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Journal articles on the topic 'Offloading'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hu, Heng-Cheng, and Pi-Chung Wang. "Computation Offloading Game for Multi-Channel Wireless Sensor Networks." Sensors 22, no. 22 (2022): 8718. http://dx.doi.org/10.3390/s22228718.

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Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of

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Liu, Jun, Xiaohui Lian, and Chang Liu. "Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network." Future Internet 13, no. 5 (2021): 128. http://dx.doi.org/10.3390/fi13050128.

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In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the moveme

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Zhang, Rui, Libing Wu, Shuqin Cao, et al. "Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV." ACM Transactions on Internet Technology 22, no. 2 (2022): 1–24. http://dx.doi.org/10.1145/3475871.

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The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the

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Amit Kumar C Jain and Apoorva HC. "Usage of Therapeutic Offloading In Diabetic Foot- A Clinical Audit." IAR Journal of Medicine and Surgery Research 2, no. 3 (2021): 1–5. http://dx.doi.org/10.47310/iarjmsr.2021.v02i03.01.

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Aim- To assess the usage of therapeutic offloading in diabetic foot patients in an expert diabetic foot centre and distribute them according to Amit Jain’s classification for offloading. Methods and materials- A retrospective audit was conducted at Amit Jain’s diabetic foot offloading clinic which is now an eponymous wing under Amit Jain’s Institute of Diabetic Foot and Wound Care, Brindhavvan Areion Hospital, Bengaluru, Karnataka, India. The audit was over one year period from July 2019 to June 2020. Results – There were 34 patients in whom offloading was used during this period. Majority was

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Zou, Jing, Zhaoxiang Yuan, Peizhe Xin, et al. "Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks." Electronics 12, no. 16 (2023): 3484. http://dx.doi.org/10.3390/electronics12163484.

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Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerab

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6

Quan, Wei, Kai Wang, Yana Liu, Nan Cheng, Hongke Zhang, and Xuemin (Sherman) Shen. "Software-Defined Collaborative Offloading for Heterogeneous Vehicular Networks." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/3810350.

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Vehicle-assisted data offloading is envisioned to significantly alleviate the problem of explosive growth of mobile data traffic. However, due to the high mobility of vehicles and the frequent disruption of communication links, it is very challenging to efficiently optimize collaborative offloading from a group of vehicles. In this paper, we leverage the concept of Software-Defined Networking (SDN) and propose a software-defined collaborative offloading (SDCO) solution for heterogeneous vehicular networks. In particular, SDCO can efficiently manage the offloading nodes and paths based on a cen

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Liu, Bin, Qi Zhu, Weiqiang Tan, and Hongbo Zhu. "Congestion-Optimal WiFi Offloading with User Mobility Management in Smart Communications." Wireless Communications and Mobile Computing 2018 (August 1, 2018): 1–15. http://dx.doi.org/10.1155/2018/9297536.

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We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion-Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational comp

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Grinschgl, Sandra, Frank Papenmeier, and Hauke S. Meyerhoff. "Consequences of cognitive offloading: Boosting performance but diminishing memory." Quarterly Journal of Experimental Psychology 74, no. 9 (2021): 1477–96. http://dx.doi.org/10.1177/17470218211008060.

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Modern technical tools such as tablets allow for the temporal externalisation of working memory processes (i.e., cognitive offloading). Although such externalisations support immediate performance on different tasks, little is known about potential long-term consequences of offloading behaviour. In the current set of experiments, we studied the relationship between cognitive offloading and subsequent memory for the offloaded information as well as the interplay of this relationship with the goal to acquire new memory representations. Our participants solved the Pattern Copy Task, in which we m

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9

Bai, Wenle, Zhongjun Yang, Jianhong Zhang, and Rajiv Kumar. "Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing." Security and Communication Networks 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/4348511.

