, Runfa Li, Mingchu Li, Rongda Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Serv Software Liaoning Prov Key Lab Ubiquitous Network Dalian 116620 Peoples R China

The edge computing paradigm is evolving to provide time-sensitive computing services for energy-constrained mobile IoT devices, with the purpose of decreasing latency. In this paper, we investigate a sustainable edge offloading system that is composed of one green mobile IoT device and multiple access points (APs), which considers minimizing the execution latency, meanwhile adapting to the mobile awareness and dynamic energy harvesting (EH) process. The IoT device is able to reduce computing latency by offloading its generated tasks to the APs. We propose a deep deterministic policy gradient (DDPG) based optimal placement strategy (DBOP) algorithm to take advantage of computation resources and improve consumer experience quality. The DBOP first applies the Lyapunov optimization to decompose the constraint problem into subproblems in each time slot. Then, the DBOP makes optimal offloading decisions by predicting the computation resources and solving the per-slot subproblems with low computational complexity. Finally, the performance bounds of the proposed algorithm are discussed and the simulation results demonstrate the effectiveness of the DBOP.

关键词： Lyapunov optimization computation offloading Energy harvesting Reinforcement learning

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JDACO: Joint Data Aggregation and computation offloading in UAV-Enabled Internet of Things for Post-Disaster Scenarios

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16529-16544页

作者： Raivi, Asif Mahmud Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

Owing to high flexibility and rapid deployment, unmanned aerial vehicles (UAVs) can offer network coverage for Internet of Things (IoT) devices in post-disaster scenarios. UAV-aided mobile edge computing (MEC) provides computational support and facilitates optimal decision-making processes for ground-based IoT devices. However, existing literature has separately examined both data aggregation and computational offloading. In this article, we introduce a joint data aggregation and computational offloading (JDACO) scheme for UAV-enabled IoT systems in post-disaster scenarios. JDACO's primary objective is to minimize the overall energy consumption and latency in the aggregation and computation processes. It achieves this by employing UAVs as MEC servers and deploying multiple UAVs. We initially design an objective function to assess the costs associated with the aggregation and offloading processes. Subsequently, we frame the optimization problem as a Markov model and employ a multiagent deep reinforcement learning algorithm. This approach utilizes value decomposition with the double deep Q -Network algorithm to optimize data aggregation and enable a cost-effective offloading process through cooperative learning. Our experimental results demonstrate that our proposed JDACO scheme surpasses existing methods in terms of training time reduction, processed data volume, energy efficiency, and mission duration by 20%, 11.4%, 5.6%, and 11.2%, respectively, compared to the conventional schemes while serving up to 98% of IoT devices.

关键词： computation offloading data aggregation Internet of Things (IoT) mobile edge computing (MEC) multiagent reinforcement learning unmanned aerial vehicle (UAV)

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