The Accuracy Of Location Services And The Potential Impact On The

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University of Derby School of Computer & Mathematics A project completed as part of the requirement for the Bsc (Hons) Computer Forensic and Security Entitled The accuracy of location services and the potential impact on the admissibility of GPS Based evidence in court cases. By Ishwar Khadka [email protected] [email protected] In the years 2012 - 2015 1 Abstract This project examines the positioning inaccuracy of location services. This project explores the potential impact from the result of this study with regards to the admissibility of GPS based evidence in court cases for both prosecution and defence. The experiment was carried out using iPhone 5 and Samsung Galaxy S4 and the results suggest that positioning inaccuracy of location service is much higher than previous studies have suggested. Therefore, the positioning error uncovered during this experiment suggests that GPS-based evidence is highly questionable and misleading. The result of this experiment could change the weight given to GPS-based evidence in court. 2 Acknowledgements I would like to thank especially my supervisor Richard J. Self who motivated me, supported me and allowed me to carry out this project with his guidance. I would like to thank my family and friends for their support during the course of this project. I would like to thank especially Hari Sapkota and Anuj Paudel for reading through and listening to me talking about it. 3 Table of Contents Abstract .................................................................................................................................................. 2 Acknowledgements ................................................................................................................................ 3 Table of Contents ................................................................................................................................... 4 Table of Figures ..................................................................................................................................... 8 Chapter 1 - Introduction ....................................................................................................................... 11 1.1 Introduction ................................................................................................................................ 11 1.2 Project Aim and Objectives: ...................................................................................................... 12 Chapter 2 - Literature Review.............................................................................................................. 13 2.1 Introduction ................................................................................................................................ 13 2.2 Overview of Global Positioning System Development ............................................................. 13 2.2.1 Basic concept of Global Positioning System ...................................................................... 14 2.3 Admissibility of GPS evidence in court cases ........................................................................... 17 2.3.1 Current use of GPS data by authorities ............................................................................... 17 2.3.2 The current use of GPS evidence in court cases ................................................................. 18 2.4 Factors affecting the accuracy and integrity of GPS based evidence ........................................ 19 2.4.1 The six standard factors ...................................................................................................... 20 2.4.2 The integrity of GPS data ................................................................................................... 22 2.5 Location services ....................................................................................................................... 23 2.5.1 Assisted GPS....................................................................................................................... 24 4 2.5.2 WiFi and IP positioning ...................................................................................................... 26 2.5.3 Cellular network positioning............................................................................................... 29 2.5.4 The positional error varies in individual devices ................................................................ 30 2.6 Conclusions ................................................................................................................................ 31 2.6.1 Key Issues ........................................................................................................................... 31 2.6.2 Refined Research Questions ............................................................................................... 32 Chapter 3 - Research Methodology ..................................................................................................... 33 3.1 Introduction ................................................................................................................................ 33 3.2 Research Strategy....................................................................................................................... 33 3.3 Data Generation Methods .......................................................................................................... 33 3.4 Data Analysis ............................................................................................................................. 34 3.5 Sampling .................................................................................................................................... 34 3.6 Ethics.......................................................................................................................................... 34 3.7 Limitations ................................................................................................................................. 34 3.8 Conclusions ................................................................................................................................ 34 Chapter 4 - Planning and Design ......................................................................................................... 35 4.1 Introduction ................................................................................................................................ 35 4.2 Data collection devices .............................................................................................................. 35 4.3 Acquisition of actual geographical coordinates ......................................................................... 35 4.4 Data collection method .............................................................................................................. 37 4.5 The method of marking locations using Google Maps .............................................................. 37 5 4.6 Sample data ................................................................................................................................ 39 4.7 Data design................................................................................................................................. 40 4.7.1 SAS Data Sets ..................................................................................................................... 40 Chapter 5 - Findings and Analysis ....................................................................................................... 44 5.1 Introduction ................................................................................................................................ 44 5.2 Analysis...................................................................................................................................... 44 5.2.1 Overall results ..................................................................................................................... 44 5.2.2 iPhone 5 vs Samsung Galaxy S4 ........................................................................................ 47 5.2.3 Outdoor vs Indoor ............................................................................................................... 51 5.2.4 Urban vs Rural .................................................................................................................... 56 5.3 Conclusions ................................................................................................................................ 56 Chapter 6 - Discussion ......................................................................................................................... 58 6.1 Introduction ................................................................................................................................ 58 6.2 Accuracy of location services .................................................................................................... 58 6.2.1 Location services current level of accuracy ........................................................................ 58 6.2.2 Location services accuracy level in different devices ......................................................... 59 6.2.3 Location services accuracy level in different environments ............................................... 60 6.3 Prosecution ................................................................................................................................. 60 6.4 Defence ...................................................................................................................................... 61 6.5 Conclusion ................................................................................................................................. 62 Chapter 7 - Conclusions and Recommendations ................................................................................. 63 6 7.1 Introduction ................................................................................................................................ 63 7.2 Aims and Objectives .................................................................................................................. 63 7.3 Improvements and Recommendations ....................................................................................... 64 7.3.1 Improvements ..................................................................................................................... 64 7.3.2 Recommendations ............................................................................................................... 65 Chapter 8 - Personal Reflection ........................................................................................................... 66 9. References ........................................................................................................................................ 67 10. Appendices ..................................................................................................................................... 72 7 Table of Figures Figure 2-1The (GPS) satellite system (Garmin, 2014) ........................................................................ 14 Figure 2-2: Two satellite to find user position (IIT Delhi, 2009) ........................................................ 15 Figure 2-3: Three satellite to find user position (IIT Delhi, 2009) ...................................................... 15 Figure 2-4: Determining the location of receiver (IIT Delhi, 2009) .................................................... 16 Figure 2-5: Equation used to calculate GPS receiver position (IIT Delhi, 2009) ................................ 16 Figure 2-6: shows the route taken by RimasVenclovas in August 2011(Last, 2014).......................... 18 Figure 2-7: show the difference between two Position dilation of precision (PDOP) (IIT Delhi, 2009) .............................................................................................................................................................. 21 Figure 2-8: AGPS (Ma, 2003) ............................................................................................................. 24 Figure 2-9: Horizontal accuracy of iPhone A-GPS and Garmin autonomous GPS locations (Zandbergen, 2009) .............................................................................................................................. 25 Figure 2-10: Spatial distribution of positional error of WiFi positioning (Zandbergen, 2009) ........... 28 Figure 4-1: shows geographical coordinates longitude and latitude of a location (Google, 2015) ..... 