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Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a

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Dunlap, Leslie J., Eric Lew, Regina Gallegos, Richard Murdoch, and Simone Mulvihill. "Management of Diabetic Foot Ulcers with Two Forefoot Offloading Techniques: Case Series." Advances in Skin & Wound Care 37, no. 8 (2024): 434–39. http://dx.doi.org/10.1097/asw.0000000000000178.

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ABSTRACT Offloading is a key principle to healing diabetic foot ulcers. Nonremovable knee-high offloading devices are considered the criterion standard for offloading plantar forefoot ulcers. However, patients exhibit a limited tolerance for these devices, which contributes to a lack of use. In this case series describing two patients, the authors share two alternative offloading modalities for the treatment of diabetic plantar forefoot ulcers. One patient was managed using a football offloading dressing, and the other was managed with a modified felted football dressing. The football and modi

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Liu, Yanpei, Wei Huang, Liping Wang, Yunjing Zhu, and Ningning Chen. "Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9163–89. http://dx.doi.org/10.3934/mbe.2021452.

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<abstract> <p>The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically

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Dong, Jiadong, Kai Pan, Chunxiang Zheng, Lin Chen, Shunfeng Wu, and Xiaolin Zhang. "A Dual-Agent Approach for Coordinated Task Offloading and Resource Allocation in MEC." Journal of Electrical and Computer Engineering 2023 (December 21, 2023): 1–16. http://dx.doi.org/10.1155/2023/6134837.

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Multiaccess edge computing (MEC) is a novel distributed computing paradigm. In this paper, we investigate the challenges of task offloading scheduling, communication bandwidth, and edge server computing resource allocation for multiple user equipments (UEs) in MEC. Our primary objective is to minimize system latency and local energy consumption. We explore the binary offloading and partial offloading methods and introduce the dual agent-TD3 (DA-TD3) algorithm based on the deep reinforcement learning (DRL) TD3 algorithm. The proposed algorithm coordinates task offloading scheduling and resource

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13

Jin, Xiaomin, Zhongmin Wang, and Wenqiang Hua. "Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing." Mobile Information Systems 2019 (September 17, 2019): 1–17. http://dx.doi.org/10.1155/2019/8049804.

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Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynami

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Wenjuan Shao. "A Novel SDN-Based Architecture of Task Offloading in Mobile Ad-Hoc Cloud." Journal of Electrical Systems 20, no. 2 (2024): 529–46. http://dx.doi.org/10.52783/jes.1209.

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As the core function of mobile Ad-hoc cloud, task offloading has always been a research hotspot of mobile cloud computing, and the construction, offloading decision, task division and scheduling strategies of mobile Ad-hoc cloud directly affect the performance of offloading. In this paper, we propose a new mobile Ad-hoc cloud architecture based on SDN solving the performance bottleneck of opportunistic task offloading. The proposed solution constructs terminal clusters in Ad-hoc cloud to provide users with offloading service, and makes offloading decision and task allocation through SDN contro

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15

Risko, Evan F., and Sam J. Gilbert. "Cognitive Offloading." Trends in Cognitive Sciences 20, no. 9 (2016): 676–88. http://dx.doi.org/10.1016/j.tics.2016.07.002.

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Crisologo, Peter A., Lawrence A. Lavery, and Javier La Fontaine. "Conservative Offloading." Clinics in Podiatric Medicine and Surgery 36, no. 3 (2019): 371–79. http://dx.doi.org/10.1016/j.cpm.2019.02.003.

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Kumar V., Vinoth, Ramamoorthy S., Dhilip Kumar V., Prabu M., and Balajee J. M. "Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks." International Journal of e-Collaboration 17, no. 1 (2021): 60–70. http://dx.doi.org/10.4018/ijec.2021010104.

