Vehicular Sensor Networks in congested traffic: Linking STV field reconstruction and communications channel

2011 ◽  
Author(s):  
E. del Arco ◽  
E. Morgado ◽  
J. Ramiro-Bargueno ◽  
I. Mora-Jimenez ◽  
A. J. Caamano
Keyword(s):  
Sensor Networks ◽  
Field Reconstruction ◽  
Congested Traffic

Confident information coverage in sensor networks for field reconstruction

2013 ◽  
Vol 20 (6) ◽  
pp. 74-81 ◽  
Author(s):  
Bang Wang ◽  
Xianjun Deng ◽  
Wenyu Liu ◽  
Laurence Yang ◽  
Han-Chieh Chao
Keyword(s):  
Sensor Networks ◽  
Field Reconstruction ◽  
Information Coverage

scholarly journals Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Haji Said Fimbombaya ◽  
Nerey H. Mvungi ◽  
Ndyetabura Y. Hamisi ◽  
Hashimu U. Iddi
Keyword(s):  
Magnetic Field ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Success Rates ◽  
Earth’S Magnetic Field ◽  
Congested Traffic ◽  
Flow Monitoring ◽  
Flow Information

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

scholarly journals Bayesian Spatial Field Reconstruction With Unknown Distortions in Sensor Networks

2020 ◽  
Vol 68 ◽  
pp. 4336-4351
Author(s):  
Qikun Xiang ◽  
Ido Nevat ◽  
Gareth W. Peters
Keyword(s):  
Sensor Networks ◽  
Field Reconstruction
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