35 Figure 4-2: shows satellite view of fifty individual locations from where location data will be collected (Google, 2015) ...................................................................................................................... 36 Figure 4-3: shows map view of fifty individual locations from where location data will be collected (Google, 2015) ..................................................................................................................................... 36 Figure 4-4: shows the location in google maps (Google, 2015) .......................................................... 37 Figure 4-5: shows the location coordinates of a positon marked (Google, 2015) ............................... 38 Figure 4-6: shows the Microsoft Excel spreadsheet which is ready to import in SAS........................ 40 Figure 4-7: Master data after importing in SAS .................................................................................. 41 8 Figure 4-8: SAS Data set- containing IPhone 5 and Samsung Galaxy S4 positioning error in meters .............................................................................................................................................................. 41 Figure 4-9: SAS Data set- containing outdoor error from IPhone 5 and Samsung Galaxy S4............ 42 Figure 4-10: SAS Data set- containing Indoor error from IPhone 5 and Samsung Galaxy S4 ........... 43 Figure 4-11: SAS Data set- containing Urban and rural error from IPhone 5 and Samsung Galaxy S4 .............................................................................................................................................................. 43 Figure 5-1: displays the statistical information for all errors ............................................................... 44 Figure 5-2: displays the basic statistical measures .............................................................................. 45 Figure 5-3: test for location.................................................................................................................. 45 Figure 5-4: displays extreme errors ..................................................................................................... 45 Figure 5-5: Histogram demonstrating the distribution of distance error in meters.............................. 46 Figure 5-6: displays the statistical information for IPhone 5 errors .................................................... 47 Figure 5-7: test for location.................................................................................................................. 47 Figure 5-8: Histogram demonstrating the distribution of distance error for IPhone 5 in meters ......... 48 Figure 5-9: displays the statistical information for Samsung galaxy S4 errors ................................... 48 Figure 5-10: test for location................................................................................................................ 49 Figure 5-11: Histogram demonstrating the distribution of distance error for Samsung Galaxy S4 in meters ................................................................................................................................................... 49 Figure 5-12: Comparison of error difference between IPhone 5 and Samsung galaxy S4 .................. 50 Figure 5-13: displays the distribution of error for IPhone 5 and Samsung galaxy S4 ......................... 51 Figure 5-14: displays the statistical information for outdoor errors .................................................... 52 Figure 5-15: displays the statistical information for indoor errors ...................................................... 52 9 Figure 5-16: comparison between indoor and outdoor errors .............................................................. 52 Figure 5-17: equality of variances for indoor and outdoor error ......................................................... 53 Figure 5-18: displays the distribution of indoor and outdoor error ..................................................... 53 Figure 5-19: Paired profiles and agreement comparing indoor error for iPhone 5 and Samsung galaxy S4 …………………………………………………………………………………............................ 54 Figure 5-20: Paired profiles and agreement for iPhone 5 and Samsung galaxy S4 outdoor errors ..... 55 Figure 5-21: displays the agreement of error for Urban and rural environments…………………….56 10 Chapter 1 - Introduction 1.1 Introduction Globally the use of smartphone will cross the two billion mark in 2015 and it is predicted that it will increase sharply around 49%, nearly half of the mobile phone users globally are likely to own smartphone by 2017 (Srivastava, 2014). The adoption of location services in our smartphones has increased dramatically in recent years and as a result location services is a default feature in today's smartphones (Zhang and Mao, 2012). Yet, questions regarding the accuracy of location services in our smartphones prevail (Lallie and Benford, 2011). The quest to discover reliable technology that can detect our location has led to the revolution of Global Positioning System (GPS) and it has been adapted as the most reliable technology to find location in today’s smartphones (Zandbergen, 2009). Whilst GPS is mostly accurate to approximately 10–30 meters outdoors with clear view of the sky, meanwhile it is difficult to achieve that level of accuracy indoors and in urban areas where the view of the sky is obstructed by tall buildings (Ma, 2003). Assisted Global Positioning System (A-GPS) is used in smartphones to address the issues of inaccuracy, faster response time, higher energy consumption and obstructed satellite view. GPS receivers in smartphones are powerful enough to perform the tasks of finding your location independently. However, in order to increase accuracy, response time and to save battery in smartphones A-GPS is utilised (Ma, 2003). Location services also utilize information from cellular and WiFi because A-GPS performance inside buildings and in urban areas is very poor. WiFi positioning technique is becoming very popular in urban areas with smartphones. Smartphones can fall back on traditional cellular positioning technique, however, this is the least accurate among the four (Zandbergen, 2009). This widespread adoption of GPS technology in our smartphones means location services data from the smartphones is relied upon for investigations where the individual’s location is a key issue. It is becoming apparent that the accuracy of location services in smartphones is extremely questionable. 11 GPS receivers in smartphones use four or more satellites to calculate the current position based on trilateration (Kuseler and Lami, 2012). Geo-tags are geographical coordinates that are stored as metadata (Exchangeable image format) data in smartphones such as iPhone and Android phones. Geo-tags are contained within the photographs taken using smartphones while location service is turned on. This information is taken from the location services within the smartphones and location service has to be enabled for geo-tag to work (Lallie and Benford, 2011). This data is highly questionable and forensic investigators must handle such data with caution. There are various factors that make this data unreliable, such as GPS positioning error, snoop attack and integrity of data contained in the device (Lallie and Benford, 2011). GPS based evidence might be the easiest way of providing a proof of an individual’s location when their location is in question. However, studies have indicated that the accuracy of location services is highly questionable and misleading. This project will examine, in detail the accuracy of location services and whether it can be used as evidence in court cases. 1.2 Project Aim and Objectives: The aim of this project is to uncover the accuracy of location services in our smartphones and the potential impact on the admissibility of GPS based evidence in court cases. The project Aims will be accomplished by completing the following objectives: 1. Research and write a literature review on GPS technology, Location services, current accuracy levels, factors affecting accuracy and admissibility of GPS based evidence in court cases. 2. Capture between 200 to 500 data points at various locations in different circumstance using a smartphone. 3. Analyse the collected coordinates and perform critical analyses to discover differences between the actual physical position and the location captured using smartphone to uncover the positional error. 4. The potential impact on the admissibility of GPS based evidence in court cases. 12 Chapter 2 - Literature Review 2.1 Introduction The use of GPS based technology has increased dramatically in recent years and GPS technology has improved some aspects of our lives. The use of location service in our smartphones has also seen a dramatic increase and it is important to uncover the accuracy of location services in our smartphones. As the use of GPS base technology increases in our lives it is inevitable these will be used as evidence in court. In a study carried out by (Berman, Glisson and Glisson, 2015) the use of GPS based evidence in court cases is increasing sharply. Therefore, it is vital to test the accuracy of location services which is increasingly playing a significant part in the outcome of the verdict in court. This literature review will focus on the current use of GPS based evidence in court cases and the current use of GPS technology by the authorities. It will also describe and discuss court cases where GPS evidence has played a major part in the outcome of a verdict. It will uncover the current accuracy levels by reviewing recent studies that have tested the accuracy levels of GPS based technology and also the factors affecting the accuracy of location services. It will also describe and discuss different positioning techniques used by location services. 2.2 Overview of Global Positioning System Development The eagerness to explore the world that lead to the discovery of navigation that started in early human history. The discovery of a compass, which was used in early wars to navigate during adverse weather conditions. United States Air Force first developed global Positioning System. The first satellite was launched in 1978 and by August 1993 there were 24 GPS satellites orbiting the earth. The following year (1994) the Federal Aviation Agency (FAA) confirmed GPS was ready to be used for aviation purpose (Tsui, 2000). This technology was initially designed for military and was partly developed which was used in the Persian Gulf War of 1991. This technology allowed allied forces to navigate their way through desert landscape. It is extremely difficult to navigate using traditional maps due to the lack of standing landmarks they could use as a reference point. This technology allowed them to track and effectively 13 manage their troops and it was extremely helpful in difficult weather conditions where sandstorms are frequent and visibility is low (Clarke, 1992). Since the success of this technology in the Persian Gulf War, the military realised that knowing your exact location is extremely beneficial for not only military purpose but also for civilian purpose. Since the turn of the century, GPS has been widely used for civilian and commercial applications. Currently, there are 31 GPS satellites in orbit at the altitude of 20,000 km above earth and there are plans to launch more to increase accuracy and replace ageing satellites (Ma and Zhou, 2014). There are vast amount of GPS based devices which assist us to find our location. The most popular are (Satellite Navigation) Sat Nav and location services in our smartphones which uses Global Positioning System (GPS), cellular and Wi-Fi to calculate our location. Location based applications utilise the data from your smartphones location services to find the nearest coffee shop, train station, and etc. iPhone and other smartphones use Geo-tags for photos which are captured using them which stores the location where photograph was taken (Lallie and Benford, 2011). 