Master Project
LAWS: Location Accuracy based on Wireless Signals.
Sri Naga Jahnavi Yeddanapudy
9/4/2015
Approved by Date
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Advisor: Dr. Edward Chow
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Committee member: Dr. Kristen Walcott-Justice
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Committee member: Dr. Jonathan Ventura
2. Introduction
For all of its amenities, the pervasive use of mobile devices in the work place and beyond has brought up the need for location accuracy [1]. Location Accuracy has significant impact on various fields like public safety and disaster relief to save lives within reduced time, to track people during outdoor adventures like hiking …etc. In hospitals to disclose the patient records to authorized person located in that building. Also in work place and in military to send the confidential data to the employee in that particular office building. As the dependence of various location based applications are increasing rapidly, the need to improve the location accuracy is becoming dominant.
Initially GPS information was confined to military purpose. Later this was introduced to the civilians. That led to several applications in various areas like space, roads and highways, marine, 911 …etc. Mobile code and apps are also introducing new avenues for the developers proving the location accurately because of ubiquitous use of location based applications such as maps, emergency alerts …etc. The main goal in this project is to determine the location accurately based on Wi-Fi and GPS information in order to validate the user’s location. In this project, we assume there are no hackers trying to change or spoof the GPS and Wi-Fi location information. We will demonstrate the feasibility of the technique by developing an Android based prototype.
2.1 Background
In recent years, several location based applications started using the GPS information and among them, exists error in determining the user’s location accurately and this error sometimes may result in heavy loss especially during the emergency situations like disasters or amber alerts...etc [references]. Hence in this project we use both Wi-Fi and GPS Signals to validate the location accuracy. We use the Wi-Fi information like:
· Wireless access point(AP) which is a device that allows wireless devices to connect to a wired network usingWi-Fi, or related standards. The AP usually connects to a router(via a wired network) as a standalone device, but it can also be an integral component of the router itself. An AP is differentiated from ahotspot, which is the physical space where the wireless service is provided [2].
· BSSID is the MAC address of the access point.
· Signal to Noise ratio (SNR) compares the level of the Wi-Fi signal to thelevel of background noise [3].
Since we are using Wi-Fi signal information there is a possibility for Wireless interference and this may cause some interrupts in the applications while accessing the information. Therefore in our experiments we are focusing on outdoor area. Let us now look into what is a shielding effect and how the wireless networks get affected by the interference.
Shielding Effect: Wireless interference typically comes from three sources: walls and floors blocking wireless signals, other Wi-Fi networks using the same channel as your own Wi-Fi network, and appliances and electronics emitting radio frequency interference. Many wireless networks are affected by all three types.
· Interference from walls and floors: The construction of the building can greatly affect wireless communication speed and range. Some common types of materials, such as wood and glass, don't have much of an effect. However, denser materials such as concrete, brick and metal can make it difficult to connect, slow network speeds or even completely block wireless signals from reaching certain parts of the building.
· Interference from competing Wi-Fi networks: Another type of interference is caused by Wi-Fi networks that are set to use the same frequency channel. In North America, a Wi-Fi network can operate on one of 11 channels, while most other countries have 13 channels available. If more than one Wi-Fi network uses the same channel, they're constantly competing with each other to use limited bandwidth. It's similar to a traffic jam that never ends because everyone not only wants to drive on 13 lane highway, but they all want to drive in the same lane too. They're an especially common source of interference in cities, apartment buildings and densely populated areas where there are usually several Wi-Fi networks nearby.
· Interference from other electronics: Interference can also come from other electronics and appliances that aren't connected to our wireless network, but use the same 2.4GHz or 5GHz frequencies to communicate. Cordless phones, bluetooth devices, baby monitors and wireless video security systems are some examples. Some electronics and appliances, like microwave ovens, generate radio frequency noise as a byproduct, so we may notice a network slowdown or get disconnected only when we are reheating dinner. Some larger electronics, such as TVs, can affect Wi-Fi signals even when they're asleep or turned off since their power supplies may generate short range interference. [4]
2.2 Related Work
Several works have been proposed in the past related to GPS spoofing. Tippenhauer et al [5] work starts off by defining the GPS spoofing problem proceeding with analyzing spoofing attack on single and multiple receivers in both civilian and military GPS system. Further they investigated on the requirements for civilian GPS spoofing by seamless satellite-lock take over varying power, timing and location precision of the attacker’s spoofing signals. In our work we are using Wi-Fi information in addition to GPS information to predict the location accurately.
Humphreys et al [6] designed their own portable civilian GPS spoofer which is implemented on digital signal processor. It is used to characterize the spoofing attacks and the counter measures from the attacks. Their work is intended to the GNSS users and receivers. They tried to see the problem in a different way by assessing the spoofing attack based on the level of the attack like simplistic, intermediate and sophisticated. Finally provided the counter measure to overcome these spoofing attacks. They explained how their GPS spoofer can determine amplitude discrimination, time-of-arrival discrimination, consistency of navigation inertial measurement unit (IMU) cross-check, polarization discrimination, angle-of-arrival discrimination, cryptographic authentication. My proposed work concentrates on BSSID, signal to noise ratio and AP’s information along with the GPS information to check the user’s location.
Whipple et al [7] came up with a novel application using GPS information. The authors developed an Android application which runs in the background and when the driver drives through the school zone with more than 20 MPH an alarm sounds to alert the driver. So the author’s used the GPS information to solve this problem. Using maps which uses GPS information may result in location inaccuracy. The research results of our proposed project can help improve the location accuracy for the aforementioned mobile applications.
