1
Energy Savings from Using Mobile Smart Technologies
Veton Këpuska1, Paul Karaffa2, Guinevere Shaw1, Jacob Zurasky1,
Christopher Kovalik1, Jordan Arnold1, Salvador Macaraig1
1
Florida Institute of Technology,
2
U.S. Environmental Protection Agency ENERGY STAR
This paper presents the most recent results of energy saving benefits from the
convergence of consumer products into a multi-function smart device, such as a
smartphone or tablet, compared to single-function products (e.g., an electronic clock).
Although individual users are predominantly driven only by the marked trends and the
individual energy savings are moderate, the sheer number of users makes this global
trend promising and a model for sustainable energy.
The energy consumption of selected smart devices was tested using 42 frequently used
applications and utilities (e.g., portable gaming, scanning, music players, etc.). A range of
Operating Systems (OS) were selected for testing: Android OS (Google), Blackberry OS
(RIM), iOS (Apple), and Windows 7 OS (Microsoft). Testing was conducted using the
following smartphones: Blackberry Curve 9300, iPhone 4, Samsung Focus, Samsung
Galaxy S; and tablets: iPad 2 and Samsung Galaxy Tablet 7". In order to investigate the
battery consumption, two programs were developed in-house for iOS and Android
platforms. For the Windows phone, the built-in program was utilized, while a third-party
application was used for the Blackberry phone. To further test the battery, each device
was also decoupled from its battery and hooked up through a measuring device and
experiments were then repeated.
This study shows smart devices save consumers up to $150 annually compared to singlefunction devices in a range of applications. The results from this study demonstrate how
2
smart devices are transforming our society by providing superior functionality at a lesser
cost in energy, paving the way for other technologies to be integrated into smart devices,
and energy reduction to continue.
I. INTRODUCTION
Traditional consumer electronics were designed to perform a single function, and were rarely
designed to act as multi-function devices. Many single-function devices draw power constantly, even when
turned off (e.g., stand-by, hibernation, or sleep mode), creating an increase in energy consumption [1], and
thus an increase in the monetary burden of the product. The advent of smart devices has allowed software
developers to create applications that reduce the necessity of owning many single-function devices.
Replacing electronic products with software applications could effectively reduce household energy
consumption, and simplify product purchasing and product maintenance.
For this research, 4 smart-phones and 2 tablets were tested (see Table I) to validate the stated
hypothesis: that there is a significant energy saving benefit by using a smart device versus several singlefunction devices; namely for smartphones: Samsung Focus, Samsung Galaxy S, Blackberry Curve 9300,
iPhone 4; and for tablets: Samsung Galaxy Tablet 7” and iPad 2.
TABLE I. Smartphones and tablets with corresponding OS and information regarding the application used
for non-invasive measurement.
Device
OS
Application
Samsung
Focus
Samsung
Galaxy S
Blackberry
Curve 9300
iPhone 4
Samsung
Galaxy
Tablet 7”
iPad 2
Windows 7
Android
Blackberry
iOS
Android
iOS
Built-in
Developed
in-house
Downloaded
Developed
in-house
Developed
in-house
Developed
in-house
Smart devices were chosen based on the U.S. market share of their OS in mid-2011 (Figure 1) [2].
Android was shown to have the largest share of the market at approximately 36% followed by iOS at 26%.
Two devices were tested using each of these operating systems (i.e., smartphones and tablets). Blackberry
3
held 23% of the market in 2011 but the majority of their phones were not comparable with Android and
iOS, as many did not support applications, and were therefore not considered “smart”. The Windows 7 OS
did not hold a significant share of the market, so only one device was tested.
All smart devices were subjected to invasive and non-invasive testing (using an internal software
application as indicated in Table I). The Samsung Focus (Windows 7 OS) had a built-in application for
measuring energy consumption. However, to non-invasively measure all smart devices in this study,
several comparable applications had to be developed in-house to include those smart devices running
Android and iOS. The Blackberry Curve 9300 had a third-party application which was used to noninvasively measure battery consumption1. Figure 2 depicts the screen shots of all the applications used for
non-invasive measurements.
Three parameters were monitored during non-invasive testing to include battery life, battery
temperature, and battery voltage. Not all parameters could be tested on each device due to OS restrictions.
Table II identifies those parameters supported by each device and application.