2.2.1 Basic concept of Global Positioning System Figure 0-1: The (GPS) satellite system (Garmin, 2014) Global positioning system (GPS) needs a minimum of four satellites to find the user position. Out of the many GPS satellites currently orbiting the earth, at any given position in the planet there should be four satellites in the horizon so that GPS receiver is able to receive signal from the satellites 14 considering satellite view is not obstructed (Tsui, 2000). The complex and expensive process of launching and maintaining the GPS satellite is contrary to the concept of finding the user position that is relatively simple (Spencer, 2003). The GPS receiver in your smartphone can perform acquisition to find a signal from the satellite. A satellite has a unique C/A code that is used by the GPS receiver to perform acquisition and then it is tracked to find satellite position. Similarly, the GPS receiver acquires signals from other three satellites using the same method. These satellites send messages to the receiver that contains their positions and the time message was sent from the satellite (Spencer, 2003). The message travels from the satellite to the receiver at the speed of light and eventually arrives to the receiver. A receiver can use messages from four of these satellites and a process called trilateration to calculate the user's position (Tsui, 2000). R1 R2 S1 S2 B A Figure 0-2: Two satellite to find user position (IIT Delhi, 2009) Figure 2.2 shows that two satellite and two distance give two possible positions because two circles intersect twice. R1 S1 R2 A B S2 R3 C S3 Figure 0-3: Three satellite to find user position (IIT Delhi, 2009) 15 Figure 2.3 shows that three satellites and three distance give one user position because three circles intersect at one position at once. Figure 0-4: Determining the location of receiver (IIT Delhi, 2009) Figure 2.4 shows a GPS receiver uses four satellite signals to calculate the location (X,Y,Z) and the bias clock error (d). The satellite uses an atomic clock that is accurate to the nanosecond; the receiver clock is not that precise which mean there is a clock bias error present, and fourth satellite is required to solve this problem. Figure 0-5: Equation used to calculate GPS receiver position (IIT Delhi, 2009) Figure 2.5 show using this equation the position of the GPS receiver is calculated. The figure illustrates that (X1, Y1, Z1) and so on are the coordinates of the satellites and R1 is the distance from the satellite to the receiver. The clock error is (d) and there are four equations, so the receiver can solve the four equation in four unknowns to find its position as expressed by x, y, and z. It will also compute (d) (Tsui, 2000). 16 2.3 Admissibility of GPS evidence in court cases Over the past decade, the rapid integration of GPS enabled devices in our everyday life have increased dramatically, and it is increasingly playing a major part in the digital forensic investigation. The location data from these devices is used as evidence in criminal and civil court cases (Berman, Glisson and Glisson, 2015). The use of location data in court cases has seen a rise in recent years and it is more likely this will increase significantly as the volume of GPS enabled devices is set to rise to a new record high. The low cost and increasingly shrinking in size is one of the reasons for the adaptation of GPS enabled devices (Marshall, 2008). The use of GPS has been vital to the transportation system ranging from air, sea and land. Also, it is playing an important role in defence and search and rescue. The new personalised tracking and location services GPS devices will overrun the traditional GPS devices by vast numbers. In 2013 dedicated GPS devices was estimated at 35 million units, which is expected to grow sharply by 30% over the coming years (ABIresearch, 2014). GPS technology has been adopted in everything from mobile phones, shoes, fitness tracking, cars, offenders monitoring, monitoring elderly people and etc. However there are many legal arguments regarding the privacy and liberty of GPS data. 2.3.1 Current use of GPS data by authorities The sensitive issue of liberty is raised when authorities or individuals make an assumption based on GPS data. In United Kingdom police carried out a forced entry into a wrong house because they were using iPhone navigation system for directions. In United States Ohio, the bank mistakenly repossessed a wrong house and contents based on inaccurate GPS data (Berman, Glisson and Glisson, 2015). In Australia, authorities point out the inaccuracies in Apple Maps for causing dangerous inaccuracies in a bushfire information application (Bostic, 2013). The monitoring of sex offenders using GPS technology has increased significantly in United States. The legislation which requires monitoring of offenders after their release from prison has been passed in at least forty-four states (National Conference of State Legislatures [NCSL], 2008). In United States, a study by (Armstrong and Freeman, 2011) examining GPS monitoring alerts triggered by sex offenders discovered that there was a huge gap between the legislative goals and practical application in the community correction. The study which was carried out over the two year period found that, there was significant number of times the alert is triggered due to loss of satellite 17 signals, which resulted in officers increased workload and offenders were inconsistent fear that they would be sent back to prison for violating their bail condition. The study concluded that GPS technology is far too limited and this should be used as a tool rather than entirely depending on it (Armstrong and Freeman, 2011). In recent studies (Armstrong and Freeman, 2011), (Bostic, 2013) and (Berman, Glisson and Glisson, 2015) have highlighted the issues of using unreliable GPS data by the authorities. The studies suggest that rather than using GPS data as a tool they are becoming dependent on highly questionable and unreliable GPS data which is having a more negative impact rather than positive (Armstrong and Freeman, 2011). 2.3.2 The current use of GPS evidence in court cases Over the past decade the increased use of GPS enabled devices in our life has also see an increase in the number of GPS based evidence presented in court cases. In a recent case of RimasVenclovas 2011 (BBC, 2012) the suspect departed from his home in Lithuania. He drove across Europe via Poland, Germany, the Netherlands, Belgium, and France and arrived at his former wife's house in Peterborough, England. He then abducted and murdered his former wife. Then on the way back home he went off the main road in to the forest and buried her in a shallow grave in Poland and returned to Lithuania. He was found guilty of murder, sentenced to life in prison and during the trial the jury was shown the tracks he followed which was recovered from his Satellite Navigation (Last, 2014). Figure 2.6 below shows the route taken by RimasVenclovas in August 2011. Figure 0-6: shows the route taken by RimasVenclovas in August 2011(Last, 2014) In United States in the case of United States V Jones, the authorities suspected Jones, a nightclub owner, of drug trafficking in 2005. The authorities convinced a judge to issue a warrant to attach GPS tracker to his Jeep in Washington, D.C. However, the authorities attached the device after the 18 warrant expired and outside of the jurisdiction of Washington. The authorities monitored the suspect for four weeks and charged him with drugs trafficking (Marcus and Wilson, 2011). He was convicted of conspiracy to distribute more than five kilograms of cocaine and sentenced to life in person. Subsequently, he appealed the guilty verdict because the GPS tracker violated the Fourth Amendment’s protection against unreasonable search and seize (Law.cornell.edu, 2013). In 2012 The Supreme Court justices voted unanimously that officers physically attaching GPS tracking device to his car without valid warrant constituted to search under the Fourth Amendment’s protection against unreasonable search and seize and therefore authorities have committed a trespass against Jones (Thompson II, 2012). The authorities have withdrawn GPS evidence and instead they are going to use cell site data for retrial. In a study (Berman, Glisson and Glisson, 2015) investigating the impact of GPS evidence within United Kingdom and Europe from 01 June 1993 to 01 June 2013, it has been found that the GPS evidence was presented in verity of court cases from criminal to civil. The study uncovered the use of GPS based evidence in court cases in the past decade has increased dramatically since first of June 2003 (Berman, Glisson and Glisson, 2015). Since 2007 onwards, 75 % of cases were recorded and overall the trend is increasingly for both criminal and civil cases. The study demonstrates that GPS evidence is playing a significant role in court case verdicts. Most of the land-based GPS evidence was used for criminal cases whereas most of water based GPS evidence was used for civil cases. The study concludes that further research needs to be carried out into expert witness involving GPS based Evidence, individual court cases. Also the issues regarding the security, integrity and privacy need to be addressed (Berman, Glisson and Glisson, 2015). Although GPS based evidence in courts cases have increased in the past decade (Berman, Glisson and Glisson, 2015) there are huge questions regarding the acquisition of GPS data by authorities (Marcus and Wilson, 2011) and admissibility of GPS based evidence is highly questionable (Last, 2014). GPS based evidence in court cases cannot be taken at a face value because it is highly questionable due to technical errors, inaccuracy and integrity (Last, 2014). 2.4 Factors affecting the accuracy and integrity of GPS based evidence There are six different types of standard error which affect the accuracy of GPS enabled devices. These are Ionosphere, Troposphere, Ephemeris data, Satellite clock drift, Multipath and Position dilution of precision (PDOP). The first five are cumulative and the last one is multiplicative 19 (Spencer, 2003). The increasing use of GPS based devices in our lives has certainly made our life much easier. Parallel to the increase of GPS based devices in our life courts have also seen an increase of GPS based evidence in recent years. However, the legal literature has not critically analysed the integrity of GPS based evidence because it is much easier to alter the location data than anticipated without leaving any trace of alteration(Iqbal and Lim, 2008). 2.4.1 The six standard factors The first two Ionosphere and Troposphere error is the result of unique atmospheric changes which happens in the earth atmosphere. Ephemeris errors are generated by the satellite and happens when the GPS signals are not transmitted to the correct location of the satellite (EI-Rabbany, 2002). The master control monitor station in Colorado updates satellite position regularly and forecasts satellite’s next path in advance using the previous paths. The error occurs when the satellite takes the different path than forecasted. This data is saved in a file called almanacs which is updated to the GPS receiver. This in turn produces inaccurate location coordinate that affects the accuracy of GPS (Spencer, 2003). Suppose a GPS receiver receives signals from four satellite, say, A, B, C and D. At time tf, satellite f (f = A, B, C, or D) transmits a message (xf, yf, zf, tf) the coordinates of the satellite x, y, and z and the time of transmission respectively (Noureldin, Karamat and Georgy, 2013). The speed of light is not infinite and for this reason, the message gets delivered to the receiver sometime later. The receiver has a clock that is used to note the time the messaged arrived. Which is tr. The receiver clock is not an atomic clock which means it is not perfect. It will always have an error of b, meaning the time of arrival is actually t + b. Depending on whether the message arrived early or late, b will be positive or negative. Atomic clock is accurate to nanosecond; however, GPS receiver normally is not as precise as the atomic clock in satellites which means there is always latency so this creates a bias clock error. This bias clock error affects the accuracy of location data theoretically up to 1.5 m (Biondi and Neubert, 2013). Multipath interface occurs because GPS satellite sends a signal to the receiver using radio waves that are not strong enough to penetrate through the strong solid object. They also easily get reflected by tall buildings or objects that are on the path that affects the accuracy of the GPS coordinates, and in turn produces inaccurate GPS data. The multipath interface is a large source of GPS error, 20 particularly when you are standing near a tall building. This can produce an error up to 15 m or more (Duberstein, 2012). Figure 0-7: show the difference between two Position dilation of precision (PDOP) (IIT Delhi, 2009) Position dilution of precision (PDOP) is one of the most important errors between the six different standard errors. This error occurs as shown in the figure above due to the geometric position of the satellite (Sun, 2013). A poor geometric dilution of precision (GDOP) occurs when the four satellites that are used to find your location clustered together in the sky. A good (GDOP) is when the satellite used to find your current location are evenly distributed in the sky. When the satellite is not in an ideal location, the PDOP increases (Milbert, 2008). The PDOP is a mixture of two DOPs, the horizontal and vertical dilution of precision. The mathematical calculation, although, is beyond the scope of this paper (see Milbert, 2008). However, it is important to keep in mind that PODP values multiply the first five errors which effect the error distance. In general the error values are low and the median PDOP value worldwide is 2.7 m (Spencer, 2003). The PDOP multiplies the other errors and this is for any given coordinates. For example if you have an error of 6 m from the first five error source, if you have an error of 3 m then that gives you 15 m. Whereas if you have an error of 6m PDOP then the error is 36 m. As you can see, PDOP error has a greater contribution to the error than the first five which means small PDOP error can cause a large positional error (Spencer, 2003). 