{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:39:37Z","timestamp":1768343977179,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,4]],"date-time":"2018-04-04T00:00:00Z","timestamp":1522800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In this paper, the authors describe the realization and testing of a Wireless Sensor Network (WSN) framework aiming at measuring, remotely and in real time, the level variations of the sand layer of sandy beaches or dunes. The proposed framework is based on an innovative low cost sensing structure, able to measure the level variations with a 5-cm degree of precision and to locally transfer the acquired data through the ZigBee protocol. The described sensor is integrated in a wider ZigBee wireless sensor network architecture composed of an array of sensors that, arranged according to a grid layout, can acquire the same data at different points, allowing the definition of a dynamic map of the area under study. The WSN is connected to a local Global System for Mobile Communications (GSM) gateway that is in charge of data processing and transmission to a cloud infrastructure through a General Packet Radio Service (GPRS) connection. Data are then stored in a MySQL database and made available any time and anywhere through the Internet. The proposed architecture has been tested in a laboratory, to analyze data acquisition, processing timing and power consumption and then in situ to prove the effectiveness of the system. The described infrastructure is expected to be integrated in a wider IoT architecture including different typologies of sensors, in order to create a multi-purpose tool for the study of coastal erosive processes.<\/jats:p>","DOI":"10.3390\/ijgi7040141","type":"journal-article","created":{"date-parts":[[2018,4,4]],"date-time":"2018-04-04T06:45:37Z","timestamp":1522824337000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Wireless Sensor Network Framework for Real-Time Monitoring of Height and Volume Variations on Sandy Beaches and Dunes"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-8858","authenticated-orcid":false,"given":"Alessandro","family":"Pozzebon","sequence":"first","affiliation":[{"name":"Department of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3178-6168","authenticated-orcid":false,"given":"Alessandro","family":"Andreadis","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-2437","authenticated-orcid":false,"given":"Duccio","family":"Bertoni","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, University of Pisa, 56126 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmine","family":"Bove","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,4]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Methods for assessing coastal vulnerability to climate change","volume":"1","author":"Ramieri","year":"2011","journal-title":"ETC CCA Tech. 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