Master’s Project Report

LAWS: LOCATION ACCURACY BASED ON WIRELESS SIGNA\LS

Sri Naga Jahnavi Yeddanapudy

A ProjectSubmitted to the Graduate School Faculty of the

University of Colorado Colorado SpringsIn Partial Fulfilment of the Requirements

For the Degree of Master of Science in Computer ScienceDepartment of Computer Science

Fall 2015

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This project for the Master of Science in Computer Science degree bySri Naga Jahnavi Yeddanapudy

Has been approved for theDepartment of Computer Science

By

__________________________________________________________________________________Dr. C. Edward Chow Date

__________________________________________________________________________________ Dr. Kristen – Walcott Justice Date

__________________________________________________________________________________ Dr. Jonathan Ventura Date

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Abstract

The ubiquitous use of mobile devices and various applications made this world so small. This modern era helped us in a way where we can contact anyone. The need to ameliorate the user location plays a dominant role in our lives. Several applications like E-911, amber alerts and rescue efforts during the emergency situation are very important. So, in this project our goal is to use the GPS and Wi-Fi signals information and locate the user accurately. We used the android applications to facilitate our experiments. We collected the validation data from Department of Geography. Our experiments are conducted at UCCS Alpine soccer field. An android application is used to get the latitude and longitude values using GPS information. When we run the application the user location latitude and longitude values are retrieved. We also used developed an android application which is used to scan the nearby Wi-Fi base stations and using the Free Space path loss formula we calculated the distance from each Wi-Fi base station. After obtaining the distances we used the GPS information of the Wi-Fi base stations that are collected from Google Earth and applied the trilateration algorithm to determine the user location. Our experiment result shows that locating the user using GPS is more accurate compared to the use of Wi-Fi signals.

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Acknowledgement:

I would like to express my deepest gratitude to my advisor Dr. Edward Chow, for his excellent guidance, patience and selecting me to work on this project. I admire his commitment and his expertise in problem solving. I thank him for all the support and constructive feedback, which pushed me to ameliorate my thought processing everyday. His insights into the project helped a lot.

I would also like to thank my committee members Dr. Kristen Walcott Justice and Dr. Jonathan Ventura for their enthusiasm, encouragement and feedback which really helped me to think about the problem in detail.

I would also like to thank Department of Geography Professor Dr. John Harner and Instructor Eric Billmeyer for all the help and allowing me to borrow their High end GPS units.

I would also like to thank Mr.Donovan Wireless Network Manager Tech Team of UCCS Campus IT for helping me in collecting the Wi-Fi information. I would also like to thank Ms.Mariness Falcon, Facilties and Services and Alpine Soccer Field Staff for their support in conducting my experiments.

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Table of Contents1. Introduction.....................................................................................................................................6

1.1 Motivation...................................................................................................................................6

1.2 Goal of the Project.......................................................................................................................6

2. Background and Prior work...........................................................................................................8

3. Design..............................................................................................................................................14

4. Implementation..............................................................................................................................17

4.1 Overview...................................................................................................................................12

4.2 Implementation details...............................................................................................................12

A GPS .........................................................................................................................................13

B Wi-Fi........................................................................................................................................13

4.3 Challenges faced in this project.................................................................................................14

5. Performance Evaluation of LAWS System..................................................................................23

5.1 LAWS Testbed..........................................................................................................................23

5.2 Test Results:..............................................................................................................................23

5.3 Performance Analysis:...............................................................................................................24

6. Lessons Learnt...............................................................................................................................25

7. Future Work..................................................................................................................................27

8. Conclusion......................................................................................................................................29

9. References:.....................................................................................................................................30

Appendix A: Configuration and Installation of LAWS System............................................................31

Appendix B: Demonstration Steps of LAWS System..........................................................................34

A. GPS.........................................................................................................................................34

B Wi-Fi........................................................................................................................................37

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1. Introduction

1.1 MotivationFor all of its amenities, the pervasive use of mobile devices in the work place and beyond has

brought up the need for location accuracy. Location Accuracy has significant impact on various fields like public safety and disaster relief to save lives within reduced time, to track people during outdoor adventures like hiking...etc. In hospitals, to disclose the patient records to authorized person located in that building. Also in work place and in military to send the confidential data to the employee in that particular office building. As the dependence of various location based applications are increasing rapidly, the need to improve the location accuracy is becoming dominant.

Initially GPS information was confined to military purpose. Later this was introduced to the civilians. That led to several applications in various areas like space, roads and highways, marine, 911...etc. Mobile code and apps are also introducing new avenues for the developers proving the location accurately because of ubiquitous use of location based applications such as maps, emergency alerts...etc. Based on GPS information many applications have been proposed in the past. But still there exists some offset in determining the location accuracy. Mainly during the emergency the locating the user accurately will help in rescue activities.

