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 effective applications and availability of network access made this world so small. These modern technologies helped us reach almost anyone instantly. The need to locate the current locations of the persons plays a dominant role in our lives. The precision of location service in several applications like E-911, amber alerts and rescue efforts during the emergency situation are very important. In this project our goal is to use the GPS and Wi-Fi signals information for locating the user accurately. Android applications were developed to collect the GPS and the Wi-Fi data in our experiments. We borrowed high precision GPS equipment from Department of Geography to collect the reference GPS data on a set of locations. Android application on the same set of locations are compared against the reference GPS data. The average and variance of the smart phone GPS errors were analyzed. The average GPS location error from the android app is around 8 meters. An Android app was developed to collect the signal strengths from of all observed base stations in the area. Trilateration algorithm is implemented to compute the latitude and longitude of the smartphone using the strongest three Wi-Fi signals. Again, the Wi-Fi derived GPS data are compared with the reference GPS data and the average and variance values are analyzed. The results show the average Wi-Fi derived GPS location error is around 39 meters. Our experiment result shows that geolocation data using smart phone GPS software is more accurate compared to using Wi-Fi signals. It is also observed the geolocation displayed on the Google Earth could have errors as big as two meters. Based on data we analyzed, Wi-Fi derived GPS location software can be used for implementing the E-911 service within a campus to locate a Wi-Fi user bur beware of potential errors. The distance and direction from the base station with strongest signal may be used. They can also be used for verifying the presence of a Wi-Fi device and potentially the presence of a user in a secure geolocking service.

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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 every day. 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 Thorpe 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, Facilities and Services and Alpine Soccer Field Staff for their support in conducting my experiments.

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Table of Contents1. Introduction.....................................................................................................................................8

1.1 Motivation........................................................................................……. ……….. ……………………..8

1.2 Goal of the Project.......................................................................................................................8

2. Background and Prior work.........................................................................................................10

3. LAWS Design...................................................................................................................................17

4. LAWS Implementation.................................................................................................................19

4.1 Overview...................................................................................................................................19

4.2 Implementation details...............................................................................................................19

4.3 Challenges faced in this project.................................................................................................23

5. Performance Evaluation of LAWS System..................................................................................27

5.1 Test Results:..............................................................................................................................27

5.2 Performance Analysis:...............................................................................................................36

6. Lessons Learnt...............................................................................................................................37

7. Future Work..................................................................................................................................39

8. Conclusion......................................................................................................................................40

9. References:.....................................................................................................................................41

Appendix A: Configuration and Installation of LAWS System............................................................43

Appendix B: Demonstration Steps of LAWS System..........................................................................49

5. LAWS_GPS............................................................................................................................49

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List of Tables and FiguresFigure 3.1 - Trilateration implementation ....................................................17

Figure 3.2 - Alpine Soccer Field Plan indicating the experimented points...18

Figure 4.2.1 - Pseudo code of GPS Smart Phone application........................19

Figure 4.2.2 - Pseudo code to generate the Wi-Fi information. ...................20

Figure 4.2.3 - Pseudo code of Trilateration algorithm..................................21

Figure 4.2.4 - User location generated by LAWS_GPS application sample 122

Figure 4.2.5 - User location generated by LAWS_GPS application sample 222

Figure 4.2.6 - Wi-Fi information generated by the wifistri application.........23

Figure 4.2.7 - Alpine center Geolocation generated based on Wi-Fi trilateration....................................................................................................................... 23

Figure 4.3.1 - Offset errors of that pointed out by the Google Earth is shown for inselected location

on the Alpine Soccer field plan ……………………………………………………………………24

Figure 4.3.2- Nine points GPS data collected using High End precision GPS unit projected on

Google Earth..................................................................................................25

Figure 4.3.3 - Error in Longitude during implementation of Wi-Fi trilateration....................................................................................................................... 26

Figure B.1 - Retrieve and display current GPS location using LAWS_GPS app........................................................................................................................ 49

Figure B.2 - Base stations observed by LAWS_WIFI (wifistri) app................50

Figure B.3 - Alpine Soccer field user points generated by wifistrilatera (Left four points)............................................................................................................51

Figure B.4 - Alpine soccer field user points generated by wifistrilater app (Right four points)....................................................................................................52

Figure B.5 -Alpine Center GPS location generated by wifistrilaterat...........53

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Table 5.1.1 - Left Corner Samples.................................................................27

Table 5.1.2 - Left X marked Samples.............................................................28

Table 5.1.3 - Left End Corner Samples..........................................................28

Table 5.1.4 - Left End X marked Samples......................................................29

Table 5.1.5 -Right Corner Samples................................................................29

Table 5.1.6 -Right X marked Samples...........................................................30

Table 5.1.7 - Right End Corner Samples.......................................................30

Table 5.1.8-Right End X marked Samples.....................................................31

Table 5.1.9 -Center Samples.........................................................................31

Table 5.1.10 -Wi-Fi Base stations Latitude and Longitude Values................32

Table 5.1.11 - Wi-Fi Data that is used for the determining the user position32

Table 5.1.12 -High End Precision GPS data, Smart Phone GPS Data, Wi-Fi Derived Geolocation Data ..........................................................................................33

Table 5.1.13 - Error values of Smart Phone GPS Data, Wi-Fi Derived Geolocation compared the High Precision GPS data.................................................34

Table 5.1.14(a) -Average and Variance of the Smart Phone GPS data (90 samples) and Wi-Fi Derived

Geo location Data..........................................................................................35

Table 5.1.14(b) - Average and Variance of the Smart Phone GPS data and Wi-Fi Derived Geo location

data ………………………………………………………………………………………………..35

Table B.1 - User location based on Wi-Fi Trilateration (left points on Alpine Field)....................................................................................................................... 51

Table B.2 - User location based on Wi-Fi Trilateration (Right points on Alpine Field)....................................................................................................................... 52

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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 [1]. 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 enforce the disclosure of the patient records only to authorized person located in that building. Also in work place and in military to send the confidential data to the employee in specific office building. As the dependence of various location based applications are increasing rapidly, the need to improve the location accuracy is increasing.

