Relevant bibliographies by topics / Edge Computation Offloading

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Edge Computation Offloading'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Edge Computation Offloading"

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Patel, Minal Parimalbhai, and Sanjay Chaudhary. "Edge Computing." International Journal of Fog Computing 3, no. 1 (2020): 64–74. http://dx.doi.org/10.4018/ijfc.2020010104.

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In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.
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Xiao, Yong, Ling Wei, Junhao Feng, and Wang En. "Two-tier end-edge collaborative computation offloading for edge computing." Journal of Computational Methods in Sciences and Engineering 22, no. 2 (2022): 677–88. http://dx.doi.org/10.3233/jcm-215923.

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Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative off
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Man, Junfeng, Longqian Zhao, Bowen Xu, Cheng Peng, Junjie Jiang, and Yi Liu. "Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode." Journal of Database Management 34, no. 1 (2023): 1–29. http://dx.doi.org/10.4018/jdm.318451.

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Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates
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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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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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Maftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai, and Ali Ouacha. "Intelligent task processing using mobile edge computing: processing time optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 143. http://dx.doi.org/10.11591/ijai.v13.i1.pp143-152.

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<p>The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capab
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Maftah, Sara, Ghmary Mohamed El, Bouabidi Hamid El, Mohamed Amnai, and Ali Ouacha. "Intelligent task processing using mobile edge computing: processing time optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 143–52. https://doi.org/10.11591/ijai.v13.i1.pp143-152.

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The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities,
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Lin, Li, Xiaofei Liao, Hai Jin, and Peng Li. "Computation Offloading Toward Edge Computing." Proceedings of the IEEE 107, no. 8 (2019): 1584–607. http://dx.doi.org/10.1109/jproc.2019.2922285.

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Sheng, Jinfang, Jie Hu, Xiaoyu Teng, Bin Wang, and Xiaoxia Pan. "Computation Offloading Strategy in Mobile Edge Computing." Information 10, no. 6 (2019): 191. http://dx.doi.org/10.3390/info10060191.

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Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy
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Huang, Yan-Yun, and Pi-Chung Wang. "Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing." Sensors 23, no. 3 (2023): 1155. http://dx.doi.org/10.3390/s23031155.

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Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of
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Dissertations / Theses on the topic "Edge Computation Offloading"

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Yu, Shuai. "Multi-user computation offloading in mobile edge computing." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS462.

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Mobile Edge Computing (MEC) est un modèle informatique émergent qui étend le cloud et ses services à la périphérie du réseau. Envisager l'exécution d'applications émergentes à forte intensité de ressources dans le réseau MEC, le déchargement de calcul est un paradigme éprouvé réussi pour activer des applications gourmandes en ressources sur les appareils mobiles. De plus, compte tenu de l'émergence de l'application collaborative mobile (MCA), les tâches déchargées peuvent être dupliquées lorsque plusieurs utilisateurs se trouvent à proximité. Cela nous motive à concevoir un schéma de déchargem
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Hansson, Gustav. "Computation offloading of 5G devices at the Edge using WebAssembly." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85898.

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With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices. This master thesis investigates the use of WebAssembly in the context of computa¬tional offloading at the Edge. The focus is on utilizing WebAssembly to move computa¬tional heavy parts of a system from an end device to an Edge Server. An objective is to improve program performa
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Bozorgchenani, Arash <1989&gt. "Energy and Delay Efficient Computation Offloading Solutions for Edge Computing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9356/1/PhD%20Thesis_Arash%20Bozorgchenani.pdf.

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This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of a
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Soto, Garcia Victor. "Mobility-Oriented Data Retrieval for Computation Offloading in Vehicular Edge Computing." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38836.

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Vehicular edge computing (VEC) brings the cloud paradigm to the edge of the network, allowing nodes such as Roadside Units (RSUs) and On-Board Units (OBUs) in vehicles to perform services with location awareness and low delay requirements. Furthermore, it alleviates the bandwidth congestion caused by the large amount of data requests in the network. One of the major components of VEC, computation offloading, has gained increasing attention with the emergence of mobile and vehicular applications with high-computing and low-latency demands, such as Intelligent Transportation Systems and IoT-base
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Messaoudi, Farouk. "User equipment based-computation offloading for real-time applications in the context of Cloud and edge networks." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S104/document.

