Transcript

Resilient Wireless Sensor Nodes Embedded on Road Reflectors for Vehicle Traffic Monitoring Through Car Engine Sounds

by

Benjielon D. PascualNorbert U. OngMichael Aldwin S. ReyesCzarina Marie B. Rivera

A Thesis Proposal Submitted to the School of Electrical, Electronics and Computer Engineering in Partial Fulfillment of the Requirements for the Degree

Bachelor of Science in Electronics Engineering

Mapa Institute of TechnologyOctober 2012

Chapter 1INTRODUCTIONThe Philippines is listed as one of the worst in transport related international survey. Manila is the 3rd most worst city for driving according to CNNGo, the travel news website of cable news network or more commonly known as CNN. CNNGo.com added that according to one report, Filipinos perceive traffic congestion as their number one problem. Aside from manila other cities of Metro Manila, being the center of Philippine socio-economic and political activities, has been experiencing heavy traffics and plagued by air pollution because of the large volume of vehicles and vast amount of commuters. Heavy traffic might cause to several problems to the commuters. Some of the problems that might be encountered are delay, fuel consumption and pollution, and road rage. Heavy traffic might also cause car accidents.There are a lot of studies regarding on how to monitor the traffic in highways. Some of the researches are based on 3D video processing technique to monitor the traffic. Here in the Philippines there is an application for Smartphones, MMDA for Android. The said application for Smartphones provide information for the drivers regarding the status of the traffic. The data for this application is gathered through the agencys CCTV camera network and reports from the traffic enforcers. Aside from the traffic monitoring system provided by the MMDA and from the traffic information gathered from the news, there are no other systems or ways to notify and inform the motorists here in the Philippines regarding the traffic. Unfortunately this system is only accessible and applicable for the drivers who can access the Internet within the highways and have Android Smartphones. Applying the concept of wireless sensor nodes, we have thought of a way of monitoring the traffic through the sounds emitted by the vehicles.

The main objective of the study is to build resilient wireless sensor nodes. The study aims to design and create a circuit that detects sound emitted by the engine of moving and stationary vehicles. The study also aims to transmit information from one sensor to another until it reaches the main server by integrating the circuit into a transmitter/receiver. The study also covers to mount the circuit into a traffic reflector found in high ways and to test its resiliency.The primary significance of building resilient wireless sensor nodes is to contribute to the development of future traffic systems. The proposed study will help to inform the drivers ahead of time about the condition of traffic on their way and with this, they can avoid the heavy traffic and take other routes instead. Furthermore, they will also save fuel if there is no heavy traffic. The sensor nodes are mounted on the reflector and it will send the information from one sensor node to another sensor node until it reaches the main server.The proposed study focuses on building resilient wireless sensor for future traffic systems. Resilient Wireless Sensor Nodes are used to detect sounds coming from the engine of the vehicles and to use those sounds to identify the flow of traffic. The study does not cover the classification of vehicles. The device cannot differentiate the sound emitted by the engine of a vehicle from another. We limit our study up to 2 cars. The vibration of the ground produced by the vehicles can affect the resiliency of the circuit.

Chapter 2REVIEW OF RELATED LITERATURE2.1 Possible ways to determine the presence of vehiclesDetermining the presence of a vehicle is one important factor to be considered in this study and there are several technologies that have been developed to detect or sense the presence of these motor vehicles. By far the most common technique is with the use of a CCTV camera. CCTV monitoring systems not only provides security but also prevents people from engaging in criminal or unlawful activities. These are generally used in many cases including real-time monitoring of traffic. As road networks become busier and more congested there is a growing need to monitor the transport network for it is essential for road users to be up-to-date in road occurrences. The automated parking system is another good example in detecting cars. These kinds of system have sensors installed to each parking space that can tell whether or not the space is occupied. Once a car is parked an indicator will mark the bay as occupied and this information is picked up by a Data collector which will be then transmitted and processed in a central data station. The information is released and displayed on a monitor that will tell how many slots are still available. In terms of scientific application for detection of motor vehicles, they generally use the presence of sound. Based on the study SOLAR: sound object localization and retrieval in complex audio environment by D. Hoiem, Y. Ke, and R. Sukthankar, they created this system in order to identify certain types of sound within a complex audio environment. In order for the system to be able to localize and retrieve specific sounds they prefer to identify, they used two classification techniques. The first is with the use of decision trees to select discriminating features and the use of Adaboost to improve classification with an ensemble of trees. In their experiment, they included as object of interest the presence of vehicles through detection of car horns. SOLAR was able to achieve good results on many objects, including the sound of car horns with 46% for 10 FP/hr, 66% for 50FP/hr and 76% for 100FP/hr. FP/hr or false positive per hour is how they measure the datas accuracy. Another technique is the application of inductive loop as shown in Figure 2.1, wherein it works by detecting the change in inductance. A study by Sheik Mohammed Ali, S. involving a Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic was conducted and a sensor was designed which is capable of applying a new measurement scheme for multiple loop system. Its basic feature is to sense and segregate different types of vehicle and to count the number of vehicle in a mixed traffic flow condition. Basically, an inductive loop is simply a continuous coil of wire embedded in the road's surface wherein it enters and exits at the same point. When current first starts flowing in the coil, the coil will build up a magnetic field. The loop resonates at a constant frequency that the detector monitors. When a large metal is detected by the loop such as vehicles, the corresponding inductance and resonance frequency will change. Generally, a compact car will cause a greater increase in frequency than a full size car or truck. Therefore the shift in frequency will help classify and determine the various types of vehicles along the road.

