Sensor Networks for Automobile Tracking and Routing

By

Seong-Moo Yoo, Ph.D. Associate Professor

Electrical and Computer Engineering Department The University of Alabama in Huntsville

Huntsville, AL 35899

Prepared by

UTCA

University Transportation Center for Alabama The University of Alabama, The University of Alabama at Birmingham,

and The University of Alabama in Huntsville

UTCA Report 03303 March 2004

ii

Technical Report Documentation Page

1. Report No FHWA/CA/OR-

2. Government Accession No. 3. Recipient Catalog No.

5. Report Date March 2004

4. Title and Subtitle Sensor Networks for Automobile Routing

6. Performing Organization Code 7. Authors

Seong-Moo Yoo, Ph.D.

8. Performing Organization Report No. UTCA Report 03303

10. Work Unit No.

9. Performing Organization Name and Address Electrical and Computer Engineering Department College of Engineering The University of Alabama in Huntsville Huntsville, Alabama 35899

11. Contract or Grant No. DTSR 0023424 13. Type of Report and Period Covered

Final Report: January 2003 – March 2004

12. Sponsoring Agency Name and Address University Transportation Center for Alabama The University of Alabama P.O. Box 870205 Tuscaloosa, AL 35487-0205

14. Sponsoring Agency Code

15. Supplementary Notes 16. Abstract Wireless sensor networking is an emerging technology that has a wide range of potential applications including environment monitoring, smart spaces, medical systems and robotic exploration. In this report the researcher tried to present the intermediate result on the design of traffic control systems related to automobile tracking based on the wireless sensor networks. Crossbow’s sensor network developer’s kit (Crossbow Technology, 2003) was used as the hardware and TinyOS (University of California Berkeley, 2003), an operating system for wireless sensors, was used as the software. The Crossbow Mica mote was used as a node in the sensor network. The motes were programmed using NesC language. All data collected by the sensor nodes was aggregated at a base station. The base station also enabled connection to a PC or a computer platform. The Crossbow MPR300CB processor/radio board acted as a base station. This report presents the preliminary implementations of three sub-systems: (1) safe vehicular passing, (2) traffic violation detection, and (3) automobile tracking. Safe vehicular passing was developed to serve rural roads where many accidents happen during passing maneuvers. This application allows the users to acquire knowledge of the surroundings to enhance vehicular passing safety. Traffic violation detection was developed to capture traffic violations with minimal costs. In this application light sensors were used to either count traffic or to detect a violation. Many assumptions were made when developing the automobile tracking system, but these assumptions can be implemented with the sophisticated equipment available. This research offers three theoretical examples of how the device might be used, and even though the researcher realizes that these examples do not have “real world” applications because of legal, traffic operations and other issues, they do illustrate use of the device. 17. Key Words automobile tracking, safe vehicular passing, traffic violation detection, wireless sensor networks.

18. Distribution Statement

19. Security Classification (of this report) Unclassified

20. Security Classification (of this page) Unclassified

21. No of Pages 19

22. Price

Form DOT F 1700.7 (8-72)

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Contents

Contents ..………………………………………………………………………………….. iii List of Figures ...…………………………………………………………………………... iv Executive Summary……….……………………………………………………………… v

1.0 Introduction, Problem Statement, Overall Project Approach …….…………………. 1 1.1 Introduction …………………………………….………………………………. 1 1.2 Problem Statement ……………………………………….……………………… 1 1.3 Overall Project Approach ………………………………………………….……. 2 2.0 Methodology …………………………………………………………………………. 3 2.1 Hardware and Software Used …………………………………………………… 3 3.0 Project Findings and Results…………………………………………………………. 4 3.1 Safe Vehicular Passing …………………………………………………………. 4 3.1.1 Background ………………………………………………………………. 4 3.1.2 Design Overview ………………………………………………………… 4 3.1.3 Preliminary Implementation …………………………………………….. 5 3.2 Traffic Violation Detection …………………………………………………….. 7 3.2.1 Background ………………………………………………………………. 7 3.2.2 Design Overview ……………………………………………………….. 7 3.2.3 Preliminary Implementation …………………………………………….. 7 3.3 Automobile Tracking ………………………………………………………………… 8 3.3.1 Background ………………………………………………………………. 8 3.3.2 Design Overview ……………………………………………………….. 9 3.3.3 Simulation ……………………………………………………………. 10 3.3.4 Preliminary Implementation ……………………………………………. 13 4.0 Project Conclusions and Recommendations ……………………………..………….. 14