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Le délestage de calcul ou de code est une technique qui permet à un appareil mobile avec une contrainte de ressources d'exécuter à distance, entièrement ou partiellement, une application intensive en calcul dans un environnement Cloud avec des ressources suffisantes. Le délestage de code est effectué principalement pour économiser de l'énergie, améliorer les performances, ou en raison de l'incapacité des appareils mobiles à traiter des calculs intensifs. Plusieurs approches et systèmes ont été proposés pour délester du code dans le Cloud tels que CloneCloud, MAUI et Cyber Foraging. La plupart
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Djemai, Ibrahim. "Joint offloading-scheduling policies for future generation wireless networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS007.

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Les défis posés par le nombre croissant d'appareils connectés, la forte consommation d'énergie et l'impact environnemental dans les réseaux sans fil d'aujourd'hui et de demain retiennent de plus en plus l'attention. De nouvelles technologies telles que le cloud mobile de périphérie (Mobile Edge Computing) ont vu le jour pour rapprocher les services en nuage des appareils et remédier à leurs limitations en matière de calcul. Le fait de doter ces appareils et les nœuds du réseau de capacités de récolte d'énergie (Energy Harvesting) est également prometteur pour permettre de consommer de l'énergi
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Krishna, Nitesh. "Software-Defined Computational Offloading for Mobile Edge Computing." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37580.

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Computational offloading advances the deployment of Mobile Edge Computing (MEC) in the next generation communication networks. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. Therefore, by leveraging the idea of Software-Defined Networking (SDN) and Service Composition (SC), we propose a Software-Defined Service Composition model (SDSC). In this model, the SDSC controller is deployed at the edge of the network and composes service in a centralized manner to reduce the latency of the ta
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Silva, Joaquim Magalhães Esteves da. "Adaptive Computation Offloading in Mobile Edge Clouds." Doctoral thesis, 2021. https://hdl.handle.net/10216/139189.

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Maurício, Bruno Alexandre de Salabert. "Modelling edge computation offloading for automotive video analytics." Master's thesis, 2021. https://hdl.handle.net/10216/135579.

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Intelligent vehicles are becoming more common and affordable, and with each new model come complex and resource-intensive applications, starting at simple sensors, into assistant AI, and more recently full vehicle automation. These applications can be mostly segmented into two categories, infotainment and driving assistance. The latter category requires strict adherence to time limits, lest they become useless or even dangerous to the driver, and is the focus of the present work. The spread and availability of powerful computation devices throughout city streets as a result of a variety of fac
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Maurício, Bruno Alexandre de Salabert. "Modelling edge computation offloading for automotive video analytics." Dissertação, 2021. https://hdl.handle.net/10216/135579.

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Intelligent vehicles are becoming more common and affordable, and with each new model come complex and resource-intensive applications, starting at simple sensors, into assistant AI, and more recently full vehicle automation. These applications can be mostly segmented into two categories, infotainment and driving assistance. The latter category requires strict adherence to time limits, lest they become useless or even dangerous to the driver, and is the focus of the present work. The spread and availability of powerful computation devices throughout city streets as a result of a variety of fac
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Books on the topic "Edge Computation Offloading"

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Chen, Ying, Ning Zhang, Yuan Wu, and Sherman Shen. Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2.

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Zhang, Ning, Ying Chen, Yuan Wu, and Sherman Shen. Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing AG, 2022.

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Book chapters on the topic "Edge Computation Offloading"

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Taheri, Javid, Schahram Dustdar, Albert Zomaya, and Shuiguang Deng. "AI/ML for Computation Offloading." In Edge Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22155-2_4.

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Zhang, Yan. "Mobile Edge Computing." In Simula SpringerBriefs on Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83944-4_2.

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AbstractMobile edge computing is a promising paradigm that brings computing resources to mobile users at the network edge, allowing computing-intensive and delay-sensitive applications to be quickly processed by edge servers to satisfy the requirements of mobile users. In this chapter, we first introduce a hierarchical architecture of mobile edge computing that consists of a cloud plane, an edge plane, and a user plane. We then introduce three typical computation offloading decisions. Finally, we review state-of-the-art works on computation offloading and present the use case of joint computat
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Ma, Xiao, Mengwei Xu, Qing Li, Yuanzhe Li, Ao Zhou, and Shangguang Wang. "Edge Computing Based Computation Offloading." In 5G Edge Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0213-8_4.