21 2.4.2 The integrity of GPS data In recent years since the wide adaptation of GPS enabled devices in our day to day life and data from these devices are increasingly presented in courts as evidence. GPS data from these devices are used for both purposes to prove one's innocence and law enforcement are using it to convict individuals criminality. GPS data is being used in different type of court cases ranging from criminal to civil. GPS is a radio navigation system that is subject to venerability in itself without any malicious attempts to edit GPS data as examined in the previous section of this paper. GPS enabled devices collect location data which is stored within the device or external memory and location data is not encrypted which leaves the door open for individual with malicious intent to alter the data (Iqbal and Lim, 2008). The integrity of GPS data is one of the most important factors which affects the outcome of a case in court where GPS evidence is the key evidence. The integrity of GPS evidence is highly questionable according to (Iqbal and Liam, 2008) to investigate whether it was possible to alter GPS data without any trace or tamper with GPS data without needing great deal of technical knowledge. In the study, a volunteer’s car was fitted with GPS device that had a clear view of the sky. Then the volunteer used his car to go to work related place and came back. He handed the GPS devices back and two of the GPS receiver generated NMEA output, which was connected to the mobile phone and PDA. The NMEA output was saved as a text file on the mobile phone and PDA. The GPS data was extracted and altered to show on the return leg of his journey a false stop of 30 minutes in front of sports bar was made. The volunteer was presented with this data, and he denied making a stop in front of the sports bar (Iqbal and Liam, 2008). This highlights that it was possible to alter the date, time, speed and position and there is no mechanism to verify the GPS data for authenticity. Smartphones such as iPhone and others provide location information to the application. In iphone when location service is on the camera application tags the pictures with the location coordinates of where the picture was taken. These images can be relied upon in an investigation, however this study demonstrates that it is possible to alter the geographic coordinates of the pictures taken from iPhone and there was nothing in the file system of iPhone which suggested that the data was tampered with so the integrity of data is lost (Lallie and Benford, 2011). 22 GPS spoofing attacks are possible to carry out however this requires a great deal of technical knowledge and equipment. An experiment carried out by (Iqbal and Liam, 2008) illustrates that it is possible to target individual GPS receiver where a transmitter is used, which transmits similar or identical signals as GPS satellite and the GPS receiver would lock onto the signals because these signals are much stronger than the satellite signals. The study concludes that is possible to spoof attack individual GPS receiver which in turn can produce false location data and there is no mechanism to verify the location data (Iqbal and Liam, 2008). This study illustrates that individuals can implant false evidence against another person or someone can edit the GPS data to cover their crime. The above literature demonstrates that there are some factors, technical and malicious which makes GPS data high questionable. There are not only questions regarding the accuracy of GPS data however, the legal literature has paid minimal attention to the integrity and technical factors which affect the admissibility of GPS based evidence in court cases (Lallie and Benford, 2011). The issue of integrity of GPS data cannot be solved by encrypting GPS data because there is another issue that is spoofing attacks on individual GPS receivers. The admissibility of GPS based evidence is highly questionable due to inaccuracy and integrity (Last, 2014). 2.5 Location services Over the years the usage of location services has increased dramatically, 69% of users smartphone users use their phone for maps (Ofcom, 2013). Location services provide location data to location based applications, websites, maps, camera and other applications in smartphones. Location information is acquired using GPS, A-GPS, Wi-Fi and the cellular network to provide the approximate location (Zandbergen, 2009). Over 79% of smartphone users use their smartphones to take photos and videos which contains Geotagging when location service is enabled (Ofcom, 2013). When the location of that individual is a key factor authorities use the images captured from individual’s smartphone to find previously visited locations (Lallie and Benford, 2011). Every smartphone uses location service; however, the method used to acquire location data may depend on their individual make or model. Different manufacturers use a different algorithm to obtain GPS coordinates. 23 2.5.1 Assisted GPS Assisted Global Positioning System (A-GPS) is a technology that is employed by most GPS enabled Cell phones to calculate their position. A-GPS is the preferred choice of technology for smartphones. A-GPS uses the similar concept as normal GPS receiver. However, the full functions of GPS is performed by the remote location server. Most of the complex task is performed by the remote GPS server - it provides satellite orbit and clock information, the initial position and time estimate, satellite selection, range and range data and position computation (Zandbergen, 2009). Most of the smartphones contain a basic GPS receiver that connects to the given satellite and transfer pseudo range information to the location server (Singhal and Shukla, 2012). Figure 0-8: AGPS (Ma, 2003) A-GPS might be the preferred choice for smartphones. However, there are issues regarding the accuracy and availability of satellite while indoor and in urban areas. A-GPS does not work very well inside buildings, large infrastructures and train stations. Hence, its availability is limited because these are the places where people tend to spend more time (Prentzas, 2010). High-sensitivity GPS (HSGPS) chip sets allow GPS signals to measured and tracked to improve positioning in some indoor environments. There are some improvements in accuracy and reliability than the conventional GPS receivers however due to absent of some satellites, multipath errors and measurement noise linked with low power of the remaining signals which affects the improvement and it is not that significant in terms of reliability and accuracy (Lachapelle et al., 2004). 24 Figure 0-9: Horizontal accuracy of iPhone A-GPS and Garmin autonomous GPS locations (Zandbergen, 2009) The figure 2.9 shows accuracy levels of A-GPS according to the experiment carried our using iPhone 3G and Garmin unit in autonomous mode (Zandbergen, 2009) in ideal conditions for 20 minutes field test were able to record valid position fixes 100% at all time. The test reveals that A-GPS positional error which is much greater stretch with maximum error 18.5 m and RMSE of 8.3m whereas Garmin positional fixes are very closely plotted from the original benchmark location with maximum horizontal error of approximately 1.4 m and RMSE of 1.0 m. Although the positional accuracy of iPhone A-GPS compared to Garmin was not accurate to the same level however it was fairly consistent. During the experiment, the largest errors recorded in all test combined (2,400 positions in total) was 27.7 m horizontal and 48.4 m vertical. This shows that the requirements of Federal Communication Commission (FCC) are relatively easily met however, it does not meet the 20 m performance of unaided GPS receiver in ideal outdoor conditions (Zandbergen, 2009). In a more recent study carried out by using Motorola i580 and Sanyo SCP-750 to test the accuracy levels of A-GPS, both devices have a built-in high-sensitivity receiver and A-GPS. The experiment that was conducted outdoor in a static position on a sample size of 1791 for Motorola the horizontal average positional error was 7.18 m and vertical 25.92 m. The maximum horizontal error was 23.85 m whereas maximum vertical error was 58.75 m. The same test was conducted using Sanyo with a sample size of 1748 which showed the horizontal average positional error of 5.60 m that is slightly smaller than Motorola. The maximum horizontal positional error for Sanyo was 17.85 m (Zandbergen and Barbeau, 2011) 25 Although these two separate studies (Zandbergen, 2009) and (Zandbergen and Barbeau, 2011) demonstrated, the positional errors using various A-GPS enabled mobile devices. However, there has been very little research ever since. The last study was carried out four years ago and since then more improvements have been made in both hardware and software side of A-GPS enabled smartphones. The lack of recent research studies testing the accuracy levels of A-GPS using latest smartphones shows that there is a need for new research which should be carried out to uncover the accuracy levels of location services on our smartphones. 2.5.2 WiFi and IP positioning WiFi and IP positioning system utilize terrestrial-based WiFi access point (APs) to discover the location. Over the past decade, WiFi usage has grown in popularity. WiFi (APs) using 802.11 standards have been developed by an individual, small businesses, homeowners, public places, large corporate organisations, academic institution and retail outlets. WiFi broadcasts wireless signals to several hundred meters and in urban areas the density of the wireless signal is so high they overlap each other providing seamless signals for WiFi positioning (Gartner and Rehrl, 2009). WiFi positioning software distinguish the existing WiFi signals and using the WiFi-enabled mobile device it discovers the device location (Zandbergen, 2009). WiFi positioning system works particularly well in urban and heavily populated areas because the density of WiFi network is high. This positioning system works relatively well indoor and in certain outdoor locations thean GPS or A-GPS because strength of wireless signals from private and public WiFi are strong in urban areas, whereas GPS is not particularly accurate because satellite view is obstructed indoors and in urban areas (Köbben, 2007). WiFi positioning system uses WiFi signals from the nearby network to establish current location. Smartphones do not necessarily need to connect to WiFi because WiFi signals are used to record their unique Media access control (MAC) address and signal strength at a specific location. The advantage of WiFi positioning system is that it can use signals from encrypted networks and week signals to locate devices without the need to establish WiFi connection (Zandbergen, 2012). WiFi positioning technology uses three different methods of discovering the location of the user or device. (1) Triangulation is that it requires three or more signals from WiFi (APs), since the position of three access points are known and measuring the strength of the signals relative position of the device can be found. (2) Angle of arrival (AOA) is calculated using at least two signals from WiFi 26 (APs) or beacon when the orientation are known and at least three are needed to uncover the location and orientation (Bunningen and Muthukrishnan, 2004). Most of the geometric and statistical positioning technique are highly unreliable in realistic terms, the difficulty to implement these in metropolitan cities are; there are thousands of WiFi (APs), at one time there are more that 10 to 15 WiFi (APs) and it is difficult for devices to choose specific ones. It is also difficult to calculate the Angle of Arrival (AOA) because the signal is venerability to obstruction, reflection and noise. For instance, the noise levels in city is very high hence receivers will not be able to hear the beacon and there is traffic on the road which will obstruct the Angle of Arrival (AOA) or reflect the signal (Bunningen and Muthukrishnan, 2004). (3) Location fingerprinting schemes is one of the preferred methods out of the three WiFi positioning methods because fingerprint of the location is unique to a particular location. The characteristic of individual network signal is stored in a database with the location coordinates and matched to calculate the current location of the receiver. The method used to map the WiFi location is known as ‘Wardriving’. This is carried out using a vehicle which is driven around the city with wireless enabled laptop which is attached to a GPS device and wireless signals and GPS coordinates are recorded as the vehicle moves through an area (Zandbergen, 2012). There are commercial WiFi positioning systems such as Skyhook wireless positioning systems, Google, Microsoft, Apples, Navizon, PlaceEngine and WeFi. Skyhook Wireless is one of the oldest among these technology. Skyhook Wireless produced a white paper in 2008 which stated that it could provide accuracy within 20m, indoor and outdoor. These claims have not been independently tested by published research however the research was carried out in Sydney, Australia to evaluate the skyhook wireless in an urban area (Gallagher et al., 2009). The test was carried out using HTC Dream phone and older HTC phone running Windows Mobile 5. To test the repeatability of the result, the experiment was carried out on three different dates and times. Similarly different devices were used to test the repeatability of the results. The test was carried out in 17 indoor and 10 outdoor locations. The result shows an average of 63 m positional error indoor and an average of 63 m positional error outdoor. Whereas the maximum estimated observed positional error was 400 m (Gallagher et al., 2009). 