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In recent years, WiFi offloading provides a potential solution for improving ad hoc network performance along with cellular network. This paper reviews the different offloading techniques that are implemented in various applications. In disaster management applications, the cellular network is not optimal for existing case studies because the lack of infrastructure. MANET Wi-Fi offloading (MWO) is one of the potential solutions for offloading cellular traffic. This word combines the cellular network with mobile ad hoc network by implementing the technique of Wi-Fi offloading. Based on the appl

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18

Zhang, Haibo, Xiangyu Liu, Xia Bian, Yan Cheng, and Shengting Xiang. "A Resource Allocation Scheme for Real-Time Energy-Aware Offloading in Vehicular Networks with MEC." Wireless Communications and Mobile Computing 2022 (February 10, 2022): 1–17. http://dx.doi.org/10.1155/2022/8138079.

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With the emergence of new vehicular applications, computation offloading based on mobile edge computing (MEC) has become a promising paradigm in resource-constrained vehicular networks. However, an unreasonable offloading strategy in offloading can cause serious energy consumption and latency. A real-time energy-aware offloading scheme for vehicle networks, based on MEC, is proposed to optimize communication and computation resource to decrease energy consumption and latency. Because the problem of computation offloading and resource allocation is the mixed-integer nonlinear problem (MINLP), t

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Kou, Jinfeng, Yang Xiao, and Dong Wang. "An Economic User-Centric WiFi Offloading Algorithm for Heterogeneous Network." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/341292.

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An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular ne

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Attalah, Mohamed Amine, Sofiane Zaidi, Naçima Mellal, and Carlos T. Calafate. "Task-Offloading Optimization Using a Genetic Algorithm in Hybrid Fog Computing for the Internet of Drones." Sensors 25, no. 5 (2025): 1383. https://doi.org/10.3390/s25051383.

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Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs

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Liu, Xiaokai, Fangmin Xu, Ye Xiao, et al. "Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach." Mathematical Problems in Engineering 2022 (September 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/1486580.

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To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge comp

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Son, Yunsik, Junho Jeong, and YangSun Lee. "An Adaptive Offloading Method for an IoT-Cloud Converged Virtual Machine System Using a Hybrid Deep Neural Network." Sustainability 10, no. 11 (2018): 3955. http://dx.doi.org/10.3390/su10113955.

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A virtual machine with a conventional offloading scheme transmits and receives all context information to maintain program consistency during communication between local environments and the cloud server environment. Most overhead costs incurred during offloading are proportional to the size of the context information transmitted over the network. Therefore, the existing context information synchronization structure transmits context information that is not required for job execution when offloading, which increases the overhead costs of transmitting context information in low-performance Inte

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Zhang, Guowei, Shengjian Zhang, Zhiyi Man, Chenlin Cui, and Wenli Hu. "Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode." Electronics 13, no. 13 (2024): 2668. http://dx.doi.org/10.3390/electronics13132668.

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Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective me

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Shan, Nanliang, Yu Li, and Xiaolong Cui. "A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing." Mathematical Problems in Engineering 2020 (June 27, 2020): 1–17. http://dx.doi.org/10.1155/2020/4124791.

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Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed tha

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Lazzarini, Peter A., and Gustav Jarl. "Knee-High Devices Are Gold in Closing the Foot Ulcer Gap: A Review of Offloading Treatments to Heal Diabetic Foot Ulcers." Medicina 57, no. 9 (2021): 941. http://dx.doi.org/10.3390/medicina57090941.

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Diabetic foot ulcers (DFU) are a leading cause of the global disease burden. Most DFUs are caused, and prolonged, by high plantar tissue stress under the insensate foot of a person with peripheral neuropathy. Multiple different offloading treatments have been used to try to reduce high plantar tissue stress and heal DFUs, including bedrest, casting, offloading devices, footwear, and surgical procedures. The best offloading treatments are those that balance the benefits of maximizing reductions in high plantar tissue stress, whilst reducing the risks of poor satisfaction, high costs and potenti

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Xu, Shilin, and Caili Guo. "Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing." Sensors 20, no. 23 (2020): 6820. http://dx.doi.org/10.3390/s20236820.

