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Modelling and Simulation in Engineering
Volume 2018, Article ID 2591304, 11 pages
https://doi.org/10.1155/2018/2591304
Research Article

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

1Department of Electronics and Telecommunication Engineering, College of Information and Communication Technology, University of Dar es Salaam, Dar es Salaam, P.O. Box 33335, Tanzania
2Department of Computer Science & Engineering, College of Information and Communication Technology, University of Dar es Salaam, Dar es Salaam, P.O. Box 33335, Tanzania

Correspondence should be addressed to Haji Said Fimbombaya; zt.ca.tid@ayabmobmif.ijah

Received 22 May 2018; Accepted 9 September 2018; Published 16 October 2018

Academic Editor: Jing-song Hong

Copyright © 2018 Haji Said Fimbombaya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.