Koch et al [9] in their work used the geo location information for strategic pre incident preparation of an IT Forensics Analysis. According to the author’s definition strategic pre incident refers to all measures, which, in anticipation of a potential incident, can support the investigation of an incident, significantly more/additional data is available for a forensic examination. The author’s idea is to create an optimal starting point for forensic analysis. They tried to create good database which uses the IP geo-location information. They used the IP to determine the physical location and use the Intrusion detection technique utilizing the database to prove the identity. In contrast our work is more specific in using the GPS and Wi-Fi for determining the user’s location.
3. Project Plan
The main goal of this project is to design and develop the software prototype called Location Accuracy based on Wireless Signals, (LAWS) for computing location accurately using Android smart devices with the GPS and Wi-Fi signals
The main tasks of this project are:
· Conduct experiments to evaluate location accuracy based on Wi-Fi signals and GPS signals. Our experiments focus on outdoor such UCCS Alpine Soccer Field or Alpine parking garage or central court or older building because we have better shielding effect on the wall door.
· Develop an Android Application for collecting Wi-Fi base station information like the BSSID, Noise, AP’s (Access Points) … etc, signal strength, device GPS Signals and reported location.
· Develop and algorithm to calculate android phone location based in the Wi-Fi signal strength.
· Based on the availability of the base station data, compare the android phone signal strength with those observed by the base station.
Metrics for evaluating the experiments are
· Accuracy
· Time Performance
3.1 Tasks Schedule
Task / Task Description / Length / Status1 / Literature Survey / 6 weeks / Completed
2 / Develop Android Application for collecting
Wi-Fi signal Information / 2 weeks / Yet to be done
3 / Develop Android Application for collecting GPS Signal information / 2 weeks / Yet to be done
4 / Algorithm to calculate location based on Wi-Fi signal strength / 4 weeks / Yet to be done
5 / Evaluate location accuracy / 2 weeks / Yet to be done
6 / Write Project Report / 1 week / Yet to be done
3.2 Deliverables
· Android Application for Location accuracy based on Wi-Fi and GPS signals.
· A Project report documenting the LAWS Application.
4. References
[1] http://www.gps.gov/applications/
[2] https://en.wikipedia.org/wiki/Wireless_access_point
[3] http://help.netspotapp.com/what-is-the-signal-to-noise/
[4] https://nest.com/support/article/What-is-wireless-interference-and-how-do-I-troubleshoot-it
[5] Nils Ole Tippenhauer, Christina Popper, Kasper B. Rasmussen, Srdjan Capkun, “On the Requirements for Successful GPS Spoofing Attacks”, CCS '11Proceedings of the 18th ACM conference on Computer and communications security, Pages 75-86, October 17 2011.
[6] Todd E. Humphreys, Brent M. Ledvina, Mark L. Psiaki, Brady W. O'Hanlon, and Paul M. Kintner, Jr., “Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer”, ION GNSS Conference Savanna, GA, September 16–19, 2008.
[7] John Whipple, William Arensman, Marian Starr Boler, “A Public Safety Application of GPS-Enabled Smartphones and the Android Operating System”, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, Pages 2059-2061, October 11-14 2009.
[8] Robert Koch, Mario Golling, Lars Stiemert, and Gabi Dreo Rodosek, “Using Geolocation for the Strategic Preincident Preparation of an IT Forensics Analysis”, Systems Journal, IEEE, Pages 1-12, February 16 2015.
[9] Tyler Nighswander , Brent Ledvina , Jonathan Diamond , Robert Brumley , David Brumley ,“GPS Software Attacks”, CCS '12Proceedings of the 2012 ACM conference on Computer and communications security, Pages 450-461, October 16 2012.
[10] Jun Wang, Xiaoli Xi, Jiangfan Liu, “The design of GPS IF signal Software simulator”, Proceedings of International Symposium on Signals, Systems and Electronics (ISSSE2010), Pages 1-3, September 17-20 2010.
[11] Kexiong (Curtis) Zeng, Sreeraksha Kondaji Ramesh and Yaling Yang, “Location Spoofing Attack and Its Countermeasures in Database-Driven Cognitive Radio Networks”, Communications and Network Security (CNS), 2014 IEEE Conference, Pages 202-210, October 29-31 2014.
[12] Samer Khanafseh, Naeem Roshan, Steven Langel, Fang-Cheng Chan, Mathieu Joerger, and Boris Pervan ,“GPS Spoofing Detection using RAIM with INS Coupling”, Position, Location and Navigation Symposium - PLAN– 2014, 2014 IEEE/ION, Pages 1232 - 1239, May 5-8 2014.
[13] Zhenghao Zhang, Matthew Trinkle, Lijun Qian, and Husheng Li, “Quickest Detection of GPS Spoofing Attack”, Military Communications Conference,2012 – MILCOM 2012, Pages 1-6, October 29 2012 – November 1 2012.
[14] Der-Yeuan Yu, Aanjhan Ranganathan , Thomas Locher, Srdjan Capkun, David Basin,“Short Paper: Detection of GPS Spoofing Attacks in Power Grids”, WiSec '14Proceedings of the 2014 ACM conference on Security and privacy in wireless & mobile networks, Pages 99-104, July 23 2014.