TABLE II
PARAMETERS OF APPLICATIONS USED FOR NON-INVASIVE MEASUREMENTS
D
evice
T
est
S
S
S
i
i
ams
ams
ams
Pho
Pa
lackb
ung
ung
ung
ne 4
d2
erry
Gal
Gal
Foc
Curv
axy
axy
us
e
S
Tab
B
9300
let
1
Tiny Meter: http://appworld.blackberry.com/webstore/content/32468/?lang=en
4
7”
P
ercent
age of
batter
X
X
X
X
X
X
X
X
X
X
X
X
y
Rema
ining
B
attery
Temp
X
eratur
e
B
attery
Volta
ge
A total of 42 applications were selected from a number of more popular applications reflecting a
range of categories to include business, news, games, utilities, etc. It was not possible to test all devices
against all the applications. Nevertheless, a large number of applications were tested across most of the
devices (see Table VI & VII).
5
II. TESTING PROCEDURE
Testing was separated into two procedures: invasive testing and non-invasive testing. Each test
began with the smart device battery charged to 100% capacity. Note that there were different energy
capacities for different devices as indicated in Table III.
TABLE III
3
3
3
iPad 2
3
Samsung
3
Galaxy Tablet 7”
s
Focus
it
iPhone 4
n
Curve 9300
Samsung
Samsung
U
Galaxy S
Blackberry
BATTERY SPECIFICATION FOR TESTED SMART DEVICES
V
o
lt
s
.
.
.
.
.
.
[
7
7
7
7
7
7
5
5
5
1
3
V
]
W
a
tt
h
o
u
.
.
5
5
5
5
4
.
4
2
.
5
8
2
.
3
5
6
r
s
[
W
h
]
M
il
li
a
m
p
e
r
1
1
1
1
4
e
5
5
1
4
0
4
-
0
0
5
2
0
0
h
0
0
0
0
0
0
o
u
r
s
[
m
A
4
7
h
]
C
C
a
a
T
p
p
r
a
a
a
c
c
n
L
i
c
r
i
t
-
i
i
m
v
v
L
C
C
D
D
s
tS
C
D
M
M
u
u
i
e
e
e
s
e
T
T
s
n
o
o
i
T
u
u
v
y
c
c
e
p
h
h
T
M
u
l
s
c
s
i
-
-
-
t
i
i
-
l
lt
t
e
L
t
t
t
o
o
u
u
c
c
h
h
o
F
u
T
c
c
h
r
r
L
e
e
C
e
e
D
n
n
8
S
c
r
e
2
e
n
”
4
.4
.
"
4
5
”
”
S
3
9
.7
”
7
”
i
z
e
Each application was tested in increments of 5 minutes, during which time the application was run
as authentically as possible to mimic its actual usage, after expiration of which the results were recorded.
Note that for iOS devices the time was extended for non-invasive measurements from 5 minutes to 25
minutes to compensate for insufficient accuracy of the iOS that gives the resultant battery capacity to the
nearest multiple of 5%.
The smart device battery was then re-charged to its maximum load for the next measurement. This
procedure ensured that the different experiments started from the same charge level; that is, they were
started from the same initial conditions.
Experiments were repeated 7 times and each result was logged-in. The overall measurement was
computed as the
average of all 7 measurements. The experimental cycle was then repeated with a
different application and after each cycle the battery was recharged to its maximum level.
Significant care was taken so that individual experiments were conducted under realistic
circumstances for each application. The measurement was kept under the same conditions for each
9
application, and each application was tested in the same manner irrespective of the smart device in order to
ensure equality during the measurement procedure.
Finally, the battery specification data, as indicated in Table III, was used to compute the average
battery capacity drop for each tested smart device using the non-invasive measurement.
The testing procedure was repeated invasively with an external energy measurement module
("Watts Up" Meter2) to confirm the accuracy of each individual OS as presented in Figure 3. The actual
laboratory setup for invasive testing is depicted in Figure 4.
Energy consumption was made comparable by calculating the actual battery level drop. The
average drop of battery level 𝒍̅ is computed as indicated in Table IV. This table is a typical measurement
example for the Samsung Galaxy S (Android) smartphone obtained for the Tetris game. As specified in the
table the number, in this case, is 1.25%. This value is then used to compute battery charge by using the
capacity of the battery (see Table III); that is by applying the formula:
𝑷 = −𝑪 ∗ 𝒍̅
where 𝒍̅ is the average drop of the battery level (e.g, 1.25/100), and C is the capacity of the battery
in [Wh] (obtained from Table III); in this case C = 5.55 [Wh]. Hence the actual drop is computed to be P =
-0.069375 [Wh].