4.1 Conclusions ……………………………………………………………………… 14 4.2 Recommendations ……………………………………………………………….. 15 5.0 References ……………………………………………………………………………. 16

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List of Figures

Number Page

3-1 Design overview of safe vehicular passing 5 3-2 A flow chart for safe vehicular passing 6 3-3 A flow chart of the algorithm for traffic violation detection 8 3-4 An overview of automobile tracking 9 3-5 Pseudo code of the automobile tracking algorithm 11 3-6 System tracking times 12 3-7 Power consumption 12

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Executive Summary Wireless sensor networking is an emerging technology that has a wide range of potential applications including environmental monitoring, smart spaces, medical systems and robotic exploration. However, there is not much research on traffic-related security systems using sensor networks. In this report the researcher tried to present the intermediate result on the design of traffic control systems related to automobile tracking based on the wireless sensor networks. Crossbow’s sensor network developer’s kit (Crossbow Technology, 2003) was used as the hardware, and TinyOS (University of California Berkeley, 2003), an operating system for wireless sensors, was used as the software. The Crossbow Mica mote was used as a node in the sensor network. The motes were programmed using NesC language. All data collected by the sensor nodes was aggregated at a base station. The base station also enabled connection to a PC or a computer platform to transfer the data or to process the data for further applications. The Crossbow’s MPR300CB processor/radio board, plugged into a mote interface board, acted as a base station. This report presents the preliminary implementations of three sub-systems: (1) safe vehicular passing, (2) traffic violation detection, and (3) automobile tracking. Safe vehicular passing was developed to serve rural roads where many accidents happen during passing maneuvers. This application allows the users to acquire knowledge of the surroundings to enhance vehicular passing safety. Traffic violation detection was developed to capture traffic violations with minimal costs. In this application light sensors were used to either count traffic or to detect a violation. The light sensors can be replaced with acceleration/motion sensors which would provide more accurate results. Many assumptions were made when developing the automobile tracking system, but these assumptions can be implemented with the sophisticated equipment available today. This research offered three theoretical examples of how the device might be used, and even though the researcher realizes that these examples do not have “real world” applications because of legal, traffic operations and other issues, they do illustrate use of the device.

1

Section 1.0

Introduction, Problem Statement, Overall Project Approach 1.1 Introduction

Mobility Technology (Mobility Technology, 2003) is creating an efficient, innovative traffic information system for the movement of people, goods, and services. In the United States, an estimated $75 billion is lost annually due to traffic congestion, so Mobility Technology developed Mobility Solutions, which employs an advanced Intelligent Transportation Systems (ITS) technology to meet the needs of businesses, government agencies, and consumers. They are constructing a wireless digital sensor network for traffic data collection. The Digital Traffic Pulse Sensor Network is the first web-based, distributed ITS information system designed to serve tens of millions of concurrent users. Installed along major highways, the network collects key traffic information including vehicle speeds, counts, and density, and transmits the data over a wireless network every 60 seconds to a data center. This is one example of how wireless sensor networks are applied to industrial and commercial environments.

1.2 Problem Statement Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered over short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, leverage the idea of sensor networks. Wireless sensor networking is an emerging technology that has a wide range of potential applications including environmental monitoring, smart spaces, medical systems and robotic exploration. While the technology is very promising, it raises serious challenges in network and system design. Sensor networks differ in many ways from the traditional Internet protocol (IP) or voice networks, and have unique features and requirements. During the last three years, much research has been done on sensor networks (Akyildiz et al., 2002, Chen et al., 2002, Deverapalli, 2003). Applications of sensor networks are being developed in a wide range of industrial and commercial environments. However, there is not much research, to the knowledge of the researchers, on traffic-related security systems using sensor networks incorporated in the Mobility Technology sensor networks. Traffic management in large cities is not easy and, consequently, control of traffic is needed to reduce the possibility of traffic accidents and to improve traffic flow. Using a wireless sensor network may be a possible solution in the near future. For example, wireless intelligent sensors could be installed on top of traffic signals throughout a metropolitan area. These sensors could coordinate with each other to detect congested roadways, accidents, and stranded motorists. Emergency crews could use this aggregated information for early identification of accidents, and motorists could use this information to minimize their travel time. The sensor networks could also make it

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possible to locate a specific vehicle or moving object, and to monitor its movement with integrated results from more than one type of sensor (images from a camera, vibration from a seismic sensor, noise from an audio sensor, etc.). 1.3 Overall Project Approach This project developed applications closely associated with automobile security, which can complement the Mobility Technology sensor network. Trial implementations were made on three sub-systems: (1) safe vehicular passing, (2) traffic violation detection, and (3) automobile tracking. This report presents the intermediate results of the design of traffic-control-system-related automobile tracking based on wireless sensor networks.

