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Route Optimization via Environment-Aware Deep Network and Reinforcement Learning [article]

Pengzhan Guo, Keli Xiao, Zeyang Ye, Wei Zhu
2021 arXiv   pre-print
A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring.  ...  In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task.  ...  Specifically, we first input the current pick-up frequencies and related travel records of the previous year to determine parameters related to the self-check mechanism via a deep neural network.  ... 
arXiv:2111.09124v1 fatcat:fbffsrk2t5hwbonbj5e25buiwq

Towards Cognitive Routing based on Deep Reinforcement Learning [article]

Jiawei Wu, Jianxue Li, Yang Xiao, Jun Liu
2020 arXiv   pre-print
Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL).  ...  It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.  ...  Based on the definition, we proposed a deep reinforcement learning (DRL) based cognitive routing framework by defining the DRL factors in the cognitive routing environment.  ... 
arXiv:2003.12439v1 fatcat:lrneydxbxngarimhel7zel2v4u

Table of Contents

2021 2021 IEEE 46th Conference on Local Computer Networks (LCN)  
and Resource Allocation for FANETs with Deep Reinforcement Learning 315 A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking 319 Optimal Placement of Recurrent  ...  Slices Monitoring 185 Delay-Aware DNN Inference Throughput Maximization in Edge Computing via Jointly Exploring Partitioning and Parallelism 193 v To Forward or Not to Forward: Optimal Message  ... 
doi:10.1109/lcn52139.2021.9524933 fatcat:bopsc4l2qrc7bobzfyb6343iou

Guest editorial: Collaborative intelligence for vehicular Internet of Things

Celimuge Wu, Kok-Lim Alvin Yau, Carlos Tavares Calafate, Lei Zhong
2021 China Communications  
Some collaborative learning approaches, such as federated learning and multi-agent systems, have been used to reduce network traffic and improve the learning efficiency of some smartphone applications.  ...  a certain message in a multi-access Internet-of-moving-things (IoMT) environment where the cellular, vehicular WiFi, and low power wide area network technologies coexist.  ...  ., "Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks," considers the optimization of task offloading with ultra-reliable and low-latency communications  ... 
doi:10.23919/jcc.2021.9495349 fatcat:3zybrihi4ze37lvmokf7yqjtlu

Intelligent Network Traffic Control Based on Deep Reinforcement Learning

Fei Wu, Ting Li, Fucai Luo, Shulin Wu, Chuanqi Xiao
2022 North atlantic university union: International Journal of Circuits, Systems and Signal Processing  
On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement  ...  The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.  ...  based on deep reinforcement learning more and more inadequate.  ... 
doi:10.46300/9106.2022.16.73 fatcat:ih3ceqodgjgvbk2hqj4vjhjowy

Dynamic Vehicle Traffic Control Using Deep Reinforcement Learning in Automated Material Handling System

Younkook Kang, Sungwon Lyu, Jeeyung Kim, Bongjoon Park, Sungzoon Cho
Our deep reinforcement learning model consists of a Q-learning step and a recurrent neural network, through which traffic states and action values are predicted.  ...  Additionally, we find evidence the reinforcement learning structure proposed in this study can autonomously and dynamically adjust to the changes in traffic patterns.  ...  Then, we adjust traffic weights using these key paths in our deep reinforcement learning model which utilizes Q-learning and a recurrent neural network.  ... 
doi:10.1609/aaai.v33i01.33019949 fatcat:yntis4hjqjfthnus77prz5pjxy

Table of Content

2021 2021 IEEE 7th International Conference on Network Softwarization (NetSoft)  
9 Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Transfer over Wide Area Networks 166 LiONv2: An Experimental Network Construction Tool Considering Disaggregation of Network Configuration  ...  Deep Q-Learning for Job Offloading Orchestration in a Fleet of MEC UAVs in 5G Environments 186 In-network Solution for Network Traffic Reduction in Industrial Data Communication 191 Managing Video Processing  ... 
doi:10.1109/netsoft51509.2021.9492551 fatcat:kczddggaonawbh2ylhgwtxaxba

