{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:57:42Z","timestamp":1760151462484,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data distribution is a cornerstone of efficient automation for intelligent machines in Industry 4.0. Although in the recent literature there have been several comparisons of relevant methods, we identify that most of those comparisons are either theoretical or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. In this respect, as a first contribution of this paper, we develop more detailed and fine-tuned solutions for robust data distribution in smart factories on stationary and mobile scenarios of wireless industrial networking. Using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. We implement the presented methods in the highly detailed OMNeT++ simulation environment and we evaluate their performance via an extensive simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to an increased data delivery success rate and low data access latency. Next, we describe two test cases demonstrated in an industrial smart factory environment. First, we show the collaboration between robotic elements and wireless data services. Second, we show the integration with an industrial fog node which controls the shop-floor devices. We report selected results in much larger scales, obtained via simulations.<\/jats:p>","DOI":"10.3390\/s22072533","type":"journal-article","created":{"date-parts":[[2022,3,27]],"date-time":"2022-03-27T21:31:25Z","timestamp":1648416685000},"page":"2533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2906-584X","authenticated-orcid":false,"given":"Theofanis P.","family":"Raptis","sequence":"first","affiliation":[{"name":"Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Formica","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Milan, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7162-5997","authenticated-orcid":false,"given":"Elena","family":"Pagani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Milan, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1694-612X","authenticated-orcid":false,"given":"Andrea","family":"Passarella","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4064","authenticated-orcid":false,"given":"Marco","family":"Conti","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97052","DOI":"10.1109\/ACCESS.2019.2929296","article-title":"Data Management in Industry 4.0: State of the Art and Open Challenges","volume":"1","author":"Raptis","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23484","DOI":"10.1109\/ACCESS.2017.2765544","article-title":"Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance","volume":"5","author":"Yan","year":"2017","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/JSAC.2020.2980917","article-title":"Distributed Data Access in Industrial Edge Networks","volume":"38","author":"Raptis","year":"2020","journal-title":"IEEE J. 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