Several applications are invented every day and are limited by the human imagination. These applications are serving variety of purposes in our day to day life. Now the question arises can we improve the location accuracy by utilizing other information like Wi-Fi information in addition to the GPS information to determine the location accuracy. So in our project we used the Wi-Fi and GPS information to determine the user location.

The main goal in this project is to determine the location accurately based on Wi-Fi and GPS information in order to validate the user’s location. In this project, we assume there are no hackers trying to change or spoof the GPS and Wi-Fi location information.

1.2 Goal of the ProjectThe standard solutions for the location accuracy is by using the GPS information. Based on

the GPS information the trilateration technique is applied to determine the user location. In general, using the GPS information from more than three satellites the location of the user is determined. There exists different maps like the google maps, map quest, navigator …etc. Most of these applications use the GPS information in order to determine the user location. Based on the latitude and the longitude information from more than three satellites the user location is calculated. Currently the GPS information used by the civilians is different from the military GPS information which is more secure and accurate without much interruptions. In our project we used the civilians GPS information like the latitude and longitude values.

Along with the GPS information we used the Wi-Fi information to determine the user location. The Wi-Fi information used for the project are the BSSID, SSID, Signal to noise ratio and frequency. Our main focus is to use both the latitude and longitude information in addition to the Wi-Fi to find the user location accurately. We developed and Android application to facilitate our experiments. Initially Location based applications or simulations were developed depending on GPS to determine the location accuracy. Zirari et al [] proposed a Wi-Fi GPS based combined positioning algorithm. Their simulation results were determined based on distance from AP’s and Satellites. Our work, is addition to their work. We are also using other Wi-Fi information like BSSID, signal to noise

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ratio, frequency and developed an android application in order to correlate. We used to trilateration technique to determine the user location.

We validated our experiments results with the information collected using sophisticated GPS units from Department of Geography and Environmental Sciences.

In summary the main contributions are:

• To validate user location based on Wi-Fi and GPS information.

• Developed the Android application to facilitate the experiments

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2. Background and Prior work

In recent years, several location based applications started using the GPS information and among them, exists error in determining the user’s location accurately and this error sometimes may result in heavy loss especially during the emergency situations like disasters or amber alerts...etc. [2]. Hence in this project we used both Wi-Fi and GPS Signals information to validate the location accurately.

2.1. GPS

The Global Positioning System (GPS) is a space-based navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites. The system provides critical capabilities to military, civil, and commercial users around the world. The United States government created the system, maintains it, and makes it freely accessible to anyone with a GPS receiver.

The US began the GPS project in 1973 to overcome the limitations of previous navigation systems, integrating ideas from several predecessors, including a number of classified engineering design studies from the 1960s. The U.S. Department of Defense (DoD) developed the system, which originally used 24 satellites. It became fully operational in 1995. Roger L. Easton, Ivan A. Getting and Bradford Parkinson are credited with inventing it.

Advances in technology and new demands on the existing system have now led to efforts to modernize the GPS and implement the next generation of GPS Block IIIA satellites and Next Generation Operational Control System (OCX). Announcements from Vice President Al Gore and the White House in 1998 initiated these changes. In 2000, the U.S. Congress authorized the modernization effort, GPS III.

In addition to GPS, other systems are in use or under development. The Russian Global Navigation Satellite System (GLONASS) was developed contemporaneously with GPS, but suffered from incomplete coverage of the globe until the mid-2000s. There are also the planned European Union Galileo positioning system, India's Indian Regional Navigation Satellite System, China's BeiDou Navigation Satellite System, and the Japanese Quasi-Zenith Satellite System.

For our experiments we used the GPS information like the latitude and the longitude values which are collected from the google earth.

2.2. Wi-Fi

Wi-Fi (or WiFi) is a local area wireless computer networking technology that allows electronic devices to connect to the network, mainly using the 2.4 gigahertz (12 cm) UHF and 5 gigahertz (6 cm) SHF ISM radio bands.

The IEEE 802.11 standard is a set of media access control (MAC) and physical layer (PHY) specifications for implementing wireless local area network (WLAN) computer communication in the 2.4, 3.6, 5, and 60 GHz frequency bands. They are created and maintained by the IEEE LAN/MAN Standards Committee (IEEE 802). The base version of the standard was released in 1997, and has had subsequent amendments. The standard and amendments provide the basis for wireless network products using the Wi-Fi brand.