3

Section 2.0

Methodology 2.1 Hardware and Software Used Crossbow’s sensor network developer’s kit (Crossbow Technology, 2003) was used as the hardware, and TinyOS (University of California Berkeley, 2003), an operating system for wireless sensors, was used as the software. The Crossbow Mica mote was used as a node in the sensor network. The motes were programmed using NesC language. The Crossbow MTS300CA sensor board has a Photo diode, thermistor, microphone and sounder. All the data collected by the sensor nodes is aggregated at a base station. The base station also enables connection to a PC or a computer platform to transfer the data or to process it for further applications. The Crossbow MPR300CB processor/radio board, plugged into a mote interface board, acted as a base station.

4

Section 3.0

Project Findings and Results 3.1 Safe Vehicular Passing 3.1.1 Background Safe vehicular passing (SVP) can be implemented through a real time application where one vehicle (car, truck, etc.) equipped with a sensor mote wants to communicate with a vehicle in front of it, also carrying a similar mote, for safe passing (overtaking). This report merely addresses the applicability of the device to communicate between vehicles equipped with it, without regard to passing issues. For example, this report assumes that the driver of a preceding vehicle judges the speed of an approaching vehicle and sends a true signal to the driver of the following vehicle. The motes have three different colored LEDs which illuminate when triggered. The idea behind this application comes from a real life problem which every person faces. Suppose there are two vehicles traveling in the same direction on a road with only two lanes (one in each direction). The trailing vehicle wants to overtake the leading vehicle. How can this be done safely? Since it is a two-lane road, the second vehicle will have to move into the lane which has traffic in the opposite direction, and then pass the first vehicle without communicating with it. The risk involved in such situations has led to the development of this application. 3.1.2 Design Overview Every vehicle can be equipped with a sensor mote and each mote can have a unique identification (ID), which can be the vehicle identification number. Each mote broadcasts its ID at equal intervals of time. Since a mote can sense other motes lying in its communication range, it can be assumed that as soon as the vehicles approach each another, both motes exchange their IDs. Once the ID is found, they can communicate. This application is developed for a base case in which there are two vehicles, one behind the other, and the trailing vehicle wants to pass the leading vehicle. Figure 3-1 shows the design overview of SVP. Let us assume that vehicle 1 wants to pass vehicle 2. Mote 1 in vehicle 1 sends a request packet to mote 2 in vehicle 2 using mote 2’s ID. Any other mote receiving this packet will discard the request packet. As soon as mote 2 receives this packet, its red LED glows to indicate to driver 1 that the following vehicle wants to overtake and pass. Mote 2 sends an acknowledge packet to mote 1 indicating that it has received the request packet. This acknowledgement will make mote 1 stop broadcasting its request. If the acknowledgement is not received, mote 1 will keep broadcasting its request until it times out. The acknowledgement packet will also trigger on the yellow LED on mote 1, which lets the driver know that the request has been received. Mote 2 now sends the request reply packet using mote 1’s ID. Depending upon the reply, either the green (for passing) or the red (not-passing) LED of mote 1 is illuminated. Mote 1 also sends an acknowledgement to this

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request reply packet. Upon receiving the acknowledgement the yellow LED of mote 2 glows indicating to the driver that the reply was received successfully.

Figure 3-1: Design overview of safe vehicular passing 3.1.3 Preliminary Implementation Figure 3-2 shows the flow chart for implementation of SVP. For this implementation the researchers took two sensors and fixed their node IDs. The key components used in this design were CntToLedsAndRfm and RfmToLeds in TinyOS. An application timer was used to turn the LEDs on and off. The component CntToLedsAndRfm sensed the request from another mote and stored the source mote's ID. Then it sent the acknowledgement packet to the LEDs of the source mote. The reply packet was broadcast with the destination address through the radio. It also received the request reply from the source and accordingly illuminated the appropriate LEDs based on the request reply. This application can be used to improve the safety of vehicles during passing maneuvers on two-lane roads.