27 Figure 0-10: Spatial distribution of positional error of WiFi positioning (Zandbergen, 2009) Another test was carried out using iPhone 3G in Albuquerque, New Mexico (Zandbergen, 2009). The figure 2.10 above show the WiFi positional error indoor using skyhook wireless. The test was conducted in 65 different indoor location, the median positional error of 74 m was discovered and maximum observed positional error was around 500 m. Which is a sharp contrast to the 20 m accuracy reported by Skyhook Wireless (2008) (Zandbergen, 2009). The more recent research was carried out in Albuquerque, New Mexico (Zandbergen, 2012). The tests were carried out in 90 different location using WiFi positioning Skyhook’s system. The location was close to nearby roads which means it is most likely that Skyhook data collector would have picked up the APs. The test was carried out in three urban cities San Diego, Miami and Las Vegas using IPhone 3GS and laptop. The median positional error differences between the laptop and IPhone in three different cities was not much different. In San Diego the difference was 3.8 m, Miami it was 2.7 and whereas the biggest difference was in Las Vegas of 12.7 m approximately. The median positional error using IPhone in San Diego was 42.6 m, Miami 41.6 m and Las Vegas 92.4 whereas the median positional error using Laptop in San Diego was 46.4 m, Miami 38.9 and Las 28 Vegas 79.7. The maximum positional error using IPhone was in Las Vegas 1986.4 m and using Laptop the maximum positional error was in San Diego 462.9 m (Zandbergen, 2012). Although three separate studies (Zandbergen, 2009), (Gallagher et al., 2009) and (Zandbergen, 2012) have demonstrated that WiFi positioning error varies in different cities. However, it also shows that very little has improved with regards to positional accuracy between these studies (Zandbergen, 2009), (Gallagher et al., 2009) and (Zandbergen, 2012). The above literature also independently highlights that the claims made by companies such as Skyhook Wireless that produced a white paper in 2008 which stated that it could provide accuracy within 20m, indoor and outdoor. As the three separate studies reveal that Skyhook wireless was not even meeting their accuracy target in 2012. 2.5.3 Cellular network positioning Cellular network positioning is used to find a mobile phone location in a network. Cellular positioning utilizes the cell data from the base station (cellular tower) and these cells are assigned to a base station. The mobile device and the cell tower have two-way communication when the mobile device wants to use the cellular tower. The cellular tower that is transmitting a strongest signal to the device is allocated the device (Markoulidakis, 2010). Location accuracy depends on the strength of the cell size. There are methods and algorithms to correct the location accuracy however the accuracy depends largely on the number of cell towers available. A research study carried out to test accuracy level of cellular positioning indoors where A-GPS and GPS positioning is not available in the city of Albuquerque, United States. The test was carried out in 65 different indoor location. The study revealed cellular positioning was able to make all 65 observations and the number of valid position fixes was 64. The result of this study was that the minimum horizontal error was 30 m whereas the maximum was 2,731. The median horizontal error was 599 m and observations with error <100 m were 4 (Zandbergen, 2009). Another research carried out to test the accuracy of positioning data on smartphones in Switzerland using a data set of 2289 location coordinates collected using iPhone application. The study uncovered that data collected using the same application in iPod, iPad and iPhone accuracy level deferred significantly. The study reveals that the positioning error of Cellular positioning is above 500 m. The study concludes that although cellular positioning has greater coverage. However, the 29 positioning error is significantly higher than other positioning systems (von Watzdorf and Michahelles, 2010). Cellular positioning depends on base stations (cell towers) and it is vital to know from which direction the mobile phone is transmitting signal to the nearest cell tower in order to discover the physical position of the phone. A research study which purposes GSM-based positional technique using relative received signal strength. This study was carried out in Egypt roads using realistic data and Android smartphones. The result showed that the mean positional accuracy was around 29 m in urban areas and velocity approximately around 1 km/h in rural areas (Abdel Meniem, Hamad and Shaaban, 2013). It is clear that these two separate studies (Zandbergen, 2009) and (von Watzdorf and Michahelles, 2010) have produced similar results in two different part of the world. They have demonstrated the positional error using cellular positioning system and the third study (Abdel Meniem, Hamad and Shaaban, 2013) has proposed a different positioning technique that could improve positioning accuracy. However, the challenge will be to implement this new technique and then to test the positioning accuracy level in the larger scale. 2.5.4 The positional error varies in individual devices Over the past decade due to the increased accuracy and affordability of GPS technology has contributed to the dramatic increase in GPS-enabled devices. The most common ones are Sat Nav, mobile phones and handheld GPS devices. The positional accuracy of GPS-enabled devices is dependent on individual devices(Köbben, 2007). In a research study carried out using iPhone 3G found that using WiFi and Cellular positioning the median positional error using WiFi positioning was 74 m whereas the median positional error using cellular positioning was 599 m. This experiment shows that WiFi positioning is much more accurate than cellular positioning. When using cellular positioning the highest positional error was around 2600 m however in few instances cellular positioning was more accurate than WiFi positioning (Zandbergen, 2009). In a research carried out to test commercial WiFi positioning systems for indoor and urban canyons using HTC one and O2 smartphone produced similar results other independent research (Gallagher et al., 2009). 30 In the same study (Zandbergen, 2009) testing A-GPS using iPhone and Garmin unit in autonomous mode in ideal conditions found that the positional accuracy of the Garmin unit was much more accurate than the iPhone from the benchmark position. The highest horizontal Positional error for the Garmin unit was around 1.4 m and RSME of 1.0 m whereas for iPhone the error were much larger and highest horizontal error of 18.5 m and a RMSE of 8.3m (Zandbergen, 2009). In a more recent study (Zandbergen, 2012) testing the accuracy of WiFi positioning using iPhone 3GS and Dell Latitude D630 laptop in three different cities. The results of the study show that there was a small difference in the positional error between two different devices however the positional error between three different cities varied considerably more. Comparing the positional errors from two different study (Zandbergen, 2009) and (Zandbergen, 2012) shows that there has been very little improvement in the accuracy levels of WiFi positional and other various positioning system explored in above literature. It is clear to see from these separate studies (Zandbergen, 2009), (Gallagher et al., 2009), (von Watzdorf and Michahelles, 2010) and (Zandbergen, 2012) that the positional accuracy varies in different devices although the positioning technique might be the same. This will affect the admissibility of GPS based evidence in court cases because of the positional accuracy in different devices. 2.6 Conclusions 2.6.1 Key Issues There have been numerous research studies carried out around the world testing the accuracy of location services, however this issue remains largely unresolved and recent studies show that the number of GPS-based evidence in court cases has increased significantly over the past decade. As the adoption of GPS-enabled devices increase in our lives it is predicated that these will be increasing used in court as evidence. The existing literature has highlighted the positioning inaccuracy of different positioning systems used by location services (Zandbergen, 2009), (Gallagher et al., 2009), (Zandbergen, 2012) and (von Watzdorf and Michahelles, 2010) however, new and improved technology is introduced to the smartphone market frequently. This illustrates that need for existing literature to catch up with new technology hence the need for more research using state of the art technology. 31 Research studies have been carried out to discover the use of GPS based evidence in courts cases (Berman, Glisson and Glisson, 2015), however there has been very little comprehensive research that has tested the positional accuracy of location services and how this could potentially impact the admissibility of GPS based evidence in court cases. For this reason, the accuracy of location services and the potential impact on the admissibility of GPS based evidence in court cases will be the focus of this study. 2.6.2 Refined Research Questions How accurate are the location services on our smartphones and what impact this will have on the admissibility of GPS based evidence in court cases? 32 Chapter 3 - Research Methodology 3.1 Introduction The literature review has identified two key issues, the positional inaccuracy of location services and admissibility of GPS based evidence in court cases. Research into the inaccuracy of location services has shown that location data is highly questionable and this has an impact on the admissibility of GPS evidence in court cases. The research methodology has three main objectives: 1. In order to generate and capture the location data to uncover the positional inaccuracy using location services, two different smartphone will be used namely, iPhone 5 and Samsung Galaxy S4. Using these two smartphones 400, geographical data points will be collected in various locations indoors and outdoors. 2. Collected location data will be analysed, the geographic coordinate’s latitude and longitude captured using smartphones location services will be compared with the physical geographic coordinates of the location to uncover the positional error. 3. The potential impact on the admissibility of GPS based evidence in court cases. 3.2 Research Strategy This research strategy involves formal experiments that will generate quantitative data. The data will be generated using two smartphones using location services. The physical location from which geographic coordinates will be captured will be chosen at random and set of the procedure will be followed before capturing the coordinates to maintain the quality of data. While the experiment is running, careful observations are made of the outcome. This research strategy was chosen over others because primary data generated particularly for this study can be used to find positional error patterns and relationship between two different variables which affects the outcome (Oates, 2006). A conclusion can be drawn from this formal experiment that will uncover the positional inaccuracy of location services. 