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Offload Strategy for edge computing in Satellite Networks Based on Software Defined Network

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Computers, Materials & Continua 2025年第1期82卷 863-879页

作者： Zhiguo Liu Yuqing Gui Lin Wang Yingru Jiang Communication and Network Laboratory Dalian UniversityDalian116622China College of Environment and Chemical Engineering Dalian UniversityDalian116622China

Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable *** sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user *** this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their ***,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN ***,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of *** results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.

关键词： Satellite network edge computing task scheduling computing offloading

Providing Robust and Low-Cost edge computing in Smart Grid:An Energy Harvesting Based Task Scheduling and Resource Management Framework

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China Communications 2025年第2期22卷 226-240页

作者： Xie Zhigang Song Xin Xu Siyang Cao Jing School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Engineering Optimization and Smart Antenna Research Institute Northeastern UniversityQinhuangdao 066004China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Mathematics and Information Science&Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart ***,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack ***,solutions are derived by two different ***,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability ***,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy *** simulation results show the efficiency and superiority of our proposed framework.

关键词： edge computing energy harvesting energy storage unit renewable energy sampling average approximation task scheduling

Research on the Optimal Scheduling Model of Energy Storage Plant Based on edge computing and Improved Whale Optimization Algorithm

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Energy Engineering 2025年第3期122卷 1153-1174页

作者： Zhaoyu Zeng Fuyin Ni School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology Jiangsu University of TechnologyChangzhou213001China

Energy storage power plants are critical in balancing power supply and ***,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy *** tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network *** architecture effectively reduces transmission costs by minimizing data travel *** addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation *** IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal *** results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and *** successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy *** edge-computing framework significantly reduces transmission delays between energy ***,IWOA outperforms traditional algorithms in optimizing the objective function.

关键词： Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm

Dynamic Task Offloading Scheme for edge computing via Meta-Reinforcement Learning

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Computers, Materials & Continua 2025年第2期82卷 2609-2635页

作者： Jiajia Liu Peng Xie Wei Li Bo Tang Jianhua Liu Teacher Development and Teaching Evaluation Center Civil Aviation Flight University of ChinaGuanghan618307China Institute of Electronics and Electrical Engineering Civil Aviation Flight University of ChinaGuanghan618307China

As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.

关键词： edge computing adaptive meta task offloading joint optimization

edge computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： edge computing task scheduling blockchain task caching industrial security

edge computing-Based Tasks Offloading and Block Caching for Mobile Blockchain

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Computers, Materials & Continua 2020年第2期62卷 905-915页

作者： Yong Yan Yao Dai Zhiqiang Zhou Wei Jiang Shaoyong Guo Electric Power Research Institute State Grid Zhejiang Electric Power CompanyHangzhouChina State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications BeijingChina State Grid Corporation of China BeijingChina

Internet of Things(IoT)technology is rapidly evolving,but there is no trusted platform to protect user privacy,protect information between different IoT domains,and promote edge ***,we integrate the blockchain technology into constructing trusted IoT ***,the application of blockchain in IoT is hampered by the challenges posed by heavy computing *** solve the problem,we put forward a blockchain framework based on mobile edge computing,in which the blockchain mining tasks can be offloaded to nearby nodes or the edge computing service providers and the encrypted hashes of blocks can be cached in the edge computing service ***,we model the process of offloading and caching to ensure that both edge nodes and edge computing service providers obtain the maximum profit based on game theory and auction ***,the proposed mechanism is compared with the centralized mode,mode A(all the miners offload their tasks to the edge computing service providers),and mode B(all the miners offload their tasks to a group of neighbor devices).Simulation results show that under our mechanism,mining networks obtain more profits and consume less time on average.

关键词： edge computing blockchain mining offloading block caching

edge computing-Based mobile object tracking in internet of things

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High-Confidence computing 2022年第1期2卷 19-27页

作者： Yalong Wu Pu Tian Yuwei Cao Linqiang Ge Wei Yu Department of Computer Science and Engineering North Central CollegeNapervilleUSA Department of Computer and Information Sciences Towson UniversityTowsonUSA Department of Computer Science University of Illinois at ChicagoChicagoUSA School of Computer Science Columbus State UniversityColumbusUSA

Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical ***,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing *** address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation ***,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location *** framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing *** have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile *** addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

关键词： Internet of things edge computing Architecture Mobile object tracking Vector auto regression

edge computing oriented virtual optical network mapping scheme based on fragmentation prediction

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High Technology Letters 2024年第2期30卷 158-163页

作者：何烁 BAI Huifeng HUO Chao ZHANG Ganghong China Internet Network Information Center Beijing 100190P.R.China Beijing SmartChip Microelectronics Technology Company Limited Beijing 100089P.R.China

As edge computing services soar,the problem of resource fragmentation situation is greatly worsened in elastic optical networks(EON).Aimed to solve this problem,this article proposes the fragmentation prediction model that makes full use of the gate recurrent unit(GRU)*** on the fragmentation prediction model,one virtual optical network mapping scheme is presented for edge computing driven *** the minimum of fragmentation degree all over the whole EON,the virtual network mapping can be successively *** results show that the proposed approach can reduce blocking rate,and the supporting ability for virtual optical network services is greatly improved.

关键词： elastic optical networks virtual optical network fragmentation self-awareness edge computing

VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

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Digital Communications and Networks 2023年第4期9卷 981-989页

作者： Jiale Zhang Yue Liu Di Wu Shuai Lou Bing Chen Shui Yu School of Information Engineering Yangzhou UniversityYangzhou225009China Water Conservancy and Civil Engineering College Inner Mongolia Agricultural UniversityHohhot010018China Deakin Blockchain Innovation Lab School of Information TechnologyDeakin UniversityMelbourneVIC3125Australia College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Computer Science University of Technology SydneySydney2007Australia

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central ***,the frequently transmitted local gradients could also leak the participants’private *** protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been ***,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification *** tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission ***,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex ***,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge *** security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and *** last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

关键词： Federated learning edge computing Privacy-preserving Verifiable aggregation Homomorphic cryptosystem