{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T05:50:49Z","timestamp":1773640249933,"version":"3.50.1"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"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":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2020,2,29]]},"abstract":"<jats:p>\n            The Internet-of-Things (IoT) has engendered a new paradigm of integrated sensing and actuation systems for intelligent monitoring and control of smart homes and buildings. One viable manifestation is that of IoT-empowered smart lighting systems, which rely on the interplay between smart light bulbs (equipped with controllable LED devices and wireless connectivity) and mobile sensors (possibly embedded in users\u2019 wearable devices such as smart watches, spectacles, and gadgets) to provide automated illuminance control functions tailored to users\u2019 preferences (e.g., of brightness, color intensity, or color temperature). Typically, practical deployment of these systems precludes the adoption of sophisticated but costly location-aware sensors capable of accurately mapping out the details of a dynamic operational environment. Instead, cheap\n            <jats:italic>oblivious mobile sensors<\/jats:italic>\n            are often utilized, which are plagued with uncertainty in their relative locations to sensors and light bulbs. The imposed volatility, in turn, impedes the design of effective smart lighting systems for uncertain indoor environments with multiple sensors and light bulbs. With this in view, the present article sheds light on the adaptive control algorithms and modeling of such systems. First, a general model formulation of an oblivious multisensor illuminance control problem is proposed, yielding a robust framework agnostic to a dynamic surrounding environment and time-varying background light sources. Under this model, we devise efficient algorithms inducing continuous adaptive lighting control that minimizes energy consumption of light bulbs while meeting users\u2019 preferences. The algorithms are then studied under extensive empirical evaluations in a proof-of-concept smart lighting testbed featuring LIFX programmable bulbs and smartphones (deployed as light sensing units). Lastly, we conclude by discussing the potential improvements in hardware development and highlighting promising directions for future work.\n          <\/jats:p>","DOI":"10.1145\/3369392","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T13:33:12Z","timestamp":1576848792000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Multisensor Adaptive Control System for IoT-Empowered Smart Lighting with Oblivious Mobile Sensors"],"prefix":"10.1145","volume":"16","author":[{"given":"Areg","family":"Karapetyan","sequence":"first","affiliation":[{"name":"Masdar Institute, Khalifa University, and Research Institute for Mathematical Sciences (RIMS), Kyoto University, Kyoto, Japan"}]},{"given":"Sid Chi-Kin","family":"Chau","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australia"}]},{"given":"Khaled","family":"Elbassioni","sequence":"additional","affiliation":[{"name":"Masdar Institute, Khalifa University, Abu Dhabi, UAE"}]},{"given":"Syafiq Kamarul","family":"Azman","sequence":"additional","affiliation":[{"name":"Masdar Institute, Khalifa University, Abu Dhabi, UAE"}]},{"given":"Majid","family":"Khonji","sequence":"additional","affiliation":[{"name":"Masdar Institute, Khalifa University, Abu Dhabi, UAE"}]}],"member":"320","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.07.077"},{"key":"e_1_2_1_2_1","first-page":"721","article-title":"Lighting System for Controlling the Color Temperature of Artificial Light under the Influence of the Daylight Level","volume":"5","author":"Begemann Simon H. A.","year":"1998","unstructured":"Simon H. A. Begemann , Ariadne D. Tenner , and Gerrit J. Van Den Beld . 1998 . Lighting System for Controlling the Color Temperature of Artificial Light under the Influence of the Daylight Level . US Patent 5 , 721 ,471. Simon H. A. Begemann, Ariadne D. Tenner, and Gerrit J. Van Den Beld. 1998. Lighting System for Controlling the Color Temperature of Artificial Light under the Influence of the Daylight Level. US Patent 5,721,471.","journal-title":"US Patent"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008731.1008733"},{"key":"e_1_2_1_4_1","volume-title":"Human Factors in Lighting","author":"Boyce Peter Robert","unstructured":"Peter Robert Boyce . 2014. Human Factors in Lighting . CRC Press . Peter Robert Boyce. 2014. Human Factors in Lighting. CRC Press."},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"D. Caicedo S. Li and A. Pandharipande. 2017. Smart lighting control with workspace and ceiling sensors. Lighting Research 8 Technology 49 4 (2017) 446--460.  D. Caicedo S. Li and A. Pandharipande. 2017. Smart lighting control with workspace and ceiling sensors. Lighting Research 8 Technology 49 4 (2017) 446--460.","DOI":"10.1177\/1477153516629531"},{"key":"e_1_2_1_6_1","unstructured":"Meghan Clark. 2018. Python Library for Accessing LIFX Devices Locally Using the Official LIFX LAN Protocol. Retrieved from https:\/\/github.com\/mclarkk\/lifxlan.  Meghan Clark. 2018. Python Library for Accessing LIFX Devices Locally Using the Official LIFX LAN Protocol. 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Phillips , Rui Tan , Mohammad-Mahdi Moazzami , Guoliang Xing , Jinzhu Chen , and David K. Y. Yau . 2013. Supero: A sensor system for unsupervised residential power usage monitoring . In IEEE International Conference on Pervasive Computing and Communications (PerCom\u201913) . Dennis E. Phillips, Rui Tan, Mohammad-Mahdi Moazzami, Guoliang Xing, Jinzhu Chen, and David K. Y. Yau. 2013. Supero: A sensor system for unsupervised residential power usage monitoring. In IEEE International Conference on Pervasive Computing and Communications (PerCom\u201913)."},{"key":"e_1_2_1_16_1","volume-title":"ACM Conference on Embedded Networked Sensor Systems (SenSys\u201905)","author":"Singhvi Vipul","unstructured":"Vipul Singhvi , Andreas Krause , Carlos Guestrin , James H. Garrett Jr ., and H. Scott Matthews . 2005. Intelligent light control using sensor networks . In ACM Conference on Embedded Networked Sensor Systems (SenSys\u201905) . Vipul Singhvi, Andreas Krause, Carlos Guestrin, James H. Garrett Jr., and H. Scott Matthews. 2005. Intelligent light control using sensor networks. In ACM Conference on Embedded Networked Sensor Systems (SenSys\u201905)."},{"key":"e_1_2_1_17_1","volume-title":"Tromble","author":"Smith Noah A.","year":"2004","unstructured":"Noah A. Smith and Roy W . Tromble . 2004 . Sampling uniformly from the unit simplex. Johns Hopkins University , Technical Rep 29 (2004). Noah A. Smith and Roy W. Tromble. 2004. Sampling uniformly from the unit simplex. Johns Hopkins University, Technical Rep 29 (2004)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.02.016"},{"key":"e_1_2_1_19_1","volume-title":"IEEE Wireless Hive Networks Conference. 1--7.","author":"Wen Yao-Jung","unstructured":"Yao-Jung Wen and A. M. Agogino . 2008. Wireless networked lighting systems for optimizing energy savings and user satisfaction . In IEEE Wireless Hive Networks Conference. 1--7. Yao-Jung Wen and A. M. Agogino. 2008. 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