Department of Civil, Environmental,and Geo-Engineering (CEGE)

Battery-Efficient Location Change Detection for Smartphone-Based Travel Data Collection:A Wi-Fi Fingerprint Approach (#16-2090)

Chen-Fu Liao, Yingling Fan, Gediminas Adomavicius, and Julian Wolfson2 3 41

Dept. of Civil, Environmental, and Geo-Engineering

Hubert H. Humphrey School of Public Affairs

Carlson School of Management

School of Public Health

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Abstract Methodology

Summary and Conclusion

With the advance of smartphone technology and prevalence of smartphone ownership, numerous apps are being

made available in the market to enhance the quality of our life. In transportation, a plethora of apps have been

developed by researchers to collect trip and activity data for travel mode detection, trip experience, and traveler

behavior analysis. However, one fundamental concern about the smartphone-based data collection apps is the

battery power consumption. When the battery power depletes rapidly from continuous operation of multiple

sensors (especially the GPS receiver) on the smartphone, the requirement (and inconvenience) of frequent

battery recharge often outweighs the potential benefit of using those apps. In this work, we present an approach

to conserving smartphone’s battery power using the information from the surrounding Wi-Fi networks, i.e.,

using the Wi-Fi signal fingerprint technique. We use two indices, Jaccard and normalized weighted signal level

change (NWSLC), using the Wi-Fi signal fingerprint technique to detect a location change. Both measures are

computed based on the information about the received Wi-Fi signal strength, obtaining which is substantially

less demanding on the smartphone battery than obtaining continuous GPS information. The proposed measures

were implemented on a test app for verification and validation. Experiments were conducted at 10 different

indoor locations (representing potential trip origins) to determine the appropriate numeric thresholds for location

change detection. The results suggest that proposed Wi-Fi fingerprint methodology can reliably detect the

change of a person’s location prior to resuming the GPS service for trip data collection.

Objective

The objective of this study is to develop a Wi-Fi signal fingerprint methodology for detect location change when

a person initiates a trip. The GPS service will be turned off to conserve battery power when no motion is

detected by the accelerometers for a period of time. In areas when Wi-Fi signals are available, the GPS service

will not be turned back on until a location change or beginning of a trip is detected by the Wi-Fi fingerprint

technique.

Smartphone Sensors

Data Analysis and Results

Introduction

We build upon the SmarTrAC app (recently developed by our research group). This smartphone app is designed

to incorporate smartphone sensing with advanced statistical and machine learning techniques to automatically

detect, identify, and summarize attributes of a traveler’s daily trips and activities. We learned that the battery

power consumption rate for the GPS sensor is significantly larger than that of the accelerometer (i.e., motion

sensor). We found that phones consumed minimal power (1.0% - 1.1% per hour) when GPS sampling was

turned off, and that phones consumed 4.9% - 7.0% power per hour when GPS sampling was turned on.

Table 1 Comparison of Accelerometers on Different Smartphones

Smartphone Brand Samsung Galaxy S5 Samsung Galaxy S4 HTC One Sony Xperia Z

MEMS Manufacturer Invensense ST Microelectronics Bosch Bosch

Model MPU-6500 K330 (LSM330DLC) BMA-250 BMA-250

Type 6-Axis 6-Axis 3-Axis 3-Axis

Acceleration

Tri-Axis accelerometer

programmable full scale

range of ±2g, ±4g, ±8g and

±16g

Tri-Axis accelerometer

programmable full

scale range of ±2g, ±4g,

±8g and ±16g

Tri-Axis

accelerometer,

programmable

full scale range

of ±2g, ±4g, ±8g

and ±16g

Tri-Axis

accelerometer,

programmable full

scale range of ±2g,

±4g, ±8g and ±16g

Angular Rate

Tri-Axis angular rate sensor

(gyro) with a sensitivity up

to 131 LSBs/dps and a full-

scale range of ±250, ±500,

±1000, and ±2000dps

Tri-Axis angular rate

sensor (gyro) with a

full-scale range of

±250, ±500, and

±2000dps

NA NA

Linear

Acceleration

Sensor

Range 0...19.613 (±2g) 0...19.613 (±2g) 0...39.227 (±4g) 0...156.96 (±16g)

Resolution 0.001 (0.003%) 0.001 (0.003%) 0.038 (0.098%) 20 (12.742%)

Power 0.25mA 0.25 mA 0.1 mA 0.003 mA

Web Site invensense.com www.st.com ae-bst.resource.bosch.com

We used a Jaccard index and a normalized weighted signal level change (NWSLC) index for detecting

location changes. We also develop a general-purpose battery power conservation methodology for travel

data collection apps to determine when to suspend/resume the GPS sensor on the smartphone.

