{"title":"Measure It Yourself - Why Smart Cities Need Custom Weather Data Sources","authors":"Raphaela Erbel, Philipp Brune","doi":"10.1109/FiCloud57274.2022.00037","DOIUrl":null,"url":null,"abstract":"Smart city applications could help to improve citizens’ well-being, health and mobility by providing, e.g., intelligent recommendations for personalized leisure activities and transport options in urban areas. One important factor that influences peoples’ choices and behavior is the weather. Hence, accurate data are needed for making reliable predictions. Those weather data may be obtained either from publicly available open data services or measured by a custom sensor network. Despite the importance of weather data, the advantages and disadvantages of both approaches have not been studied systematically so far. Therefore, in this paper the design and implementation of a custom sensor network and results from its comparative evaluation with open weather data within a field experiment in a German city will be presented. Results indicate that for adequate accounting of local weather deviations (i.e. micro weather) within a city or urban area, public data services might not be sufficient and a custom sensor network should be implemented.","PeriodicalId":349690,"journal":{"name":"2022 9th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 9th International Conference on Future Internet of Things and Cloud (FiCloud)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FiCloud57274.2022.00037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Smart city applications could help to improve citizens’ well-being, health and mobility by providing, e.g., intelligent recommendations for personalized leisure activities and transport options in urban areas. One important factor that influences peoples’ choices and behavior is the weather. Hence, accurate data are needed for making reliable predictions. Those weather data may be obtained either from publicly available open data services or measured by a custom sensor network. Despite the importance of weather data, the advantages and disadvantages of both approaches have not been studied systematically so far. Therefore, in this paper the design and implementation of a custom sensor network and results from its comparative evaluation with open weather data within a field experiment in a German city will be presented. Results indicate that for adequate accounting of local weather deviations (i.e. micro weather) within a city or urban area, public data services might not be sufficient and a custom sensor network should be implemented.