fieldworkers to monitor traffic in a specified area. The ... wireless sensor network specifically tomotor vehicle ... rely heavily on these wvired sensor networks.
2006 6th Intertional Conference on ITS Telecommunications Proceedings
Monitoring System with PowerEfficient Wireless Sensor etworks
AVLehicular
Jatupom CHINRUNGRUENG, Udompom SUNUNTACHAIKUL, Satien TRIAMLUMLERD 112 Thailand Science Park National Electronics and Computer Technology Center Klong Nueng, Klong Luang Pathumthani 12120, THAILAND
loops, have been brought in for an automatic traffic
Abstract- Efficient and effective vehicular traffic planning and management call for an accurate and up-to-date traffic data. In Bangk-ok the data collection process often involves assigning fieldworkers to monitor traffic in a specified area. The collection process is often time-consuming and inaccurate due to human error. The city has brought in several technologies, such as pneumatic tubes, inductive loops and camera videos, for automatic traffic data collection and monitoring purposes. However, these technologies involve costly installation process and maintenance. It is difficult, or in cases of inductive loops, is impossible to relocate. In this work we study an application of wireless sensor network specifically to motor vehicle monitoring. It delivers several advantages including smaller size, easier installation and maintenance, and relocatable. We propose a star-based topology with a simple polling MIAC protocol. It is shown that power efficiency can be easily achieved by setting MAC parameters appropriately. The energy consumption is calculated. I. INTRODUCTION
statistical data collection. Uniform or slow changing traffic are characterized by these statistical data, which can then be analyzed and used to control traffic liglhts during light and uniform traffic. Rush-hour traffic, however, often changes rapidly due to many unpredictable situations, and are niot characterized well with the statistic data. A real-time automatic traffic data collection must be employed for efficient management of rush-hour traffic. Vanrous technologies have been studied and proposed for real-time collection of traffic data. Research on this topic is considered as part of the Intelligent Transport System research community. Intelligent Transportation System (ITS) is the application of the computers, commuications, and sensor technology to surface transportation. When integrated into infrastructure of the transportation system, and in vehliicles themselves, these technologies provide monitoring and managing traffic flow in order to reduce congestion. Travelers take the best route for their commuting based on traffic information collected real-time. One of ITS main reqnirements is to accurately obtain traffic data, such as nmnber of cars, average speed, throughput, classification and occupanqy. A broad range of sensor technologies, such as inductive loops, ultrasonic devices and video cameras, have been employed with wired connection for both power and signal transmission. Transportation networks in many cities today rely heavily on these wvired sensor networks. However, an installation of wired sensors can be costly and strenuous. For example installinig an inductive loop requires several days of work. Resulting lane closures from installation and maintenance can cause unpleasant traffic congestion. A Wireless Sensor Network (WSN), known for its aptness of smart environment monitonrng, has gain more popularity among ITS commnunity. This is nainiy due to its capability to communicate between a sensor node and a server node (data collection point) via radio frequency. Its wireless feature and small size make the installation process quick and easy. The installation can be finished at night time to avoid traffic congestion due to lane closure. Temporary placement of WSN allows traffic data collection for a few days. Our view is that the WSN can emulate the manner the traffic police handle rush-hour traffic: observing and
Traffic congestion is eveir big city's major concern around the world. It hinders substantial economic and social development. Much of the attempt today has been to find solutions to traffic congestion. It is widely agreed that efficient traffic planming and management often alleviate the congestion to a certain degree. As a fundamental practice duning rush hours, Bangkok often manages the traffic by resorting to the traffic police force. Police officers are dispatched to main streets and junctions to help relieve traffic congestion. Police officers often manually control traffic lights at junctions based on real-time traffic condition being observed and communicated anong them Over their trunked-radio. Traffic data may be collected and analyzed in a statistical fashion. The collection process in Bangkok is carried out by assigning feldworkers to various streets and junctions. These agents then manually count the number and record the type of vehicles passing the assigned locations. In order to obtain average speed, a number of fieldworkers drive their probe vehicles along xvith the traffic, then record average speed, time between txxo assigned locations'. The proceduLres are repeated several ti:mes and data are compiled and presented in a statistic fishion. These manually collected data are not accurate due to human error. Some electronic devices, such as pneumatic tubes and inductive
Information obtained from a visit to the Office of Transport and Traffic Policy and Planning, Bangkok.
0-7803-9586-7/06/$20.00 C 2006 IEEE.
