Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without impacting the application’s semantics adversely. To solve the fundamental problem, we propose a dynamic programming algorithm and prove its optimality in terms of energy savings. Then, we perform postoptimal analysis to explore the tolerance of the algorithm to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues.We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging and demonstrate the applicability of the proposed algorithm towards signal strength fluctuations.

1. Extend Your Journey:
Introducing Signal Strength into
Location-based Applications
Postdoctoral Fellow
Chih-Chuan Cheng
Embedded and Mobile Computing LAB
Research Center for IT Innovation, Academia Sinica

2. Outline
• Motivation
• Existing Solutions
• Introducing Signal Strength into Location-based
Applications
A virtual tour system
An optimal algorithm
• Real-world Case Studies
• Postoptimal Analysis
• Open Issues
• Conclusion
2

3. 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

4. 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.
4

5. Existing Solutions
• Basically, existing approaches leverage the
complementary characteristics of WiFi and 3G
WiFi to improve energy efficiency
3G to maintain ubiquitous connectivity
Ref1: Prediction-based approaches
for delay-tolerant applications
5
Ref2: Context-based approaches
for delay-sensitive applications
Ref3: Batch scheduling
and fast dormancy

6. Where Communication Energy Consumption
Comes From?
• Receiving energy
Signal strength has a direct
impact on the receiving energy.
• Tail energy
8x
 3G does not switch from the high to
the low power state immediately
after each communication.
PING
Tail Energy
(6.67 joules)
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
6

7. Extend Your Journey: Introducing Signal
Strength into Location-based Applications
7
Problem 1: How to exploit these two
observations in location-based applications
to save communication energy?
• A virtual tour system
• A fundamental algorithm for data reception
Problem 2: Because signal strength
fluctuates, how the proposed algorithm
tolerate signal strength fluctuations?
• Postoptimal analysis

8. A Virtual Tour System
8
Virtual Tour Server
Signal Strength DB
LBS Providers
Src & Dst
Estimated SS
Fetch schedule
LBS Info.
Signal Strength DB
Example applications The mobile platform

9. Data Fetch Scheduling Problem (DFSP)
-77 -73 -75 -86 -72 -90 -91
1 2 3 4 5 6 7

SS
(dBm)
650 4478 500 800 4200 300 0
Dispatch constraint
1 1 1 1 1 1 1
5 4 1 4 4 3 4
Fetch constraint
• 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
9
Objects
9 3 3 3 3 3 0
0 0 0 1 0 0 1
MFC
(Kbytes)
To
Taipei
101
Mitsukoshi
is there!
a cinema
is nearby!?
The firework
of Taipei 101
is awesome.
Availability constraint

10. 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.
Subproblem
E(u,i)
Subproblem
E(v,j)
10
To fetch or not to fetch

11. Case Studies
Route@campus Route@downtown
11
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

12. 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
12

13. Publication
13
• 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%)

14. How Signal Strength Fluctuations Affect
The Proposed Algorithm?
14
• 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
휺 = ±ퟓ 풅푩풎
5.8e-006
4.7e-006
234000
180000

15. 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
15
Search direction
critical points
Difference
In energy
consumption
optimal and feasible ranges
∵ fetch size↑
flexibility ↑
Violation!

16. Case Studies
Route@campus Route@downtown
16
Route
Ch.
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 Hall
MRT Station
VIESHOW & Taipei 101
Main Entrance of
Academia Sinica
The Institute of
History and Philology
[-5.048, 7.689]

17. 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
17
Optimal range
Optimal range
Optimal range
Feasible6 r.0a6n7g ejoules
Optimal range
Optimal range
Underestimate the maximum fetch sizes
The decreasing rate grows
considerably, attributing to
the hundreds of objects
Optimal range
The proposed algorithm can tolerate
signal strength fluctuations very well
when the objects along a route are spare.
The large size videos accelerate
the reaching of the maximum fetch sizes
at those checking locations with stronger signal.

18. 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 approaches
Handle unexpected situations
18

19. Publication
19
• 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.

20. 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.
20

21. Thank You
21