Table of Contents

2020 2020 IEEE 45th Conference on Local Computer Networks (LCN)  
Wireless Networks 325 IQoR: An Intelligent QoS-Aware Routing Mechanism with Deep Reinforcement Learning 329 Secure and Reliable Data Transmission in SDN-Based Backend Networks of Industrial IoT Detecting  ...  and Reliable Routing Protocol for Mobile Robotic Networks with Deep Reinforcement Learning 465 Rate Adaptation Techniques Using Contextual Bandit Approach for Mobile Wireless LAN Users 469 Doctoral-track  ... 
doi:10.1109/lcn48667.2020.9314824 fatcat:ijv6a3vurbd2zjmdmkt7bxle4q

Special Issue on Deep Reinforcement Learning for Emerging IoT Systems

Jia Hu, Peng Liu, Hong Liu, Obinna Anya, Yan Zhang
2020 IEEE Internet of Things Journal  
Jia Hu received the B.Eng. and M.Eng. degrees in electronic engineering from the Huazhong  ...  Peng Liu received the B.S. and M.S. degrees in computer science and technology from Hangzhou Dianzi University, Hangzhou, China, in 2001 and 2004, respectively, and  ...  The article titled "Deep-reinforcement-learning-based QoSaware secure routing for SDN-IoT" proposes a DRLbased Quality-of-Service (QoS)-aware secure routing protocol (DQSP) to defend against the internal  ... 
doi:10.1109/jiot.2020.2998256 fatcat:rct75tsesbh7lkjmhuyogfi4ym

Table of contents

2021 IEEE Internet of Things Journal  
Yang 4657 Priority-Aware Reinforcement-Learning-Based Integrated Design of Networking and Control for Industrial Internet of Things .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Shen 4548 Deep-Reinforcement-Learning-Based User Profile Perturbation for Privacy-Aware Recommendation .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jiot.2021.3060607 fatcat:veucgc5syvbijd5vcgt5dvhb3m

Traffic Engineering in Software-defined Networks using Reinforcement Learning: A Review

Delali Kwasi Dake, James Dzisi Gadze, Griffith Selorm Klogo, Henry Nunoo-Mensah
2021 International Journal of Advanced Computer Science and Applications  
With the exponential increase in connected devices and its accompanying complexities in network management, dynamic Traffic Engineering (TE) solutions in Software-Defined Networking (SDN) using Reinforcement  ...  Learning (RL) techniques has emerged in recent times.  ...  The DQSP agent through the controller is aware of the underlying network environment and generates routing policies for the controller to executive.  ... 
doi:10.14569/ijacsa.2021.0120541 fatcat:3osugiglqrhwlegcc4x2mdsrxe

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues [article]

Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, Shiwen Mao
2019 arXiv   pre-print
networking focuses on the applications in clustering, base station switching control, user association and routing.  ...  in the network layer, and localization in the application layer.  ...  Moreover, training a reinforcement learning model in a complex environment can also cost much time, and it is possible that the elements of the communication environment, such as the environmental dynamics  ... 
arXiv:1809.08707v2 fatcat:6tnzliwthfehrpuxpmm45hs4vq

Towards Resilient Access Equality for 6G Serverless p-LEO Satellite Networks [article]

Lin Shih-Chun, Lin Chia-Hung, Chu Liang C., Lien Shao-Yu
2022 arXiv   pre-print
The proposed design dynamically orchestrates communications and computation functionalities and resources among heterogeneous physical units to efficiently fulfill multi-agent deep reinforcement learning  ...  , and learning performance.  ...  Also, multi-user access schemes to non-terrestrial bases stations are investigated in [8] via deep reinforcement learning to provide high throughput and fewer handovers for 6G traffic.  ... 
arXiv:2205.08430v1 fatcat:75l42xqr5zcbnat4ott22yeweq

2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32

2022 IEEE Transactions on Parallel and Distributed Systems  
., +, TPDS July 2021 1765-1776 Petrel: Heterogeneity-Aware Distributed Deep Learning Via Hybrid Synchronization.  ...  ., +, TPDS July 2021 1765-1776 Petrel: Heterogeneity-Aware Distributed Deep Learning Via Hybrid Syn- chronization.  ... 
doi:10.1109/tpds.2021.3107121 fatcat:e7bh2xssazdrjcpgn64mqh4hb4

A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments [article]

Manish Bhattarai, Manel Martinez-Ramon
2020 arXiv   pre-print
The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment.  ...  Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in  ...  We would also like to thank Sophia Thompson for her valuable suggestions and contributions to the edits of the final drafts.  ... 
arXiv:2011.06450v1 fatcat:vakae5gun5frhd7px64zwckuha
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