N. Moretti, J. D. Blanco Cadena, A. Mannino, T. Poli, F. Re Cecconi
{"title":"基于超声传感器网络的智能建筑维修服务优化","authors":"N. Moretti, J. D. Blanco Cadena, A. Mannino, T. Poli, F. Re Cecconi","doi":"10.1080/17508975.2020.1765723","DOIUrl":null,"url":null,"abstract":"ABSTRACT Occupancy monitoring in smart buildings has great potential to improve their operational performance. One of the most common applications concerns the dynamic adaptation of indoor conditions according to the occupancy variation. However, other implementations are possible. Occupancy data could also enhance maintenance smart contracts management, especially if coupled with a contracts’ management system as blockchain through which it is possible to achieve higher reliability and trust in transactions. In this article, a methodology to monitor occupancy data with a low-cost network, composed by a set of ultrasonic sensors, is presented. To ensure the collection of consistent data, different tests were performed for defining a convenient configuration for their installation. Following the proposed methodology, gathered data are processed and stored into a digital asset model associated with the building maintenance plan. Once a predefined threshold is reached, the system triggers a maintenance alert to the contractor to activate cleaning operations. The proposed approach enables an enhancement of the automation of maintenance management operations in a cost-effective manner. However, further validation trials are required to test the flexibility of its application in different space types.","PeriodicalId":45828,"journal":{"name":"Intelligent Buildings International","volume":"13 1","pages":"4 - 16"},"PeriodicalIF":2.1000,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17508975.2020.1765723","citationCount":"16","resultStr":"{\"title\":\"Maintenance service optimization in smart buildings through ultrasonic sensors network\",\"authors\":\"N. Moretti, J. D. Blanco Cadena, A. Mannino, T. Poli, F. Re Cecconi\",\"doi\":\"10.1080/17508975.2020.1765723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Occupancy monitoring in smart buildings has great potential to improve their operational performance. One of the most common applications concerns the dynamic adaptation of indoor conditions according to the occupancy variation. However, other implementations are possible. Occupancy data could also enhance maintenance smart contracts management, especially if coupled with a contracts’ management system as blockchain through which it is possible to achieve higher reliability and trust in transactions. In this article, a methodology to monitor occupancy data with a low-cost network, composed by a set of ultrasonic sensors, is presented. To ensure the collection of consistent data, different tests were performed for defining a convenient configuration for their installation. Following the proposed methodology, gathered data are processed and stored into a digital asset model associated with the building maintenance plan. Once a predefined threshold is reached, the system triggers a maintenance alert to the contractor to activate cleaning operations. The proposed approach enables an enhancement of the automation of maintenance management operations in a cost-effective manner. However, further validation trials are required to test the flexibility of its application in different space types.\",\"PeriodicalId\":45828,\"journal\":{\"name\":\"Intelligent Buildings International\",\"volume\":\"13 1\",\"pages\":\"4 - 16\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2020-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/17508975.2020.1765723\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligent Buildings International\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/17508975.2020.1765723\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Buildings International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17508975.2020.1765723","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Maintenance service optimization in smart buildings through ultrasonic sensors network
ABSTRACT Occupancy monitoring in smart buildings has great potential to improve their operational performance. One of the most common applications concerns the dynamic adaptation of indoor conditions according to the occupancy variation. However, other implementations are possible. Occupancy data could also enhance maintenance smart contracts management, especially if coupled with a contracts’ management system as blockchain through which it is possible to achieve higher reliability and trust in transactions. In this article, a methodology to monitor occupancy data with a low-cost network, composed by a set of ultrasonic sensors, is presented. To ensure the collection of consistent data, different tests were performed for defining a convenient configuration for their installation. Following the proposed methodology, gathered data are processed and stored into a digital asset model associated with the building maintenance plan. Once a predefined threshold is reached, the system triggers a maintenance alert to the contractor to activate cleaning operations. The proposed approach enables an enhancement of the automation of maintenance management operations in a cost-effective manner. However, further validation trials are required to test the flexibility of its application in different space types.