UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings
| dc.contributor.author | Laidi, Roufaida | |
| dc.contributor.author | Djenouri, Djamel | |
| dc.date.accessioned | 2017-12-26T06:47:37Z | |
| dc.date.available | 2017-12-26T06:47:37Z | |
| dc.date.issued | 2017-12-25 | |
| dc.description.abstract | A solution for motion sensors deployment in smart buildings is proposed. It diferentiates 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 rst that de nes 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 e ective, 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.isrn | 17-000000017-DZ | fr_FR |
| dc.identifier.uri | http://dl.cerist.dz/handle/CERIST/908 | |
| dc.publisher | CERIST | |
| dc.relation.ispartof | Rapports de recherche internes | |
| dc.relation.place | Alger | |
| dc.structure | Réseaux de capteurs et Applications | fr_FR |
| dc.subject | Smart Buildings | fr_FR |
| dc.subject | Sensor Deployment | fr_FR |
| dc.subject | Machine learning | fr_FR |
| dc.subject | Occupancy monitoring | fr_FR |
| dc.title | UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings | fr_FR |
| dc.type | Technical Report |