This work was supported by the LOEWE Priority Program Cocoon (www.cocoon.tu-darmstadt.de)

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Location Data Availability for User #5969

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Phone off

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GPS required

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Instantaneous entropy (in bits)

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Average Instantaneous Entropy (in bits) for 38 Users (GSM)

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People, sensors, and cities !  Number of people living in cities is constantly rising !  Challenges for critical infrastructures (e.g., transport, communication) !  Opportunity: Widespread of smartphones equipped with sensors

!  Detect and predict human behavior (e.g., mobility) !  Use predictions to support users and operate critical infrastructures

Predicting human mobility !  Well-known algorithms to predict human activities [1,2] and mobility [3,4,5,6] !  Challenge: Behavior and thus “predictability” changes over time

!  Best performing prediction algorithm accordingly changes over time !  Research question: How to select which algorithm to run depending on

users' long- and short-term predictability?

Adaptive sensor cooperation !  Use sensor-equipped smartphones to capture human behavior

!  Mobility patterns (GSM, Wi-Fi, GPS) [3,4,5,6] !  Social ties (call logs, Bluetooth) [1,2,5,11] !  Routines (calendar, Bluetooth, GPS) extraction [1,2]

!  Trade-off between data accuracy and resource usage !  E.g., energy consumption of some sensors depend on the

number of visible devices at the time of the location reading [7] !  Research question: How to select which sensors to interrogate?

Sensor Energy costs for location reading

Wi-Fi 545.07 mJ

Bluetooth 1299 * N(t) + 558 mJ

GPS (cold start) 5700 mJ

GPS (warm start) 1425 mJ

GSM data available locally

Adaptive Sensor Cooperation for Predicting Human Mobility Paul Baumann, Silvia Santini Wireless Sensor Networks Lab, TU Darmstadt, Germany

Motivation and Goals

References

Cocoon Workshop, November, 8th 2012

Household A Household B

Wi-Fi-based

GSM-based

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Future Work Predictability index !  Further explore correlation between instantaneous entropy and prediction

accuracy of state-of-the-art algorithms !  Adapt a non-linear approach [4] for calculating instantaneous entropy

!  Search for similar instead of the exact location sequences !  Cope with missing sensor data and variable length of stays in specific places

Self-adaptivity !  Explore trade-off between accuracy and energy costs with respect to users’

short-term predictability !  Define strategies to select prediction algorithms based on users’

instantaneous entropy

[1] Eagle, N. and Pentland, A., Eigenbehaviors: Identifying Structure in Routine. Behavioral Ecology and Sociobiology. 63, 7 (Apr. 2009), 1057–1066.

[2] Do, T.M.T. and Gatica-Perez, D., Human Interaction Discovery in Smartphone Proximity Networks. Personal and Ubiquitous Computing 2011.

[3] Krumm, J. et al., E. Predestination: Inferring Destinations from Partial Trajectories. UbiComp 2006. [4] Scellato, S. et al., Nextplace: A Spatio-temporal Prediction Framework for Pervasive Systems.

Pervasive 2011. [5] Domenico, M.D. et al., Interdependence and Predictability of Human Mobility and Social Interactions.

Pervasive 2012. [6] Montoliu, R. et al., Discovering Places of Interest in Everyday Life from Smartphone Data.

Multimedia Tools and Applications 2012. [7] Lin, K. et al., Energy-accuracy Aware Localization for Mobile Devices. MobiSys 2010.

[8] McInerney, J. et al., Exploring Periods of Low Predictability in Daily Life Mobility. Pervasive 2012. [9] Laurila, J. et al., The Mobile Data Challenge: Big Data for Mobile Computing Research.

Pervasive 2012. [10] Scott, J. et al., PreHeat: Controlling Home Heating Using Occupancy Prediction. UbiComp 2011. [11] Eagle, N. and Pentland, A., Reality Mining: Sensing Complex Social Systems.

Personal and Ubiquitous Computing. 10, 4 (Nov. 2006), 255–268.

Current Results Instantaneous entropy computed on GSM data from Nokia dataset !  Variability of users' predictability over time

Correlation between instantaneous entropy and prediction accuracy !  Evaluation of schedule-based and PreHeat-based [10] occupancy prediction

!  Occupancy: user’s presence at home

!  Evaluation of modified PreHeat-based [10] next place prediction

0

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Correctness of Made Decision vs Entropy for Different Algorithms ! 38 Users (GSM)

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ccupancy

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pre

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k =

5

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Heat!

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ext

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k =

5

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k =

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Instantaneous entropy !  Metric to measure users’ instantaneous predictability [8] !  Instantaneous entropy is computed using a sequence

of location data (GSM, Wi-Fi, GPS)

Adaptive algorithm and sensor selection !  Explore correlation between prediction accuracy and instantaneous entropy

!  State-of-the-art prediction algorithms (next place, activities, etc.) !  Detect causes of performance degradations of different algorithms

!  E.g., short-term changes in mobility patterns !  Develop adaptive strategies to perform algorithm and sensor selection

Evaluation !  Dataset from Nokia Lausanne Data Collection Campaign [9] (Nokia dataset) !  185 participants, 36 months !  Nokia N95 mobile phones, data logger run continuously

!  Location (GPS, Wi-Fi, GSM) and proximity (Bluetooth) !  Motion (accelerometer) !  Communication (calls and SMS) !  User interactions

Our Approach

!! = !log!(!)Γ!

!!

Unpredictable

Highly predictable

Target group

Algorithms and sensors selector

Prediction algorithms

Hidden Markov Model

Exponential smoothing

Regression

…

Human behavior prediction

Predictability index

Predictability estimator

Selected algorithms and sensors

Sensor values

Urban public navigation

Home automation

: instantaneous entropy i : time instant Γi : shortest previously unseen location sequence ending at instant i

HiFeature'#1'

Pred

ictability'inde

x'

Feature #1

Pred

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dex

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

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Instantaneous Entropy (in bits) vs Schedule!based Prediction Error for 15 users (GSM)

Instantaneous entropy (in bits)0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

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Instantaneous Entropy (in bits) vs PreHeat!based Occupancy Prediction Error for 15 users (GSM) ! K=5

Occ

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Instantaneous entropy (in bits)

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Instantaneous Entropy (in bits) vs PreHeat!based Next Place Prediction Error for 15 users (GSM) ! K=5

Ne

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Instantaneous entropy (in bits)

…