Figure 2.1 Illustration of an inductive loop-based vehicle detection scheme at a junction. [10]

Table 2.1 Summary of different approaches on detecting vehiclesApproaches on Detecting vehiclesAdvantagesDisadvantage

CCTV cameraRoad users to be up-to-date in road occurrences. Not only provides security but also prevents people from engaging in criminal or unlawful activities.Usually not able to display every square inch of a facility and sometimes causes controversy.

Automated parking systemLess time consuming for drivers to look for a place to park. It would also reduce the traffic congestion caused by those drivers and reduce carbon emissions.Necessitates a maintenance contract with the supplier, higher construction cost per space and redundant systems will result in a higher cost.

Presence of soundHas the ability to measure the speed of vehicles.Other insignificant sounds are always present and must be able to localize sounds created by vehicles

Inductive loopCan help classify and determine the various types of vehicles along the road.

Inability to directly measure speed, requires extensive traffic control and results in congestion and motorist delay and underground wires, are susceptible to being damaged by utility work.

The primary goal of this study is to create a wireless sensor that would help monitor the traffic condition in a specific location. Therefore, the approach that will be used is the detection of sound created by vehicles specifically cars. Sensors will determine the speed of cars through measuring the sound of car engines. The traffic condition will then be established through data processing.

2.2 Different Ways of Detecting the Speed of VehiclesQuickBird Images In year 2009, Wen Liu developed a new method of detecting the speed of vehicle using QuickBird. QuickBird is a satellite that uses Ball Aerospaces Global Imaging System 2000 that captures very high image resolution, which can make houses and buildings visible to our naked eyes from outside of the Earth. He used a pair of QuickBird (QB) panchromatic (PAN) and multi-spectral (MS) images to automatically detect the speed of moving vehicles. This method is tested on different parts of QB images (Figure 2.2 and Figure 2.3). The speed of moving vehicles is detected by analyzing the motion between the images of the PAN and MS in the time lag because PAN and MS sensors have only a 0.2 seconds lag. By using a resolution of 0.6m produced by a PAN image, images of vehicles can be extracted using an object-based approach and with these results, speed of vehicles can be determine. On the other hand, area correlation method is performed to calculate the location of vehivles from MS image in a sub-pixel level and the level of accuracy of the gathered data has been verified.

Figure 2.2 QuickBird PAN image of central Tokyo, Japan [1]

Figure 2.3 A part of the PAN image (up) and the result of vehicle detection (down) from scene 1 in Figure 2.1 [1]

Magneto-Impedance (MI) Sensor Built-in Disk SetUsing an amorphous wire CMOS magneto-impedance (MI) sensor that is built in a disk set on the road is used by Uchiyama, T, Mohri, K, Itho, H and Nakashima, K to detect the presence of a car. The device is composed of two MI sensors, a microcomputer, and a semiconductor IC memory built-in. Figure 2.4 shows a construction of the proposed car sensing system. With these materials, velocity, size of the car (length) and the time when the car had passed can be acquired and processed to be able to interpret what is the condition of the traffic on a certain road. The MI sensor attached into a disk detects stray fields coming from the car when passes above it. The disk can only process up to 2000 cars. Figure 2.5 shows a picture of the prototype disk.

