{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:33:32Z","timestamp":1771659212869,"version":"3.50.1"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T00:00:00Z","timestamp":1553817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2019,3,29]]},"abstract":"<jats:p>Smart thermostats, with multiple sensory abilities, are becoming pervasive and ubiquitous, in both residential and commercial buildings. By analyzing occupants' behavior, adjusting set temperature automatically, and adapting to temporal and spatial changes in the atmosphere, smart thermostats can maximize both - energy savings and user comfort. In this paper, we study smart thermostats for refrigerant leakage detection. Retail outlets, such as milk-booths and quick service restaurants set up cold-rooms to store perishable items. In each room, a refrigeration unit (akin to air-conditioners) is used to maintain a suitable temperature for the stored products. Often, refrigerant leaks through the coils (or valves) of the refrigeration unit which slowly diminishes the cooling capacity of the refrigeration unit while allowing it to be functional. Such leaks waste significant energy, risk occupants' health, and impact the quality of stored perishable products. While store managers usually fail to sense the early symptoms of such leaks, current techniques to report refrigerant leakage are often not scalable. We propose Greina - to continuously monitor the readily available ambient information from the thermostat and timely report such leaks. We evaluate our approach on 74 outlets of a retail enterprise and results indicate that Greina can report the leakage a week in advance when compared to manual reporting.<\/jats:p>","DOI":"10.1145\/3314401","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T11:57:40Z","timestamp":1554206260000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Beyond Control"],"prefix":"10.1145","volume":"3","author":[{"given":"Milan","family":"Jain","sequence":"first","affiliation":[{"name":"IIIT-Delhi, Delhi, India"}]},{"given":"Mridula","family":"Gupta","sequence":"additional","affiliation":[{"name":"University of California Davis, Davis, California, USA"}]},{"given":"Amarjeet","family":"Singh","sequence":"additional","affiliation":[{"name":"IIIT-Delhi, Delhi, India"}]},{"given":"Vikas","family":"Chandan","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, Richland, Washington, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,3,29]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/10789669.2006.10391186"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2011.02.005"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939735"},{"key":"e_1_2_2_4_1","volume-title":"Matrix Factorisation for Scalable Energy Breakdown. 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