For our experiments we used the Wi-Fi information like:

• Wireless access point (AP) which is a device that allows wireless devices to connect to a wired network using Wi-Fi, or related standards. The AP usually connects to a router (via a wired network) as a standalone device, but it can also be an integral component of the router

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itself. An AP is differentiated from a hotspot, which is the physical space where the wireless service is provided [].

• BSSID is the MAC address of the access point.

• Signal to Noise ratio (SNR) compares the level of the Wi-Fi signal to the level of background noise [].

Since we are using Wi-Fi signal information there is a possibility for Wireless interference and this may cause some interrupts in the applications while accessing the information. Therefore, in our experiments we are focusing on outdoor area. Let us now look into what is a shielding effect and how the wireless networks get affected by the interference.

SHEILDING EFFECT

Wireless interference typically comes from three sources: walls and floors blocking wireless signals, other Wi-Fi networks using the same channel as your own Wi-Fi network, and appliances and electronics emitting radio frequency interference. Many wireless networks are affected by all three types.

• Interference from walls and floors: The construction of the building can greatly affect wireless communication speed and range. Some common types of materials, such as wood and glass, don’t have much of an effect. However, denser materials such as concrete, brick and metal can make it difficult to connect, slow network speeds or even completely block wireless signals from reaching certain parts of the building.

• Interference from competing Wi-Fi networks: Another type of interference is caused by Wi-Fi networks that are set to use the same frequency channel. In North America, a Wi-Fi network can operate on one of 11 channels, while most other countries have 13 channels available. If more than one Wi-Fi network uses the same channel, they’re constantly competing with each other to use limited bandwidth. It’s similar to a traffic jam that never ends because everyone not only wants to drive on 13 lane highway, but they all want to drive in the same lane too. They’re an especially common source of interference in cities, apartment buildings and densely populated areas where there are usually several Wi-Fi networks nearby.

• Interference from other electronics: Interference can also come from other electronics and appliances that aren’t connected to our wireless network, but use the same 2.4GHz or 5GHz frequencies to communicate. Cordless phones, Bluetooth devices, baby monitors and wireless video security systems are some examples. Some electronics and appliances, like microwave ovens, generate radio frequency noise as a byproduct, so we may notice a network slowdown or get disconnected only when we are reheating dinner. Some larger electronics, such as TVs, can affect Wi-Fi signals even when they’re asleep or turned off since their power supplies may generate short range interference. []

2.3 Free Space Path Loss

In telecommunication, free-space path loss (FSPL) is the loss in signal strength of an electromagnetic wave that would result from a line-of-sight path through free space (usually air), with no obstacles nearby to cause reflection or diffraction. It is defined in "Standard Definitions of Terms for Antennas", IEEE Std 145-1983, as "The loss between two isotropic radiators in free space, expressed as a power ratio." Usually it is expressed in dB, although the IEEE standard does not say that. So it assumes that the antenna gain is a power ratio of 1.0, or 0 dB. It does not include any loss associated with hardware imperfections, or the effects of any antennas gain.

Free Space path loss in decibels

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A convenient way to express FSPL is in terms of dB:

where the units are as before.

For typical radio applications, it is common to find   measured in units of GHz and   in km, in which case the FSPL equation becomes

For   in meters and kilohertz, respectively, the constant becomes   .

For   in meters and megahertz, respectively, the constant becomes   .

For   in kilometers and megahertz, respectively, the constant becomes   .

2.4 TRILATERATION

The intersections of the surfaces of three spheres is found by formulating the equations for the three sphere surfaces and then solving the three equations for the three unknowns, x, y, and z. To simplify the calculations, the equations are formulated so that the centers of the spheres are on the z = 0 plane. Also, the formulation is such that one center is at the origin, and one other is on the x-axis. It is possible to formulate the equations in this manner since any three non-collinear points lie on a unique plane. After finding the solution, it can be transformed back to the original three dimensional Cartesian coordinate system.

We start with the equations for the three spheres:

d is the x coordinate of point P2. You have to subtract it from x to get the length of the base of the triangle between the intersection and r2 (x, y, z are coordinates, not lengths).

We need to find a point located at (x, y, z) that satisfies all three equations.

We need to use r1 and r2 to eliminate y and z from the equation and solve for x:

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Assuming that the first two spheres intersect in more than one point, that is that

In this case, substituting the equation for x back into the equation for the first sphere produces the equation for a circle, the solution to the intersection of the first two spheres:

Substituting   into the formula for the third sphere and solving for y there results:

Now that the x- and y-coordinates of the solution point are found, the formula can be rearranged for the first sphere to find the z-coordinate:

Now the solution to all three points x, y and z is found. Because z is expressed as the positive or negative square root, it is possible for there to be zero, one or two solutions to the problem.

