Abstract
The remote surveillance and tracking of vehicles on roads is generally achieved by means of a network of sensors deployed along the roadside. The effectiveness of such networks depends fundamentally on the precision with which the sensor locations are known Global Positioning Systems provide an effective means of pinpointing the sensor locations. However, such systems are expensive, and are therefore impractical for large-scale sensor deployment. Accordingly, this paper proposes a low-cost sensor localization service based on binary detection information and a road map. In the proposed algorithm, the sensors are classified as either intersection nodes or non-intersection nodes in accordance with the stochastic characteristics of the binary detection information. Having identified all of the non-intersection nodes along each road segment, the intersection nodes are connected with the non-intersection nodes by means of a clustering technique. Finally, the positions of the roadside sensors are identified by mapping the clustered sensors to the known road map. The simulation results show that the proposed algorithm outperforms existing localization algorithms reported in the literature.
| Original language | English |
|---|---|
| Pages (from-to) | 323-339 |
| Number of pages | 17 |
| Journal | Journal of High Speed Networks |
| Volume | 23 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Hardware and Architecture
- Computer Networks and Communications
Cite this
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Low-cost localization service for roadside sensors based on passive information. / Li, Jung Shian; Kao, Chuan Kai; Tsai, Chia Yang; Liu, I. Hsien; Liu, Chuan Gang.
In: Journal of High Speed Networks, Vol. 23, No. 4, 01.01.2017, p. 323-339.Research output: Contribution to journal › Article
TY - JOUR
T1 - Low-cost localization service for roadside sensors based on passive information
AU - Li, Jung Shian
AU - Kao, Chuan Kai
AU - Tsai, Chia Yang
AU - Liu, I. Hsien
AU - Liu, Chuan Gang
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The remote surveillance and tracking of vehicles on roads is generally achieved by means of a network of sensors deployed along the roadside. The effectiveness of such networks depends fundamentally on the precision with which the sensor locations are known Global Positioning Systems provide an effective means of pinpointing the sensor locations. However, such systems are expensive, and are therefore impractical for large-scale sensor deployment. Accordingly, this paper proposes a low-cost sensor localization service based on binary detection information and a road map. In the proposed algorithm, the sensors are classified as either intersection nodes or non-intersection nodes in accordance with the stochastic characteristics of the binary detection information. Having identified all of the non-intersection nodes along each road segment, the intersection nodes are connected with the non-intersection nodes by means of a clustering technique. Finally, the positions of the roadside sensors are identified by mapping the clustered sensors to the known road map. The simulation results show that the proposed algorithm outperforms existing localization algorithms reported in the literature.
AB - The remote surveillance and tracking of vehicles on roads is generally achieved by means of a network of sensors deployed along the roadside. The effectiveness of such networks depends fundamentally on the precision with which the sensor locations are known Global Positioning Systems provide an effective means of pinpointing the sensor locations. However, such systems are expensive, and are therefore impractical for large-scale sensor deployment. Accordingly, this paper proposes a low-cost sensor localization service based on binary detection information and a road map. In the proposed algorithm, the sensors are classified as either intersection nodes or non-intersection nodes in accordance with the stochastic characteristics of the binary detection information. Having identified all of the non-intersection nodes along each road segment, the intersection nodes are connected with the non-intersection nodes by means of a clustering technique. Finally, the positions of the roadside sensors are identified by mapping the clustered sensors to the known road map. The simulation results show that the proposed algorithm outperforms existing localization algorithms reported in the literature.
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U2 - 10.3233/JHS-170574
DO - 10.3233/JHS-170574
M3 - Article
AN - SCOPUS:85032936341
VL - 23
SP - 323
EP - 339
JO - Journal of High Speed Networks
JF - Journal of High Speed Networks
SN - 0926-6801
IS - 4
ER -