development of wireless sensor network for traffic

International Journal of Reconfigurable and Embedded Systems (IJRES) Vol. 3, No. 3, November 2014, pp. 119~132 ISSN: 2089-4864 119

Journal homepage: http://iaesjournal.com/online/index.php/IJRES

Development of Wireless Sensor Network for Traffic Monitoring Systems

Sanket Dessai*, N.D. Gangadhar*, M.P. Bheema Rao* * Departement of Computer Engineering, M.S. Ramaiah School of Advanced Studies, Bangalore

Article Info ABSTRACT

Article history:

Received May 14, 2013 Revised Jul 25, 2013 Accepted Aug 18, 2013

Traffic congestion has been a major problem on roads around the world. In addition, there is increase in volume of traffic vehicle density at a steady rate. Thus traffic on major roads has to be controlled to keep the traffic flowing at an acceptable rate. Several schemes for replacing the predominantly used Round Robin (RR) scheme for reducing congestion at traffic junctions have been proposed. Dynamic traffic control schemes adapt to the changing traffic by monitoring the state (such as the number queued up on each lane). These need appropriate sensing and monitoring systems. In this paper a traffic monitoring and control system based on AMR (Anistropic Magneto Resistive) vehicle sensors, wireless sensor network and a proiritised Weighted Round Robin (WRR) scheduling technique, is developed.AMR sensors installed in road pavement detect the number of vehicles waiting in a traffic lane. The AMR sensors are connected to the master controller to form a Zigbee based sensor network. The master node consists of an ARM processor integrated with a Zigbee masternode. The traffic control algorithm is implemented at master node which is responsible for taking traffic signaling decision. It receives sensor data from all the lanes. A two level priority algorithm with weighted round robin scheduling, where first and second maximum weighted lane are to pass the signal is developed, To avoid starving the least loaded lanes, a cycle of normal round robin scheduling is performed after four rounds of proiritised weighted round robin schedule. The proposed algorithm is simulated and compared with the standard round robin algorithm. The developed algorithm decreases the average waiting time for a commuter while maintaining the average throughput up to average loads. The development traffic monitoring system is successfully demonstrated for a four lane junction.

Keyword:

AMR ARM RR WRR Zigbee

Copyright © 2014 Institute of Advanced Engineering and Science. All rights reserved.

Corresponding Author:

Sanket Dessai, Departement of Computer Engineering, M.S. Ramaiah School of Advanced Studies, #470-P,Peenya Industrial Area,4th Phase Bangalore 560058, Karnataka,India Email: [email protected]

1. INTRODUCTION

The gathering of traffic information is a base for all kinds of traffic modelling, simulation and prediction for tasks like emission reduction, efficient use of infrastructure or extension planning of the road network as well as the intervention and resource planning. Auto accidents injure at least 10 million people each year, and two or three million of them seriously. The hospital bill, damaged property, and other costs are expected to add up to 1%-3% of the world’s gross domestic product. With the aim of reducing injury and accident severity, pre-crash sensing is becoming an area of active research among automotive manufacturers, suppliers and universities. Vehicle accident statistics disclose that the main threats a driver is facing are from other vehicles. In these systems, robust and reliable vehicle detection is the first step. A successful vehicle detection algorithm will pave the way for vehicle recognition, vehicle tracking, and collision avoidance.

ISSN: 2089-4864

IJRES Vol. 3, No. 3, November 2014 : 119 – 132

120

Current highway monitoring applications require the installation of bulky magnetic loop detectors or single mount high resolution video cameras in order to detect passing vehicles. These devices are both cumbersome and expensive to install and maintain. It is proposed that sensor networks be used as a replacement for these current technologies, since these are much simpler to install and may be less costly to maintain in the long run. The traffic parameters on the different sections of the road are not uniform, because the propagation of traffic flow is vulnerable to the influence of drivers’ personality and skill, pedestrians crossing the roads, intersections of minor roads, accidents and so on. The precision and robustness of traditional traffic forecasting methods cannot meet the requirements of developing traffic control and guidance technologies.