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To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more cha

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Shen, Hui, Yujing Jiang, Fangming Deng, and Yun Shan. "Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning." Electronics 11, no. 14 (2022): 2188. http://dx.doi.org/10.3390/electronics11142188.

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Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a “device-edge-cloud” collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the

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Zhang, Kangjie, Xiaodong Xu, Jingxuan Zhang, Shujun Han, Bizhu Wang, and Ping Zhang. "Multi-Connectivity Enhanced Communication-Incentive Distributed Computation Offloading in Vehicular Networks." Electronics 10, no. 20 (2021): 2466. http://dx.doi.org/10.3390/electronics10202466.

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Flexible resource scheduling and network forecast are crucial functions to enhance mobile vehicular network performances. However, BaseStations (BSs) and their computing unit which undertake the functions cannot meet the delay requirement because of limited computation capability. Offloading the time-sensitive functions to User Equipment (UE) is believed to be an effective method to tackle this challenge. The disadvantage of the method is offloading occupies communication resources, which deteriorate the system capability. To better coordinate offloading and communication, a multi-connectivity

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Gong, Bencan, and Xiaowei Jiang. "Dependent Task-Offloading Strategy Based on Deep Reinforcement Learning in Mobile Edge Computing." Wireless Communications and Mobile Computing 2023 (January 4, 2023): 1–12. http://dx.doi.org/10.1155/2023/4665067.

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In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task-offloading problems and neglect the impact of the dynamic change of the channel on the offloading strategy. To solve the offloading problem of dependent tasks in a dynamic network environment, this paper establishes the dependent task model as a directed acyclic graph. A Dependent Task-Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network s

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Pang, Shanchen, Huanhuan Sun, Min Wang, Shuyu Wang, Sibo Qiao, and Neal N. Xiong. "An Efficient Computing Offloading Scheme Based on Privacy-Preserving in Mobile Edge Computing Networks." Wireless Communications and Mobile Computing 2022 (June 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/5152598.

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Computation offloading is an important technology to achieve lower delay communication and improve the experience of service (EoS) in mobile edge computing (MEC). Due to the openness of wireless links and the limitation of computing resources in mobile computing process, the privacy of users is easy to leak, and the completion time of tasks is difficult to guarantee. In this paper, we propose an efficient computing offloading algorithm based on privacy-preserving (ECOAP), which solves the privacy problem of offloading users through the encryption technology. To avoid the algorithm falling into

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Zheng, Yi-fan, Ning Wei, and Yi Liu. "Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge Computing." Mobile Information Systems 2022 (July 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/4840801.

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Computation offloading and caching strategy is a well-established concept for allowing mobile applications that are high in resources. Furthermore, the unloaded duties can be replicated when several customers are within easy access because of the rising mobile cooperation applications. However, the problematic characteristics of offloading and caching strategy delay bandwidth transfer from mobile computing devices to cloud computing. A new technical approach to restrict the issues and unwanted functions in offloading and caching is called the intellectual power computing framework (IPCF). IPCF

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Chen, Zhixiong, Nan Xiao, and Dongsheng Han. "A Multilevel Mobile Fog Computing Offloading Model Based on UAV-Assisted and Heterogeneous Network." Wireless Communications and Mobile Computing 2020 (July 7, 2020): 1–11. http://dx.doi.org/10.1155/2020/8833722.

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The mobile fog computing (MFC) network that integrates unmanned aerial vehicles (UAV) fully exerts its advantages of flexible deployment, load balance, and rapid response. Under complex network environment, proposing a reasonable offloading model and according resource optimization of the MFC network is important to satisfy high-requirement offloading standard. In this paper, a multilevel MFC offloading model where UAV and fog nod undertake relay nodes and offloading computing nodes are established for computation-intensive and latency-critical tasks, considering heterogeneous network selectio

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Ni, Mingchang, Guo Zhang, Qi Yang, and Liqiong Yin. "Research on MEC computing offload strategy for joint optimization of delay and energy consumption." Mathematical Biosciences and Engineering 21, no. 6 (2024): 6336–58. http://dx.doi.org/10.3934/mbe.2024276.