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Figure 3-2: A flow chart for safe vehicular passing

Exchange mote ids

Mote 2 receives request

Mote 1 sends request

Mote 2 sends ack

Mote 2 red LED glows

Mote 1 receives ack

Mote 1 yellow LED glows

Mote 2sends reply

Mote 1 green LED glows Mote 1 red

LED glows

yes no

Mote 1 sends ack

Mote 2 receives ack

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3.2 Traffic Violation Detection 3.2.1 Background This application investigated a traffic signal scenario using the sensor motes. The researchers are aware that there are currently serious legal issues to be resolved, but the device applied here might someday be a possibility for aiding in detection of violators. The trial application regulated traffic flow at a crossroad junction and detected violations at this junction. The violation detection mechanism can be further expanded to either notify a user connected to a PC or to take digital photographs of the violators by connecting it to a digital camera. 3.2 Design Overview Figure 3-3 shows the algorithm of the implementation of a traffic light scenario. The main goal is to sense the traffic at a particular instant and then decide the waiting time rather than implementing fixed waiting time. The process of making a decision for waiting time reduces the waiting time of the automobiles. Assume the junction of two roads, one in the east-west (EW) direction and the other in the north-south (NS) direction. We assume that the EW road has a higher volume of traffic than the NS road. Four motes were placed in the four directions. Since they were equipped with only light and temperature sensors, the light sensor was used to count the number of cars in each direction. Each mote had a counter that was initialized to zero. Every time the reading of the light sensor fell below a predefined value, the counter was incremented. The counters were also programmed to count the number of cars in the direction in which the traffic signal was red. To detect traffic violations at this junction, four additional motes were placed just after the stop line in each direction. These motes were programmed to beep whenever an automobile passed over this mote when the traffic signal was red in that particular direction. 3.2 Preliminary Implementation The counters present in the motes Cn, Cs, Ce, Cw were all initialized to zero, and by default the EW road signal was set to green and the NS signal was set to red. The system waited for thirty seconds during which the counters Cn and Cs were incremented according to traffic in the respective directions. After thirty seconds Cn and Cs were sampled and MAX{Cn, Cs} ∗ 3seconds/car was calculated provided MAX{Cn, Cs} > 0. This calculated the green signal time Tnsg for the NS direction. Tnsg could not exceed thirty seconds. Here, we assumed that thirty seconds was the MAX green time allowed in any direction. As soon as Tnsg was calculated, the signal for EW changed from green to yellow, stayed for 3 secs, turned red, and remained red for (Tnsg + 3 sec). Once the EW signal was changed to red, the NS signal was changed to green with a 3 sec delay. This process was repeated in the EW direction and the cycle continued. The four additional motes placed after the stop line sensed the direction in which the signal was red. They did not sense in other signal states to save power. These sensors were equipped with a sounder programmed to beep whenever a vehicle passed over the stop line when the signal was red. This application can be used to regulate traffic at a traffic signal and can also be used simultaneously to detect violators.

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Figure 3-3: A flow chart of the algorithm for traffic violation detection

3.3 Automobile Tracking 3.3.1 Background A wireless sensor network is comprised of compact, autonomous nodes equipped with data-gathering, computation, and wireless communication capabilities. The sensor nodes are used to monitor a certain geographic area. Each node collects data from the area it covers. The collected

{Cn, Cs, Ce, Cw }=0

Wait for 30 secs

If Max {Cn , Cs } > 0

Tnsg = Max{Cn , Cs }*3 secs/car

EW yellow for 3 secs Then, EW red(Tnsg + 3sec)

NS green(Tnsg )

After Tnsg , NS yellow for 3 secs Then, NS red

EW green

No

Yes

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data is then used to answer various queries. Because sensor nodes operate in a physical environment, knowledge about their own physical location is crucial. Location information can be obtained via several methods. Global Positioning System (GPS) is one of the mechanisms that provide absolute location information. For economical reasons, however, only a subset of sensor nodes need to be equipped with GPS receivers so they can function as location references. They periodically transmit beacon signals telling their own location information so that the other sensor nodes without GPS receivers can determine their approximate positions in the terrain. This application enables the sensors in the network to locate a specific vehicle-moving object and report its geographical location to the base station. The researchers are aware that there are currently serious legal issues. For example, American drivers do not want someone watching them or tracking their vehicles. In spite of this obstacle, the researchers think that the device applied here might be a possibility for future tracking of vehicles. 3.3.2 Design Overview