Figure 1. Sensors used for Road Traffic Monitiring (Carlos Sun 2000) Currently, road-traffic monitoring relies on the technology of sensors based on radar, microwaves,

tubes or loop detectors as shown in Figure 1. Radar: For accurately measuring vehicle speed Microwave Detectors: These are usually mounted on a bridge or gantry such that they point vertically down over a lane of traffic. The device emits microwaves which are reflected on the road surface and bounced back towards the sensor. A vehicle passing under the sensor will cause interference to the reflected microwaves which enables the vehicle to be detected. Tubes: A rubber tube fixed to the road surface across the width of a lane of traffic forms the basis of this sensor. One end of the tube is closed and the other is connected to a pressure sensor. As each wheel of a vehicle runs over the tube it causes a pressure. Wireless sensor networks (WSN) are new integrative technologies arising from the development of wireless communication and tiny sensors. WSN is a kind of monitoring networks consisting of a large number of low-cost, power-saving, highly integrative and self-organized sensor nodes and network coordinators. WSNs own broad and valuable application outlook including military, urban management, biomedical treatment, environmental monitoring and remote monitoring of dangerous areas. WSNs installed on roads in a sweeping manner can not only obtain the traffic flow parameters of the entries and exits of intersections, but also of the forks, crosswalks, bus stations and other special places. Therefore, with WSNs covering road networks over a great area, the globe traffic information can be observed in details in the traffic monitoring centre. This trend will certainly bring great breakthroughs in the traffic monitoring technologies.

Typically, sensor nodes are organized as sensing nodes and aggregator nodes. While a sensing node is responsible for sensing data, an aggregator node is used to process data from sensing nodes and send

IJRES ISSN: 2088-8708

Development of Wireless Sensor Network for Traffic Monitoring Systems (Sanket Dessai)

121

results data to a base station. A base station is used to collect data from the entire sensor network and reports the data to an end user.

The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms.

Figure 2. The Architecture of the Hybrid Node (Wallin 2004)

Figure 2 shows that the top and middle layers of the architecture are consistent to present traffic information network. The bottom layer makes up of WSNs, which are very flexible. With the rapid developing WSN technologies, all kinds of information of the physical world around us will be transferred to the present information system at a fine level and with high speed. As a result, the urban traffic network with distributed parameters will become more measurable and controllable. The advantage of using WSN 1) WSN can monitor and evaluate the roads automatically and continuously, with little human effort 2) WSN can work in nights and abominable weather, when there is fog or dust 3) WSN is able to accurately record the traffic flow data of the road for further analysis which is hard for video cameras 4) WSN is becoming cheaper and can be deployed in a fine-grained mode for real time and “real space” traffic monitoring

2. DESIGN Functional block diagram of the Traffic Monitoring system is shown in figure 3; the system has

mainly two blocks of master node and slave node. Master node consists of ARM based processor capable of performing multitasking operations.

Slave node consists of vehicle detection sensor and zigbee RF module. Sensor collects the vehicle detected data and sends to master node through RF module. Based on efficient algorithm Master node will allow the vehicle denser lane to be cleared Traffic Monitoring System block consists of a Junction with 4 Lanes (North Lane, South Lane, East Lane, West Lane). Magnetic Sensors are mounted on each Lane in regular fashion based on efficient sensing of the vehicle.Slave controller consists of two zones zone-1 and zone-2, Zone-1 is connected to North and West lane sensors and Zone-2 is connected to South and East Lane Sensors. Vehicles passing from these lanes are detected through sensors and sensor data are sending to zigbee modules connected at two zones. Master node consists of RF Transceiver and Traffic signal controlling is done using efficient algorithm. RF data sent from slave (zone-1 and zone-2) is collected by master depending on the sensor weight active decision is taken as a result the lane with high density is allowed to pass.

ISSN: 2089-4864


122

Figure 3. Block Diagram of Traffic Monitoring Systems

a. Hardware Design and Implementation As shown in Figure 4 high level design of traffic monitoring system consist of a junction where four

lanes meets and it is represented as North Lane, South Lane, East lane and West lane. Each lane will be having vehicle detection sensor named as NS (north sensor), SS (south sensor), ES (east sensor), WS (east sensor) in turn the corresponding lane traffic light are represent as TL-N, TL-S, TL-E, TL-W. As algorithm demands it has been sub divided into four lane junction with two zones as zone-1 and zone-2, zone-1 correspond to north and west lane similarly zone-2 corresponds to south and east lane.

