Mateusz Mikusz, Oliver Bates, S. Clinch, N. Davies, A. Friday, A. Noulas
{"title":"Poster: Understanding Mobile User Interactions with the IoT","authors":"Mateusz Mikusz, Oliver Bates, S. Clinch, N. Davies, A. Friday, A. Noulas","doi":"10.1145/2938559.2938607","DOIUrl":null,"url":null,"abstract":"The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a \"physical analytics cookie\" could raise significant privacy concerns.\n However, in many cases a more \"human-centric\" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights.","PeriodicalId":298684,"journal":{"name":"MobiSys '16 Companion","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MobiSys '16 Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2938559.2938607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a "physical analytics cookie" could raise significant privacy concerns.
However, in many cases a more "human-centric" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights.