{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:08:11Z","timestamp":1769731691184,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011337","name":"The Belt and Road Special Foundation of the State Key Laboratory of Hydrology\u2014Water Resources and Hydraulic Engineering","doi-asserted-by":"publisher","award":["2021490811"],"award-info":[{"award-number":["2021490811"]}],"id":[{"id":"10.13039\/501100011337","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011337","name":"The Belt and Road Special Foundation of the State Key Laboratory of Hydrology\u2014Water Resources and Hydraulic Engineering","doi-asserted-by":"publisher","award":["2016YFC0402710"],"award-info":[{"award-number":["2016YFC0402710"]}],"id":[{"id":"10.13039\/501100011337","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021490811"],"award-info":[{"award-number":["2021490811"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2016YFC0402710"],"award-info":[{"award-number":["2016YFC0402710"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.<\/jats:p>","DOI":"10.3390\/s23125447","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T02:03:18Z","timestamp":1686276198000},"page":"5447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1637-1511","authenticated-orcid":false,"given":"Zaipeng","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"},{"name":"Department of Computer Science and Technology, Hohai University, Nanjing 211100, China"}]},{"given":"Cheng","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Hohai University, Nanjing 211100, China"}]},{"given":"Lifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Hohai University, Nanjing 211100, China"}]},{"given":"Mingyao","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Hohai University, Nanjing 211100, China"}]},{"given":"Hongli","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"220121","DOI":"10.1109\/ACCESS.2020.3042874","article-title":"Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook","volume":"8","author":"Peres","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.comcom.2020.02.069","article-title":"Applications of artificial intelligence and machine learning in smart cities","volume":"154","author":"Ullah","year":"2020","journal-title":"Comput. 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