Initially GPS information was confined to military purpose. Later this was introduced to the civilians. That led to the development of critical applications in various areas like space, roads and highways, marine, 911...etc. Mobile code and apps are also providing new avenues for the developers to provide the location based applications such as maps, emergency alerts...etc. But existing location based apps still need significant improvement in terms of location accuracy. During the emergency rescue activities.

One way to improve the location accuracy is to utilize other information like Wi-Fi information in addition to the GPS information to determine the location accuracy. In our project we investigate the accuracy of both methods, and explore different ways to utilize 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. Note that the availability of Wi-Fi derived location information can be used to verify if the user provided GPS information is spoof or not, vice versa.

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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 map applications like the google maps, map quest, navigator 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 provided by Google geolocation API available in Android phone for computing 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 in this 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 information to find the user location accurately. We developed Android applications to facilitate our experiments. Initially Location based applications or simulations were developed depending on GPS to determine the location accuracy. Zirari et al [2] proposed a Wi-Fi GPS based combined positioning algorithm. Their simulation results were determined based on distance from AP’s and Satellites. Our work, using other Wi-Fi information like BSSID, signal to noise 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 high precision 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

Section 2 presents the background and related work. Section 3 details our experiment design. Section 4 describes our software implementation of the Android app and GPS data collected from the high precision GPS instrument, the Android Google API client, and Wi-Fi derived GPS information from our Android app. Section 5 presents the Performance evaluation of LAWS system. Section 6 discusses the lessons learnt. Section 7 future research directions. Section 8 is the concluding remarks.

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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. [3]. In this project we tried to understand the location accuracy provided by the current Wi-Fi and GPS Signals and the geolocation and Wi-Fi network API so as to integrate them more effectively in location based mobile applications and related service.

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. The U.S Patent Office received his submittal, "Navigation System Using Satellites and Passive Ranging Techniques," and on January 29, 1974 it was assigned U.S. Patent 3,789,409.

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.

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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 [4].

For our experiments we used the GPS information like the latitude and the longitude values from a high precision Trimble GeoXT [23] Geology system and a smart phone running Android 4.2.2. We also use GPS location data provided by 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 wavelength) 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 [5].

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

• 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 itself. An AP is differentiated from a hotspot, which is the physical space where the wireless service is provided [6].

• BSSID is the ID name configured by network admin for the specific AP.

• Signal to Noise ratio (SNR) which is the ratio between the level of the Wi-Fi signal to that of background noise [7].

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

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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

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

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   [8].

2.4 TRILATERATION

Trilateration is a math formula for calculating the location of an intersection from the three spheres. In practical Wi Fi applications, the sphere corresponds the set of 3D points with certain wireless signal strength a receiver from a base station. 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:

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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:

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.

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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.

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

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gives the points in the original coordinate system since   and , the basis unit vectors, are expressed in the original coordinate system. [9]

RELATED WORK

Huang et al [10] 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 [11] 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 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 [12] 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 [13] 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 [14] 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.

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Bisio et al [15] 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 [16] 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 [17] 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 [18] 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 [19] 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 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. LAWS Design The main focus of this project is to validate the user location and see which among the

ones listed below gives the accurate results. We designed an Android application called LOCATION ACCURACY BASED ON WIRELESS SIGNALS (LAWS) 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 running android application with Geolocation API.

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 at UCCS. We use them as the reference location information or reference data.

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

Figure 3.1 depicts the trilateration method for obtaining the receiver location.

In our case we used latitude and longitude values from the actual locations of three base stations and also distances calculated from the signals of each base station to obtain the receiver location.

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Figure 3.1. Trilateration implementation

The experiments are conducted in Alpine Garage Field (UCCS). Figure 3.2 is the field plan obtained from UCCS Facilities Department and the nine locations selected for the study. Those nine locations are marked with blue solid circles Figure 3.2. They are selected based on the easy-to-recognize landmarks or the pained pattern for the soccer field.

Figure 3.2. Alpine Soccer Field Plan indicating the experimented points

- Indicates the nine points selected for the experiments.

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4. LAWS Implementation

4.1 OverviewThe LAWS system consists of the following components:

1. A GPS application which retrieves latitude and longitude values.

2. A Wi-Fi application which consists of two modules:

a. First module retrieves the Wi-Fi information and then calculates the distance from each Wi-Fi base station.

b. Second module saves the latitude and longitude values of the Wi-Fi base stations manually using Google earth based on the information provided by campus IT department and verified by personal visits. Based on latitude and longitude values of the base stations and the receiver’s distances, the trilateration algorithm is applied to obtain the longitude and latitude values of the receiver.

4.2 Implementation details We used Android Studio 1.5 to develop the applications. The applications support the android smart phones or tablets which have

minimum API 15 : Android 4.0.3 (Ice cream Sandwich) The programming languages used for our experiments are Java for functionality

and xml for the user interface. We used the Google Client API [20] which uses the classes like the Location

service for requesting the last location and other methods like the get longitude, latitude values and also Camera position method to zoom to the exact location.

GPS application Pseudo code to get the latitude and longitude values of the user location and set the camera position to zoom to the exact location.