2
http://www.powerwerx.com/digital-meters/watts-up-meter-dc-inline.html
10
“Watts
To
External
To
To
Figure 3. Invasive measurement set up.
Figure 4. Actual laboratory setup for invasive measurements.
For invasive measurements actual battery level drop was computed similarly (see Table V). The
Ampere per hour [Ah] and Voltage were selected as being most reliable for computing energy
consumption in Watts per hour [Wh] since other parameters fluctuated during instantaneous readings by
the measuring device. The energy consumption was computed with the following formula:
𝑷 = −𝒄̅ ∗ 𝑽
11
where 𝒄̅ is the average change in Ampere per hour and V is the Voltage reading.
TABLE IV
EXAMPLE OF THE TETRIS GAME RECORDED USING THE SAMSUNG GALAXY S SMARPTPHONE (ANDROID OS) DURING NON-INVASIVE TESTING
T
etris
C
B hange
T
T attery
ime
in ime
min
Log
Tem
perature
in
Read
V
Level
Battery
ing
oltage
(%)
Level
(Celsius)
Reading
T
ime
at
Start
9
:12:00
1
00
9
5 :19:00
1
0
0
2
9
.113
-
9
4
34
.096
2
9
4
34
1
4
.133
-
9
9
.146
4
34
2
6
:44:00
3
9
9
33
1
7
:38:00
5
9
9
2
0
9
:34:00
.186
4
00
:27:00
5
31
1
9
1
4
4
33
-
33
.091
4
12
0
:50:00
3
5
3
1
9
:55:00
.083
9
1
-
4
2
33
.066
T
ime
9
Finished :59:00
9
0
-
4
1
33
A
.057
-
verage
1.25
TABLE V
EXAMPLE OF THE MAHJONG GAME RECORDED USING IPAD2 TABLET (IOS) DURING INVASIVE TESTING
Mahjong
T
T
ime
in ime
min
Log
mp
A
W
att
oltage
V
A
C
mp
hange
per hr
in Amp
[
Wh]
per hr
T
ime
9
at :00:00
.84
0
3
.4
.02
0
3
.3
.01
0
3
.4
.00
0
3
.4
.00
4
0
4
0
0
Start
5
9
:05:00
1
0
9
:10:00
1
5
.84
.83
9
:15:00
.84
.069
4
0
.069
0
.141
4
.277
0
.072
0
.215
0
0
.288
0
.074
0
.296
13
2
0
9
:20:00
2
5
.85
9
:25:00
3
0
.84
9
:30:00
3
5
.85
9
:35:00
T
ime
.85
0
3
.4
.99
0
3
.5
.98
0
3
.4
.97
0
3
.4
.96
3
0
.282
3
0
.067
0
.347
3
0
0
3
.267
.065
.421
0
.259
0
.074
0
.501
0
0
.294
0
.08
0
.317
9
:35:00
Finished
A
0
verage
.2823
0
.0716
0.285
The summary of each individual invasive and non- invasive measurement—a total of 42
experiments—were collected and tabulated (see Table VI and VII). Note that due to the differences in
measurement capabilities the units of the measurements are different (e.g., [mAh] for intrusive and [Wh]
for non-intrusive measurements.
