Robust Occupancy Inference with Commodity WiFi
Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibratio...
| मुख्य लेखकों: | , , , , , |
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| स्वरूप: | Conference item |
| प्रकाशित: |
Institute of Electrical and Electronics Engineers
2016
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| _version_ | 1826269219173433344 |
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| author | Lu, X Trigoni, N Wen, H Zou, H Jiang, H Xie, L |
| author_facet | Lu, X Trigoni, N Wen, H Zou, H Jiang, H Xie, L |
| author_sort | Lu, X |
| collection | OXFORD |
| description | Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. In this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity WiFi infrastructure. Depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. We evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. Experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness. |
| first_indexed | 2024-03-06T21:21:37Z |
| format | Conference item |
| id | oxford-uuid:41a2f067-b4a4-4cab-b621-f0e044e52d14 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:21:37Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:41a2f067-b4a4-4cab-b621-f0e044e52d142022-03-26T14:44:51ZRobust Occupancy Inference with Commodity WiFiConference itemhttp://purl.org/coar/resource_type/c_5794uuid:41a2f067-b4a4-4cab-b621-f0e044e52d14Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2016Lu, XTrigoni, NWen, HZou, HJiang, HXie, LAccurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. In this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity WiFi infrastructure. Depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. We evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. Experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness. |
| spellingShingle | Lu, X Trigoni, N Wen, H Zou, H Jiang, H Xie, L Robust Occupancy Inference with Commodity WiFi |
| title | Robust Occupancy Inference with Commodity WiFi |
| title_full | Robust Occupancy Inference with Commodity WiFi |
| title_fullStr | Robust Occupancy Inference with Commodity WiFi |
| title_full_unstemmed | Robust Occupancy Inference with Commodity WiFi |
| title_short | Robust Occupancy Inference with Commodity WiFi |
| title_sort | robust occupancy inference with commodity wifi |
| work_keys_str_mv | AT lux robustoccupancyinferencewithcommoditywifi AT trigonin robustoccupancyinferencewithcommoditywifi AT wenh robustoccupancyinferencewithcommoditywifi AT zouh robustoccupancyinferencewithcommoditywifi AT jiangh robustoccupancyinferencewithcommoditywifi AT xiel robustoccupancyinferencewithcommoditywifi |