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<abstract> <p>The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computatio

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Sun, Chao, and Zhihua Li. "Multi-UAV-Assisted MEC Offloading-Optimization Method on Deep Reinforcement Learning." International Journal on Semantic Web and Information Systems 21, no. 1 (2025): 1–31. https://doi.org/10.4018/ijswis.368839.

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In multi-UAV-assisted mobile edge computing (MEC), insufficient consideration of collaborative computation in inter-UAV communication can significantly reduce computational service capabilities. For this problem, we present a multi-UAV-assisted MEC offloading optimization model that jointly optimizes task offloading decision, UAV resource allocation, UAV trajectories and establish collaborative computation through inter-UAV communication. First, to solve the multi-UAV-assisted MEC offloading optimization issue, we define a weighted utility function that balances delay and energy consumption. T

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Xing, Yongli, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, and Zihui Liu. "A computational offloading optimization scheme based on deep reinforcement learning in perceptual network." PLOS ONE 18, no. 2 (2023): e0280468. http://dx.doi.org/10.1371/journal.pone.0280468.

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Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Exper

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Numani, Abdullah, Zaiwar Ali, Ziaul Haq Abbas, Ghulam Abbas, Thar Baker, and Dhiya Al-Jumeily. "Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing." Mobile Information Systems 2021 (July 2, 2021): 1–13. http://dx.doi.org/10.1155/2021/9993946.

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Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consu

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Hossen, Md Rajib, and Mohammad A. Islam. "Mobile Task Offloading Under Unreliable Edge Performance." ACM SIGMETRICS Performance Evaluation Review 48, no. 4 (2021): 29–32. http://dx.doi.org/10.1145/3466826.3466838.

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Offloading resource-hungry tasks from mobile devices to an edge server has been explored recently to improve task com- pletion time as well as save battery energy. The low la- tency computing resource from edge servers are a perfect companion to realize such task offloading. However, edge servers may su er from unreliable performance due to its rapid workload variation and reliance on intermittent re- newable energy. Further, batteries in mobile devices make online optimum offloading decisions challenging since it in- tertwines offloading decisions across di erent tasks. In this paper, we prop

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Zhu, Anqi, Huimin Lu, Mingfang Ma, Zongtan Zhou, and Zhiwen Zeng. "DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks." Drones 7, no. 11 (2023): 656. http://dx.doi.org/10.3390/drones7110656.

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With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of to

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Tu, Youpeng, Haiming Chen, Linjie Yan, and Xinyan Zhou. "Task Offloading Based on LSTM Prediction and Deep Reinforcement Learning for Efficient Edge Computing in IoT." Future Internet 14, no. 2 (2022): 30. http://dx.doi.org/10.3390/fi14020030.

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In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memo

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Wu, Suiyuan, Hongmei Xue, and Long Zhang. "Q-Learning-Aided Offloading Strategy in Edge-Assisted Federated Learning over Industrial IoT." Electronics 12, no. 7 (2023): 1706. http://dx.doi.org/10.3390/electronics12071706.

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Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss fu

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Li, Feixiang, Chao Fang, Mingzhe Liu, Ning Li, and Tian Sun. "Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing." Electronics 12, no. 5 (2023): 1254. http://dx.doi.org/10.3390/electronics12051254.