This last part can be visualized as taking the circle found from intersecting the first and second sphere and intersecting that with the third sphere. If that circle falls entirely outside or inside of the sphere, z is equal to the square root of a negative number: no real solution exists. If that circle touches the sphere on exactly one point, z is equal to zero. If that circle touches the surface of the sphere at two points, then z is equal to plus or minus the square root of a positive number.

The Derivation section pointed out that the coordinate system in which the sphere centers are designated must be such that

1. all three centers are in the plane z = 0,

2. the sphere center, P1, is at the origin, and

3. the sphere center, P2, is on the x-axis.

In general the problem will not be given in a form such that these requirements are met.

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This problem can be overcome as described below where the points, P1, P2, and P3 are treated as vectors from the origin where indicated. P1, P2, and P3 are of course expressed in the original coordinate system.

 

is the unit vector in the direction from P1 to P2.

 

is the signed magnitude of the x component, the coordinate system of the vector from P1 to P3.

 is the unit vector in the y direction. Note that the points P1, P2, and P3 are all in the z = 0 plane of the figure 1 coordinate system.

The third basis unit vector is  . Therefore,

 the distance between the centers P1 and P2 and

 

is the signed magnitude of the y component, in the figure 1 coordinate system, of the vector from P1 to P3.

Using   and   as computed above, solve for x, y and z as described in the Derivation section. Then

gives the points in the original coordinate system since   and , the basis unit vectors, are expressed in the original coordinate system.

RELATED WORK

Huang et al [] approached the GPS positioning problem in a unique way. They are tried to use the receiver behavior to improve the GPS Positioning. They used the Newton’s three laws for their model. The perspective is to collect the received GPS raw data from the user and apply noise screening and later introduce Behavior learning. Depending on the context of the receiver’s GPS data the accuracy is determined.

Tsui et al[] analyzed the performance of location accuracy between war driving and war walking in metropolitan cities. War driving is collecting of Wi-Fi data while driving a motorcycle or car and war walking is collecting the Wi-Fi data while walking. In their paper the authors tried to analyze the properties that effect the position accuracies. Depending on the signal strength of the detected APs, amount of signal data on a radio map, GPS labeling of calibration points, uniformity of the geographic distribution of calibration points. Their experiments resulted in revelation. The accuracy gap is not because of additional APs or better signal strength may lead to better results. Instead, the percentage

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of truncated RSS signals influence the results. Our work points out to the location accuracy and does not concentrate on war driving.

Lin and Hung[] approached the Location aware applications in a novice way. They proposed a task remainder based on location. As we are aware that initially we used to write down to do list. Later we started using mobiles are created the remainders based on time or date. But Lin and Hung brought forth the idea of creating the task remainders based on the location that we may be passing by or location that we are present right now.

Lin et al[] developed the location system based on accelerometer, compass, map information and GPS called ACMG location system. Their design contributes to reliable and accurate pedometer, multi-level location error correction using the A-GPS and also energy efficient outdoor location system. A-GPS is extensively used with GPS-capable cellular phones. Depending on the acceleration readings, displacement estimation is done and the peak values are noted. The location is recovery is done based on the map information. The latitude, longitude and compass data sequence are recorded and the path is determined. The authors conducted the experiments by installing the power tool on PC and developed the Android application to control different sensors on I9100. ACMG provides 24.7 7% power and accuracy less than 6.7m.

Han et al[] used the Gradient algorithm to estimate the direction of AP depending on the strong local signal strength. After calculating the signal strengths the points that are acquired will point to the direction of the AP. The authors performed the preprocessing of the data like the signal noise…etc to reduce the error in determining the direction of AP.

Bisio et al[] proposed a novice idea of using GPS/HPS and Wi-Fi Fingerprint based location recognition for check in applications over smartphones in Cloud based LBSs. In their work authors used the GPS, HPS, Wi-Fi information like the AP’s and also Wi-Fi fingerprint (FP) through a new definition of using the signal strength instead of absolute values. The FP values of the position data are stored and Wi-Fi scans are extracted simultaneously. If FP values are not available then the detecting the location they provided the Wi-Fi stability detection mechanism.

Talasila et al[] approached the problem of validating the sensed data through location. To solve the problem of validating the crowd sensed data they used the image processing technique to validate the location. They captured the images using the Bluetooth. After capturing the pictures the co- located points are compared with the validated photo task, which are marked as trusted points. .Their experiments resulted in 40% Cheating people and 25% detected the tasks with False Location claim.