TABLE VI
SUMMARY OF INVASIVE INDIVIDUAL APPLICATIONS MEASUREMENTS IN MAH
Phones
W
indows
Tablets
G
B
i
alaxy S lackberr Phone 4 verage
A
G
alaxy
i
Pad2
A
verage
14
7 Focus
Phone
y Curve
Phones
Tab
Tablets
9300
Contr
ol
0
.017
Alar
m Clock
.012
0
.016
Angr
y Birds
.029
le Breaker
Calc
ulator
s
0
0
Craig
0
.010
0
.028
0
.018
.014
0
0
.079
0
.054
0
.040
.059
.017
.040
0
0
.064
.064
0
.032
0
.016
.073
0
0
0
0
0
.029
.028
.142
.026
.073
0
0
0
0
.060
.038
.076
.029
0
0
0
0
0
.070
.060
.021
0
0
0
0
0
0
.083
.020
.090
.010
.031
.075
slist Mobile
0
0
Ches
0
0
0
0
0
.055
.043
.058
.027
.020
.012
.012
.033
0
.071
0
0
0
0
0
.043
.030
.026
.012
.022
.014
era
.015
.040
.014
.015
0
0
0
0
0
0
Cam
.021
.023
.023
.030
0
0
0
0
.016
.027
.016
0
0
0
0
Bubb
.007
.048
.023
ser
0
0
0
Brow
.016
.036
.022
0
0
0
Bloc
ked In
0
0
Barc
0
.020
.011
.027
ode Scanner
0
0
.019
0
.068
0
.072
0
.070
15
Emai
l
0
.017
ESP
N Mobile
.021
book Chat
.035
0
FM
Radio
Gas
le Voice
.009
.023
.018
0
0
.019
.086
0
0
0
.076
0
.082
0
.072
0
0
.058
.058
.018
0
.012
0
0
.018
0
.067
.050
.061
.053
0
0
0
0
0
0
.082
.066
.017
.063
.071
0
0
0
0
.050
.029
.018
.043
.052
.015
0
.060
0
0
0
0
0
0
.028
.015
.071
0
0
0
0
0
.071
.019
.036
.018
.107
Hang
.013
0
0
Goog
0
.029
.022
le Maps
.016
.061
.043
.023
0
0
0
0
0
0
Goog
0
0
0
0
0
0
0
0
.221
.069
.049
.016
.016
.018
.008
.022
Buddy
0
0
Fruit
0
0
0
0
0
.076
.052
.033
.020
.025
.017
.024
Ninja
0
.365
.016
.073
0
0
0
0
0
.023
0
0
.014
0
.015
.014
.012
.016
er
0
.015
Flixst
.018
.009
.022
0
0
0
Flash
light
.012
.019
.021
0
0
0
Face
man
.015
0
Face
book
0
0
.012
0
.022
0
.018
0
.080
0
.080
16
Kindl
e
0
.027
Mahj
ong
0
MP3
.014
.023
for Speed
ix
.039
0
.045
0
.016
.016
.030
.052
.023
.013
am
0
.020
0
0
.020
0
Tetri
0
.024
0
0
.022
.014
.009
.011
0
.082
.129
0
.069
0
0
.052
.052
0
.019
.068
0
0
.050
0
.024
0
0
0
0
0
.064
.069
.021
0
.059
.074
.177
.017
0
.080
0
0
0
0
0
.038
0
.013
.023
0
0
0
0
0
0
.021
.020
.016
.032
0
.087
.054
.015
.021
.016
0
.018
0
.066
.059
.033
0
.072
.073
.072
0
0
0
0
0
0
0
Solit
0
.014
.024
.023
aire
0
0
Shaz
.058
.030
ora
o Editor
0
0
0
.066
.072
0
Pand
Phot
.086
0
0
0
.033
.023
Scanner
.046
0
0
0
0
PDF
0
.023
.030
Man
0
0
Pac
s
.010
.010
.027
0
0
0
Netfl
.069
0
0
Need
0
.020
.023
Player
0
0
.068
0
.077
0
.064
0
.072
0
.072
17
Twitt
er
0
.025
0
.015
0
0
.014
.018
0
Uno
e Notes/Rec
her Bug
.073
.027
0
.013
0
.011
0
.068
0
.016
.019
0
0
.015
.064
Weat
0
.030
0
her Channel
0
.057
0
.087
.011
.072
.011
0
MD
0
.049
Word
0
s W/Friends
0
.024
Yout
0
.027
Aver
age
0
0
Web
ube
0
.025
0
Weat
.063
0
0
.025
Voic
0
.018
0
.016
0
.026
0
.020
0
.021
0
.021
.021
.016
0
0
0
.049
.073
0
.073
0
.021
0
.034
0
.024
0
.070
0
.076
0
.073
Cons
-
-
-
-
-
-
-
-
umption
26.474
mAh
21.014
15.557
34.234
24.320
69.576
76.429
73.002
18
III. RESULTS
The weighted average energy consumption
of tested smart devices is presented in Figure 5
(1) The results correlate well between non(invasive)
and
Figure
6
(non-invasive).
invasive and invasive measurements with the
Measurements differed during invasive and nonexception of the Samsung Focus.
invasive testing. The inconsistencies are most likely
(2) The non-invasive measurements, utilizing an
due to the fact that non-invasive measurements
on board OS, consistently underestimated its
were done by using the device being measured as
energy consumption.
the measuring device.
