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Design and Evaluation of a Wireless Magnetic-based Proximity Detection Platform for Indoor Applications

Xiaofan Jiang, Chieh-Jan Mike Liang, Kaifei Chen, Ben Zhang, Jeff Hsu, Jie Liu, Bin Cao, and Feng Zhao

Abstract

Many indoor sensing applications leverage knowledge of relative proximity among physical objects and humans, such as the notion of “within arm’s reach”. In this paper, we quantify this notion using “proximity zone”, and propose a methodology that empirically and systematically compare the proximity zones created by various wireless technologies. We find that existing technologies such as 802.15.4, Bluetooth Low Energy (BLE), and RFID fall short on metrics such as boundary sharpness, robustness against interference, and obstacle penetration. We then present the design and evaluation of a wireless proximity detection platform based on magnetic induction - PULSAR. PULSAR provides sweet spot for indoor applications that require reliable and precise proximity detection. Finally, we present the design and evaluation of an end-to-end system, deployed inside a large food court to offer context-ware and personalized advertisements and diet suggestions at a per-counter granularity.

Details

Publication typeProceedings
Published inIPSN (International Conference on Information Processing in Sensor Networks)
DOI10.1145/2185677.2185735
PublisherACM – Association for Computing Machinery
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