Figure 2.4 Car sensing system using two MI sensors and a microcomputer built into the disk set on the road [2]

Figure 2.5 Photograph of a prototype disk system with two MI sensors and a microcomputer built-in [2]

Video Detection SystemsUsing Video Detection Systems are another ways to track vehicles and measure their speed. This was proposed by, MICHAEL KYTE and HONGCHAO LIU, to track main-street through vehicles and measure speeds at two-way, stop-controlled (TWSC) intersections. Based on their research, there were two video detection systems that have similar functions and those are Mobilizer and VideoTrak. These two are used to track vehicles on the road just by matching a group of images. Using these two video detection systems, errors were found. Using a Mobilizer, about 18% of the target vehicles were traced. Compare to that, VideoTrak had more than 20% of errors on counting the volume of vehicles, errors caused by occlusion and a low-quality of video, which results to a low accuracy in tracking vehicles. On top of that, the tests were taken with no vehicle interferences among turning movements.Researchers who are in-line of general machine-vision technology knew that there is a big problem of using video detection systems in tracking vehicles because of its low accuracy. In regards with this problem, researchers found a way to increase the accuracy of video detection systems by developing advanced algorithms using machine-vision technology. These algorithms that were used focused on overcoming the problem of occlusion. An example of these advanced algorithms is the segmentation algorithm that was used to track vehicles under tough occlusion conditions. Unfortunately, such advanced algorithms have not yet been used or applied by video detection system manufacturers. Another problem using video detection systems is when tracing turning movements of the vehicles at TWSC intersections. There are previous studies attempted to overcome this problem and one of this used a method of tracking vehicles at all-way, stop-controlled intersections based on the principle of flow conservation and data redundancy. In Figure 2.6 you can see the vehicles travel path and the position of the detector. For each movement, multiple detectors were used and this method established a matrix to relate each counts of detectors to a specific turning movement. Yet, the big problem again is severe occlusion because multiple detectors assumed that only one vehicle is moving.On this study, AUTOSCOPE, a video detection system, was used and Econolite Control Products Inc manufactures this. AUTOSCOPE is composed of two detectors, one is for counting vehicles and the other is for knowing the presence of the vehicles. AUTOSCOPE develops speed traps to measure the speed of vehicles and their sizes (lengths). Yet, numerous numbers of errors were detected as they conducted the study. First, speed trap lacked the capability of differentiating turning movements. Second, huge amount of errors occurred when a turning vehicle crosses a little portion of the speed trap because the internal algorithm did not have the capability to correct such errors. Third, using a long speed trap increases the accuracy of speed measurements but this is only relevant for low volume of vehicles because having long speed trap resulted to a big errors when the volume of vehicle is heavy.

Figure 2.6 Example of vehicles travel path and detector set-up. [3]Digital Audio Signal ProcessingLori Mann Bruce, Navaneethakrishnan Balraj, Yunlong Zhang and Qingyong Yu design a system for automated traffic accident detection in intersections. The input to the system is a 3 second segment of audio signal. The system can be operated in two modes: twoclass and multi-class. The output of the two-class mode is a label of crash or non-crash. In the multi-class mode of operation, the system identifies crashes as well as several types of non-crash incidents, including normal traffic and construction sounds. The system is composed of three main signal processing stages: feature extraction, feature reduction, and classification. Five methods of feature extraction are investigated and compared; these are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. Three types of classifiers are investigated and compared; these are the nearest mean, maximum likelihood, and nearest neighbor methods. The results of the study show that the wavelet-based features in combination with the maximum likelihood classifier are the optimum design. The system is computationally inexpensive relative to the other methods investigated, and the system consistently results in accident detection accuracies of 95% to 100%, when the audio signal has a signal-to-noise-ratio of at least 0 decibels.Table 2.2 Advantages and disadvantages of different ways on determining speed of vehiclesTechnologyAdvantagesDisadvantages

QuickBird Images

Acquires very high resolution of imagesThere is only limited method on how to retrieve the images from Google Earth. There are no thermal bands, so it will be more difficult to mask out the clouds. You have less spectral variation to work with and have to work around it to get good results. Different images also show variations between them or even within the same image, so finding a method that works on one image does not necessarily work on the next.

Magneto-Impedance (MI) Sensor Built-in Disk SetThese MI micro sensors have high performances such as high sensitivity, low power consumption (10 mW) due to a pulse current excitation with a C-MOS multivibrator circuit and low cost.Signal transmission between the disks was necessary to estimate the velocity and length of a car. That is a problem for the system installed on the road.

Video Detection SystemsThe vehicle-tracking application using a video detection system overcame the shortcomings of existing commercial video-detection systems in tracking vehicle movements and measuring speeds at TWSC intersections. This application could be extended to other types of data collection, such as collecting turning movement counts at other types of intersections. Under the worst-case scenario with an unfavorable departing view, the accuracy of vehicle tracking was about 87 percent.When reporting individual speeds, the speeds were usually within an error range of 5 km/hr. between the two types of measurements

Digital Audio Signal ProcessingThe system is computationally inexpensive relative to the other methods investigated, and the system consistentlyThe accuracy of the results depends on the SNR of the acquired audio signals. When the acquired audio signals have low SNR, accuracy is low.