Figure 4.2.1 Pseudo code of GPS Smart Phone application

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In order to get the current geolocation values in the Smart phone GPS application we used the Location Manager and the method location.get Lattitude and location.get Longitude will retrieve the latitude and longitude values of the current user location. In Figure 4.2.1 the latitude and longitude values that are obtained are passed to the variables current Latitude and current Longitude.

LatLng is a public class which extends the java.lang.Object. The current latitude and longitude values are passed to LatLng public constructor.

Using the Marker options like the position and title a marker will pop on the map which gives the details of the Latitude and longitude values of the geo location.

The latlng variable is passed to the marker option position to point out the user location. Inorder to display the values on the marker title option is passed with the latlng variable.

By default the marker doesn’t zoom in the camera to pinpoint the user location. It throws out the point on the map. In our case it shows a point in Colorado. So we used the Camera position to zoom in the camera and point the user location clearly. This is to make the application user friendly.

Wi-Fi Scan application:

Figure 4.2.2 gives out the distance calculated based on the free space path loss and using the Wi-Fi Scan receiver [21] which extends the Broadcast receiver to get the Wi-Fi information. Using Wi-Fi scan Result we get the following data like the BSSID, SSID, level (signal to noise ratio) and the frequency and the distance is printed based on the distance calculation formula.

Figure 4.2.2 – Pseudo code to generate the Wi-Fi information.

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In order to calculate the distance the level (i.e signal to noise ratio) and the frequency that are obtained by using the getScanResults method.. The scan results are passed to the list. So in the Figure 4.2.2 we can see that the values like BSSID, level(S/N ratio), frequency and SSID are obtained from the scan list. We call the calculate distance method by passing the level(S/N ratio) and the frequency parameters. The value that that is generated is passed to the string variable st5.

Wi-Fi trilateration:The Trilateration algorithm is applied and the pseudo code in Figure 4.2.3 is a part

of the algorithm,

Figure 4.2.3 Pseudo code of Trilateration algorithm

The distances that are obtained during the scanning of the Wi-Fi information as mentioned in Figure 4.2.2 are used to calculate the x and y coordinates. We followed the trilateration algorithm that is explained in Background work. The P1, P2, P3 are the latitude and longitude values of the Wi-Fi base stations, ex and ey are the unit vectors that are generated based on the P1, P2, P3 values.

Figure 4.2.4 gives a clear idea of the GPS application obtained when we run the application. Below are the instances of some points in the alpine soccer field.

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Figure 4.2.4 User location generated by LAWS_GPS application sample 1

Figure 4.2.5 User location generated by LAWS_GPS application sample 2

Figure 4.2.6 shows the screenshot displaying the BSSID, signal to noise ratio, frequency, distance and the SSID of the base stations. The distances are calculated based on the Wi-Fi signal strength and using the frequency and the signal to noise ratio

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Figure 4.2.6 Wi-Fi information generated by the wifistri application

Figure 4.2.7 gives the longitude and latitude of the user location that is generated using the Wi-Fi information.

Figure 4.2.7 Alpine Center Geo location generated based on Wi-Fi trilateration

4.3 Challenges faced in this projectDuring the implementation I encountered several challenges. Collecting precise GPS

information and related Wi-Fi information are among them. In order to validate the precision of

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obtained results, we collected the geolocation data using high precision GPS units and the points collected are projected on maps using Google Earth. When projected the precise GPS data on the map, it is observed the locations shown by Google Earth are slightly offset from the selected locations. The biggest offset is about 2 meters. It causes doubts on the precision of high precision GPS equipment or our operating procedure. We contacted the Geology Department for verifying the procedure. We also contacted UCCS Facilities Services and obtained the Campus map and marked the offset on the Campus map. It is confirmed that indeed the points displayed by Google Earth are off in some cases. Some can be explained by the 3D manipulation of images and the precision of pins drawn.

The offsets of the locations are marked red in the Figure 4.3.1, while the selected locations are marked blue.

Figure 4.3.1 Offset errors of the Google Earth shown for selected location on the Alpine Soccer field plan.

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Figure 4.3.2 – Nine points GPS data collected using High End precision GPS unit projected on Google Earth

Figure 4.3.2 shows the selected locations displayed by Google Earth. The yellow pins indicates the nine points on the Alpine soccer field and the red lines indicate the offset from the original point

Wi-Fi user location calculation:

We tried to scan all the Wi-Fi base stations that are available and calculate the distance from each Wi-Fi base station. The coding error in sorting the signal strength data presented the challenge for selecting the top three Wi-Fi router that are near to the receiver. After getting the shortest distances based on the BSSID we tried to obtain the receiver location using trilateration. Since we don’t have the database for the Wi-Fi base station latitude and longitude values we had to obtain the latitude and longitude values of the Wi-Fi base stations manually using Google Earth.

Applying the Trilateration was a challenging task. When testing the code, initially we were able to obtain the latitude value but not the longitude value. Through the careful examination, it is found that the parameters passed to the algorithm were incorrect. We learnt that after following all the steps in the Trilateration process, in the final step we need to pass the ex, ey values ( which are the unit vectors of the latitude and longitude values of the three points)correctly to calculate the latitude and longitude value. If the ex, ey values are not passed properly it might result in wrong output.

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Figure 4.3.3 Error in Longitude during implementation of Wi-Fi trilateration

GPS user location determination:

During the testing phase, we initially took the average/variance value (latitude and longitude) for each selected location (total 9 locations) in Alpine soccer field. But later we collected 10 samples for each location that is total of 90 samples for the Smart GPS testing in order to compute the average/variance values for each selected location. This was one of the challenging tasks.