3.3 Data Generation Methods The data generation methods used in this study is by conducting a experiment; the experiment will be structured and planned. The experiment will involve two smartphones iPhone 5 and Samsung Galaxy S4 which will be used to capture geographical coordinates of a location using location services. This 33 method of data generation was chosen over others such as surveys because this provides data that can be analysed to discover the positional inaccuracy in location services (Oates, 2006). 3.4 Data Analysis The geographic coordinates collected from the two smartphones will be compared with the actual geographic coordinates extracted from Google maps to uncover the positional error of location services. The positioning accuracy of Google maps itself which is Root Mean Square Error (RMSE) was found to be 1.59m (Mohammed, Ghazi and Mustafa, 2013). The quantitative data collected for this experiment will be statistically analysed using SAS analytics software (Oates, 2006). 3.5 Sampling Please refer to planning and design section 4.6. 3.6 Ethics The experiment will abide by the University of Derby ethical guidelines. However, there are no ethical issues because there is no human participation. 3.7 Limitations This study will be limited to uncover the positional inaccuracy of location services and how this will impact the admissibility of GPS based evidence in court cases. There are other factors which have the potential to impact the admissibility of GPS based evidence however these have been highlighted in the literature review. While collecting data, there might be limitations regarding access to some location because they might fall under private properties or restricted areas. The solution to this problem is to find new data points on the map that are accessible. 3.8 Conclusions The research methodology designed for this study is to uncover the position inaccuracy of location services. The research strategy used to fulfil the two objectives is a experiment that will be used to collect quantitative data. The data collected will be compared with geographical coordinates of a location from Google maps and statistical analyses will be performed using SAS software to discover 34 various positioning error patterns. This study has also explored the current accuracy of Google maps which is (RSME) 1.59m and will take into account the findings from previous studies. Chapter 4 - Planning and Design 4.1 Introduction Planning and design of an experiment is a key factor to the success of an experiment. This section will demonstrate in details how location data will be generated and how the hypothesis of this study will be tested. This is a quasi (Field) experiment which will be conducted outside in the real-life setting however remaining true to the spirit of traditional laboratory experiment (Oates, 2006). 4.2 Data collection devices The devices that are going to be used to collect data are iPhone 5 and Samsung Galaxy S4. The specification of the smartphone is iPhone 5 16GB. The Location positioning system enabled are Assisted GPS, Digital compass, Wi-Fi and cellular. The software is currently running on IOS 8.2 and network coverage is 3G and 4G.The specification of Samsung Galaxy S4 16GB, Location positioning system enabled are Assisted GPS, Wi-Fi and cellular. It runs on the android mobile platform and network coverage 3G and 4G. 4.3 Acquisition of actual geographical coordinates In order to uncover the positioning inaccuracy of location services, physical geographical coordinates of the location from where location data is captured using smart phones are required and in this experiment utilizes Google maps to find the longitude and latitude of a location. Figure 0-1: shows geographical coordinates longitude and latitude of a location (Google, 2015) 35 Figure 4.1 show the location coordinates of a location which has been extracted using Google maps. To conduct this experiment, each location's longitude and latitude will be extracted using this method. Subsequently, when carrying out this experiment in the field, this location will be discovered and location data will be captured using two different smart phones location services. Figure 0-2: shows satellite view of fifty individual locations from where location data will be collected (Google, 2015) Figure 0-3: shows map view of fifty individual locations from where location data will be collected (Google, 2015) Figure 4.3 shows fifty stars that are the location from where location data will be captured. This method of marking the location prior to formal experiment will be followed throughout the data collection process. The marked location in figure 4.3 will be found in the field and location data will 36 be collected using two smartphones. To ensure location data is collected from the marked positions in the maps visual reference points will be used such as buildings, trees, road markings, etc. 4.4 Data collection method The data collection was carried out using two smart phones iPhone 5 and Samsung Galaxy S4. These two phones have built in location services that utilise three positioning systems; Assisted GPS, Wi-Fi and cellular, which can be utilised by third party applications. All three positioning system will be enabled during the collection of data and following approach will be adopted; a third party application skypro is installed in both devices. Test reading are carried out to ensure application is functioning correctly. This application provides the geographic coordinates of a current position in longitude and latitude. It also provides the number of satellites visible. However, this option is only available with Android phones. Subsequently, marked location in the map were found, using third party application on the two devices location coordinates of that location were captured and number of the satellite where applicable. The captured data is transferred to a Microsoft Excel spreadsheet. The data collection approach explained above was followed for each location. 4.5 The method of marking locations using Google Maps This section will demonstrate the techniques used to mark the locations using Google maps. The first step is to load Google maps using internet browser. To save the locations, you will need create a Google account or sign in using an existing account. Figure 0-4: shows the location in Google maps (Google, 2015) Figure 4.4 shows Google maps, to find the location coordinates of a position in the map you will need to right click over the position. An option icon will appear on the screen then select the third option (What’s here?). 37 Figure 0-5: shows the location coordinates of a position marked (Google, 2015) The Figure 4.5 shows the location coordinates of a position marked in figure 4.4 and to save this location left click on the star. This procedure can be carried out to mark as many location coordinates as you would like. The figure 4.2 illustrates how this method could be used to assist other research studies. 38 4.6 Sample data Loca Poi tion nts Date Time Actual Actual IPhone stamp Latitude Longitude Latitude IPhone Samsung Samsung Latitude Indoor/ Nu Longitude Outdoors mb Longitude er of sate llite 03/04/ 1 1 2015 52.94032 12:30 03/04/ 1 2 2015 3 2015 12:45 4 2015 11:08 5 2015 18:57 6 2015 18:45 7 2015 19:05 8 39 2015 -1.505597 2 5 4 9 19:10 2 -1.504448 -1.50725 8 outdoor n/a 6 -1.50728 outdoor n/a -1.5055 outdoor n/a -1.5045 outdoor n/a -1.50191 outdoor n/a -1.50217 outdoor n/a -1.50119 outdoor n/a -1.49942 outdoor n/a 52.9384 -1.50551 52.9386 -1.50346 52.9379 -1.503336 8 7 -1.502191 4 -1.50306 7 -1.5025 9 6 52.9364 -1.501286 52.9356 -1.499324 8 52.9372 52.9363 -1.501285 1 52.9384 52.9372 52.93548 12:35 -1.50306 52.9381 52.93635 03/04/ 1 8 52.9402 52.9386 52.9384 52.93720 02/04/ 1 -1.507262 52.93766 02/04/ 1 2 -1.50387 52.93806 02/04/ 1 4 6 52.9386 52.93844 02/04/ 1 -1.503849 52.93862 04/04/ 1 8 52.9404 2 52.9361 -1.49837 2 4.7 Data design It is vital to prepare the location data collected in an organised format which is acceptable for analysis in SAS. All the location coordinates were collected from 200 different location in Derbyshire; each device has a column for Latitude and Longitude, date, time, Actual Latitude and Longitude and Indoor and Outdoor. After sorting the raw data, Microsoft Excel spreadsheet will be imported to SAS. Figure 0-6: shows the Microsoft Excel spreadsheet that is ready to import in SAS Once this is imported in SAS, the next task is to uncover the ‘geodist’ procedure in SAS. This function in SAS will uncover the positioning difference between Actual position and phone position, and the difference will be calculated in meters. This will be explained in great details in finding and analysis section. 4.7.1 SAS Data Sets To answer the questions that have been raised in Literature review and methodology sections, there will be multiple data sets which will be created in SAS. This process makes it easier to create graphs, charts and conduct statistical analysis on the data collected. 40 Figure 0-7: Master data after importing in SAS Figure 0-8: SAS Data set- containing iPhone 5 and Samsung Galaxy S4 positioning error in meters 41 Figure 0-9: SAS Dataset- containing outdoor error from iPhone 5 and Samsung Galaxy S4 42 Figure 0-10: SAS Dataset- containing Indoor error from iPhone 5 and Samsung Galaxy S4 Figure 0-11: SAS Dataset- containing Urban and rural error from iPhone 5 and Samsung Galaxy S4 43 Chapter 5 - Findings and Analysis 5.1 Introduction The research methodology was designed to achieve the following objective; to measure the accuracy levels of location services in smartphones and whether location services in smartphone is a reliable tool to capture accurate geographical coordinates. There are a number of variables that have been explored in this study and these are; comparing iPhone 5 with Samsung Galaxy 4S, indoor/outdoor and various location urban with rural. To answer the refined research question, various graphs and charts have been developed using SAS, which presents a unique perspective on the data with regards to the accuracy of location services. 5.2 Analysis 5.2.1 Overall results To uncover the positional inaccuracy of location services in smartphones, 200 different geographical coordinates were taken from each device hence 400 readings altogether. These geographical coordinates were collected in a various location within Derbyshire. There are 21 readings out of 400 which were unable to find the position fixes. Using the SAS ‘univariate’ procedure, statistical information has been produced and charts have been created to display the error in distance. Figure 0-1: Displays the statistical information for all errors Figure 5.1 shows the statistical information for all the errors and the mean error is 139.99 meters. The number of valid positional fixes is 379 out of 400 readings. The mean error is much higher than previous studies have shown which will have an impact on the admissibility of GPS based evidence because previous studies have shown much lower mean error than the result in figure 5.1. 44 Figure 0-2: displays the basic statistical measures Figure 5.2 shows the median error is 17.32 for all errors and the standard deviation is 1197 meters. Figure 0-3: test for location Figure 0-4: displays extreme errors Figure 5.4 shows the extreme observations for all errors. The lowest error is 0.64 meters and the highest error is 22724.95 meters. These results will have an impact on the admissibility of GPS based evidence because there are a number of observation that are over a mile and in an extreme case over 14 miles. The positioning errors that are shown in figure 5.4 will make GPS based evidence highly unreliable and questionable. 45 Figure 0-5: Histogram demonstrating the distribution of distance error in meters Figure 5.5 displays the distribution of overall error. The histogram shows that (99%) of errors are within 0-995 meters and there are few errors that are above 1050 meters. The extreme error which is 22724.95 meters which are also displayed by the kernel. Although major of errors are within 0 – 995 meters this still makes the GPS based evidence highly questionable and the result shown in figure 5.5 confirms that GPS-based evidence alone without other corroborating evidence will not be admissible in court. 