{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:26:04Z","timestamp":1758270364078,"version":"3.41.2"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":33,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Young Doctor Scientific Research Foundation of Harbin University","award":["HUDF201712"],"award-info":[{"award-number":["HUDF201712"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>As an important direction of Industry 4.0, cyberphysical machine tool systems (CPMTS) can realize the deep integration and real\u2010time interaction of physical components and information to optimize manufacturing processes. Wireless sensor network (WSN), an important part of CPMTS, is responsible for data collection and transmission. However, in the process of data transmission, due to memory limitations and noise interference, unreasonable sensor distribution will affect the performance of CPMTS. At the same time, data accuracy will be affected due to the resource constraints of CPMTS. To solve the problems above, this paper firstly presented a single\u2010station transfer model to ensure the layout of sensors in each sink, which can meet the detection capability of fault\/monitoring data. Then, by using fuzzy graphs, a multihop\u2010station transfer model and data\u2010collecting model are developed to describe the data flow and memory allocation in the wireless network. Taking noise interference and data position into consideration, a MILP problem is formulated and the optimization solution is obtained by using the \u201cbranch and bound\u201d method. Finally, case studies about optimal sensor distribution on the single station and path optimization on the multihop station are presented to illustrate the proposed strategy. The case studies validated that the proposed sensor distribution in a single station can achieve higher detectability with fewer resources, and the optimization path strategy can achieve the best performance in two proposed experiments, compared to the shortest path and noninferior path strategies.<\/jats:p>","DOI":"10.1155\/2021\/6680718","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T02:05:56Z","timestamp":1612404356000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Wireless Sensor Network Deployment in Cyberphysical Machine Tool System Based on Optimal Allocation of Memory Buffers"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1352-6226","authenticated-orcid":false,"given":"Xiaoyang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2016.10.040"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-012-4264-3"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-015-7117-z"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2007.70784"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13673-019-0179-4"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.09.013"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.876256"},{"key":"e_1_2_10_8_2","first-page":"433","article-title":"A PSO based energy efficient coverage control algorithm for wireless sensor networks","volume":"56","author":"Wang J.","year":"2018","journal-title":"Computers, Materials and Continua"},{"key":"e_1_2_10_9_2","unstructured":"XuK. 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