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Multiple Occupant Indoor Localization Open Access


Other title
Indoor Localization
Activity Recognition
Smart Homes
RFID Placement
PIR sensors
Target Tracking
Type of item
Degree grantor
University of Alberta
Author or creator
Vatanpour Azghandi, Masoud
Supervisor and department
Stroulia, Eleni (Computing Science)
Nikolaidis, Ioanis (Computing Science)
Examining committee member and department
Zhang, Hong (Computing Science)
Friggstad, Zac (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Recognizing the indoor activities of people is a key functionality of smart homes, since it is a prerequisite for any supportive action in service of the occupants. In this thesis, we investigate the multiple occupant localization problem in indoor environments. We developed a method based on the use of inexpensive passive infrared motion sensors together with Radio Frequency IDentification (RFID) readers. In our method, the former type of sensors, placed throughout the space, recognize movement and RFID readers, placed selectively at key locations, unambiguously recognize individuals’ locations (some or all of the occupants are assumed to be wearing passive RFID tags) as they pass through their coverage area. Due to their high cost and generally cumbersome placement requirements, RFID readers must be judiciously placed. Thus, we study the placement of the readers such that the occupants trajectory ambiguity is reduced. We rely on a heat-map representing the frequency with which individuals visit locations as they move through the indoor space and on models of coverage for the Passive InfraRed (PIR) sensors and RFID readers and develop a heuristic for the RFID reader placement. We also revisit the problem of cost-efficient PIR sensor placement for high-quality in- door localization, extending it for sensors with diverse coverage footprints, and the oc- clusion effects due to obstructions typically found in indoor environments. The objective is the placement of the smallest number of sensors with the right combination of foot- prints. Given the vast search space of possible placement and footprint combinations, we adopt an evolutionary technique. We demonstrate that our technique performs faster and/or produces more accurate results when compared to previously proposed greedy methods. Furthermore, our technique is flexible in that adding new sensor footprints can be trivially accomplished. We evaluate the effectiveness of our method in both RFID reader and PIR sensor placement under different occupancy conditions with simulations.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Masoud Vatanpour Azghandi, Ioanis Nikolaidis, Eleni Stroulia: Multi-Occupant Movement Tracking in Smart Home Environments. ICOST 2015: 319-324Masoud Vatanpour Azghandi, Ioanis Nikolaidis, Eleni Stroulia: Sensor placement for indoor multi-occupant tracking. IISA 2015: 1-8Masoud Vatanpour Azghandi, Ioanis Nikolaidis, Eleni Stroulia: Indoor Sensor Placement for Diverse Sensor-coverage Footprints. SENSORNETS 2015: 25-35

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