Isaac Taylor and Miguel Labrador

1. Improving the Energy Consumption in Mobile Phones by Filtering Noisy GPS Fixes with Modified Kalman Filters Isaac Taylor and Miguel Labrador Center for Urban Transportation Research Department of Computer Science & Engineering

2. Location Aware State Machine • Integrated in TRACIT • A mobile application that collects a user’s travel information via GPS and provides individual feedback [1]. • Determines if valid and useful GPS data is being obtained. • Adjusts the frequency of GPS calculations depending on a user’s movement [1]. • Frequencies used: 4, 8, 16, 64, 150, 256

3. State Machine Problems Incorrect Behavior Application Performance Incorrect data may erroneously decrease the number of GPS calculations, thus losing important tracking information for the application. Energy Consumption Incorrect data may erroneously increase the number of GPS calculations unnecessarily, thus wasting the cellular phone’s energy.

4. Robust Kalman Filter (RKF) • Developed by Ting, J 2007 • Main idea • Give the static noise variable R a dynamic weighting factor • Changes to the Kalman Filter: For more information see [5] Advantages No increase in Computation Complexity No use of heuristics • Disadvantages • Tuning of the filter is required • Values for a, b, Q, and R are needed • Some knowledge of outliers are necessary Q = R = .0001 (recommended by Ting) a = b = 1 (recommended by Ting)

5. Adaptive Kalman Filter (AKF) • Developed by Rutan, S in 1991 • Main idea • Compute a new error value every time a new point is encountered • Changes to the Kalman Filter (taken from the RKF): For more information see [3] Advantages Little tuning of the filter needed (Q) Ease of implementation Disadvantages Could increase computations if window is large No way to adjust the filter if needed Untested with GPS Q = .0001 (recommended by Ting)

6. Adaptive Robust Kalman Filter (ARKF) • Simply a combination of the above two filters • R is calculated (AKF) and then weighted (RKF) Advantages Any outliers potentially overlooked by one of the other filter may be caught here • Disadvantages • Increased Complexity • R is now dynamically computed, and weighted

7. Outlier Test • Why • Prove that each filter removes outliers in GPS data • The dataset contains: • 394 GPS Fixes • 4 Outliers (3 stacked)

8. TRACIT Test • Why • To Determine if the integration of the three modified Kalman filters with the Location Aware State Machine: • Reduces energy consumption • Leaves the amount of valid travel data unchanged • How • Four phones, one with each filter and one without a filter, are carried for 30 days to produce 30 tests. • After data is recorded, each point is labeled as stationary or traveling, based on a travel log kept for a particular day.

9. TRACIT Test Metrics • Three Metrics • Incorrect State Point (ISP) • Uses the time difference between two consecutive GPS fixes. • This time difference is used to determine which state the state machine is in at a given time • A difference is counted as a ISP if the state machine is in an incorrect state • In an awake state when the user is stationary or vise versa • Stationary Fix Counts • Total fixes taken while stationary – Used to track energy consumption • Traveling Fix Counts • Total fixes taken while traveling – Used to check the amount of valid travel data received

10. Incorrect State Point(ISP) AKF increases the state machine performance by about 4 % • Uses the time difference between two consecutive GPS fixes to determine what state the Location Aware State Machine is currently in. • A point is an ISP if: • it has a difference of less than 5 seconds and is labeled “stationary” • it has a difference greater than 8 seconds and is labeled “traveling” • Values for 6 and 7 are not accounted for because • the state of the Location Aware State Machine is difficult to determine at these values due to • the behavior of GPS.

11. Stationary Fix Count AKF reduces the total number of Stationary fixes by 17.4 % • Total number of Stationary Fixes generated in a test/day • Each point is compared to a travel log for a given day • All points generated when the travel log states “stationary” are counted

12. Traveling Fix Count AKF leaves the traveling fix count unchanged • Total number of Stationary Fixes generated in a test/day • Each point is compared to a travel log for a given day • All points generated when the travel log states “traveling” are counted • The closer a filter’s Traveling Fix Count is to the Normal's, the smaller the change is in application performance

13. Conclusions • AFK shows the largest decrease (17.5% ) in Stationary fixes while not affecting the application performance

14. References • Barbeau, S., Perez, R, Labrador, M. A., Perez, A., Winters, P., Georggi, N.. LAISYC A Location-Ware Framework to Support Intelligent Real-Time Applications for GPS-Enabled Mobile Phones. IEEE Pervasive Computing (to appear 2010). • Kalman, R.. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering, 82:35–45, 1960. • Rutan, S.. Adaptive Kalman Filtering. Analytical Chemistry, 63:1103A1109A, 1991. • Sharkley, J.. Coding for Life–Battery Life, That Is. http://dl.google.com/io/2009/. Accessed Jan 2011 • Ting, J., Theodorou, E., Schaal, S.. A Kalman Filter for Robust Outlier Detection. Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2007, pages 1514–1519, 2007. • Welch, G., Bishop, G.. An Introduction to the Kalman Filter. ACM SIGGRAPH International Conference on Computer Graphics and Interactive Techniques, Chapel Hill, NC, USA, 1995. University of North Carolina at Chapel Hill.