Paper Info
Title
SpringLoc: A Device-free Localization Technique for Indoor Positioning and Tracking using Adaptive RSSI Spring Relaxation
Abstract
Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2913910
IEEE ACCESS
Keywords
Field
DocType
Device-free localization (DFL),histogram distance,indoor positioning systems (IPS),smart homes,spring-relaxation
Kernel (linear algebra),Histogram,Software deployment,Wireless,Device free localization,Computer science,Home automation,Real-time computing,Signal strength,Calibration,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Daniel Konings100.34
Fakhrul Alam2209.06
Frazer K. Noble301.69
Edmund Ming-Kit Lai412058.89