Zhang et al[] implemented a prototype Borealis which is a directional analysis user navigation. Their experiments prove that Body can be an obstacle to determine the AP. If the user is facing the AP with the mobile the accuracy was better compared to the back facing AP. They also considered another scenario of the user rotating in place the accuracy is better compared remaining two conditions.

Whipple et al [] came up with a novel application using GPS information. The authors developed an Android application which runs in the background and when the driver drives through the school zone with more than 20 MPH an alarm sounds to alert the driver. The author’s used the GPS information to solve this problem. Using maps which uses GPS information may result in location inaccuracy. The research results of our proposed project can help improve the location accuracy for the aforementioned mobile applications.

Koch et al [] in their work used the geo location information for strategic pre incident preparation of an IT Forensics Analysis. According to the author’s definition strategic´ pre incident refers to all measures, which, in anticipation of a potential incident, can support the investigation of an incident, significantly more/additional data is available for a forensic examination. The author’s idea is to create an optimal starting point for forensic analysis. They tried to create good database which uses

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the IP geo-location information. They used the IP to determine the physical location and use the Intrusion detection technique utilizing the database to prove the identity. In contrast, our work is more specific in using the GPS and Wi-Fi for determining the user’s location.

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3. Design This project main focus is to validate the user location and see which among the ones listed below gives the accurate results. We implemented the Android applications to facilitate our experiments. The project is designed as follows:

a. Based on GPS signals we tried to retrieve the Latitude and longitude information by testing through android application.

b. We also used the Wi-Fi information like the frequency, signal to noise ratio, BSSID and calculated the distances from the Wi-Fi base stations based on frequency and Signal to noise ratio.

c. Used the Trilateration algorithm to determine the users location based on the Latitude and longitude values of the base stations as well the distances that are obtained as mentioned above.

d. To validate the obtained values we used the latitude and longitude information that was collected using the High End GPS unit from Department of Geography.

We obtained the longitude and latitude values from both GPS and Wi-Fi signals and compared with the validation data.

The figure below points out to the trilateration method for obtaining the user location.

In our case we used three base stations latitude and longitude values and also distances from each base station to obtain the user location.

The experiments are conducted in Alpine Garage Field (UCCS). The figure shown below is the field plan obtained from UCCS Facilities Department.

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- Indicates the 9 points where the experiments are conducted.

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4. Implementation

4.1 OverviewThe LAWS system consists of the following components:

1. GPS application which retrieve latitude and longitude values.

2. Wi-Fi application which consists of two parts:

a. First is to retrieve the Wi-Fi information and then calculating the distances from each Wi-Fi base station.

b. Obtaining the latitude and longitude values of the Wi-Fi base stations manually using Google earth. Based on latitude and longitude values in addition to distances applying the trilateration to obtain the user location longitude and latitude values.

4.2 Implementation details4.3 Challenges faced in this project

The implementation effort encountered several challenges. Collecting the GPS information, Wi-Fi information was one among them. In order to validate the obtained result with more accurate value we had to collect the data using more sophisticated GPS units and the points collected are projected on maps using google earth. When projected on the map there was some offset that was shown by the google earth. We also contacted the UCCS Facilities Services and obtained the Campus map and marked the offset on the Campus map.

The offset of the validation data is shown in the figure below.

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We tried to scan all the Wi-Fi that are available and calculate the distance from each Wi-Fi base station. We faced the challenged of selecting the three Wi-Fi router’s information that are near to the user location. After getting the shortest distances based on the BSSID we tried to obtain the user location using trilateration. We tried to obtain the latitude and longitude values of the Wi-Fi base stations using Google Earth.

Applying the Trilateration was one of the challenging task. When testing the code initially we were able to obtain the latitude value but not the longitude value. The reason is that the parameters passed were incorrect. So we learnt that after following all the steps in the Trilateration process the final step where we need to pass the ex,ey values to calculate the latitude and longitude value if the ex, ey values are not passed properly it might result in wrong output.

During the testing phase we initially took each value (latitude and longitude) for each point (total 9 points) in Alpine soccer field. But later we collected 10 samples for each point that is total of 90 samples for the Smart GPS testing in order to have concrete results. This was one of the challenging task.

5. Performance Evaluation of LAWS System

5.1 LAWS Testbed

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5.2 Test Results:

5.3 Performance Analysis:

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6. Lessons Learnt

7. Future Work

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8. Conclusion

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9. References:

[1]

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Appendix A: Configuration and Installation of LAWS System

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Appendix B: Demonstration Steps of LAWS System

A. GPSB. Wi-Fi

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