Wi-Fi Fingerprinting Technique

Jaccard Index (or Similarity Coefficient)

The Jaccard index, or similarity coefficient, of two sets S1 and S2 is defined as:

𝐽 𝑆1, 𝑆2 =𝑆1∩𝑆2

𝑆1∪𝑆2∈ 0,1 (1)

Si = { BSSID | (BSSID, RSSI) Fi, SignalLevel (RSSI, 5) ≥ 2 }, for i = 1, 2.

Normalized Weighted Signal Level Change (NWSLC) Index

𝐴 =1

𝑁𝑛 𝑖=1𝑛 𝑆𝑖𝑔𝑛𝑎𝑙𝐿𝑒𝑣𝑒𝑙𝑟𝑒𝑓𝑖 × 𝑆𝑖𝑔𝑛𝑎𝑙𝐿𝑒𝑣𝑒𝑙𝑐𝑢𝑟𝑖 − 𝑆𝑖𝑔𝑛𝑎𝑙𝐿𝑒𝑣𝑒𝑙𝑟𝑒𝑓𝑖 (2)

Where,

𝐴 is the NWSLC index,

n is the number of intersection samples (i.e., n = | F1 F2|),

N is the total number of signal levels (i.e., N = 5 in our case),

𝑆𝑖𝑔𝑛𝑎𝑙𝐿𝑒𝑣𝑒𝑙𝑟𝑒𝑓𝑖 is the signal level of reference AP i (from scan F1),

𝑆𝑖𝑔𝑛𝑎𝑙𝐿𝑒𝑣𝑒𝑙𝑐𝑢𝑟𝑖 is the signal level of current AP i (from scan F2).

Site ID Test Site Description*Initial #

of APs

AVG Jaccard Index AVG NWSLC Index

From To From To

1 From a work office to an elevator in the building 7 1.00 0.09 0.00 1.34

2 From an elevator to the terrace outside a building 75 0.72 0.01 0.22 0.91

3 From an indoor atrium to the building’s east exit 152 0.56 0.01 0.40 1.29

4 From a student study area to a building’s east exit 73 0.71 0.01 0.38 2.58

5 From a lobby area to the building’s east exit 181 0.70 0.08 0.32 1.32

6 From a café area to the building’s north exit 112 0.92 0.00 0.15 1.65

7 From inside a Starbucks store to its north exit 37 0.69 0.07 0.47 1.24

8 From a hotel lobby to its west exit 31 0.76 0.03 0.32 1.08

9 From a home office to the neighbor’s driveway 14 1.00 0.09 0.00 0.91

10 From the lobby of an alumni center to its N. exit 158 0.91 0.02 0.19 1.59

*Notes Stay at each indoor location for about 1 min. AVG 0.80 0.04 0.25 1.39

Travel to a location outside the building or store

about 30-50 meters awaySD 0.15 0.04 0.16 0.49

Flowchart

We introduced two indices based on monitoring the Wi-Fi signal fingerprint for detecting location

changes, the Jaccard index and the normalized weighted signal level change (NWSLC) index. The

Jaccard index measures the similarity of two sample sets. The normalized weighted signal level change

(NWSLC) is another index that describes the changes of a network signature by weighting the signal

level change with respect to its previous signal strength and then taking the normalized average. Both

measures based on the received Wi-Fi signal strength were implemented on a smartphone app for

validating the proposed location change detection logics. The results from experiments at 10 indoor

locations indicate that proposed Wi-Fi fingerprinting methodology can reliably detect location change

before turning on GPS sensor for trip data collection.

Battery Power Consumption

Transportation Mode Identification