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EnLoc: Energy-Efficient Localization for Mobile Phones

EnLoc: Energy-Efficient Localization for Mobile Phones. Written By, Ionut Constandache (Duke), Shravan Gaonkar (UIUC), Matt Sayler (Duke), Romit Roy Choudhary (Duke), London Cox (Duke). Presented by, Urmila B. Shinde. OUTLINE OF PAPER. Aim Energy measurement Framework design

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EnLoc: Energy-Efficient Localization for Mobile Phones

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  1. EnLoc: Energy-Efficient Localization for Mobile Phones Written By, Ionut Constandache (Duke), Shravan Gaonkar (UIUC), Matt Sayler (Duke), Romit Roy Choudhary (Duke), London Cox (Duke) Presented by, Urmila B. Shinde

  2. OUTLINE OF PAPER • Aim • Energy measurement • Framework design • System design • Performance evaluation • Limitations and Future work

  3. Why EnLoc? • Number of location based applications on mobile phones is growing. • Most of them use GPS for its location accuracy(10m). • GPS drains off lots of energy of mobile phone reducing its battery life to less than 9 hours. • Energy efficient localization framework: EnLoc • It provides optimal localization accuracy for a given energy budget.

  4. Observations for energy-accuracy profile • Energy budget for localization is assumed to be 25% of battery capacity. • For some application continuous GPS localization is necessary. • WiFi and GSM cannot be obvious replacements to GPS because of their higher localization error.

  5. Energy Measurements • Power consumption in Nokia N95 is measured by a software monitor. • Reference work: Project Place Lab proposed using WiFi and GSM sensors for localization. • Energy consumption on Nokia N95 phones is measured for each localization sensor.

  6. Power consumption on Nokia N95

  7. Power consumption was translated to net battery life • Energy-accuracy tradeoff can be seen from this table.

  8. Framework Design • Average Location Error (ALE): Here, GPS is assumed to be the ground truth.

  9. 2. The basic aim: for a given energy budget, determine a schedule with which the location sensors should be triggered such that ALE is minimized. Schedule: set of time instants, {t1, t2, t3,..} and corresponding sensors {s1,s2,s3,..}, where si ɛ {GPS, WiFi, GSM} This is done by Dynamic Programming (DP)

  10. 3. Optimal Localization error: DP was executed on a trace obtained by a custom simulator that integrated the traces with the wireless map. energy budget: 25% duration of operation: 24 hours

  11. 4. Prediction opportunities: • Simple Linear Predictor • Human Mobility Patterns • Deviations

  12. System Design • Habitual Mobility: individuals exhibit habitual space-time movements, with reasonably small variation. GPS mobility traces of several people are plotted on a google map to form a Logic Mobility Tree (LMT). Vertices of this tree are uncertainty points. key idea: scheduling location readings right after the uncertainty points on LMT.

  13. user's mobility over two weeks Spatio-temporal LMT Spacial LMT

  14. Example • Lets assume its 8 am and phone is at home • Consider two paths of the tree P1 = home=>coffee P2 = home=>walmart • Ni: # of location readings N1 = 4+6 = 10 N2 = 4+8+3 = 15 • M = max(Ni) = 15 • F = eH/M = 4/15

  15. Example • Amount of energy for detecting its departure from home = F * Bremaining = 0.26 * 10 = 2.6 • Thus, approximately 2 GPS readings are available • Heuristic randomly chooses 2 time points out of 4 and samples the phone’s location. • Once phone is found on one of its paths going out of home, heuristic predicts phones location based on habitual velocity on that edge. • At next uncertainty point, F is recomputed.

  16. Deviation from Habits • This is coped up with exploiting mobility of large populations as a potential indicator of the individual’s mobility. • Deviation mode is switched on when scheduled location reading finds out that the phone is in an unexpected location. • In this mode, population activity map is used to predict the phone’s movement.

  17. Performance Evaluation Deviant path optimal schemes • EnLoc is evaluated by using traces collected on UIUC campus. 1. Evaluation of deviant paths: mobility of phone is predicted based on maximum probability at the intersection. Deviant Path Heuristics

  18. Performance Evaluation 2. Habitual Mobility person’s mobility profile is utilized for prediction. a student’s mobility profile was derived from 30 days of traces. LMT is manually generated and mobility profile heuristic was executed for each day. Individual Mobility Profile

  19. Limitations and Future Work • When the location of a phone is tracked while moving along a predicted path, varying speeds or pauses cause imprecise prediction. These errors are not accounted in evaluation results. • GPS reading scheduled after an intersection can be eliminated by using compass. • EnLoc does not proactively identify deviations from habitual paths. • Generating probability maps for places unlike university campuses may be difficult.

  20. Critique • Authors have not elaborated Dynamic Programming due to space constraints. • Lot of overhead in maintaining an individual habitual map. • It is not mention how much exact amount of energy is conserved by using this framework.

  21. Questions???

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