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5. Performance Evaluation of LAWS System

5.1 Test Results:

Initially we performed the testing based on only nine values for nine points in smart phone GPS data. Later we extended the testing by collecting 10 samples for each point and a total of 90 samples are collected to get the accurate Smart phone GPS data. We also scanned the Wi-Fi and found the shortest distance and compared with the High End Precision GPS data.

The tables listed below contain the information about 90 samples of the Smart phone GPS data

Table 5.1.1 points to the ten samples that are collected for the left corner of the Alpine soccer field. These samples are collected by using the GPS application. The average of the 10 samples are taken for our experiments.

Table 5.1.1 – Left Corner Samples

Left CornerLatitude Longitude

1 38.8975336 -104.80684992 38.8975639 -104.80687453 38.8975638 -104.80746834 38.8975975 -104.80727315 38.8975837 -104.80742586 38.8976804 -104.80694487 38.8975533 -104.80744278 38.8976719 -104.80728749 38.8975051 -104.807402510 38.8975413 -104.8074072Average Value 38.89757945 -104.8072376

Table 5.1.2 contains the data of the 10 samples that are collected using the GPS

application at left corner where there is symbol X in the Alpine soccer field.

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Table 5.1.2 – Left X marked Samples

Left X Marked Latitude Longitude

1 38.8975757 -104.80689082 38.897524 -104.80683873 38.8976238 -104.80691214 38.8975388 -104.80685915 38.8975388 -104.80685916 38.8975563 -104.8068847 38.8977576 -104.80727598 38.8981889 -104.81184399 38.8979963 -104.807243210 38.89788102 -104.807398Average Value 38.89771812 -104.8075005

Table 5.1.3 contains the 10 samples of the latitude and longitude values at Left end corner point using the GPS application. The average value is also calculated from those 10 samples and in the Evaluation part we used the average value.

Table 5.1.3 – Left End Corner Samples

Left End CornerLatitude Longitude

1 38.898053 -104.80783022 38.8980934 -104.80791883 38.8981477 -104.8077414 38.8980077 -104.8078775 38.8978874 -104.80782526 38.8980573 -104.80788367 38.8979872 -104.80774588 38.8979145 -104.80776959 38.8979601 -104.807739310 38.8978959 -104.80782Average Value 38.89800042 -104.807815

Table 5.1.4 points out to the 10 samples that are collected using the GPS application at the Left corner there is X mark on the Alpine fields. These samples are collected at that particular point.

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Table 5.1.4 – Left End X marked Samples

Left End X MarkedLatitude Longitude

1 38.8977088 -104.80751912 38.8980014 -104.80750023 38.8979867 -104.8074824 38.8978269 -104.80750265 38.897851 -104.80724586 38.8979356 -104.80771897 38.8981035 -104.80759838 38.898071 -104.807599 38.8980107 -104.807553110 38.8980775 -104.8075483Average Value 38.89795731 -104.8075258

Table 5.1.5 points to the ten samples that are collected for the right corner of the Alpine soccer field. These samples are collected by using the GPS application. The average of the 10 samples are taken for our experiments.

Table 5.1.5 – Right Corner Samples

Right CornerLatitude Longitude

1 38.8982288 -104.8069912 38.8975853 -104.80688613 38.8980651 -104.80660444 38.8982396 -104.80669065 38.8982288 -104.80665096 38.8980078 -104.80660187 38.8982459 -104.80655648 38.8979951 -104.80655179 38.8975463 -104.806836310 38.8981345 -104.8065134Average Value 38.89802772 -104.8066883

Table 5.1.6 points out to the 10 samples that are collected using the GPS application at Right corner where there is symbol X in the Alpine soccer field.

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Table 5.1.6 – Right X marked Samples

Right X MarkedLatitude Longitude

1 38.8980866 -104.80713542 38.8981701 -104.80715653 38.8980438 -104.80694744 38.8980025 -104.8067515 38.8978631 -104.80727246 38.8979192 -104.8071747 38.897947 -104.8072688 38.8977264 -104.8068049 38.8980794 -104.80716410 38.89808381 -104.8069522Average Value 38.89799219 -104.8070625

Table 5.1.7 contains the 10 samples of the latitude and longitude values at Right end corner point using the GPS application. The average value is also calculated from those 10 samples and in the Evaluation part we used the average value.

Table 5.1.7 – Right End Corner Samples

Right End CornerLatitude Longitude

1 38.8980879 -104.80661032 38.8983845 -104.80720763 38.8984229 -104.80676754 38.898213 -104.80708345 38.8985921 -104.80689496 38.898295 -104.80713437 38.8985478 -104.80709098 38.8985099 -104.80703419 38.8985173 -104.80710 38.898505 -104.8070987Average Value 38.89840754 -104.8069922

Table 5.1.8 points out to the 10 samples that are collected using the GPS application at the Right corner there is X mark on the Alpine fields. These samples are collected at that particular point.

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Table 5.1.8 – Right End X marked Samples

Right End X MarkedLatitude Longitude

1 38.8980382 -104.80709892 38.8984719 -104.80703333 38.8982374 -104.80716564 38.8984952 -104.8071085 38.8981375 -104.80721746 38.8984317 -104.80710117 38.897604 -104.80689928 38.8983347 -104.80709629 38.8982107 -104.807137110 38.8980824 -104.8074438Average Value 38.89820437 -104.8071301

Table 5.1.9 points out to the 10 samples that are collected at the center of the alpine soccer field using the GPS application. The average of these values are taken for evaluation.