The following initial conclusions can be
drawn by comparing those results:
TABLE VII
SUMMARY OF NON-INVASIVE INDIVIDUAL APPLICATIONS MEASUREMENTS IN [WH]
Phones
Tablets
Bl
W
G acbarry
A
indows 7 alaxy S Curve
Focus
Contr
ol
Phone
-
0.286
9300
-
0.857
i verage
Phone4
-
1.000
1.143
i verage
Tab
-
0.821
A
alaxy
Phones
-
G
Pad2
-
1.000
Tablets
-
1.143
1.071
19
Alar
m Clock
1.000
Angr
y Birds
0.714
-
1.429
Barco
de Scanner
ser
-
1.000
Calcu
lator
-
-
Came
2.143
-
0.714
-
-
-
-
0.929
-
0.714
0.857
-
1.000
1.246
-
-
-
1.167
0.714
0.857
1.000
-
-
-
-
0.857
0.571
1.000
1.190
-
-
-
-
1.286
0.857
1.321
1.429
1.000
0.429
1.429
1.429
-
-
-
-
-
-
-
1.429
-
-
-
-
-
1.286
1.095
1.143
1.143
0.286
ra
-
1.000
1.429
1.429
-
-
-
-
1.714
1.429
-
-
-
1.000
1.714
1.429
1.143
1.286
le Breaker
-
-
-
Bubb
0.810
2.143
1.286
0.429
-
0.714
1.571
1.571
Brow
-
-
-
Block
ed In
-
1.000
-
-
1.286
-
1.286
-
-
-
Chess
1.571
Craig
slist Mobile
0.714
-
1.286
1.143
-
1.571
-
1.429
-
1.571
-
1.214
-
1.286
-
0.857
-
1.429
-
1.429
-
1.429
-
1.143
-
0.714
-
1.071
-
-
Email
0.571
ESP
N Mobile
0.857
-
0.571
0.571
-
0.429
1.286
-
1.714
0.821
-
1.286
0.714
-
1.000
0.714
-
1.286
0.714
-
0.714
1.000
20
Faceb
ook
1.571
Faceb
ook Chat
1.571
er
Radio
0.571
Fruit
Ninja
0.714
0.429
Gas
Buddy
le Maps
le Voice
man
-
-
Mahj
0.714
1.429
-
0.714
1.143
-
1.143
0.714
1.000
1.143
0.714
0.714
1.333
-
-
-
-
0.929
1.143
0.929
1.143
-
-
-
-
2.071
0.714
1.095
1.429
1.286
1.536
-
-
-
-
1.429
1.143
0.786
2.857
1.143
-
-
-
-
-
-
-
-
-
-
-
0.857
0.571
1.286
1.143
1.000
0.857
1.429
-
0.571
1.000
1.238
-
-
-
-
1.571
1.143
1.107
1.429
2.000
1.286
0.714
e
1.429
1.429
1.000
-
1.143
-
-
-
-
-
Kindl
1.429
-
-
-
-
1.214
1.571
1.500
2.000
1.000
1.571
Hang
1.714
-
-
Goog
-
1.286
0.714
Goog
2.857
1.286
1.143
1.286
-
-
-
-
-
-
1.143
1.286
1.714
-
-
-
-
-
1.571
1.286
0.857
-
-
-
-
1.429
1.286
1.714
0.571
-
-
-
-
FM
1.857
1.000
0.857
-
-
-
Flixst
ong
1.429
-
Flash
light
-
0.714
-
1.429
-
1.286
-
1.214
0.714
1.000
21
MP3
Player
1.429
Need
for Speed
0.857
-
1.429
Netfli
x
-
0.857
1.143
1.286
1.429
-
Pand
-
ora
1.143
Scanner
0.429
Photo
Editor
m
-
Solita
1.429
-
1.286
-
1.000
-
1.143
1.143
1.143
1.143
0.857
1.000
1.143
-
-
-
-
0.857
0.857
1.036
-
-
-
-
-
-
-
-
0.571
1.143
1.286
0.929
0.714
1.143
0.952
1.000
-
-
-
0.857
1.000
1.190
1.571
1.143
1.000
1.143
0.643
0.571
-
-
-
-
-
-
-
-
-
-
1.000
1.333
1.143
1.000
0.714
ire
-
-
Shaza
1.286
1.286
1.286
-
0.429
1.429
1.571
-
-
-
1.000
0.857
1.667
1.429
-
-
-
-
PDF
1.071
2.286
-
PacM
-
-
1.429
an
-
0.429
0.714
-
0.714
0.429
0.571
-
-
Tetris
0.714
Twitt
er
1.286
-
0.714
0.857
-
1.286
0.952
-
1.429
1.286
1.179
0.714
1.286
-
1.000
-
Uno
0.714
1.286
0.714
0.857
22
Voice
Notes/Rec
1.429
0.857
Weat
0.875
Weat
MD
1.048
-
1.000
-
1.143
ube
2.429
1.286
Aver
age [%]
1.000
0.986
2.714
1.180
0.055
1.429
-
-
Cons
uption [Wh]
-
1.286
0.065
0.055
0.076
1.429
0.980
0.165
1.429
1.143
1.117
-
-
-
0.063
1.286
1.714
1.227
-
-
-
-
1.000
