UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings

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dc.contributor.authorLaidi, Roufaida
dc.contributor.authorDjenouri, Djamel
dc.date.accessioned2019-04-05T16:28:10Z
dc.date.available2019-04-05T16:28:10Z
dc.date.issued2018-03
dc.description.abstractA solution for motion sensors deployment in smart buildings is proposed. It differentiates the monitored zones according to their occupancy, where highly-occupied zones have higher coverage requirements over low-occupied zones, and thus are assigned higher granularity in the targeted coverage (weights). The proposed solution is the first that defines a user-driven approach, which uses sampling of occupants’ behavior to determine the zones and the coverage weights. The samples are acquired during a short learning phase and then used to derive a graph model. The latter is plugged into a greedy, yet effective, algorithm that seeks optimal placement for maximizing detection accuracy while reducing the cost (number of sensors). Practical aspects such as the scalability and the applicability of the solution are considered. A simulation study that compares the proposed solution with two state-of-the-art solutions shows the superiority of the proposed approach in the accuracy of detection (increased coverage), and scalability (reduced runtime).fr_FR
dc.identifier.isbn978-1-5386-3227-7fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/938
dc.publisherIEEEfr_FR
dc.relation.ispartofseries2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops);
dc.relation.pages209-214fr_FR
dc.relation.placeAthens, Greecefr_FR
dc.structureRéseaux de capteurs et Applicationsfr_FR
dc.subjectSensors deploymentfr_FR
dc.subjectSmart buildingsfr_FR
dc.subjectMotion sensorsfr_FR
dc.subjectMachine learningfr_FR
dc.titleUDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildingsfr_FR
dc.typeConference paper
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