Blue-Fi : Enhancing Wi-Fi Prediction using Bluetooth

Signals

Ganesh Ananthanarayanan and Ion StoicaReliable, Adaptive, Distributed Systems Lab (RAD

Lab)University of California, Berkeley

Energy-efficient data transfer◦ 5 J/MB for Wi-Fi (vs. 100 J/MB for cellular)

Idle power consumption is high◦ 0.77W for Wi-Fi vs. (~0W for cellular,

0.01W for bluetooth)

Detect Wi-Fi availability without scanning but use it whenever available◦ Background applications like Email clients

and RSS feed synchronizers

Wi-Fi: The good and the bad

Learn Wi-Fi availability (Rahmati et al.)◦ Correlate Wi-Fi availability with locations

Localization◦ Global Positioning System

Accurate Power-hungry Poor signals indoors and in urban high-rise settings

◦ Cell-tower fingerprinting Power-efficient Coarse grained granularity

Location-based prediction

Fine-grained and practical indoor localization…

Bluetooth Contact Patterns◦ Users tend to repeatedly encounter the same set

of bluetooth devices

Bluetooth Fingerprinting

I have to download an email attachment …

Ion’s DeviceRAD Lab Bluetooth Printer

Looks like I am under Wi-Fi coverage…

Bluetooth Discovery

High Mobility◦ Potentially low temporal and spatial

constancy leading to low predictability

Low range◦ Possibly within Wi-Fi hotspot but just out

of range of bluetooth devices…

Discovery Time◦ High start-up times for network jobs

Challenges

Combine with cell-

tower signatures

Learning reliable devices

Periodic discovery

and caching

Periodic logging and correlation of network signals

Identifying reliable predictors◦ Predictability: Confidence measure of a signal’s

presence indicating Wi-Fi availability “Whenever I see Ion’s phone, I have Wi-Fi

connectivity”

Constantly refined to account for new mobility patterns

Learning Process

Prediction schemes evaluated using:◦ Coverage: Fraction of Wi-Fi connectivity chances

that are predicted◦ Accuracy: Fraction of Wi-Fi connectivity

predictions that are accurate

Bluetooth-based Prediction: High accuracy but low coverage (low range)Cell-tower-based Prediction: Low accuracy but high coverage (high range)

Prediction of Wi-Fi Availability

Fine-grained learning (Accuracy) using bluetooth devices, and use cell-towers as a fall-back (Coverage)

Helps in finer prediction within a larger area covered by cell-towers

Learning phase identifies both the reliable as well as the unreliable bluetooth predictors

Hybrid Prediction Scheme

Why is the hybrid scheme better?

Erroneous Prediction

Accurate Prediction

Coverage is equal to pure cell-tower

prediction

Best of both worlds – Coverage as well as Accuracy!

What is the threshold of predictability over which we consider a device as reliable?

Predict-Signal Matrix

1. Probe for Wi-Fi network when there is Wi-Fi availability (p1)

Prediction Reliability Threshold

Prediction of Wi-Fi availability

Wi-

Fi S

ign

al

Availab

ilit

y

p

s

p__

s__

2. Use the cellular interface in the presence of Wi-Fi (p2)

3. Waste energy to probe for Wi-Fi networks (p3)

4. Use the cellular interface because there is no Wi-Fi availability (p4)

Minimize the expected energy wastage

Case 2: Function of size of data transfer as well as p2

Case 3: Function of p3

p2 and p3 are functions of Accuracy, which in turn is only dependent on the threshold◦ Please refer to the paper for the derivation

Reducing Energy Wastage

Bluetooth discovery takes ~11 seconds◦ High latency in prediction and application start-up

Periodic discovery and use last discovered list

Stationary No change in Wi-Fi prediction Euclidean distance of cell-tower signatures

Bluetooth Discovery

Landmark Devices: ◦ Stationary bluetooth devices◦ Bluetooth printers, computer peripherals

(keyboard, mouse), bluetooth access points (CoolSpots)

◦ Shared across different users

Mobile Accessories:◦ Personal bluetooth gadgets◦ Bluetooth headphone, bluetooth-enabled media

players◦ Eliminate from logs; introduces error in prediction

All bluetooth devices are not equal!

Calculate diversity for bluetooth devices◦ Variance among the set of locations sighted

using K-Medians clustering technique

Landmark Device: Any device whose diversity is low, and whenever a signature similar to its cluster occurs, it is present

Personal Accessory: Occur in high fraction of log entries

Identification Algorithm

Twelve volunteers collected logs for a period of two-three weeks◦ Graduate students in Berkeley and working

professionals in the San Francisco Bay Area◦ HTC i-mate PDAs – Windows Mobile 5.0◦ Log all <Wi-Fi SSID/BSSID, cell-tower identifiers,

bluetooth MACs> every minute

Wi-Fi connectivity varies between 32%-68%Bluetooth devices are visible up to 77% of

the time

Evaluation – Log Collection

Coverage and Accuracy

Prediction Accuracy Coverage

Bluetooth 87.25% 61%

Cell-Tower 59.66% 93.5%

Hybrid 84.2% 93.5%

Hybrid Scheme has good Accuracy as well as Coverage

Workload modeled on background synchronization applications◦ Periodically, wake up and download data◦ Starting with full charge, measure the number of

synchronizations until the device dies

Comparison with two common strategies:◦ Ecellular : Use the cellular interface always

◦ EWi-Fi : Scan for Wi-Fi networks, and use if available

Energy Consumption [1]

Improvement of 19-62% w.r.t. Ecellular and

20-40% w.r.t. EWi-Fi

Blue-Fi is most effective:◦ w.r.t. Ecellular when Wi-Fi coverage is moderate-high

◦ w.r.t. EWi-Fi when Wi-Fi coverage is low-moderate

Energy Consumption [2]

Availability of Wi-Fi Networks

Blue-Fi is most effective:◦ w.r.t. Ecellular for moderate-high downloads

◦ w.r.t. EWi-Fi for low-moderate downloads

Energy Consumption [3]

Size of data downloaded

Most devices have low diversity Users see bluetooth devices only at select

locations Landmark devices have to be sighted every

time the user is present at that location

Diversity of bluetooth devices

Multi-hop bluetooth discovery◦ Chasm between range of Wi-Fi and bluetooth

signals◦ Increase the Coverage of bluetooth-based

prediction Reference bluetooth devices

◦ Deploy bluetooth landmark devices◦ Indoor spatial monitoring system for sensor

applications E.g., cooling within an office, Wi-Fi coverage

Future Work

Wi-Fi prediction is necessary due to the dichotomy in energy characteristics

Prediction strategy using bluetooth signals◦ Fine-grained indoor localization scheme

Combination of bluetooth and cellular based predictions produce encouraging results

Summary

Questions/Feedback