M2M-Routing: Environmental Adaptive Multi-agent Reinforcement Learning based Multi-hop Routing Policy for Self-Powered IoT Systems-Reference-Cited by-同舟云学术

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M2M-Routing: Environmental Adaptive Multi-agent Reinforcement Learning based Multi-hop Routing Policy for Self-Powered IoT Systems

Published:2022-03-14 Issue: Volume: Page:
ISSN:
Container-title:2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Zhang Wen¹,Zhang Jeff²,Xie Mimi³,Liu Tao⁴,Wang Wenlu¹,Pan Chen¹

Affiliation:

1. Texas A&M University-Corpus Christi,Department of Computer Science,Corpus Christi,USA

2. Harvard University,Department of Electrical Engineering,Cambridge,USA

3. University of Texas at San Antonio,Department of Computer Science,San Antonio,USA

4. Lawrence Technological University,Department of Math and Computer Science,Southfield,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9774496/9774497/09774779.pdf?arnumber=9774779

Reference14 articles.

1. Mean shift: a robust approach toward feature space analysis

2. Pro-Energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks

3. Spatiotemporal Scenario Data-Driven Decision-Making Framework for Strategic Air Traffic Flow Management

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Survey on the authentication and key agreement of 6LoWPAN: Open issues and future direction;Journal of Network and Computer Applications;2024-01

2. Sparsity-Aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2022-11

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