This section consist of implementing master node which includes ARM processor, Zigbee receiver and Traffic controlling signals as shown in Figure 5. Samsung S3C2440A (ARM920T) based ARM board mini2440 has been used which runs at 533 MHz, has 64M SDRAM, 128M Nand Flash, 2M Nor Flash with BIOS installed, S3C2440 support 2 boot mode Nand Flash boot and Nor Flash boot. Memory map and chip selection is different based on different boot mode. It supports OS porting compatible with Linux 2.6, Android and WinCE.

Figure 4. High Level Design of the Traffic Monitoring Systems



123

Figure 5. Block Diagram of the Master Node

Figure 6.a. Master Node Flow Chart Master node flow chart has been shown in figure 6.a to 6.d. First all the initializations for the port

pins are done and other necessary settings like UART, sensor port are done. Next step is to receiving RF data from zone-1 and zone-2 slave nodes, there is particular format followed in recognizing the exact zone data, if start of packet is ‘$’ then data belongs to zone-1 and if start of packet is ‘#’ then data belongs to zone-2.Now once data is received, master controller will decode the packet and calculate for first maximum weighted lane and then second maximum weighted lane. As a result the lane particular to first maximum lane is allowed first and then second maximum lane is allowed to pass next.

ISSN: 2089-4864


124

Figure 6.b. Block Diagram of Master Node

Figure 6.c. Block Diagram of Master Node



125

Figure 6.d. Block Diagram of Master Node

b. Algorithmic Implementation This section explains about the algorithm implemented in performing efficient traffic monitoring

and signaling. As explained in earlier section the system consist of three weighted sensors located in each lane and there is traffic signal (Red, Yellow, Green) corresponding to each lane. By default the traffic signal passing sequence followed at the junction is North, East, West and South lane i.e ‘N’ ‘E’ ‘W’‘S’ this is showed in Figure 8 and 9 representing Round robin sequence and weighted round robin. Figure 7 shows the zones and its cycles of traffic flow for sender and master data reception with round robin and weighted round robin algorithm.

Figure 7. Round Robin Algorithm and Weighted Round Robin Algorithm

ISSN: 2089-4864


126

Figure 8. Flow Diagram Representing Round Robin Algorithm

Figure 9. Flow Diagram Representing Weighted Round Robin Algorithm

c. Schematic Circuit Diagram for Master Node Algorithmic Implementation The schematic circuit diagram of the Traffic monitoring system for master node is shown

in Figure 10 and 11. It consist of connecting zigbee module through UART port from ARM board and traffic signal lights are connected to port pins of ARM board.



127

Figure 10. Schematic Diagram for Master Node

Figure 11. Schematic Diagram for Master Node

ISSN: 2089-4864


128

3. RESULTS AND ANALYSIS Figure 12 shows slave zone node along with sensor and added weight, there are three magnetic

sensors which are in yellow color as shown which is interfaced to zone-1 PIC microcontroller through GPIO, when any metal piece is detected it sends the data. For experimental process metal washer and metal slab piece is used. Once the complete set-up is ready and working properly, we can ensure the wireless module to be working in good condition by checking and diagnosing through HyperTerminal set-up. The master will schedule the signaling process of allowing the vehicles based on sensor input below figure 14 shows North, South, East, West Weight and its corresponding traffic light signals showing in HyperTerminal and figure 15 shows the zone-1 one received data on hyperterminal. Vehicles are been sensed through sensor laid on pavement from slave node, it frames the collected RF data and sends to the master for further processing the RF data been collected is shown in Figure 13. Based on the weights shown the Master controller will allow the signal for the lane in sequence of East lane then South Lane, then North Lane, then West Lane, this sequence is followed for four cycles then one Round Robin is executed.

Figure 12. Implementation of the Traffic Monitoring System Setup

Figure 13. Implementation of the Slave Node with the Weighted Sensor



129

Figure 14. Results on Hyperterminal

Figure 15. Zone-1 Wireless Sensor Data

As explained the process of performing average queuing theory, average waiting time and performance throughput had been calculated. To represent the calculated performance and waiting time between RR and WRR system process graphs are been plotted as shown below. Figure 14 and 15.showing the sensor data at the hyperterminal of the Zone-1 sensor data. The below equations are been used to plot the graph Average arrival rate lambda λ = (average input / Cycle time)

ISSN: 2089-4864


130

Average input =1/2 Peak input Average waiting time Wi = Average Left over vehicles / lambda λ Cycle time is constant for RR and WRR since the average signaling duration is same for both Figure 16 shows the graph for average waiting time in the system for round robin and weighted round robin implementation X-axis represents average arrival rate vehicles per second and Y-axis represents average waiting time per seconds. Similarly system throughput graph is also plotted as shown in Figure 17, throughput is calculated by using equation shown below Throughput of system = average No of vehicle allowed / Average arrival rate lambda λ.