According to the Intelligent Transportation Society of America (ITS America). 2
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analyzing traffic on the spot, disseminating the information over their radio, and utilizing the information to achieve
installation and maintenance process requires strenuous work and needs lane closure for several days or even wxeeks. C. Vdeo Camera This technology is also widely used for traffic monitonrng as it gives video information to users. It gives a real sense of traffic information as if users were observing the traffic themselves. The main advantage is that vehicle count, speed, and classification can all be extracted with software. The accuracy of data depends largely on the extraction software and video quality. High-quality camera systems are therefore very expensive. D. Optoelectronic sensors This type of sensor technology is a semiconductor type that has energy bandgap equivalent to the energy of light (visible and invisible light). It accurately gives vehicle count as it proxides a fast response and can easil) be focused to a small area. Two sensors are required to obtain speed data. Classification can be obtained by measumrng to the vehicle length or heiht. Its small size and low price make it a candidate for WSN.
traffic flow as much as possible. As an ideal model, we automatically collect, analyze and disseminate traffic data for an effective traffic management. In this paper, we describe our design of WSN with application to vehicular monitoring. As the power source (battery) is limiited, it is important that a design of sensor node is power efficient. It must operate for several months or even years in some cases without maintence. As sensor is an essential part of WSN, we survey several existing sensor technologies used in traffic monitoring in Section TI. We also describe their applicability to WSN. In Section IlL1 we describe the design of our WSN focus on the simplicity and powxer efficient aspects. The network design is based on a star-topology that allows us to avoid the complexity of MAC protocol. We also describe our MAC protocol based on a simple power-efficient pOlling protocol. The power requirement for our wireless sensor node is provided. We conclude tliis work in Section IV. II. SURVEY OF SENSOR TECHNOLOGIES FOR VEHICULAR DETECTION
E.
Resistro-mqgnetic
sensors
This sensor is another semiconductor type that its resistance varies according to magnetic field. It passively measures a magnetic field as small as the Earth magnetic field. When a car, whose body consists of metal body, passes through the sensor, it affects the Earth magnetic field in the sensor proximity. This change of the Earth magnetic field can be detected by measuring the change in its resistance. As it has a small size and low price this sensor is another candidate for WSN. It accurately obtains vehicle counts similar to inductive loops. Two sensors are required to obtain speed data. Classification can be achieved by analyzing it magnetic field signature. According to our survey, we found that both optoelectronics sensor and resistor-magnetic sensor are good candidates for WSN. This is due to their small size, good power-efficiency, and low price. Other technologies either require high power consuimption or are too large to fit in a sensor node. In this work, we choose an optoelectronic sensor for our WSN design due to its widespread avallability on the market. TII. WSN FOR MOTOR VEHICLE MONITORING Recently WSN becomes widely adopted in ITS due to its ability to accurately monitor traffic, flexibility, easy deployment and low cost. The complexity of WSNs ranges from simple networks of a few nodes to complex networks of thousand nodes [2]. The complexity of WSNs is also reflected by MAC protocols. To monitor vehicular traffic, an area being monitored is usually small as the trafic data collected in that particular small area is generally a good representative of traffic conditions for at least several hunred meters or even several kilometers. Fig. 1 shows two placemett configurations of WSN: at a junction and on a highway. At the junction, 8 sensor nodes are reqwired to monitor 8 traffic
A sensor that suits WSN must be of a small size and
powxer conscious. It must also have a fast response so it cain detect fast flowing traffic. There are various sensor technologies in use today for monitoring vehicular traffiC. We provide a survey of these sensors in this section. We describe how each technology works and provide if it can obtain the following vital and fumdamental data for traffic managemnent: vehicle count, speed, and classification. Other information such as occupancy and traffic voluine can often be derived from the above data [1]. A. Pneumatic Tube This technology uses rubber air tube for detection of vehicles. It detects a vehicle by detecting pressure put on it. However, the tube wears easily as contact betyeen cars and tube is required for detection. Therefore, it is mostly used for temporarily monitoring of traffic. It also counts the number of axles and therefore adjustment needs to be maade in order to detect the number of cars. Since the number of axles is different for dierent types of vehicles, therefore we can only obtain statistic value for vehicle count. Two tubes can be employed to measure speed. Classification can be obtained according to the axle separation. B. Inductive Loop This technology is the most comnmonly used for vehicular detection today. It consists of a set of electrical coil either installed permanently under the road surface or temporarily above the road surface. Electric current is then fed into the coil in order to generate a strong magnetic field. When a car passes over the coil, its ferrous body chainges the magnetic field which can be detected by measuring the change of electric current in the coil. Inductive loop yields a very accurate vehicle count. Two loops are required to obtai speed. Classification can be achieved by analyzing the electric current signature in the coil. Hoxvever, its
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lanes in all directions. On the highway, 6 sensor nodes are needed. The number is only doubled when speed traps are employed. Therefore, a WSN consisting of 10 or 20 nodes is usnally practical for traffic monitoring application. A senrer node (access point) can be placed at the median strip or on the side in order to collect data from these sensor nodes. In this vork, we will describe a WSN based on a small number of sensor nodes that form a small cluster.