Figure 3-4: An overview of automobile tracking Figure 3-4 shows an overview of the application. The base station broadcasts the ID of the vehicle that must be tracked. Sensors in all vehicles listen to the broadcast and forward the message if they are not the vehicle that is the subject of the search. Once the vehicle that is being searched for receives the message, it transmits the latitude and longitude values at its location. To detect and identify an object, integrated results from more than one type of sensor (image from a camera, noise from an audio sensor, etc.) may be required. The tracking algorithm will include all issues related to the subject such as power, routing latency, accuracy, transmitting/receiving data between the nodes, and transmitting data between the nodes and the base station. It will also include how the different nodes interact when they are moving and when they are stationary. The algorithm will deal with these issues according to wireless sensor protocols. The algorithm will take into account that the vehicle being tracked is supported with a GPS. The GPS obtains the latitude and longitude coordinates of the automobile position from a satellite, and the sensor node sends its x and y values to the base station.

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Figure 3-5 shows the pseudo code for the algorithm. Parameters such as track time, re-track time, track frequency, and the target are predefined by the base station. Then each node in the network receives the broadcast message from the base station. The node which gets the message initially will activate its sensing capabilities and look for the target. During this process it will send a message to its neighbors telling them to suppress their sensing operations. If the node finds the target, it will broadcast a message saying that the target was found. This message is heard by all its neighbors and also forwarded to the base station. If the node does not find the target, one of its neighbors is randomly picked and it will perform the sensing operations trying to locate the target. These operations are performed until the target is found within the range of any one of the nodes in the network. 3.3.3 Simulation The proposed algorithm has been simulated using C++. All nodes in the network were set up as mobile, and the base station of the network was located at the center of the geographical area. One node in the network was randomly chosen as the target. Simulation runs were preformed with varying numbers of nodes in the network. Parameters such as the length of time that a node could be tracked in the network and the amount of power saved in each node were measured. Figure 3-6 shows the simulation system tracking time. The mote sensing area is set to be a circle with a 200 foot radius. All motes choose a direction in random and keep traveling in that direction. The direction of the motes changes randomly every 10 seconds. The times shown in the y-axis of the figure are the system tracking times, i.e., the amount of time for which a given target is tracked by the nodes present in the system. As seen in the figure, it is clear that the more densely populated the network, the longer the target could be tracked within the system.

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Initialization Track time Re-track time Track frequency Target

Each node will do the following:

While (receive "suppress")

Standby ()

{If (sensor gets the target)

{

- Tell all nodes to "suppress"

- Tell neighbors to "Re-track"

- Tell the sender "found "

}

If (timer expired)

{Sensor turns on

Timer for re-tracking starts}

While (receive found)

Tell the sender found

For each tracking frequency

If (sensor detects the target)

{Tell sender "found"

Tell neighbors "re-track"

}

Else

Timer re-tracks starts

If the timer for re-tracking expired, then sensor standby

If the timer for application expired, then sensor turns off

Figure 3-5: Pseudo code of the automobile tracking algorithm

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Number of Nodes in the Network

Tim

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Figure 3-6: System tracking times

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4 6 8 10 12 14 16 18 20 22 24 26 28 30

Number of Nodes in the Network

Tota

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onsu

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Case-1Case-2

Figure 3-7: Power consumption

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Figure 3-7 shows the comparison of power consumption between two cases. Case 1 is a network in which all the nodes continuously search for a target broadcast by the base station. In Case 2 only some of the nodes keep sensing for the target while they broadcast a suppress message to the remaining nodes in the network, and this mechanism moves on as the target moves. The values shown in the y-axis represent the power consumed by the motes present in the network. From the results shown in Case 1, it is clear that as the number of nodes increase in the network, the power increases linearly as all nodes in the network perform sensing operations continuously. The power shown in Case 2, for a network with thirty nodes, is about half as much as for Case 1. 3.3.4 Preliminary Implementation A simple scheme was created so that a base station could know the position of a certain vehicle at any time. The base station broadcast the request to the sensor field. This request contained the name of the node for which the position was desired. As each node received the request, it compared the node name with its own name. If they did not match, it broadcast the request again. When the request reached the desired node, it broadcast a reply that contained the node name and its position. The reply was rebroadcast by other motes until it reached the base station. The implementation consisted of two parts: (1) remote data logging and collection: initialize the position (x, y) and write them to electrically erasable programmable read-only memory (EEPROM) and then transmit position values from the EEPROM over the radio to the base station, and (2) synchronous transmission between motes. The base station mote broadcast the address of the mote being tracked, and the other motes received this broadcast address. If the address of any mote matched the broadcast address, this mote displayed its address on the LED (flag). The target sent the position forward to the base station, which displayed the position (x, y). For this implementation four nodes were programmed with the application which also contained predefined x and y co-ordinates. All the nodes were stationary. The base station broadcast a message searching for a predefined target. All the nodes in the network forwarded this message until the target heard it. Upon receiving this message the target identified itself and replied to the base station with its predefined co-ordinate values. The idea behind the implementation was that if the nodes were mobile, the x and y co-ordinates would be constantly updated as they were sent by the GPS. In a mobile scenario the x and y locations of the target will be constantly sent to the base station. The base station then can determine the distance between these co-ordinates and also the time taken to travel this distance. This will give information about how fast the target is traveling. Since the base station has all the information, like the speed of the target and the direction in which it is moving, it makes the sensing of the target more accurate. This application, with minimal changes in hardware and software, can be used to develop a commercial product.