We will use Digital Audio Signal Processing as our reference on detecting the sound of car engines and classifying the motion (moving or stationary) of cars. Since this method provides an accuracy range of 95% to 100% and is cost-effective, this would make our design more effective and more efficient.2.3 Possible Ways of Detecting Vehicle SoundThe sound of a vehicle is difficult to be characterized. The sound may come from the tires having contact with the ground. There are also internal noises present in the vehicle. The environment is also a factor that affects the sound of a vehicle. The best way on detecting the sound of a vehicle is on relying on the sound emitted by the vehicles engine. The sound emitted by the engine also varies as the speed of the vehicle changes as well.Acoustic sensors can detect sound. An example of an acoustic sensor is a microphone. A microphone can detect sound and can also transmit it. Once sound is sensed by a microphone, it then converts the sound into an electrical signal. Acoustic sensors like microphones can be used to detect the sound emitted by a vehicle. There was a previous study that was conducted wherein the sound emitted by a vehicle was used on detecting it. The microphone array was placed near the car engine as shown in Figure 2.8. The study was conducted by Toshiya Takechi, Koichi Sugimoto, Takashi Mandono, and Hideyulu Sawada. The study was entitled as Automobile identification based on the measurement of car sounds. With this system configuration in Figure 2.7, they built their design.Using a microphone array, the system estimates a sound source location to identifyparticular car and its state in noisy environment. Microphone arrays are series of microphones working in together. The microphones were used to detect the sound emitted by the vehicle. The application of this study was used on management of automatic parking system. Another study regarding the sound emitted by a vehicle was used to prevent traffic accidents. Aside from the microphone arrays, the study also made use of filters to determine the range of frequencies that are allowed to sense by the device.

Figure 2.7 System Configurations [4]

Figure 2.8 Location of microphone array under a car [4]

Again using microphone arrays system, the sound of vehicles was used to detect the direction ofapproaching vehicles in a T-intersection as shown in Figure 2.9 accurately and rapidly. Kenji Kodera, Akitoshi Itai and Hiroshi Yasukawa conducted the study. The study made use of the sound emitted by the vehicle to detect the location of the approaching vehicle in the T intersection.

Figure 2.9 Measurement Environment [5]Table 2.3 Advantages and Disadvantages of ways of detecting vehicle soundsTechnologyAdvantagesDisadvantages

Microphone array and filters.Can detect the presence of car through sounds.Can be prone to unwanted signals from the surroundings.

2.4 Resilient Wireless SensorA Wireless Sensor Network is a network of sensors that senses parameters that are related to environment. It is a collection of nodes organized into a cooperative network. The network could have several nodes wherein each node is composed of processing capability. WSN processes data locally or in a distributed manner and wirelessly communicates information to central Base Station. The Base Station is the one that analyses the information that it will receive and initiates if there are responses required from it. Information is being transmitted from node to node until it reaches its destination.

ApplicationsWireless Sensor Networks implementations to different industries are growing very fast nowadays. There are several application areas for this new technology such as environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces. According to Yang Lv and Yu Tian, the nodes usually use ZigBee, GPRS, and Ethernet techniques on ARM7 microcontroller for transmission of data. There experiment using Wireless Sensor Network has proved that it is applicable and reliable in monitoring environmental elements such as temperature, humidity, etc.

Approaches/Methods to Make Electronic System ResilientConsidering the limited power of wireless sensor networks, a novel algorithm for in-network aggregation of the traffic flow time-series was proposed by Meng Shuai, Kunqing Xie, Xiujun Ma, and Guojie Song, which reduces the communication cost between the sensor nodes and base station significantly. This method is efficient and suitable to be implemented on sensor nodes. The primary experiments on PeMS data demonstrate the effectiveness and energy efficiency of the approach.Another wireless sensor network approach for collecting data is by bilateral sanding units. This was presented by Chung-Hsien Kuo, Chun-Tzu Chen, Ting-Shuo Chen, and Yu-Chen Kuo. Three wireless sensor nodes are attached to a conventional sanding unit for automatic data collections. Two wireless sensor nodes are desired for a set of sanding hand blocks, and the remaining one is used for the reciprocal exercise platform. Three wireless sensor nodes can be easily attached to conventional sanding units with less wiring and setup efforts for fast realizing data collection functions.

