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

Postdoctoral FellowChih-Chuan ChengEmbedded and Mobile Computing LABResearch Center for IT Innovation, Academia Sinica

Outline• Motivation• Existing Solutions• Introducing Signal Strength into Location-based

ApplicationsA virtual tour systemAn optimal algorithm

• Real-world Case Studies• Postoptimal Analysis• Open Issues• Conclusion

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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

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A Major Challenge• The trend will lead to a significant boost in

mobile data traffic.Resulting in further pressure on the limited battery

capacity of mobile devices• Reducing the communication energy is an

imminent challenge in stimulating such applications.

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Existing Solutions• Basically, existing approaches leverage the

complementary characteristics of WiFi and 3GWiFi to improve energy efficiency 3G to maintain ubiquitous connectivity

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Ref1: Prediction-based approaches for delay-tolerant applicationsRef2: Context-based approaches for delay-sensitive applicationsRef3: Batch scheduling and fast dormancy

Where Communication Energy Consumption Comes From?

• Receiving energySignal 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 En

ergy

(6.67 jo

ules)

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

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

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Problem 1: How to exploit these two observations in location-based applications to save communication energy?

Problem 2: Because signal strength fluctuates, how the proposed algorithm tolerate signal strength fluctuations?

• A virtual tour system• A fundamental algorithm for data reception

• Postoptimal analysis

A Virtual Tour System

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Virtual Tour Server

Signal Strength DB

LBS Providers

Src & Dst

Estimated SS

Fetch schedule

LBS Info.

The mobile platformExample applications

Signal Strength DB

Data Fetch Scheduling Problem (DFSP)

• Goal: to schedule the fetching locations of the location-based information based on the signal strength such that the communication energy is minimized without adversely impacting the application’s semantics

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-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

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 0Dispatch constraint

Availability constraint

Fetch constraint

An Optimal Algorithm• We propose a dynamic-programming algorithm to solve the DFSP

and prove its optimality in terms of energy savings. The basis of the dynamic-programming algorithm is the recursive formula. E(u,i) is defined as the minimum energy required to reach pn from pu when the first i

objects (or files) have been available on the device already.

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Subproblem E(u,i)

Subproblem E(v,j)

To fetch or not to fetch

Case Studies

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Route@campus Route@downtown

RouteCh.

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 HallMRT Station

VIESHOW & Taipei 101

Main Entrance ofAcademia Sinica

The Institute ofHistory 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 distortion2. The round trip time of requests

1. Large number of objects2. Significantly varied signal strength

Amortized bythe videos

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Publication

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• Chih-Chuan Cheng and Pi-Cheng Hsiu, "Extend Your Journey: Introducing Signal Strength into Location-based Applications," IEEE International Conference on Computer Communications (INFOCOM), pages 2742-2750, April 2013, (280/1613 = 17%)

How Signal Strength Fluctuations Affect The Proposed Algorithm?

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• Feasibility Fetch size at a checking location

varies with the changes of downlink data rates.

• Optimality Receiving energy varies with signal

strength fluctuations.

• The technical problem How to find the optimality and

feasibility conditions

𝜺=±𝟓𝒅𝑩𝒎

5.8e-006

4.7e-006

234000180000

Postoptimal Analysis• Goals

Optimal and feasible ranges Difference in energy consumption between schedules Estimation error boundaries where the optimal schedule changes

• Assumptions for the energy and data rate models The linear assumption The monotonic characteristic

• Sensitivity analysis Feasibility condition

Only fetch constraint is related to signal strength. Minimum condition

When another schedule can save energy more than a tail energy

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Search direction

critical points

DifferenceIn energyconsumption

optimal and feasible ranges

∵ fetch size↑ flexibility ↑

Violation!

Case Studies

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Route@campus Route@downtown

RouteCh.

Route@campus

Route@downtown

Std. deviation of energy cost (dBm)

2 (avg.)4 (max)

2 (avg.)4 (max)

Analyzable range (dBm)

[-5.048, 16.703]

[-14.21, 7.689]

Taipei City HallMRT Station

VIESHOW & Taipei 101

Main Entrance ofAcademia Sinica

The Institute ofHistory and Philology

[-5.048, 7.689]

Experimental Results• Impacts of the amount of information and the velocities• LBS1 (Google maps):

-0.093 joules/dBm along Route@campus and -0.029 joules/dBm along Route@downtown

• LBS2 (Google maps and Panoramio): -0.281 joules/dBm along Route@campus and -2.314 joules/dBm along Route@downtown

• LBS3 (Google maps, Panoramio and YouTube): -1.933 joules/dBm along Route@campus and -5.054 joules/dBm along Route@downtown

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Optimal range

Optimal range

Optimal range

Optimal range

The decreasing rate growsconsiderably, attributing tothe hundreds of objects

Feasible range6.067 joules

Underestimate the maximum fetch sizes

Optimal range

Optimal range

The proposed algorithm can toleratesignal strength fluctuations very wellwhen the objects along a route are spare.

The large size videos acceleratethe reaching of the maximum fetch sizesat those checking locations with stronger signal.

Open Issues

• Checking location selection• Energy and data rate model enhancement

It would be interesting to consider multiple factors, such as the base station’s load and the user’s movement speed.

• Dynamic approachesHandle unexpected situations

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Publication

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• Chih-Chuan Cheng and Pi-Cheng Hsiu, "Extend Your Journey: Considering Signal Strength and Fluctuation in Location-based Applications," to appear in IEEE/ACM Transactions on Networking.

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.• The proposed algorithm can tolerate signal strength

fluctuations very well when the objects along a route are spare.

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

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Thank You

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