Table 5.1.9 – Center Samples

CenterLatitude Longitude

1 38.8980132 -104.80764942 38.8979793 -104.80740383 38.8979676 -104.80763364 38.8980047 -104.80740635 38.8980887 -104.80721026 38.8980174 -104.80744537 38.8980204 -104.807418 38.8980356 -104.80747179 38.8980917 -104.807334210 38.898049 -104.8072883Average Value 38.89802676 -104.8074253

Table 5.1.10 gives the details of the Wi-Fi base station latitude and longitude values

which are collected using the Google Earth. Since we know where the Wi-Fi base stations are located we collected the information using Google Earth.

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Table 5.1.10: Wi-Fi Base stations Latitude and Longitude Values

Description Latitude Longitude1 Left corner 38.897540 -104.8073612 Left end corner 38.897969 -104.8079373 Right corner 38.898053 -104.8065604 Right end corner 38.898559 -104.8069945 Center 38.897776 -104.806991

The table 5.1.11 gives details of the Wi-Fi data that is used to obtain the user location. We collected the distances based on the Wi-Fi scanning and applying the Free Space Path Loss Formula. The latitude and longitude values are taken from the Google Earth as mentioned in Table

Table 5.1.11: Wi-Fi Data that is used for the determining the user position

Description

Distance 1 Distance 2 Distance 3 Lat/Lng 1 Lat /Lng 2 Lat/Lng 3

1 Left corner 5.21 6.56 6.93 38.897540/ -104.807361

38.897776/-104.806991

38.897969/-104.807937

2 Left x marked

7.31 8.31 9.89 38.897540/ -104.807361

38.897776/-104.806991

38.897969/-104.807937

3 Left end corner

8.81 8.81 6.56 38.897540/ -104.807361

38.897776/-104.806991

38.897969/-104.807937

4 Left end x marked

11.58 16.36 10.32 38.897540/ -104.807361

38.897776/-104.806991

38.897969/-104.807937

5 Right corner

0.96 4.96 0.96 38.898053/ -104.806560

38.897776/-104.806991

38.898559/-104.806994

6 Right x marked

4.37 4.81 4.37 38.898053/ -104.806560

38.897776/-104.806991

38.898559/-104.806994

7 Right end corner

0.92 4.91 0.92 38.898053/ -104.806560

38.897776/-104.806991

38.898559/-104.806994

8 Right end x marked

4.37 4.61 4.37 38.898053/ -104.806560

38.897776/-104.806991

38.898559/-104.806994

9 Center 4.61 4.91 5.84 38.897969/-104.807937

38.897776/ -104.806991

38.897540/-104.807361

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Table 5.1.12 gives the details of the High end Precision Data, Smart Phone GPS Data after averaging the 90 samples and the Wi-Fi derived Geolocation data for the nine points in the alpine soccer field.

Table 5.1.12 - High End Precision GPS data, Smart Phone GPS Data, Wi-Fi Derived Geolocation Data

High End Precision GPS data Smart Phone GPS Data Wi-Fi Derived Geolocation DataLatitude Longitude Latitude Longitude Latitude Longitude

1 Alpine Field Left Corner

38.89758023020 -104.80739922900

38.89757945 -104.8072376 38.8980554961 -104.8068455038

2 Alpine Field X marked

38.89781919210 -104.80731095500

38.89771812 -104.8075005 38.8980554974 -104.8068455025

3 Alpine Field Left End Corner

38.89801726900 -104.80785340800

38.89800042 -104.807815 38.8980555711 -104.8068454883

4 Alpine Field Left end X marked

38.89804308980 -104.80754912700

38.89795731 -104.8075258 38.8980549406 -104.8068460593

5 Alpine Field Right Corner

38.89809631360 -104.80659569900

38.89802772 -104.8066883 38.8978887960 -104.8067250399

6 Alpine Field Right X marked

38.89807207270 -104.80689642100

38.89799219 -104.8070625 38.897887851 -104.8067251487

7 Alpine Field Right end Corner

38.89853266550 -104.80705306100

38.89840754 -104.8069922 38.897887957 -104.8067250422

8 Alpine Field Right end X marked

38.89830563100 -104.80714542900

38.89820437 -104.8071301 38.897887840 -104.8067251591

9 Alpine Field Center

38.89805676080 -104.80722240400

38.89802676 -104.8074253 38.8979905002 -104.80696949971

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Error Values

Table 5.1.13 gives the information about the error values of the Smart phone GPS Data and Wi-Fi derived Geo location data compared with the High End precision data.

Table 5.1.13: Error values of Smart Phone GPS Data, Wi-Fi Derived Geolocation compared with the High End Precision GPS data

.

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High End Precision GPS data Smart Phone GPS Data Error value

Wi-Fi Derived Geolocation Data Error value

Latitude Longitude Latitude Longitude Latitude Longitude1 Alpine

Field Left Corner

38.89758023020 -104.80739922900

0.0000007802 0.000161609 0.000475266 0.000553725

2 Alpine Field X marked

38.89781919210 -104.80731095500

0.00010107 0.000189525 0.000236308 0.000465455

3 Alpine Field Left End Corner

38.89801726900 -104.80785340800

0.000016849 0.000038368 0.0000383021 0.00100792

4 Alpine Field Left end X marked

38.89804308980 -104.80754912700

0.0000857798 0.000023297 0.0000118508 0.000703068

5 Alpine Field Right Corner

38.89809631360 -104.80659569900

0.0000685936 0.000092561 0.000207518 0.000129341

6 Alpine Field Right X marked

38.89807207270 -104.80689642100

0.0000798817 0.0002166069 0.0001844222 0.000171272

7 Alpine Field Right end Corner

38.89853266550 -104.80705306100

0.000125125 0.000060891 0.000644708 0.000328019

8 Alpine Field Right end X marked

38.89830563100 -104.80714542900

0.000101261 0.000015369 0.000417791 0.00042027

9 Alpine Field Center

38.89805676080 -104.80722240400

0.0000300008 0.000202876 0.000062606 0.000252904

Average and Variance

The average and the variance of both the smart phone GPS data which is generated based on the 90 samples and the Wi-Fi derived geolocation data are shown in the table 5.1.14a.