46 5.2.2 iPhone 5 vs Samsung Galaxy S4 This section demonstrates the positioning error for iPhone 5 and Samsung Galaxy S4, with regards to distance in meters. For the purpose of this research study, 200 unique location were visited to collect the location coordinates however, for iPhone 5, 14 out of 200 location was unable find the GPS position fixes. Whereas Samsung Galaxy S4 was unable to find 7 out of 200 position fixes. Using the SAS ‘univariate’ procedure, statistical information has been produced and charts have been created to display the error in distance. In addition to that, another function in SAS ‘ttest’ procedure has been used to perform confidence limits for one sample, two independent samples, paired observations and AB/BA crossover design. The SAS ‘ttest’ procedure also produces graphs and charts which displays the distribution of error for the two independent data and Q-Q plots of error. iPhone 5 These are the results which display the performance of iPhone 5 Figure 0-6: Displays the statistical information for iPhone 5 errors Figure 5.6 shows the mean error for iPhone 5 which is 84.89 meters. The number valid positional fixes are 186 out of 200. Figure 0-7: test for location 47 Figure 0-8: Histogram demonstrating the distribution of distance error for iPhone 5 in meters Figure 5.8 demonstrates the error difference in meters (95%) of the errors are within 300 meters. However there some errors that are above 400 meters and the highest error is around 2900 meters. The positioning error that are displayed in figure 5.8 suggests that GPS-based evidence is highly unreliable and this will not be admissible evidence in court cases. Samsung Galaxy S4 These are the results which display the performance of Samsung Galaxy S4. Figure 0-9: Displays the statistical information for Samsung Galaxy S4 errors 48 Figure 5.9 shows the statistical information for Samsung Galaxy S4. The mean error is 193.08 meters and standard error mean is 118.69. The mean error is much higher than iPhone 5 and if this result was used as evidence in court, it would not be admissible evidence due to the positioning inaccuracy. Figure 0-10: Test for location Figure 0-11: Histogram demonstrating the distribution of distance error for Samsung Galaxy S4 in meters Figure 5.11 demonstrates the error difference in meters (99%) of the Samsung error are within 1260 meters. There are few errors that are above 1320 meters and the extreme error is 22000 meters. The 49 positioning error which are shown in figure 5.11 provides evidence that GPS based evidence is highly questionable, for example, a crime has been committed and an individual has been subsequently arrested for the crime and the authorities extract GPS data from his smartphone which suggests that he was at location at that time. However due to the positioning inaccuracy of location services the individual positions was shown 13 miles from his actual location. This level of inaccuracy of location services could have devastating consequences if it is used as evidence in court without other corroborating evidence. Comparison Below is the comparison between iPhone 5 and Samsung Galaxy S4. Figure 0-12: Comparison of error difference between iPhone 5 and Samsung Galaxy S4 Figure 5.12 shows the comparison between the two devices. There is a large difference between the mean errors for the two phones. The mean error for iPhone is 84.89 meters whereas for Samsung Galaxy S4 it is 193.1 meters there is a difference of 108.2 meters. 50 Figure 0-13: displays the distribution of error for iPhone 5 and Samsung Galaxy S4 Figure 5.13 shows the distribution of error for Samsung Galaxy S4 is much higher than for iPhone 5. This demonstrates that GPS positioning error varies significantly in individual devices. The figure 5.13 displays the Samsung outlier that is around 14 miles which has influenced the mean error that is much higher than iPhone 5. 5.2.3 Outdoor vs Indoor This section below displays the compression between data collected Outdoor and indoor with regards to distance error. Again SAS ‘univariate’ procedure has been used to produce statistical data for outdoor and indoor errors. In addition to that, SAS ‘ttest’ procedure has been used to perform confidence limits for one sample, two independent samples, paired observations and AB/BA crossover design. The SAS ‘ttest’ procedure also produces graphs and charts which display the distribution of error for the two independent data and Q-Q plots of error. Outdoor These are the result of positioning error outdoor in meters. 51 Figure 0-14: Displays the statistical information for outdoor errors Figure 5.14 shows the statistical information for outdoor errors. The mean error is 169.59 meters and standard error mean is 82.74 meters. These are the result of indoor positioning error in distance. Figure 0-15: Displays the statistical information for indoor errors Figure 5.15 shows the statistical information for indoor errors. The mean error is 55.10 meters. Comparison This section compares the positioning error difference between outdoors and indoors. However, it is crucial to note that the number reading indoors are 98 whereas 281 readings outdoor. Figure 0-16: comparison between indoor and outdoor errors Figure 5.16 shows that the mean error for outdoor is much higher than indoor. 52 Figure 0-17: equality of variances for indoor and outdoor error Figure 5.17 shows that there is a significant difference between indoor and outdoor errors. Figure 0-18: Displays the distribution of indoor and outdoor error Figure 5.18 shows the distribution of indoor and outdoor error. The graph demonstrates the distribution of error for outdoor is much higher that indoor. The mean error for indoor is much lower than outdoor. iPhone and Samsung Indoor This section compares the performance of iPhone 5 and Samsung Galaxy S4 in an indoor environment. This type of analysis will be interesting to perform because this will provide statistical data on individual devices performance in different environments. 53 Figure 0-19: Paired profiles and agreement comparing indoor error for iPhone 5 and Samsung galaxy S4 Figure 5.19 paired profiles show that indoor mean errors for both devices similar and a large majority of errors are clustered together. The agreements graph for both devices shows that highest indoor error for iPhone 5 is around 650 meters and over 1000 meters for Samsung Galaxy S4. This shows that for both devices there are a number of errors that are particularly large and this will have an impact on the admissibility of GPS based evidence in court cases. 54 IPhone and Samsung outdoor This section compares the performance of iPhone 5 and Samsung Galaxy S4 in an outdoor environment. Figure 0-190: Paired profiles and agreement for iPhone 5 and Samsung galaxy S4 outdoor errors Figure 5.20 paired profiles shows that a large majority of outdoor error from both devices are clustered together. There are some errors for both devices which will have an impact on the admissibility of GPS evidence due to the positioning inaccuracy. These errors will make GPS based evidence unreliable and misleading evidence in court cases. 55 5.2.4 Urban vs Rural This section displays the positioning error for urban and rural environments for both phones. Figure 0-21: Displays the agreement of error for Urban and rural environments Figure 5.21 shows the error difference for urban and rural locations. The graphs shows that the majority of error for both environments are within 500 meters, however, there are around eight errors for rural environments that are above 500 meters. This demonstrates that GPS in an urban environment is more accurate than rural environments. The result in the figure 5.21 is different to previous studies because in rural area while collecting the data in some location there was no cellular signal that could be one of the reasons positioning inaccuracy is much higher than in urban areas. 5.3 Conclusions The purpose of this chapter was to analyse and present the data collected for this research study in a way that enables questions about the research topic to be answered. The research suggests that the level of GPS positioning error is much higher than the recent studies have shown. It is clear to see that GPS positioning error varies in individual devices. It is also clear to see that different environments, for example, indoor and outdoor or urban and rural also have an impact on the level of accuracy. 56 The findings and analysis section has helped to answer the question regarding the accuracy levels of location services in smartphones. The information uncovered in this section will be used in the following section to answer the questions and counter check that questions remain to be answers. 57 Chapter 6 - Discussion 6.1 Introduction This study has examined the accuracy of location service in two smartphones and during this study 400 GPS coordinates were collected from two devices. These GPS coordinates were analysed to uncover the positional inaccuracy in iPhone 5 and Samsung Galaxy S4. This study examined two major elements of location services:  Accuracy of location services  Potential impact on the admissibility of GPS based evidence in court cases. This chapter will investigate the findings from the study, what knowledge has been gained and what implication they have for the wider research topic. 6.2 Accuracy of location services 6.2.1 Location services current level of accuracy The study carried out by Zandbergen, 2009 showed that using iPhone 3G and Germin unit, on both devices was able to record valid position fixes 100% at all time. The collection of studies investigated in the literature review has highlighted the issue of GPS positional inaccuracy however these studies have been conducted in ideal conditions. The study conducted in 2009 shows a lower positioning error, the maximum being 18.5 m. However, another study carried out by (Zandbergen and Barbeau, 2011) showed higher positioning error where the maximum error was 23.85 m. While this could be interpreted as a negative findings, the accuracy levels still do not seem to be improving. The findings from this study included shows much higher level of positioning inaccuracy. The results from this research study have produced three vital pieces of information with regards to the accuracy levels of location services:  Firstly, the result from this study shows the percentage of record valid positional fixes (94.75%). 58  Secondly, the result of this study shows the maximum positioning errors which is 22724.95 m. While this error is the highest, there are a large number of errors that are much larger than previous studies have suggested (see Chapter 5 – figure 5.4).  Thirdly, the result from this study shows that the mean error is 139.99 m with the median error of 17.32 m which is much higher than previous studies have shown. The result of this study could change the significance given to GPS evidences in court cases. This could have an impact on both the prosecution and defence reliance on GPS based evidence. The accuracy levels of GPS positioning is much lower than currently claimed by experts. GPS evidence is much more venerable than previously assumed. Therefore, it is very likely it may not be an admissible evidence without other supporting evidence (Last, 2014). Firstly, the result of this study shows that only (94.75%) of recorded valid positional fixes were achieved. The result of (100%) recorded valid positional fixes that were achieved by Zandbergen, 2009 could not be confirmed. The recorded valid positional fixes achieved in this study is slightly higher than the previous study by von Watzdorf and Michahelles, 2010. Secondly, the results from previous studies (Zandbergen, 2009); (von Watzdorf and Michahelles, 2010) and (Zandbergen, 2012) have maximum positioning error much smaller than the result of this study. Thirdly, the mean error is 139.99 m whereas the median error is 17.32 m that is higher than previous studies. The results that location services positioning inaccuracy level are much higher than suggested by previous studies. 6.2.2 Location services accuracy level in different devices Previous studies have shown that GPS positioning accuracy levels in different devices varies. Studies including (Zandbergen, 2009); (von Watzdorf and Michahelles, 2010); (Zandbergen and Barbeau, 2011) and (Zandbergen, 2012) have shown that the positioning technique might be the same. However, the positioning accuracy levels vary. The finding from this study also shows that the positioning accuracy varies in different devices. The mean error from iPhone 5 is 84.89 m and Samsung Galaxy S4 is 193.1 m, there is an error difference of 108.21 m between the two devices. The maximum error from iPhone 5 is 2945.9 m whereas that for Samsung Galaxy S4 is 22725 m. The study suggests there is a substantial difference between the two devices. 