Paper Info
Title
Highly Reliable Signal Strength-Based Boundary Crossing Localization in Outdoor Time-Varying Environments.
Abstract
Detecting and locating outdoor boundary crossing events is valuable information in curbing drug trafficking, reducing poaching, and protecting high-asset equipment and goods. However, boundary sensing is notoriously challenging, prone to false alarms and missed detections, with serious consequences. Weather events, like rain and wind, make it even more challenging to maintain a low level of missed detections and false alarms. In this paper, we propose and test an automated system of wireless sensors which uses received signal strength (RSS) measurements to localize where a boundary crossing occurs. In addition, we develop new RSS-based statistical models and methods that can quickly be initialized and updated on-line by using link RSS statistics to adapt to time-varying RSS changes due to weather events. These models are implemented in two new classifiers that localize boundary crossings with few missed detections and false alarms. We validate our proposed methods by implementing one of the classifiers in a three month long deployment of a solar-powered, real-time system that captures images of the boundary for ground truth validation. Furthermore, over 75 hours of RSS measurements are collected with an emphasis on collection during weather events, like rain and wind, during which we expect our classifiers to perform the worst. We demonstrate that the proposed classifiers outperform four other baseline classifiers in terms of false alarm probability by 1 to 4 orders of magnitude, and in terms of the misclassification probability by 1 to 2 orders of magnitude.
Year
DOI
Venue
2016
10.1109/IPSN.2016.7460678
IPSN
Keywords
Field
DocType
signal strength-based boundary crossing localization,outdoor time-varying environments,outdoor border crossing events,drug trafficking,poaching,high-asset equipment,border sensing,false alarms,weather events,rain,wind,wireless sensors,received signal strength measurements,RSS-based statistical models,link RSS statistics,time-varying RSS changes,ground truth validation,false alarm probability
Wireless,False alarm,Software deployment,Computer science,Real-time computing,Ground truth,Statistical model,RSS,Wireless sensor network,Source separation
Conference
ISBN
Citations 
PageRank 
978-1-5090-0802-5
2
0.38
References 
Authors
13
3
Name
Order
Citations
PageRank
Peter Hillyard184.01
Anh Luong2246.05
Neal Patwari33805241.58