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Journals / Politeknik Dergisi / 2018 / Cilt: 21 - Sayı: 2

Detection of the Vehicle Direction with Adaptive Threshold Algorithm Using Magnetic Sensor Nodes

SERCAN VANÇİN
EBUBEKİR ERDEM
Pages
333–340
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The simplicity of the proposed algorithms, the absence of any complex computations, the low cost of the sensor node and integration and the low power depletetion of the communication system show the avantage of this system in comparison with the other studies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we describe how, so as to perform vehicles direction detection in road traffic using proposed method along with the average-constant threshold and contributions of adaptive threshold detection algorithm (ATDA) thanks to special-purpose sensor nodes integrated with magnetic sensors.
📊
Key Findings & Data:In this mean, it is clear that detected adaptive threshold generates more correct result for the systems like vehicle existence, direction assignment, and speed detection in different grounds where magnetic field is changeable as a result of environmental measurements.
💡
Conclusion & Impact:The simplicity of the proposed algorithms, the absence of any complex computations, the low cost of the sensor node and integration and the low power depletetion of the communication system show the avantage of this system in comparison with the other studies.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Wireless sensor networks · magnetic sensor · adaptive threshold detection algorithm (ATDA)

Abstract

In this paper, we describe how, so as to perform vehicles direction detection in road traffic using proposed method along with the average-constant threshold and contributions of adaptive threshold detection algorithm (ATDA) thanks to special-purpose sensor nodes integrated with magnetic sensors. In this study, proposed algorithm with the adaptive threshold value as a magnetic resultant force has produced more pronounced and precise results than the average-fix threshold value. In this mean, it is clear that detected adaptive threshold generates more correct result for the systems like vehicle existence, direction assignment, and speed detection in different grounds where magnetic field is changeable as a result of environmental measurements. The direction of motion of the vehicles on the x-axis was determined as well as whether it was from left to right or from right to left, and the results were 97% average accurate. The simplicity of the proposed algorithms, the absence of any complex computations, the low cost of the sensor node and integration and the low power depletetion of the communication system show the avantage of this system in comparison with the other studies.

Özet

In this paper, we describe how, so as to perform vehicles direction detection in road traffic using proposed method along with the average-constant threshold and contributions of adaptive threshold detection algorithm (ATDA) thanks to special-purpose sensor nodes integrated with magnetic sensors. In this study, proposed algorithm with the adaptive threshold value as a magnetic resultant force has produced more pronounced and precise results than the average-fix threshold value. In this mean, it is clear that detected adaptive threshold generates more correct result for the systems like vehicle existence, direction assignment, and speed detection in different grounds where magnetic field is changeable as a result of environmental measurements. The direction of motion of the vehicles on the x-axis was determined as well as whether it was from left to right or from right to left, and the results were 97% average accurate. The simplicity of the proposed algorithms, the absence of any complex computations, the low cost of the sensor node and integration and the low power depletetion of the communication system show the avantage of this system in comparison with the other studies.

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Cite This Article

VANÇİN, S., ERDEM, E. (2018). Detection of the Vehicle Direction with Adaptive Threshold Algorithm Using Magnetic Sensor Nodes. Politeknik Dergisi, Cilt: 21 - Sayı: 2, 333–340. https://ajindex.com/articles/307388

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References (14)

  1. Haoui A., Kavaler R. and Varaiya P., "Wireless magnetic sensors for traffic surveillance," Transportation Research Part C: Emerging Technologies, 16 (3): 294-306, (2008)
  2. Lei Z., Wang R. and Cui L., “Real-time Traffic Monitoring with Magnetic Sensor Networks”, Journal of information science and engineering, 27(4): 1473-1486, (2011)
  3. Vancin S. and Erdem E., “Design and Simulation of Wireless Sensor Network Topologies Using ZigBee Standard”, International Computer Networks and Applications, 2(3):135-143, (2015)
  4. Mihajlov B. and Bogdanoski M., “Overview and analysis of the performances of Zigbee based wireless sensor networks”, International Journal of Computer Applications, 29(12):28-35, (2011)
  5. Wang X. and Zhang S., “Comparison of Several Sensor Deployments in Wireless Sensor Networks”, International Conference on E-Health Networking, Digital Ecosystems and Technologies, 1: 236-239, (2010)
  6. Jimenez V-P-G. and Fernandez M- J., “Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance”, Journal of Sensors, 2015: 1-7, (2015)
  7. Nooralahiyan A-Y-H., Kirby R. and McKeown D., “Vehicle classification by acoustic signature,” Mathematical and Computer Modelling, 27(9):205–214, (1998)
  8. Jo Y. and Jung I., “Analysis of vehicle detection with wsn-based ultrasonic sensors,” Sensors, 14(8): 14050–14069, (2014)
  9. Leitloff J., Rosenbaum D., Kurz F., Meynberg O. and Reinartz P., “An operational system for estimating road traffic information from aerial images,” Remote Sensing, 6(11): 11315– 11341, (2014)
  10. Michael J., Caruso L. and Withanawasam S., “Vehicle detection and compass applications using AMR magnetic sensors”, Honeywell, SSEC, 12001 State Highway 55, Plymouth, MN USA 55441, (1999)
  11. Chen W. and Chen L., "A Realtime Dynamic Traffic Control System Based on Wireless Sensor Network". In Proceedings of the 2005 International Conference on Parallel Processing Workshops (ICPPW’05), 258-264, (2005)
  12. Nadeem T., Dashtinezhad S., Liao C. and Iftode L., TrafficView: “A Scalable Traffic Monitoring System”, IEEE International Conference on Mobile Data Management (MDM’04), 1-14, (2004)
  13. Ng E-H., Tan S-L. and Guzman J-G., “Road traffic monitoring using a wireless vehicle sensor network”, International Symposium on Intelligent Signal Processing and Communication System. Bangkok, Thailand, (2008)
  14. Haijian L., Honghui D., Limin J. and Moyu R., “Vehicle classification with single multi-functional magnetic sensor and o