{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T13:13:03Z","timestamp":1778159583028,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technologies R&amp;D Program","award":["2022YFB430 0400"],"award-info":[{"award-number":["2022YFB430 0400"]}]},{"name":"National Key Technologies R&amp;D Program","award":["202106475002"],"award-info":[{"award-number":["202106475002"]}]},{"name":"National Key Technologies R&amp;D Program","award":["DL2023107003L"],"award-info":[{"award-number":["DL2023107003L"]}]},{"name":"National Key Technologies R&amp;D Program","award":["61801035"],"award-info":[{"award-number":["61801035"]}]},{"name":"China Scholarship Council","award":["2022YFB430 0400"],"award-info":[{"award-number":["2022YFB430 0400"]}]},{"name":"China Scholarship Council","award":["202106475002"],"award-info":[{"award-number":["202106475002"]}]},{"name":"China Scholarship Council","award":["DL2023107003L"],"award-info":[{"award-number":["DL2023107003L"]}]},{"name":"China Scholarship Council","award":["61801035"],"award-info":[{"award-number":["61801035"]}]},{"name":"Belt and Road Initiative Innovative Talent Exchange Foreign Expert Project","award":["2022YFB430 0400"],"award-info":[{"award-number":["2022YFB430 0400"]}]},{"name":"Belt and Road Initiative Innovative Talent Exchange Foreign Expert Project","award":["202106475002"],"award-info":[{"award-number":["202106475002"]}]},{"name":"Belt and Road Initiative Innovative Talent Exchange Foreign Expert Project","award":["DL2023107003L"],"award-info":[{"award-number":["DL2023107003L"]}]},{"name":"Belt and Road Initiative Innovative Talent Exchange Foreign Expert Project","award":["61801035"],"award-info":[{"award-number":["61801035"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB430 0400"],"award-info":[{"award-number":["2022YFB430 0400"]}]},{"name":"National Natural Science Foundation of China","award":["202106475002"],"award-info":[{"award-number":["202106475002"]}]},{"name":"National Natural Science Foundation of China","award":["DL2023107003L"],"award-info":[{"award-number":["DL2023107003L"]}]},{"name":"National Natural Science Foundation of China","award":["61801035"],"award-info":[{"award-number":["61801035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.<\/jats:p>","DOI":"10.3390\/s24051632","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T11:19:24Z","timestamp":1709291964000},"page":"1632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Jinyu","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Knowledge Based Technology and Energy, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of Korea"}]},{"given":"Jingyi","family":"Peng","sequence":"additional","affiliation":[{"name":"China Industrial Control Systems Cyber Emergency Response Team, Beijing 100040, China"}]},{"given":"Qing","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Gang","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Honglin","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Zili","family":"Liu","sequence":"additional","affiliation":[{"name":"China Academic of Electronics and Information Technology, Beijing 100041, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1109\/JIOT.2021.3102130","article-title":"Improving the Software-Defined Wireless Sensor Networks Routing Performance Using Reinforcement Learning","volume":"9","author":"Younus","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19886","DOI":"10.1109\/JSEN.2022.3205017","article-title":"Energy Consumption Minimization for Data Collection from Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design with DRL","volume":"22","author":"Xu","year":"2022","journal-title":"IEEE Sens. 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