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Autonomous Passive Localization Algorithm for Road Sensor Networks
November 2011 (vol. 60 no. 11)
pp. 1622-1637
ASCII Text
x 
 
Jaehoon (Paul) Jeong, Shuo Guo, Tian He, David H.C. Du, "Autonomous Passive Localization Algorithm for Road Sensor Networks," IEEE Transactions on Computers, vol. 60, no. 11, pp. 1622-1637, November, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TC.2010.260,
author = {Jaehoon (Paul) Jeong and Shuo Guo and Tian He and David H.C. Du},
title = {Autonomous Passive Localization Algorithm for Road Sensor Networks},
journal ={IEEE Transactions on Computers},
volume = {60},
number = {11},
issn = {0018-9340},
year = {2011},
pages = {1622-1637},
doi = {http://doi.ieeecomputersociety.org/10.1109/TC.2010.260},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Computers
TI - Autonomous Passive Localization Algorithm for Road Sensor Networks
IS - 11
SN - 0018-9340
SP1622
EP1637
EPD - 1622-1637
A1 - Jaehoon (Paul) Jeong,
A1 - Shuo Guo,
A1 - Tian He,
A1 - David H.C. Du,
PY - 2011
KW - Sensor network
KW - passive localization
KW - road network
KW - binary vehicle detection
KW - time stamp
KW - prefiltering
KW - graph matching.
VL - 60
JA - IEEE Transactions on Computers
ER -
 
Jaehoon (Paul) Jeong, University of Minnesota, Twin Cities, Minneapolis
Shuo Guo, University of Minnesota, Twin Cities, Minneapolis
Tian He, University of Minnesota, Twin Cities, Minneapolis
David H.C. Du, University of Minnesota, Twin Cities, Minneapolis
Road networks are one of important surveillance areas in military scenarios. In these road networks, sensors will be sparsely deployed (hundreds of meters apart) for the cost-effective deployment. This makes the existing localization solutions based on the ranging ineffective. To address this issue, this paper introduces a novel approach based on the passive vehicular traffic measurement, called Autonomous Passive Localization (APL). Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection time stamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of the road map, in order to identify where sensors are located on roadways. We evaluate our design outdoors on Minnesota roadways and show that our distance estimate method works well despite traffic noises. In addition, we show that our localization scheme is effective in a road network with 18 intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.07 sec and a vehicle speed deviation of 10 km/h.

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Index Terms:
Sensor network, passive localization, road network, binary vehicle detection, time stamp, prefiltering, graph matching.
Citation:
Jaehoon (Paul) Jeong, Shuo Guo, Tian He, David H.C. Du, "Autonomous Passive Localization Algorithm for Road Sensor Networks," IEEE Transactions on Computers, vol. 60, no. 11, pp. 1622-1637, Nov. 2011, doi:10.1109/TC.2010.260
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