large and cover a large area. Mnlti-hop MAC protocol can save transmission power and extend the battery life. For example, PEDAMACS is a MAC protocol that schedules node transmissions to achieve higher power efficiency [3]. However, like most multi-hop ad-hoc MAC protocols, PEDAMACS is rather complicated that its implementation reqnires a high performance processor. In this work, we focus on a small area monitoring traffic, which reqwires a small nnmber of sensor nodes as mentioned above. The proposed star-based network topology allows us to eliminate the complexity resulting from multi-hop ad-hoc MAC protocols.
.t Server Node
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Figure 1 . Examples of senlsor placement at (lef) junctionl anld (right) 6-lae higjhway. EighAt sensor nlodes are reiquired at the junction, whlile six sensor nofdes at the highway Inl either case, the server nofde is inl proximit to the
sensor node.
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A. Cofiuation ofProposed Monitoring Sytem Fig. 2 sliov%s oulr WSN coiigrtioii. It consists of a sewer noedeJ and a nm:lber of sen:sor noedes, Sin:ce the sensor nodes are placed to mon4itor a small trafic a3rea,: onr network ot a simple sta-based coFigurationm is chosen to base topology ing vhich each senisor node commrmcates directly the repaedt server node.nnio the with Tsmission power canp thereore noe sWltrfi ewr re,Or limited to a loxv level as it involves small area mon:itoring. Sensor Node #1
Optical Sensor or Magnetic
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-1
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-Transmit Data to Server - Clear Counter
Server Node at dPolice booth
Figure 3. Flowchart showing the operation ofthe sensor node.
With onr star-based WSN topology, we propose a simple MAC protocol employing a polling strategy. The operation flow charts of the protocol are shown in Fig. 3 and 4. Without power constraint, the server node schedules sensor nodes in round-robin fashion. Each sensor node will monitor the traffic and only report when schedLuled by the senrer node. Restrained with battery power, the sensor node should be put in sleep mode during wvhich the power reqnirement is very low. It only wakes up and reqnires more power performing one of these twvo functions: vehicle detection or communication to the server node. The timing diagrams are shown in Fig. 5. Fig. 5(a) shoxvs timing diagram that the sensor node wakes up periodically to detect vehicles. It was shown that the minimnm sampling rate was 50 ms in order to detect vehicles traveling at a speed np to 200 kim/hr [2J. At this sampling rate, two successive vehicles must not be closer than 2 m when travel at 200 km/hr so that the sensor node will not misidentify as the same vehicle. The separation between two successive vehicles is less than 2 m when they travel at a lower speed. We assnmed in our calculation that the minimmn length of vehicle is 3 m. The detection dnty cycle lasts for 0.3 ms requinng 200 mA for the photoelectric sensor head and processor core. Otherwise the sensor node goes sleep and requires only 29.4 uA. According to this current consumption and dnty cycle, the average current consumption is therefore 1.23 mA.
.q.oncr
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etecte
Fiber Optic or Wireless Channel to
Centralized Siensor NSlode #22 Database Figure 2. Wireless sensor netxork configuration showing a simple starbased network topology. Server node is located close to every sensor nodes and communicates with center either via wired or wireless channels.