Table 5.1.14a. Average and Variance of the smart phone GPS data (90 samples) and Wi-Fi Derived Geo location Data

Average VarianceLatitude Longitude Latitude Longitude

Smart Phone GPS Data Error value

0.0000609342

6.77m 0.0000950565

10.58m

0.0000000018123

0.0002m

0.0000000005613 0.00006m

Wi-Fi Derived Geolocation Data Error value

0.000253581 28.18m

0.000447997 49.87m

0.0000000470235

0.005m 0.00000000863057

0.00095m

Below is Table 5.1.14b of Average and Variance generated based on one value of the smart Phone GPS data for each point (nine points).

Table 5.1.14b Average and Variance of the smart phone GPS data and Wi-Fi derived geolocation Data

Average VarianceLatitude Longitude Latitude Longitude

Smart Phone GPS Data Error value

0.000134676

14.96697m

0.000118667

13.21m

0.0000000318012

0.003m

0.00000000301528

0.00033m

Wi-Fi Derived Geolocation Data Error value

0.000253581

28.18m 0.000447997

49.87m

0.0000000470235

0.005m

0.00000000863057

0.00095m

For converting the latitude and longitude values of the average and variance to meter we used the formula in [22].

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5.2 Performance Analysis: After collecting several samples of data for our experiments the results shows that the

performance of the GPS is 75% better than the Wi-Fi derived information based on the 90 samples that were collected for the Smart Phone GPS data.

Based on the nine values of the smart phones geolocation data, the performance of the GPS is 50% better than the Wi-Fi derived information.

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6. Lessons Learnt

While working on this project I learnt many new things which I was not aware of. I was new to Android Studio and I had to learn how to program in it from scratch. Learning the Location based API’s was very interesting where initially it gives us the opportunity to track where we are and it was quite fun.

I also learnt how to run the application on the emulator and deploying the application on to a mobile was a challenging task. Thus I learnt that figuring out the actual problem is the biggest challenge and once known, it can be a cake walk. But every day new technologies come in and keeps our mind adapt to new changes.

I started off my project working on the GPS application and it was quite interesting and challenging as well. I was looking at the Location Manager API and the methods that belong to the API. The methods like the get latitude and longitude are quite straight forward and getting those values was easy but marking those values using the marker options was a bit difficult. It displays the maps in scale that covered the whole Colorado. Then I figured out that we need to zoom the camera to see the exact location. I also learnt how to apply the methods related to Camera position like the zoom, tilt. Position the camera right makes the presentation easier to understand with proper context.

Below are my findings on Wi-Fi information:

1. I learnt many new things about the Wi-Fi and how it works. I started learning what are BSSID, SSID, signal to noise ratio, frequency and how all these components impact on calculating the user positions.

2. My initial task was to scan the Wi-Fi base stations in Android. This gave me an opportunity to learn Wi-Fi manager API and the related classes and methods.

There is a Wi-Fi scan receiver which extends the broadcast receiver. Using the getScanResults we can obtain the Wi-Fi information. To get the BSSID we can use wifiScanResult.get (index).BSSID. We can also get the other Wi-Fi information in similar way.

3. I also learnt how to apply the trilateration algorithm with the signal to noise ratio and the frequency that was obtained as inputs to determine the user location.

4. It is also interesting to observe that the Wi-Fi derived distance varies and through the experiments we were able to analyze the accuracy of these Wi-Fi derived distances.

Wi-Fi API and the Google Client API are very interesting to learn. We get to know that how we can utilize the wireless information to develop applications that will help the society.

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Android Studio is one of the best tools to work with. It is an open source tool and we have the freedom to put into practice our thoughts and come up with the new ideas. Any person can learn it very quickly and is also user friendly. Once we understand the basic concept that it is going to be super easy.

I also learnt how to use the Google Earth and overlay the selected locations as pinpoints. Google Earth is also user friendly. It consists of the tool options where we can use Ruler to draw lines and it gives us the details measured in meters or centi-meters, angle, etc.

I must never forget to mention the data collection from different departments was fun and I got a chance to learn in detail how each department works. I won’t forget all that I have learnt from those people. As we know no two persons think alike is so true that the information that I collected was satisfying their own problem. But that same information could also be used for our project as well. That was quite interesting.

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7. Future Work

1. Design and develop E-911 Service.  With Donovan Thorpe of UCCS campus IT, we have identified a plan to push the individual Wi-Fi signal info observed from some base stations to a Linux server with MySQL database in real time to allow the design and implement of related software for correlating user accounts with their Wi-Fi devices. Then we can integrate with an enhanced version of LAWS trilateration algorithm that was developed for estimating the user’s location. 

2. GPS Verification Service.  In a research project “Secure Enhanced Geolocking “Dr. Chow has been identified Wi-Fi derived geolocation service such as E-911 can be used for verifying the GPS location reported by the receiver. It can be used to detect GPS location spoofing since the base stations near the geolocation area can detect the Wi-Fi signal of the intended receiver.   The software developed in these two master projects can be integrated to provide a reliable secure service which ensures data are only received by designated parties in the specified geolocation areas.

3. The evaluation of this project was done statically. We can enhance this project to dynamically generate the GPS coordinates of the Wi-Fi base stations provided we have the database of the Latitude and Longitude values of the Wi-Fi base stations depending on the BSSID. Also while evaluating, collecting several samples of the data will improve the accuracy of the user location.