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Section 4.0 Project Conclusions and Recommendations

4.1 Conclusion Traffic control systems using wireless sensor networks are just beginning to emerge. This report presented the preliminary implementations of three sub-systems, safe vehicular passing, traffic violation detection, and automobile tracking. Safe vehicular passing was developed to serve rural roads where many accidents happen during passing maneuvers. This application allows users to acquire knowledge of the surroundings to enhance vehicular passing safety. Traffic violation detection was developed to capture traffic violations with minimal costs. In this application light sensors were used to either count traffic or to detect a violation. The light sensors can be replaced with acceleration/motion sensors which would provide more accurate results. The violation was output to the sounder component of the application, which can also be replaced with a digital camera to take photographs at every violation. Many of assumptions were made when developing the automobile tracking system, but these assumptions can be implemented with the sophisticated equipment available today. Connecting a GPS receiver to a node and updating the global co-ordinates of the node in real time would allow tracking of vehicles which are equipped with a GPS system. All three applications can be combined to form a robust system to assist departments of transportation and law enforcement. A commercial application can be envisioned using sensor networks, which would be able to track automobiles, regulate traffic, take photographs of all violations and also send the data to a base station/central computer which would be notified of the violations. The motes can be placed in automobiles which are equipped with GPS, and can be programmed to be identified using the vehicle identification numbers of the automobiles. The same motes can be used for safe vehicular passing, although additional hardware in the form of a switch may initiate and end a request. Similarly the motes used to regulate traffic can be equipped with motion sensors so that they can accurately determine the volume of traffic. The violation software can be modified so that for every violation, it would not initiate the sounder component but would activate external hardware (a digital camera). The applications developed in this report can be used in other areas. For example, the sensor boards can be changed to environmental sensors and the motes can be deployed to monitor the environment in an area. The data can be collected at a base station, and data repeaters can be used to send this data onto a host location where this information can be gathered. This research offers three theoretical examples of how the device might be used, and even though the researcher realizes that these examples do not have “real world” applications because of legal, traffic operations and other issues, they do illustrate use of the device.

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4.2 Recommendations The field of sensor networking is developing rapidly. Recent advances in micro-electro-mechanical systems (MEMS) technology have provided tiny sensors that can also be called smart dust. With the development of electronics technology which is providing tiny sensors, we need to develop application algorithms that consume minimal amounts of power. Development of routing techniques, which can remain robust throughout the system at all times, is also necessary. Design factors such as scalability and production costs need to be further studied to provide better outputs. The low-level energy constraints of the sensor nodes combined with the data delivery requirements leave a clearly defined energy budget for all other services. Tight energy bounds and the need for predictable operation will guide the development of application architecture and services. Micro-Electro-Mechanical Systems (ME

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Section 5.0 References

Akyildiz, I., Su, W., Sanakarasubramaniam, Y., and Cayirci, E., A Survey on Sensor Networks,

IEEE Communications, August 2002. Chen, C., Srisathapornpat, C., and Jaikaeo, C., Sensor Information Networking Architecture and

Applications. IEEE Pers. Communications, August 2002. Crossbow Technology Inc. “Mote Kits & Overview,”.

http://www.xbow.com/Products/productsdetails.aspx?sid=61. Accessed August 29, 2003. Deverapalli, C., A Simulation Study of a Mobile Adhoc Network, Master's thesis, The

University of Alabama in Huntsville, August 2003. Mobility Technology. “Traffic Pulse Technology.”, http://www.mobilitytechnologies.com/ntdc/.

Accessed September 11, 2003. University of California Berkeley. “TinyOS,”. http://webs.cs.berkeley.edu/tos/. Accessed

September 15, 2003.