671 Hits in 6.1 sec

Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning [article]

Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
2019 arXiv   pre-print
To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their mining tasks to a nearby MEC server via wireless  ...  We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states and wireless channel qualities between  ...  This study aims to fill the gap in the joint optimization of task offloading and privacy preservation in MEC-enabled mobile blockchain.  ... 
arXiv:1908.07467v1 fatcat:gwp3l7idqrflfpyrly22ukaita

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Hongjing Ji, Osama Alfarraj, Amr Tolba
2020 IEEE Access  
Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay.  ...  With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks.  ...  Reference [13] studied the multi-user computing offloading problem of mobile edge cloud computing under multi-channel wireless interference, and put forward a distributed computing offloading algorithm  ... 
doi:10.1109/access.2020.2983609 fatcat:b45abdrxbracnbfpvtvtu5uxui

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., TWC Jan. 2020 650-664 Huang, A., see He, H., TWC Dec. 2020 7881-7896 Huang, C., Molisch, A.F., He, R., Wang, R., Tang, P., Ai, B., and Zhong, Z., Machine Learning-Enabled LOS/NLOS Identification  ...  ., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, C., see Li, M  ...  Cui, Q., +, TWC July 2020 4519-4534 Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach [article]

Dinh C Nguyen, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne
2019 arXiv   pre-print
As an emerging paradigm, Multi-access Edge Computing (MEC) has been widely used to provide computation and storage resources to mobile user equipments (UE) at the edge of the network for improving the  ...  We then propose a novel Deep Reinforcement Learning (DRL) approach using a double deep Q-network (DQN) algorithm to solve the proposed problem.  ...  Index Terms-Blockchain, Multi-access Edge Computing (MEC), smart contract, offloading, blockchain mining, Deep Reinforcement Learning (DRL). I.  ... 
arXiv:2001.08165v1 fatcat:qcdnfs67fvd3hovszrkei34ada

Guest Editorial Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications

Jiannong Cao, Deyu Zhang, Haibo Zhou, Peng-Jun Wan
2019 IEEE Internet of Things Journal  
We hope that the special issue can serve as a good reference for scientists, engineers, and academicians in the area of computation offloading in IoTs. JIANNONG CAO  ...  Special thanks are due to the Editor-in-Chief of the IEEE INTERNET OF THINGS JOURNAL, Dr. Xuemin Shen, for his help in the publication process.  ...  In "Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning," the authors propose an optimal computation offloading policy by modeling offloading  ... 
doi:10.1109/jiot.2019.2921217 fatcat:yxc2v2whm5gtzhpefivtgt5uxy

Table of contents

2021 IEEE transactions on intelligent transportation systems (Print)  
Ersoy QoE-Based Task Offloading With Deep Reinforcement Learning in Edge-Enabled Internet of Vehicles ................ ..................................................................................  ...  Hu Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution .......... ..................................... Z. Ning, K. Zhang, X. Wang, L. Guo, X.  ... 
doi:10.1109/tits.2021.3067407 fatcat:pl4edrxprzbvtjnhubkpt7flvm

A new task offloading algorithm in edge computing

Zhenjiang Zhang, Chen Li, ShengLung Peng, Xintong Pei
2021 EURASIP Journal on Wireless Communications and Networking  
In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management  ...  expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this  ...  Acknowledgements This work was supported by the National Natural Science Foundation of China (NO. 61772064), the National Key Research and Development Program of China (2018YFC0831900).  ... 
doi:10.1186/s13638-021-01895-6 fatcat:jvc4npe5q5gxrni76eyacnfpee

Vehicular Edge Computing via Deep Reinforcement Learning [article]

Qi Qi, Zhanyu Ma
2020 arXiv   pre-print
We formulate the offloading decision of multi-task in a service as a long-term planning problem, and explores the recent deep reinforcement learning to obtain the optimal solution.  ...  Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular services.When offloading the complicated service to the vehicular  ...  The deep learning-based auction mechanism is used for optimization of edge computing resources [17] .  ... 
arXiv:1901.04290v3 fatcat:2ubcmtfm7ne3djdoj6pwxm6aie

Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing [article]

Xiaolan Liu, Jiadong Yu, Yue Gao
2020 arXiv   pre-print
Here, each end user is a learning agent observing its local environment to learn optimal decisions on either local computing or edge computing with the goal of minimizing long term system cost by choosing  ...  To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud  ...  A joint optimization of computation offloading scheduling and transmit power allocation scheme has been proposed in MEC system with single mobile user [8] , while joint optimizing radio and computational  ... 
arXiv:2004.02315v1 fatcat:xlw5ubccnjgsvgu2g4inglidi4

Efficient Computation Offloading in Edge Computing Enabled Smart Home

Bocheng Yu, Xingjun Zhang, Ilsun You, Umer Sadiq Khan
2021 IEEE Access  
To our knowledge, it is the first to joint optimization for devices' cost and energy in multi-layer networks, and attempt to adopt deep learning to mimic variables selection.  ...  Hence, we study the joint optimization of user cost and energy of the smart device. It may be the first research on user cost in edge computing so far.  ... 
doi:10.1109/access.2021.3066789 fatcat:irhla7zrbbantbzbgwwbkrfihu

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning [article]

Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen
2019 arXiv   pre-print
Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.  ...  In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement  ...  methods of optimizing learning computation tasks by the support of edge [14] .  ... 
arXiv:1809.07857v2 fatcat:2sav5fnozbc3rd2atcgpxzg7jq

A User-centric QoS-aware Multi-path Service provisioning in Mobile Edge Computing

Saif U. R. Malik, Tehsin Kanwal, Samee U. Khan, Hassan Malik, Haris Pervaiz
2021 IEEE Access  
Machine learning methods like Deep Reinforcement Learning (DRL) based schemes with Q-learning models like DDQN and DRL have been efficiently utilized for task optimization and resource allocation in multi-user  ...  A cacheassisted scheme optimize task offloading and preserve a minimum delay in the mobile edge work.  ... 
doi:10.1109/access.2021.3070104 fatcat:d37qpth7o5buzk4xrznlbnzaw4

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey [article]

Tianxu Li, Kun Zhu, Nguyen Cong Luong, Dusit Niyato, Qihui Wu, Yang Zhang, Bing Chen
2021 arXiv   pre-print
Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal  ...  Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies.  ...  The system model consists of a single BS and multiple UAVs  ... 
arXiv:2110.13484v1 fatcat:m5rbd6q4cjfwpca62erocyky7e

Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

Yawen Zhang, Yifeng Miao, Shujia Pan, Siguang Chen, Arijit Karati
2021 Security and Communication Networks  
In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed.  ...  In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper  ...  Zhang, “Joint computation algorithm and then obtain the optimal offloading and re- offloading and user association in multi-task mobile edge source allocation strategy.  ... 
doi:10.1155/2021/8167796 fatcat:mgewkdlfizd4vis5q4am53mqqy

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach [article]

Zhao Chen, Xiaodong Wang
2018 arXiv   pre-print
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC  ...  Nevertheless, by considering a MEC system consisting of multiple mobile users with stochastic task arrivals and wireless channels in this paper, the design of computation offloading policies is challenging  ...  Besides, double deep Q-network (DQN) based strategic computation offloading algorithm was proposed in [35] , where an mobile device learned the optimal task offloading and energy allocation to maximize  ... 
arXiv:1812.07394v1 fatcat:hhqdkn4bi5gldpyp7uzausehn4
« Previous Showing results 1 — 15 out of 671 results