{"title":"FloorViz - a bricked dashboard: demo abstract","authors":"Aslak Johansen, M. Kjærgaard","doi":"10.1145/3276774.3281014","DOIUrl":"https://doi.org/10.1145/3276774.3281014","url":null,"abstract":"Porting an application from one building to another requires a common format for modeling buildings. Brick provides such a format, but details still need to be flushed out and the community has to agree on how to apply the Brick ontology. In this paper, we demonstrate one way of building a dashboard application which uses a Brick model as its configuration gateway. From the Brick model it extracts links to secondary resources, that (i) don't fit naturally in an RDF model like Brick, and (ii) can be shared by other applications. We hope that availability of concrete applications will foster a grounded discussion how to apply Brick.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122695831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An early resource characterisation of wi-fi sensing on residential gateways","authors":"Chulhong Min, Mohammed Alloulah, F. Kawsar","doi":"10.1145/3276774.3276789","DOIUrl":"https://doi.org/10.1145/3276774.3276789","url":null,"abstract":"Recent research has successfully shown brand new models with Wi-Fi signals explaining space dynamics, assessing social environments, and even tracking people's posture, gesture and emotion. However, these models are seldom used in real execution and operating environments, i.e., on residential gateways with networking tasks. In this paper, we present the first, albeit preliminary, measurement study of common Wi-Fi sensing models on a residential gateway. This investigation aims to understand the performance characteristics, resource requirements, and execution bottlenecks for Wi-Fi sensing when being used in parallel with communication tasks. Based on our findings, we propose two optimisation techniques - i) dynamic sampling and ii) dynamic planning of inference execution - for optimum Wi-Fi sensing performance without compromising the quality of communication service. The results and insights lay an empirical foundation for the development of optimisation methods and execution environments that enable sensing models to be more readily integrated into next-generation residential gateways.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inferring occupant ties: automated inference of occupant network structure in commercial buildings","authors":"A. Sonta, Rishee K. Jain","doi":"10.1145/3276774.3276779","DOIUrl":"https://doi.org/10.1145/3276774.3276779","url":null,"abstract":"To design and manage office buildings that are both energy-efficient and productive work environments, we need a better understanding of the relationship between building and occupant systems. Past data-driven building research has focused on energy efficiency and occupant comfort, but little work has used building sensor data to understand occupant organizational behavior and dynamics in buildings. In this initial work, we present a methodology for using distributed plug load energy consumption sensors to infer the social/organizational network of occupants (i.e., the relationships among occupants in a building). We demonstrate how plug load data can be used to model activities, and we introduce how statistical methods---in particular, the graphical lasso and the influence model---can be used to learn network structure from time-series activity data. We apply our method to a seven-person office environment in Northern California, and we compare the inferred networks to ground truth spatial, social, and organizational networks obtained through validated survey questions. In the end, a better understanding of how occupants organize and utilize spaces could enable more contextual control and co-optimization of building-human systems.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Trust me, my neighbors say it's raining outside: ensuring data trustworthiness for crowdsourced weather stations","authors":"Alexander B. Chen, Madhur Behl, J. Goodall","doi":"10.1145/3276774.3276792","DOIUrl":"https://doi.org/10.1145/3276774.3276792","url":null,"abstract":"Decision making in utilities, municipal, and energy companies depends on accurate and trustworthy weather information and predictions. Recently, crowdsourced personal weather stations (PWS) are being increasingly used to provide a higher spatial and temporal resolution of weather measurements. However, tools and methods to ensure the trustworthiness of the crowdsourced data in real-time are lacking. In this paper, we present a Reputation System for Crowdsourced Rainfall Networks (RSCRN) to assign trust scores to personal weather stations in a region. Using real PWS data from the Weather Underground service in the high flood risk region of Norfolk, Virginia, we evaluate the performance of the proposed RSCRN. The proposed method is able to converge to a confident trust score for a PWS within 10--20 observations after installation. Collectively, the results indicate that the trust score derived from the RSCRN can reflect the collective measure of trustworthiness to the PWS, ensuring both useful and trustworthy data for modeling and decision-making in the future.","PeriodicalId":294697,"journal":{"name":"Proceedings of the 5th Conference on Systems for Built Environments","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126859074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}