Extend Your Journey:Introducing Signal Strength into Location-based Applications

Chih-Chuan Cheng and Pi-Cheng Hsiu

Research Center for IT Innovation, Academia Sinica

Outline

• Motivation

• Existing Solutions

• Introducing Signal Strength into Location-based Applications

A virtual tour system

An optimal algorithm

An optimality condition

• Real-world Case Studies

• Conclusion

2

Location-based Applications

• A variety of location-based applications and services have progressively permeated people’s daily life

Services for directions or recommendations about nearby attractions

Social interaction with friends via location sharing

3

A Major Challenge & Existing Solutions

• Reducing the communication energy is an imminent challenge in stimulating such applications.

• Basically, existing approaches leverage the complementary characteristics of WiFi and 3G

WiFi to improve energy efficiency

3G to maintain ubiquitous connectivity

4

3G

WiFi

Where Communication Energy Consumption Comes From?• Receiving energy

Signal strength has a direct impact on the receiving energy.

• Tail energy 3G does not switch from the high to

the low power state immediately after each communication.

PIN

G

Tail E

nerg

y

(6.6

7 jo

ule

s)

Signal strength (dBm) -50 -60 -70 -80 -90 -100

Energy cost (Joule/byte) 0.00001 0.00002 0.00004 0.00005 0.00006 0.00008

*measured based on an Android smarphone of HTC EVO 3D in practice

8x

5

Extend Your Journey: Introducing Signal Strength into Location-based Applications

A well-know observation Receiving energy ∝ 1/signal strength

The technical problem How to prove the concept of

introducing SS into LBA?

Contributions A virtual tour system

A fundamental algorithm for data fetch scheduling

An optimality condition w.r.t signal strength accuracy

6

A Virtual Tour System

7

Virtual Tour Server

Signal Strength DB

LBS Providers

Src & Dst

Estimated SS

Fetch schedule

LBS Info.

The mobile platformExample applications

Signal Strength DB

An Optimal Algorithm-77 -73 -75 -86 -72 -90 -91

1 2 3 4 5 6 7

Objects

SS

(dBm)

9 3 3 3 3 3 0

0 0 0 1 0 0 1

5 4 1 4 4 3 4

1 1 1 1 1 1 1

• Goal: to schedule the fetching locations of the location-based informationbased on the signal strength such that the communication energy is minimized without adversely impacting original user experience

MFC

(Kbytes)

To

Taipei

101

Mitsukoshi

is there!

a cinema

is nearby!?

The firework

of Taipei 101

is awesome.

650 4478 500 800 4200 300 0

8

An Optimality Condition

1,0

2,3 3,5

4,6

2,2 3,41.1

1.38

1.31

0.72

0.98

1.04

1.3

1

∞

1,0

2,3 3,5

4,6

2,2 3,41.11

1.4

1.35

0.73

1.01

1.07

1.25

0.97

∞

• Complete directed graph with respect to estimated signal strength constructed based on our algorithm

Fluctuations

Our proposed

algorithm

Our proposed

algorithm

𝑟∗ ≡ 1,0 → 2,2 → 3,4 → 4,6 𝑟∗ ≡ 1,0 → 2,2 → 3,4 → 4,6Identical

9

• Complete directed graph with respect to real signal strength constructed based on our algorithm

Case Studies

10

Route@campus Route@downtown

Route

Ch.

Route@

campus

Route@

downtown

Signal

strength

(dBm)

Relatively weak

(i.e., -77,-75,-

78,-86,-79,-91,-

91)

Relatively strong

(i.e., -65,-72,-

78,-76,-58,-60)

Location-

based

Info.

Sparse (i.e., 54

objects

including 24

map tiles, 7

street views, 22

photos, and 1

video)

Dense (i.e., 239

objects including

21 map tiles, 1

street view, 214

photos, and 3

videos)

Taipei City Hall

MRT Station

VIESHOW & Taipei 101

Main Entrance of

Academia Sinica

The Institute of

History and Philology

Experimental Results• Impacts of the amount of information and the velocities

• LBS1 (Google maps):

59-70% reduction along

Route@campus and

61% reduction along

Route@downtown

• LBS2 (Google maps

and Panoramio): 49-

53% reduction along

Route@campus and

18-35% reduction along

Route@downtown

• LBS3 (Google maps,

Panoramio and

YouTube): 35-46%

reduction along

Route@campus and

27-43% reduction along

Route@downtown

1. Signal strength distortion

2. The round trip time of requests

1. Large number of objects

2. Significantly varied signal strength

Amortized by

the videos

11

Demo – HTC EVO 3D

12

Taipei City Hall

VIEWSHOW

http://www.youtube.com/watch?v=NGVi1JPzxeE

Conclusions

• This work introduces signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception.

• We have deployed a virtual tour system to prove this concept.

An HTC EVO 3D smartphone can achieve 30-70% of energy savings for data reception.

We will import Taiwan’s signal database acquired from OpenSignalMaps and release the mobile application program.

13

Thank You

14

How To Estimate Energy Cost

15

OpenSignalMaps

Power monitor

(Downlink DR, SS)

Power consumption

at each power state

Polynomial

regression

method

How To Determine Maximum Fetch Size

16

RF signal tracker Effective regions83 m/min 216 m/min 667 m/min

Maximum fetch size =

(Distance/Speed)*Downlink DR