Table 2.4 Advantages and Disadvantages of different methods to make electronic systems resilient.MethodsAdvantagesDisadvantages

In-network aggregation

This method is energy efficient and suitable to be implemented on sensor nodes.

Lower transmission efficiency will be present between the main server and the sensor node that will transmit the data on it.

Bilateral sanding unitsData is collected automatically when wireless sensor nodes are attached to a conventional sanding unit.The approach is more complex and the experimentation would involve at least three sensor nodes.

Since our study is mainly about sending the data from a node to another and involves less concern to the main servers response to the received data, we can lessen the communication cost from the main server. This was present in the in-network aggregation approach wherein energy efficiency is improved and at the same time, the effectiveness of the sensor nodes is maintained.

Factors Affecting ResiliencyOperation resiliency is also very important for network traffic. Current network interface speeds are at multiple Gigabits per second and can cause significant packet drops, which can lead to lost time and productivity for users. Network operators and administrators are constantly challenged in ensuring high availability of the network. With this concern, planning/design and resilient networking elements are achieved by Janardhanan, P.N. and Sekar, G.H. They stated that the key principles of resiliency are the (1) elimination of single points of failures, (2) early detection and failover around defective parts, (3) localization of failures and containment of their impact, and (4) restoration of failed components without impacting other subsystems.

References:

[1] Liu W., and F. Yamazaki,. (2009). Speed detection of moving vehicles from one scene of QuickBird images. 2009 Urban Remote Sensing Joint Event, 1-4[2] T. Uchiyama, K. Mohri, H. Itho, K. Nakashima, J. Ohuchi, and Y. Sudo. (2000). Car Traffic Monitoring System Using MI Sensor Built-In Disk Set on the Road. Transactions on Magnetics, Volume 5 (36), 3670-3672.[3] Z. Tian, M. Kyte, and H. Liu. (2009). Vehicle Tracking and Speed Measurement at Intersections Using Video Detection Systems. ITE Journal,42-45.[4] Toshiya Takechi, Koichi Sugimoto, Takashi Mandono, and Hideyulu Sawada, (2004). Automobile identification based on the measurement of car sounds. The 30th Annual Conference of the IEEE Industrial Electronics Society, 1784-1789.[5] K. Kodera, A. Itai and H. Yasukawa, Approaching Vehicle Detection Using Linear Microphone Array, International Symposium on Information Theory and its Applications, 2008.[6] N.R. Rosa, P.R.F. Cunha, Behavioural Specification of Wireless Sensor Network Applications, 2007.[7] H. Garcia, W.C Lin, and S. Meerkov, A Resilient Condition Assessment Monitoring System, 2012.[8] P. Janardhanan and G.H. Sekar, Highly Resilient Network Elements, IEEE-International Conference on Signal Processing, Communications and Networking Madras Institute ofTechnology, Anna University Chennai India, 2008.[9] D. Hoiem, Y. Ke, and R. Sukthankar, SOLAR: Sound Object Localization and Retrieval in Complex Audio Environments, ICASSP, 2005.[10] S. Sheik Mohammed Ali, Boby George, Lelitha Vanajakshi, and Jayashankar Venkatraman, A Multiple Inductive Loop Vehicle Detection Systemfor Heterogeneous and Lane-Less Traffic, IEEE Transactions on Instrumentation and Measurement, VOL. 61, 2012.[11] Bruce L.R., Navaneethakrishnan B., Zhang Y. and Yu Q. (2003). Automated Accident Detection in Intersections via Digital Audio Signal Processing. TRB 2003 Annual Meeting CD-ROM, 2-7

Chapter 3METHODOLOGY

RESILIENT WIRELESS SENSOR NODES EMBEDDED ON ROAD REFLECTORS FOR VEHICLE TRAFFIC MONITORING THROUGH CAR ENGINE SOUND

The study Resilient wireless Sensor Nodes Embedded on Road Reflectors for Vehicle Traffic Monitoring through Car Engine Sound introduced a way on detecting the condition of vehicles. The study focuses on the detection of sound emitted by the engine of the vehicle and to transmit the data from one sensor to another. The said data is whether the vehicle is moving or stationary, from this data it can be determined if there is a traffic congestion or none. The sensor and the transmitter/receiver would be integrated in a single circuit and would be mounted on traffic reflectors, in Figure 3.1, found on the roads.

Figure 3.1 Traffic reflector

SOUND SENSOR

TX/RX

SERVER

MCU

SOUNDFig. 3.2 Block Diagram of the Design


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