Each sensor node consists of an optoelectronic sensor head, a microcontroller and a radio-frequency transceiver. The microcontroller can be progranmed to control the sensor head and communication via radio-frequency. The server node consists of a microcontroller and a radiofreqnency transceiver. It may feature interfacing to data center either via wired network e.g. fiber optic and modem or via wireless network e.g. GPRS. This feature is useful when data from several clusters of WSN are to be analyzed at the center. Our wireless sensor node features Banner Engineering's Q45BB6DL photoelectric sensor head and Chipcon's cclO10 transceiver set with built-in 8051 processor core. B. AV4C Protocol and Sensor Node 's Power Consuptfion Many researches on WSNs focns on an ad hoc-based communication as the number of sensor niodes is usually
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,Receive Data
After data is transmitted, the sensor node clears the counter, and goes to sleep. Therefore, the sensor node will wake np at most twice before it can send data to the server node. Each time it remains active for at most I sec. The power consumption can only be calculated statistically. Assume that the sensor node commuiicates with the server node every 5 minntes on avTerage. Each traffic data npdate reqnires at most 2 sec. Therefore, the power consumption is 296 uA. The total average power reqnirement is 1.53 mA. It lasts about 50 days on a 1800 mA-hr battery [1]. The battery life can be extended if a higher power-efficient sensor head is employed. Our calculation based on resistor-magnetic sensor showed that the system could last longer than 5 years
No
Yes
Update Data at Server
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Figure 4. Flowchart showing the operation of the sei-ver node.
The other sonrce of powxer load is when the sensor node commnncates with the server node in order to tranismit
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collected traffic data. When the sensor node wants to commnnmcate, the transceiver and processor core must be active reqnirinng a maximnmu of 40 mA. Fig. 5(b) shows timing diagrWa that the sensor node wakes np to commnnmcate with the sewer node. Consider a WSN consisting of n sensor nodes and a server node. Each sensor node is assigned an ID from I to n. The server node polls all n sensor nodes in rommd-robin fashion starting from sensor node 1; the polling period is I sec to allow for switching time between transmitter mode and receiver mode on both sensor node and server node. Sensor node m is alloxxed to send data only when a polling packet with ID m is received. To save poWer, the sensor node does not actively listen to the server node's polling schedule. It only wakes np and
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Qunt Chwnpim (MA) 14-
Im) Thms(ns)
(a)
m
01
(b)
m
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Figure 5. Current Consumption Timing diagrams: (a) sensor node viakes up every 50 ms to monitor traffic consuming 200mA during 0.3 ms active and 29.4 us duLring sleep mode. Average cui ent consumption is 1.23 mnA; (b) sensor node wakes up when commtunicate to senrer. It wakes up at mtost twice fbr each communrication.
listens to the poll when it wants to communicate with the server. The sensor node is not required to transmit data imnediately. Instead, the sensor node accnmulates traffic data before each transmission. As we collect vehicle count, the sensor node reports to the server node only when enongl vehicle count exceeds a limit, for example 50 vehicles. However during light traffic, it may take too long before a specified nwmber of vehicles (50 cars) has passed, and the npdating of data may be ont of date. To prevent this delay, we also set a time limit to keep the sensor node from being idle too long. For example, each sensor node mnlst report to the server node at least every 10 minntes. In other words, the period of npdating traffic data will not be longer than 10
IV.
CONCLUSION
We have demonstrated an application of WSN to vehicle monitoring. Onr WSN nettwork design is based on a star-base topology conpled with a simple polling MAC protocol. It was shown that power consumption can be lowered by having the node accumulating traffic data before commmimicating with the server. Our simple WSN provide a simple platform which traffic data collection, specifically vehicle count, can be obtained effectively. The sensor node lasts about 50 days on 1800 mA-hr battery. A higher power-efficient sensor head can further lengthen battery life. Future work includes study on applying resistor-magnetic sensor to our system.
minntes dunring light traffic. Dnring rush hotr the npdate period will be shorter. As traffic condition during light hour nsnally changes slowly, therefore real-time update of data may not be necessary. Updating data once every I to 10 minntes will be siffcicent in most cases. When either of the two above coniditions is met, the sensor node wakes np anid listens for a polling packet. it then checks the ID specified in the packet is its ID. If so, it starts sending its data to the server node. If not, it synchronizes with the polling packet sequence using the detected ID and goes to sleep. It wakes up again xvhen the polling packet with its ID is expected. It then sends data.
REFERENCES
Jatuporn ChinruLngrueng, "WAireless Sensor Network- for Vehicular Monitoring Application," Nectec's Technical Report, 2006 [2] Ian F. Akyildiz, Weilian Su, Yogesh Sankarasuhramaniam, and Erdal Cayirci, "A Sui-vey on Sensor Networks," IEEE Communications Magazine, August 2002, pp. 102-114. [3] Sinem Coleri and Pravin Varaiya, 'PEDAMACS: Power Efficient and Delay Aware Medium Access Protocol for Sensor Networks (July 1, 2004). CaIfornia Partners for Advanced Transit and Highways (PATH). otrking Ropers: Paper UCB-ITS-PWP-2004-6. [1]
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