4. The location can also be determined accurately if combine the latitude and longitude values obtained from High End precision GPS units and distances generated from the Wi-Fi information.

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8. Conclusion

In this modern era where people are using many social networking sites like Facebook, Twitter..etc to post their current status or where they are actually located tells us that user location information is playing a vital role. Improving user location accuracy will definitely make a big difference during emergency situations.

This project investigates whether we can improve the location accuracy based on the Wi-Fi and GPS information.

LAWS system was developed to provide the user location accurately. Based on our observations the GPS data from an Android phone is more accurate than the Wi-Fi derived data from the observed Wi-Fi base station signals. GPS was approximately 50 % more accurate than the Wi-Fi. The average of the GPS was around 10 meters where as it was around 39 meters. We also found an offset of approximately 2 meters with the Google Earth when we projected the High End precision GPS unit data.

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9. References

[1] http://www.gps.gov/applications/

[2] Soumaya Zirari, Philippe Canalda and Francois Spies, “WiFi GPS based Combined positioning Algorithm”, Wireless Communications, Networking and Information Security (WCNIS), 2010 IEEE International Conference, Pages 684-688, June 25-27 2010.

[3] Szakacs-Simon, P, Moraru, S.A. and Perniu L, ”Android application developed to extend health monitoring device range and real-time patient tracking”, Computational Cybernetics (ICCC), 2013 IEEE 9th International Conference, Pages 171 -175, July 8-10, 2013.

[4] https://en.wikipedia.org/wiki/Global_Positioning_System#cite_note-2

[5] https://en.wikipedia.org/wiki/Wi-Fi

[6] https://en.wikipedia.org/wiki/Wireless_access_point

[7] http://help.netspotapp.com/what-is-the-signal-to-noise/

[8] https://en.wikipedia.org/wiki/Free-space_path_loss

[9] https://en.wikipedia.org/wiki/Trilateration

[10] Jiung-yao Huang, Chung-Hsien Tsai, “Improve GPS Positioning Accuracy with Context Awareness”, Ubi-Media Computing, 2008 First IEEE International Conference, Pages 94-99, July 31 – Aug 1 2008.

[11] Arvin Wen Tsui, Wei-Cheng Lin, Wei-Ju Chen, “Accuracy Performance Analysis between War Driving and War Walking in Metropolitan Wi-Fi Localization”, Pages 1551-1562, IEEE Transactions on Mobile Computing, VOL. 9, NO. 11, November 2010

[12] Chi-Yi Lin & Ming-Tze Hung, “A location-based personal task reminder for mobile users”, Pages 303-314, Personal and Ubiquitous Computing archive, Volume 18 Issue 2, February 2014.

[13] Anhua Lin , Jianzhong Zhang ; Kai Lu ; Wen Zhang, “An efficient outdoor localization method for smartphones”, Pages 1-8, Computer Communication and Networks (ICCCN), 2014 23rd International Conference, 4-7 Aug 2014.

[14] Dongsu Han, David G. Andersen, Michael Kaminsky, Konstantina Papagiannaki, and Srinivasan Seshan, “Access Point Localization Using Local Signal Strength Gradient”, Pages 99 – 108, PAM '09 Proceedings of the 10th International Conference on Passive and Active Network Measurement.

[15] Igor Bisio, Member, Fabio Lavagetto, Mario Marchese, and Andrea Sciarrone, “GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs”, Pages 858 – 869, Multimedia, IEEE Transactions, June 2013.

[16] Manoop Talasila, Reza Curtmola, and Cristian Borcea, “Improving Location Reliability in Crowd Sensed Data with Minimal Efforts”, Pages 1-8, Wireless and Mobile Networking Conference (WMNC), 2013 6th Joint IFIP, 23-25 April 2013.

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[17] Zengbin Zhang, Xia Zhou, Weile Zhang, Yuanyang Zhang, Gang Wang, Ben Y. Zhaoand Haitao Zheng, “I Am the Antenna: Accurate Outdoor AP Location using Smartphones”, Pages 109-120, MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking.

[18] John Whipple, William Arensman, Marian Starr Boler, “A Public Safety Application of GPS-Enabled Smartphones and the Android Operating System”, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, Pages 2059-2061, October 11-14 2009.

[19] Robert Koch, Mario Golling, Lars Stiemert, and Gabi Dreo Rodosek, “Using Geolocation for the Strategic Preincident Preparation of an IT Forensics Analysis”, Systems Journal, IEEE, Pages 1-12, February 16 2015.

[20] https://developers.google.com/android/reference/com/google/android/gms/common/api/GoogleApiClient

[21] http://developer.android.com/reference/android/net/wifi/WifiManager.html

[22] https://recalll.co/app/?q=mapping%20-%20transform%20longitude%20latitude%20into%20meters.

[23] http://www.trimblesupport.com/GeoXT.html

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Appendix A: Configuration and Installation of LAWS System

The following are the steps to be followed to install and configure the web system in this project:

1. Download the source code from http://walrus.uccs.edu/~gsc/pub/master/jyeddana/src/

2. Extract the contents of the compressed file. You will see wifistri directory show up in <WIFISTRIDir> directory where the directory where the zip file is expanded

3. Install the Android Studio 1.5 http://developer.android.com/sdk/index.html

4. If we want to enhance the project just go the C:\<Downloaded Filelocation>\LAWS Systems\wifistri\app\src\main\java\student\com\wifistri – which has the java file and we can perform modifications to the java file. If we want to change the layout design then the location C:\<downloaded File location>\LAWS Systems\wifistri\app\src\main\res\layout – contains the content_main file which is an XML file. We can customize this file for the user layout. You might also have to change the ‘local’ file information in the application. In the application it consists of my local information, i.e., ndk.dir=C\:\\Users\\jahna_000\\AppData\\Local\\Android\\sdk7\\ndk-bundle.

sdk.dir=C\:\\Users\\jahna_000\\AppData\\Local\\Android\\sdk9) where the Android sdk is installed. After you install Android Studio, you get the path of your sdk<versionnumber> installed directory. Once these configuration files are set properly, you can run the application successfully.