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Edge computing is a promising technology to enable user equipment to share computing resources for task offloading. Due to the characteristics of the computing resource, how to design an efficient computation incentive mechanism with the appropriate task offloading and resource allocation strategies is an essential issue. In this manuscript, we proposed an intelligent computation offloading mechanism with content cache in mobile edge computing. First, we provide the network framework for computation offloading with content cache in mobile edge computing. Then, by deriving necessary and suffici

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Liu, Xiang, Xu Zhao, Guojin Liu, Fei Huang, Tiancong Huang, and Yucheng Wu. "Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing." Sensors 22, no. 18 (2022): 6760. http://dx.doi.org/10.3390/s22186760.

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Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted b

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Chen, Shuang, Ying Chen, Xin Chen, and Yuemei Hu. "Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks." Complexity 2020 (May 4, 2020): 1–14. http://dx.doi.org/10.1155/2020/7016307.

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With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission

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Zaidi, Sofiane, Mohamed Amine Attalah, Lazhar Khamer, and Carlos T. Calafate. "Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones." Drones 9, no. 1 (2024): 23. https://doi.org/10.3390/drones9010023.

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Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the Io

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Um, Taegeon, Byungsoo Oh, Byeongchan Seo, Minhyeok Kweun, Goeun Kim, and Woo-Yeon Lee. "FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline." Proceedings of the VLDB Endowment 16, no. 5 (2023): 1086–99. http://dx.doi.org/10.14778/3579075.3579083.

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When training a deep learning (DL) model, input data are pre-processed on CPUs and transformed into tensors, which are then fed into GPUs for gradient computations of model training. Expensive GPUs must be fully utilized during training to accelerate the training speed. However, intensive CPU operations for input data preprocessing (input pipeline) often lead to CPU bottlenecks; correspondingly, various DL training jobs suffer from GPU under-utilization. We propose FastFlow, a DL training system that automatically mitigates the CPU bottleneck by offloading (scaling out) input pipelines to remo

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Lin, Jin, ZeQin Li, Ruofei Wang, RuYue Gong, and HongJing Wu. "Task Offloading and Collaborative Backhaul System based on Multi-level Edge Computing in the Internet of Vehicles." Scalable Computing: Practice and Experience 26, no. 3 (2025): 1035–47. https://doi.org/10.12694/scpe.v26i3.4199.

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With the development of 5G and the Internet of Vehicles, diverse in-vehicle services continue to emerge. Computation-intensive and delay-sensitive in-vehicle tasks pose significant challenges to in-vehicle devices and represent one of the bottlenecks limiting the development of Internet of Vehicles technology. This paper proposes a Speed-Sensitive Offloading (SSO) and collaborative backhaul solution to address the problem of task offloading and result backhaul failure caused by vehicle movement, including a multi-level MEC architecture solution, speed sensitive task offloading and an MEC colla

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Wu, Guilu, and Zhongliang Li. "Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario." Electronics 10, no. 23 (2021): 3038. http://dx.doi.org/10.3390/electronics10233038.

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Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulate

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Yao, Bingxin, Bin Wu, Siyun Wu, Yin Ji, Danggui Chen, and Limin Liu. "An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System." International Journal of Circuits, Systems and Signal Processing 16 (January 5, 2022): 115–21. http://dx.doi.org/10.46300/9106.2022.16.15.

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In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize

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Zhang, Wenzhu, and Kaihang Tuo. "Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm." Electronics 12, no. 11 (2023): 2533. http://dx.doi.org/10.3390/electronics12112533.

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With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require

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Sutrawe,, Ms Rajashree. "Smart Offloading Strategies by Optimizing Fog Computing Through Reinforcement Learning Strategies." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34134.

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With the proliferation of Internet of Things(IoT) devices and the exponential growth in data volume, fog computing has emerged as a promising paradigm to address the limitations of cloud-centric architectures by bringing computation and storage closer to the data source. In fog comp uting environments, efficient task offloading plays a crucial role in optimizing resource utilization and minimizing latency. This paper proposes a novel approach to smart offloading strategies utilizing reinforcement learning (RL) techniques. To validate the effectiveness of our approach, we conduct extensive simu

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