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

Year 2018, Volume: 21 Issue: 2, 333 - 340, 01.06.2018

Sercan Vançin Ebubekir Erdem

https://doi.org/10.2339/politeknik.389586

Cited By: 1

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. 

Keywords

Wireless sensor networks, magnetic sensor, adaptive threshold detection algorithm (ATDA), vehicle direction detection

References

• [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 optimal MNS-based CART”, Measurement, 55: 142-152, (2014).
• [15] Lifu W., Nong Z. and Haiping D., “Real-time identification of vehicle motion-modes using neural networks”, Mechanical Systems and Signal Processing, 51: 632-645, (2015).
• [16] Nabeel M-M., EI-Dien F-M. and EI-Kader A-S. “Intelligent vehicle recognition based on wireless sensor network”. Int. J. Comput. Sci. 10:164–174, (2013).
• [17] Padmavathi G., Shanmugapriya D. and Kalaivani M., “A study on vehicle detection and tracking using wireless sensor networks”, Wirel. Sens. Netw., 2: 173–185, (2010).
• [18] Arbabi H. and Weigle C-M. “Monitoring free flow traffic using vehicular networks”. In Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 272–276, 9–12 January 2011.
• [19] Wang Y., Wu F. and Tseng Y., “Mobility management algorithms and applications for mobile sensor networks”. Wirel. Commun. Mobile Comput., 12: 7–21,( 2012).
• [20] Chakraborty S.P., Nair P., RajSinha P. and Ishan Kumar B., “Real time optimized traffic management algorithm”. Int. J. Comput. Sci. Inf. Technol., 6: 119–136, (2014).
• [21] Potdar M. and Wani S., "Wireless Sensor Network in Vehicles," SAE Technical Paper 2015-01-0241, https://doi.org/10.4271/2015-01-0241, (2015).
• [22] Sifuentes E., Casas O. and Pallas-Areny R., “Wireless magnetic sensor node for vehicle detection with optical wake-up,” IEEE Sensors Journal, 11(8), 1669–1676, (2011).
• [23] Muheden K., Erdem, E. and Vançin S., “Design and implementation of the mobile fire alarm system using wireless sensor networks”, In Computational Intelligence and Informatics (CINTI), 2016 IEEE 17th International Symposium on (pp. 000243-000246). IEEE., 243-246, (2016).
• [24] Dener M., "WiSeN: A new sensor node for smart with wireless sensor networks", Computers and Electrical Engineering, 64: 380-394, (2017).
• [25] Cheung S., Y. and Varaiya P.,“Traffic Wireless Sensor Networks: Final Report”, California PATH Research Report UCB-ITS-PWP-2004-7, (2004).
• [26] Vancin S. and Erdem E., “Implementation of the vehicle detection system with adaptive threshold algorithm in wireless sensor networks”, International Artifical Intelligence and Data Mining Symposium, Inonu University, Malatya, (2016).
• [27] Vancin S. and Erdem E., “Implementation of the vehicle recognition systems using wireless magnetic sensors”. Sadhana Springer, 42(6): 841-854, (2017).