Figure 16. Comparision based on the average Average waiting Time

Figure 17. Comparision based on the Throughput Performance

‐200

0

200

400

600

800

1000

1200

1400

1600

0 0.05 0.1 0.15 0.2 0.25 0.3

Avg

. Wai

tin

g T

ime

(s)

Avg. Arrival Rate (Vehicles/s)

Avg Waiting Time

RR

WRR

0

50

100

150

200

250

300

350

400

450

500

0 0.05 0.1 0.15 0.2

Avg

. No

of v

ehic

les

allo

wed

Avg. Arrival Rate (Vehicles/s)

Throughput Performance

RR

WRR



131

4. CONCLUSION Traffic Monitoring system for four Lane Junction has been demonstrated successfully. An efficient

wireless sensor networked algorithm based has been implemented (weighted Round robin) on master-slave controller. The following conclusions are Vehicle density is one of the main metrics used for assessing road traffic condition In the traffic information system, the flow rate is obtained by counting the number of vehicles that pass

through a detection device, such as an inductive loop detector or a surveillance camera, over a period of time

Manual Traffic signal can be overcome Wireless nodes can be used to send collected vehicle detection sensor data Using the Efficient Algorithm, waiting time at traffic signal junction for commuter can be reduced The following enhancements of the system can be considered for future: Ambulance and VIP vehicles priority should be considered by using wireless technology at master node. Placement and installing the clusters of magnetic sensor at road pavement in order to detect the vehicle

accurately. Vehicle detection method can be improved in order to identify what kind of vehicle it is and length of

vehicle (car, truck, LMV’s). REFERENCES [1] Allan R.H., “Introduction to Electronics”, Prentice Hall. Upper Saddle River, New Jersey, 2nd Edition, 2002. [2] Carlos S., “An Investigation in the Use of Inductive Loop Signatures for Vehicle Classification,” Research Reports,

Institute of Transportation Studies, PATH, University of California, Berkeley, 2000. [3] Cirronet, “White paper on ZigBee Wireless Transceiver Engineering Options”, April, 2005. [4] Datasheet for CC2500 Based Wireless Module available from

http://www.robosoftsystems.co.in/roboshop/media/catalog/product/pdf/RFpro%20Module%20-%20how%20to%20use%20v4%20.pdf (2010). Accessed September 2010.

[5] Ding. J, Cheung S.Y., Tan C.W. and Varaiya P, “Signal processing of sensor node data for vehicle detection”, IEEE Intelligent Transportation Systems Conf., 2004, pp. 70-75. 2004.

[6] Englund and Wallin. “RF in Wireless Sensor Network, Technical Report”, Department of Signals and Systems, Chalmers University Of Technology, Sweden, 2004.

[7] Gordon, R.L., Reiss R.A., Haenel H., Case E.R., French R.L, Mohaddes A., and Wolcott R., “Traffic Control Systems Handbook”, FHWA-SA-95-032, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C., Feb., 1996.

[8] Gutierrez. J.A., “On the use of IEEE 802.15.4 to enable wireless sensor networks in building automation”, Proc.of IEEE Int. Conf. Personal, Indoor and Mobile Radio Communications (PIMRC’04), Barcelona, Vol 3, pp.1865-1869, 2004.

[9] Hong-Zhong H., Backens, J., Min Song., “A High-Performance Vehicle Detection Algorithm for Wireless Sensor Parking Systems”, Mobile Ad-hoc and Sensor Networks, 2009. MSN '09. 5th International Conference, pp.327 – 333, 2009.

[10] Jiagen (Jason) D., Sing-Yiu C., Chin-woo T., and Pravin V., “Vehicle Detection by Sensor Network Nodes PATH Report”, UCB-ITS-PRR-2004-39, 2004.