6. Below are the series of steps of how to run the android application

Step 1: After you select the File-> Open,the file with .proj extension. In this case, wifistri is the name of the project we just installed. You can list of files as shown below. Select the app option.

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Step2: After selecting the app options it triggers the options as shown below. Select the src option.

Step3: Once we select the src, it again gives us some options. Select the main option

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Step4: In main select the java option.

Step5: Once the java option is selected, it shows the MainActivity in which the source code is present. If we want to edit the file, we can.

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Step6: If we want to modify the layout, we can do that as well. Repeat the Steps1, 2, 3 steps and in main select the res option. Which looks like the image shown below.

Step7: After selecting the res options, it shows list of other options as shown below. Select the layout option.

Step8: After selecting the layout option, select the content_main.xml option and it shows the layout design as shown below.

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Step9: After selecting that option, you should now see the screen as shown below. In the screenshot below we observe there are two tabs Design and Text at the bottom. This screenshot shows the design layout. We can also drag and drop the different layouts from the Palette on the left panel.

Step10: Let us now look into how the text tab looks like. The screenshot below shows the text tab in the content_main.xml. In text tab it consists of the xml code for the layout design.

Step 11: Run the application by clicking the run symbol in the toolbar.

Once we click the run symbol or start the run option, it asks us to select the device to run the app on. We can see the list of the devices that are currently connected. Select one to deploy the application. We can see the expected output accordingly.

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4. Repeat the same process for LAWS3, wifistr wifistrilater, wifistrilatera, wifistrilaterat. The user location details will be displayed.

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Appendix B: Demonstration Steps of LAWS System5. LAWS_GPS Download the LAWS3 application and run the app in the device. It displays the latitude and longitude value of the user location as shown in Figure B.1.

Figure B.1. Retrieve and display current GPS location using LAWS_GPS app.

LAWS_GPS app is started by retrieving the device’s GPS location through the Google API Client.

It uses the Location Services API to retrieve the location information like the latitude and the longitude values.

Once the values are retrieved those values are added to the marker to pinpoint the user location. We have Marker options like the position and the title to mark the location.

Camera position is set using the options like the zoom, tilt to set the camera to make the presentation easier and clear to the end user.

LAWS_WIFI app

For the Wi-Fi first we need to scan all the observable Wi-Fi base stations and calculate the distances from each base station. The application is named as the “wifistri”. Once you click on the application, it gives us the details of all the Wi-Fi base stations that are near along with the distances, shown below in Figure B.2. like the MAC address of

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the base station, the S/N ratio, estimated distances, and SSID for each base station. In order calculate the distance we did not use any API instead coded the method that uses the Free Space Path Loss Formula. First the S/N ratio and frequency are retrieved and passed as a parameter to distance calculation method.

Figure B.2. Base stations observed by LAWS_WIFI (wifistri) app.

Once the distances are calculated, we statically passed the distances in the Trilateration code to generate the receiver location.

The screen shot in Figure B.3 shows the values that are generated for four points on the left side of the Alpine field. The left side values indicate the four points i.e Left corner, Left x marked and the Left End corner and the Left End X marked of the Alpine soccer field. The application wifistrilatera generate these values. These values are generated based on the Wi-Fi Trilateration algorithm. The algorithm uses the three nearest Wi-Fi Base stations latitude and longitude values and distances from those base stations.

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Figure B.3. Alpine Soccer field user points generated by wifistrilatera (Left four points)

TableB.1 is the table representation of the FigureB.3. The table gives the details of the four points in the Alpine soccer field based on the Wi-Fi trilateration.

Table B.1 – User location based on Wi-Fi Trilateration (left points on Alpine field)

Description Latitude Longitude

1 Left corner 38.89805549615078 -104.806845549742307

2 Left X marked 38.89805549742307 -104.80684550257692

3 Left End corner 38.898055571163795 -104.8068454288362

4 Left End X marked

38.89805494067022 -104.80684605932977

The screen shot in Figure B.4 shows the values that are generated for four points on the Right side of the Alpine field. The right side values indicate the four points i.e Right corner, Right x marked and the right End corner and the Right End X marked of the Alpine soccer field. The application wifistrilater generate these values. These values are generated based on the Wi-Fi Trilateration algorithm. The algorithm uses the three nearest Wi-Fi Base stations latitude and longitude values and distances from those base stations.

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Figure B.4. Alpine soccer field user points generated by wifistrilater app (Right four points)

TableB.2 is the table representation of the FigureB.4. The table gives the details of the four points in the Alpine soccer field based on the Wi-Fi trilateration.

Table B.2 – User location based on Wi-Fi Trilateration (Right points on Alpine Field)

Description Latitude Longitude

1 Right corner 38.897887960084084 -104.80672503991592

2 Right X marked 38.89788785125235 -104.80672514874765

3 Right End corner 38.89788795776624 -104.80672504223377

4 Right End X marked

38.89788784081291 -104.8067251591871

Figure B.5 indicates the Alpine center GPS location is generated based on the three nearest Wi-Fi base stations and latitude and longitude values of those Wi-Fi base station. The user location information like the latitude and longitude are generated using the Trilateration algorithm which uses three Wi-F I base stations information

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Figure B.5. Alpine Center GPS location generated by wifistrilaterat

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