Paper Info
Title
Design and evaluation of a wireless magnetic-based proximity detection platform for indoor applications
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 in-terference, and obstacle penetration. We then present the design and evaluation of a wireless proximity detection platform based on magnetic induction - LiveSynergy. LiveSynergy 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-aware and personalized advertisements and diet suggestions at a per-counter granularity.
Year
DOI
Venue
2012
10.1109/IPSN.2012.6920959
IPSN
Keywords
Field
DocType
bluetooth low energy,electromagnetic induction,indoor application,various wireless technology,magnetic induction,livesynergy,indoor sensing applications,applications leverage knowledge,personalized advertisement,boundary sharpness,precise proximity detection,proximity zone,magneto,context aware advertisement,virtual zone,tracking,mobile radio,localization,wireless proximity detection platform,wireless technology,diet suggestion,wireless sensor networks,relative proximity,inductive,wireless magnetic-based proximity detection,end-to-end system,indoor radio,wireless communication,support vector machines
Proximity detection,Obstacle,Sensing applications,Wireless,Computer science,Real-time computing,Robustness (computer science),Granularity,Wi-Fi array,Bluetooth Low Energy
Conference
Citations 
PageRank 
References 
16
1.10
15
Authors
8
Name
Order
Citations
PageRank
Xiaofan Jiang1949110.46
Chieh-Jan Mike Liang280443.13
Kaifei Chen3656.86
Ben Zhang41087.28
Jeff Hsu5696.92
Jie Liu642948.44
Bin Cao78512.64
Feng Zhao84593455.17