[11] Jinhui L., Yuqiao S., “Vehicle detection and recognition based on a MEMS magnetic sensor, Nano/Micro Engineered and Molecular Systems”, NEMS 2009. 4th IEEE International Conference, 404 – 408, 2009.

[12] Kanathantip P., Kumwilaisak W., Chinrungrueng J., “Robust Vehicle Detection Algorithm with Magnetic Sensor”, ECTI-CON 2010, 1060 – 1064, 2010.

[13] Kell, J.H., and Fullerton I.J., “Traffic Detector Handbook”, Second Edition, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C., 1990.

[14] Kiratiratanapruk, K., Siddhichai, S., “Vehicle Detection and Tracking for Traffic Monitoring System”, TENCON 2006. 2006 IEEE Region 10 Conference, 2006.

[15] Kistler Instrumente A.G., “Weigh In Motion, The First System for Monitoring at Any Speed”, Winterthur, Switzerland, October, 1997.

[16] Klein, L.A., “Data Requirements and Sensor Technologies for ITS”, Norwood, MA, Artech House, 2001. [17] Kranig J., Minge E., and Jones C., “Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive

Technologies”, FHWA-PL-97-018, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C., May, 1997.

[18] Yongfang L. and Haitao L., “Optimization and Simulation of Wireless Sensor Networks Routing Algorithm Based on ZigBee”, Proceedings of the Third International Symposium on Computer Science and Computational Technology (ISCSCT ’10) Jiaozuo, P. R. China, 14-15, August 2010, pp. 319-321, 2010.

[19] MacCarley, C.A., Hockaday S., Need D., Taff S., “Evaluation of Video Image Processing Systems for Traffic Detection”, Transportation Research Record No. 1360, National Research Council, Washington D.C., 1992.

[20] McErlean and Narayanan S., “Distributed detection and tracking in sensor networks”, in Proc. ASILOMAR 2002, 1174-1178, 2002.

ISSN: 2089-4864


132

[21] Mini2440 Friendly Arm Board available from, http://www.friendlyarm.net/dl.php?file=mini2440_manual.pdf (2011). Accessed 03 March 2011.

[22] Sampey, H.R., “Vehicle Magnetic Imaging, Nu-Metrics”, Vanderbilt, PA, May 1994. News, 4. Oct. 6, 1999. [23] Samuel M., Michael J.F., Joseph M.H., Wei H.T., “A Tiny Aggregation Service for Ad-Hoc Sensor Networks”, 5th

Symposium on Operating System Design and Implementation, 2002. [24] Sing-Yiu C., and Pravin V., “Traffic Surveillance by Wireless Sensor, Research Reports”, Institute of

Transportation Studies, PATH, University of California, Berkeley, 2007. [25] Sun. Z., Bebies G., and Miller R., “On-road vehicle detection using optical sensors: a review”, IEEE Intelligent

Transportation Systems Conf., pp.585-590, 2004. [26] Tamersoy, B., Aggarwal, J.K., “Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using

Unsupervised Learning, 2009”, AVSS '09. Sixth IEEE International Conference, pp.529 – 534, 2009. [27] Tubaishat, M., Shang, Y., Hongchi S., “Adaptive Traffic Light Control with Wireless Sensor Networks”, Consumer

Communications and Networking Conference, CCNC 2007. 4th IEEE, 2007. [28] Wen. Y., Pan J.L., and LE J.F., “Survey on Application of Wireless Sensor Networks for Traffic Monitoring”, ASCE

Conference Proceedings, pp.246-340, 2007. [29] Xiaoguang N., Huang X., Zhao Z., Yuhe Z., “The Design and Evaluation of a Wireless Sensor Network for Mine

Safety Monitoring”, Global Telecommunications Conference, GLOBECOM '07. IEEE, 2007. [30] Yan N., Yinjiang Z., and Lala L., “Road Monitoring and Traffic Control System Design”, Information Engineering

and Computer Science, 2009. ICIECS 2009, 2009. [31] Yifei W., Dahnoun N., Achim., A novel Lane Feature Extraction Algorithm Implemented on the TMS320DM6437

DSP Platform, Digital Signal Processing, 2009 16th International Conference, pp. 1–6, 2009.

development of wireless sensor network for traffic

Documents