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Zhou, Xiaobo, Shuxin Ge, Jiancheng Chi, and Tie Qiu. "Computation Offloading in Industrial Edge Computing." In Industrial Edge Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4752-8_3.

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Peng, Kai, Yiwen Zhang, Xiaofei Wang, Xiaolong Xu, Xiuhua Li, and Victor C. M. Leung. "Computation Offloading in Mobile Edge Computing." In Encyclopedia of Wireless Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_331.

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Peng, Kai, Yiwen Zhang, Xiaofei Wang, Xiaolong Xu, Xiuhua Li, and Victor C. M. Leung. "Computation Offloading in Mobile Edge Computing." In Encyclopedia of Wireless Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_331-1.

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Cha, Narisu, Celimuge Wu, Tsutomu Yoshinaga, and Yusheng Ji. "Virtual Edge: Collaborative Computation Offloading in VANETs." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64002-6_6.

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Chen, Ying, Ning Zhang, Yuan Wu, and Sherman Shen. "Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing." In Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2_2.

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Chen, Ying, Ning Zhang, Yuan Wu, and Sherman Shen. "Energy-Efficient Multi-Task Multi-Access Computation Offloading via NOMA." In Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2_5.

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Chen, Ying, Ning Zhang, Yuan Wu, and Sherman Shen. "Deep Reinforcement Learning for Delay-Aware and Energy-Efficient Computation Offloading." In Energy Efficient Computation Offloading in Mobile Edge Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16822-2_4.

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Conference papers on the topic "Edge Computation Offloading"

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Liu, Chang, Yan Yuan, Weihua Cao, and Yabin Hu. "Multi user computation offloading decision in edge networks." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661830.

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Bovee, Nicholas, Paolo Rommel Sanchez, Shen-Shyang Ho, Suraj Bitla, Gopi Krishna Patapanchala, and Stephen Piccolo. "Poster: Computation Offloading for Precision Agriculture using Cooperative Inference." In 2024 IEEE 8th International Conference on Fog and Edge Computing (ICFEC). IEEE, 2024. http://dx.doi.org/10.1109/icfec61590.2024.00023.

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Rajwar, Dipankar, and Dinesh Kumar. "A PSO based Computation Offloading Model in Edge Computing." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649152.

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Lin, Zihong, Haopeng Chen, Yucheng Tao, Chang Liu, Shengyang Liu, and Fei Han. "Task Scheduling and Computation Offloading in Space Edge Computing." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00255.

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Shi, Jinming, Dedong Lv, Te Chen, and Yinqiao Li. "Learning-Based Inter-Satellite Computation Offloading in Satellite Edge Computing." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671510.

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Droob, Alexander, Daniel Morratz, Frederik Langkilde Jakobsen, et al. "Fault Tolerant Horizontal Computation Offloading." In 2023 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2023. http://dx.doi.org/10.1109/edge60047.2023.00036.

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Wei, Xiaojuan, Shangguang Wang, Ao Zhou, et al. "MVR: An Architecture for Computation Offloading in Mobile Edge Computing." In 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, 2017. http://dx.doi.org/10.1109/ieee.edge.2017.42.

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Zhang, Letian, and Jie Xu. "Fooling Edge Computation Offloading via Stealthy Interference Attack." In 2020 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 2020. http://dx.doi.org/10.1109/sec50012.2020.00062.

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Ma, Weibin, and Lena Mashayekhy. "Truthful Computation Offloading Mechanisms for Edge Computing." In 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2020. http://dx.doi.org/10.1109/cscloud-edgecom49738.2020.00043.

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Cheng, Lei, Gang Feng, Yao Sun, Mengjie Liu, and Shuang Qin. "Dynamic Computation Offloading in Satellite Edge Computing." In ICC 2022 - IEEE International Conference on Communications. IEEE, 2022. http://dx.doi.org/10.1109/icc45855.2022.9838943.

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