59 6.2.3 Location services accuracy level in different environments This study compared the data collected in a different environment to uncover the positioning differences. There are very few previous studies to compare the findings from this study. The results show that location services in an urban environment is much more accurate than in rural environment (see Chapter 5 – figure 5.23). The result from this study shows that in an urban area, the maximum positioning error was less than 500 m whereas in a rural it was nearly 3000 m. 6.3 Prosecution This section will discuss what potential impact the result of this study could have on the admissibility of GPS based evidence in court cases with regards to prosecution. The use of GPS based evidence in court case has increased dramatically in recent years as explored in the literature review (Berman, Glisson and Glisson, 2015). The prosecution will use GPS-based evidence that is collected from defendant or victims in a case to prove or disprove an argument. This GPS based evidence can be extracted from mobile phones or handheld GPS devices. The results from this study suggest that GPS-based evidence is highly questionable and misleading due to the positioning inaccuracy. There are a large number of positioning errors uncovered in this study that are well above 500 m and prosecution cannot rely on GPS evidence alone to secure a successful conviction. As demonstrated by the recent court case, a delivery driver working for a large delivery company was accused of stealing iPads that he was supposed to deliver to a shop. The evidence was extracted from a handheld terminal that is used to sign for delivery by customers. Whenever he takes parcels out of the van, he would scan the barcode that records his GPS fixes; another fixes are recorded when the parcel is signed and final fixes are taken when the transaction is completed. When this case came to court, all of the three fixes were identical and were outside, where his van was parked. However, CCTV in the shop showed the customer signing for the parcel inside the shop and the case collapsed (Last, 2014). There are some cases where GPS evidence has helped to solve very complex crimes. For instance, there is a recent murder case in which a former husband travelled from Poland to England in his car and he abducted and murdered his former wife and, on the way back to Poland, buried her in the woods. The prosecution used GPS evidence from his satellite navigation device along with other forms of evidence to prove him guilty and he was sentenced to life in prison (Last 2014). 60 The result from this study suggests that due to large positioning errors in location services, this will increase the probability that GPS-based evidence will be inadmissible in court. The prosecution service needs to understand that the level of position accuracy that is 15 to 20 m claimed by service providers is not possible to achieve in most instances which are demonstrated by the results of this study. There are different types of GPS-enabled devices, the location data collected from these devices could be used as evidence in court. However, the result of this study and several previous studies have suggested that positioning accuracy varies in individual devices. The prosecution service needs to consider carefully whether they can support GPS-based evidence with other evidence which will make the GPS evidence admissible in court. The prosecution need to prove that the device was operating correctly, but as the result of this study suggests, this will be very difficult to prove due to the positioning inconsistency and inaccuracy. Overall, the prosecution service needs to examine the GPS based evidence thoroughly and must present other supporting evidence for GPS based evidence to be admissible evidence in court. This study has suggested that GPS-based evidence can be misleading and inaccurate. 6.4 Defence This section will discuss the potential impacts the result of this study could have on the admissibility of GPS based evidence in court cases with regards to defence. In recent years, defence lawyers have been increasingly using GPS-based evidence in court to prove their client's innocence. The defence lawyers will use GPS-based evidence from their clients GPS-enabled mobile phones or other handheld devices. The result of this study suggests that due to the inaccuracy in location services GPS base evidence will be inadmissible in court without another form of supporting evidence. The result of this study suggests that there are some advantages and disadvantages to the defence team. For example, if the prosecution is presenting GPS evidence then they can argue that it is unreliable. However, the same applies to the defence team if they are presenting GPS-based evidence. 61 The result of this study suggests that the accuracy of GPS-based evidence is highly questionable and unreliable due to the positioning inaccuracy. When presenting GPS based evidence in court to support their client’s case defence lawyers will need to carefully examine the strength of the GPS based evidence before them and also need to analyse whether it can withstand the scrutiny from the prosecution. For their clients GPS based evidence to be admissible in court defence lawyers will need to support that evidence with other forms of corroborating evidence. For example, GPSevidence supported by CCTV footage and eye witness. GPS based evidence alone cannot be relied upon to prove or disprove a claim due to the positioning inaccuracy. For a defendant to be found guilty, the jurors or judge must have no doubt as to the defendant’s guilt. For example; a defendant is accused of a crime but he claims that at the time of the incident he was somewhere else and provides his Smartphones location data as proof. If there is another form of evidence that the prosecution have not disclosed, then GPS evidence alone will not be enough to prove that he was not present when the incident occurred. The result from this study suggests that it will be extremely difficult for the defence team to prove their client’s innocence with GPS based evidence alone due to the positioning inaccuracy. Overall, the defence lawyers need to evaluate the weight that the GPS based evidence carries that might make the difference between their clients being sent to prison and set free. 6.5 Conclusion This study has identified the inaccuracy in location services, and the potential impact this could have on the admissibility of GPS based evidence. The inaccuracy levels of location services is higher than previous study have suggested and the accuracy levels vary in different devices and locations. This clearly makes GPS based evidence highly questionable and unreliable in court. Further research needs to be carried out to examine different variables that have the potential to affect the accuracy of location services. 62 Chapter 7 - Conclusions and Recommendations 7.1 Introduction This section will explore how each aims and objectives at the start of the project have been achieved and where during the course of the project they have been accomplished. 7.2 Aims and Objectives 1. “Research and perform a literature review on GPS technology, location services, current accuracy levels, factors affecting accuracy and admissibility of GPS based evidence in court cases”. At the beginning of the project involved extensive research on GPS technology, examining the concept of GPS technology. The literature review also involved extensive research on location services, the current levels of positioning accuracy and admissibility of GPS based evidence in court cases. The research was gathered from various sources that specialised in GPS technology. When the research was merged, it gave a clear picture on the current level of accuracy in location services and the use of GPS based evidence in court cases. 2. “Captured between 200 to 500 data points at a various location in different circumstance using smart phone”. To capture location coordinates, iPhone 5 and Samsung Galaxy S4 were used and 200 location coordinates were collected from each device. The location coordinates were collected at several locations in Derbyshire. Some of the data points were collected indoors or outdoors and in urban or rural areas. 3. “Analyse the collected coordinates and perform critical analyses to discover differences between the actual physical position and the position captured using smartphone to uncover the positional error”. The collected data was analysed using SAS (Statistical Analysis System). In SAS various functions were used to analyse the data collected. In SAS to uncover the positioning difference between physical location and Phone location, ‘geodist’ function was used. Using the SAS ‘univariate’ 63 procedure and ‘ttest’ procedure, statistical information was produced and charts were created to display the error in distance. 4. “The potential impact on the admissibility of GPS based evidence in court cases”. This objective was fulfilled in the discussion section, the result of this study was discussed and the potential impact on the admissibility of GPS based evidence in court. The above objectives were accomplished in order to achieve the overall aim of the project that was: “ To uncover how accurate location services are on our smartphones and the potential impact on the potential impact on the admissibility of GPS based evidence in court cases”. To uncover the accuracy levels of location services 200 location coordinates were collected from each phone and analysed to discover the positioning inaccuracy. To maintain the quality of data collected one method was followed to throughout the collection process. 7.3 Improvements and Recommendations The following improvements and recommendations are made which could further improve the project if this were carried again. 7.3.1 Improvements Throughout the data collection process, the methods remained the same. Using different methods to collect the data could reveal a different results. The data collected from different methods could be analysed to uncover the difference. There was a large difference between the number of indoor and outdoor data points. The improvement to the project would be to conduct the same number of tests both indoors and outdoors which would give a more levelled comparison between the positioning error under different circumstances. The other variables that could be explored are whether the time of day made any difference to the accuracy of location service and weather conditions at the time of data collection. Comparing the data collected could give new insight into location service positioning accuracy. 64 Also, it would be useful to examine previous expert witness reports that have been presented as evidence in court cases. 7.3.2 Recommendations This study has examined the accuracy of location services in smartphones with single reading from each device in an individual data point. However, further research could be carried out taking more readings from a single location to examine whether it produces the same results. The data can be collected in different weather conditions and compare the data to see if weather make any difference to the accuracy of location services. 65 Chapter 8 - Personal Reflection Throughout the project, there were both positives and negatives developments that made the project both challenging and interesting to undertake. The literature review proved one of the most challenging parts of the project. While conducting the literature review, there was a death in my family which meant I was unable to work on it for over three weeks. The time and mental strain during this period tested the skills and knowledge I had learnt during the first two years of my university education. While writing the literature review, I performed an extensive research on the topic and collected the resources from reading books, academic journals and websites. The resources collected was used to find the key issue. The key issue was the inaccuracy of location services and the use of GPS based evidence in court cases. The literature provided a clear picture of the current level of accuracy levels in location services. Collecting the location data using two smartphones was particularly challenging due to unpredictable weather conditions and finding the data points marked on the map was particularly difficult in rural areas. Data collection was long and painstaking work both mentally and physically. The finding and analysis chapter was ambitious because I had to learn SAS in a short period as well as perform complex data analysis. Learning SAS was much more hard work than I had anticipated. During this project time, management played a major role as I also had commitment to other modules. Overall, the project was both interesting and challenging to complete. The result of this study was interesting and I was surprised by some of the positioning errors uncovered. The information uncovered was very interesting and important to the subject area. While undertaking this project, I have learnt the importance of research and time management. 66 9. References Abdel Meniem, M., Hamad, A. and Shaaban, E. (2013). GSM-Based Positioning Technique Using Relative Received Signal Strength. International Journal of Handheld Computing Research, 4(4), pp.38-51. ABIresearch, (2012). Personal Tracking to Be the Next Billion Dollar GPS Market. 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