1.571
1.607
1.455
-
-
-
-
1.286
1.000
1.929
1.429
-
-
-
-
2.857
1.286
0.857
-
-
-
-
Yout
2.857
1.143
-
Word
0.857
-
-
0.857
-
0.857
1.006
1.857
s W/Friends
-
-
-
0.429
Web
-
0.857
-
her Bug
her Channel
-
1.195
0.245
0.137
Using invasive measurements (see Figure 4),
and their average capacity drop was found to be
Blackberry Curve 9300 (Blackberry OS) and the
(through invasive testing) 0.2907 [Wh]. The
Samsung Galaxy S (Android OS) preformed the
Samsung Galaxy Tablet 7” was found to be more
best.
energy efficient than the iPad 2, which may be
Tablets were found to be 19.44% less attributable to its smaller screen size (7" vs. 9.7").
efficient than smartphones in both measurements,
23
Smaller screen size radiates less and hence
leading to minimal energy consumption of the
consumes less power to light its screen [3].
device during testing.
The results would presumably have been
A detailed discussion of LCD displays and
impacted by the screen size and type (i.e. touch-
how the screen size and type affects the
screen features) of particular smart devices, such as
consumption of battery power can be found in the
the
reference publication of Wiley Series in Display
Blackberry
Curve
9300,
which
has
a
significantly smaller screen size (2.4") and no
Technology [3].
touch-screen capabilities (Transmissive TFT LCD)
Average Capacity Drop [Wh] - Invasive Measurement
Tablets
-0.2913
-0.3060
-0.2765
Phones
-0.0612
-0.0812
-0.3
-0.25
-0.2
-0.15
Power Consumtion [Wh]
Figure 5. The average energy consumption of smart devices given in [Wh] using invasive measurements.
Samsung Galaxy Tab 7"
Apple iPhone4
Blacbarry Curve 9300
Samsung Galaxy S
Samsung Focus
-0.1052
-0.35
Apple iPad2
Average Phones
-0.0969
-0.1398
Average Tablets
-0.1
-0.05
0
24
Average Capacity Drop [Wh] - Non-invasive Measurement
Tablets
-0.1374
-0.2451
-0.1652
Average Tablets
Apple iPad2
Samsung Galaxy Tab 7"
-0.0630
Apple iPhone4
Phones
-0.0764
-0.0553
Blacbarry Curve 9300
-0.0654
-0.0547
-0.350
-0.300
-0.250
-0.200
-0.150
-0.100
Average Phones
Samsung Galaxy S
Samsung Focus
-0.050
Power Consumtion [Wh]
Figure 6. The average energy consumption of smart devices given in [Wh] using non-invasive measurements.
0.2584 [kWh] which in turn translates to $59.82
IV. CONCULSION
annually. Notebook and Laptop computers on
Results for tested smartphones and tablets
average consume about 59 [kWh] annually [3],[4],
were compared against energy data on the most
and [6],
efficient units in their product category (see Table
VIII) using the results from both non-invasive and
invasive testing. For example, the Analog TV
consumes
140
[kWh]
annually,
while
the
measurements in this study indicate smart devices
while smart devices on average consumes
consumed 0.2584 [kWh] for the same time period.
less than 0.2888 [kWh], an annual savings of $6.76.
With the average U.S. residential electric rate at
The most recent reports show smartphones
$0.1151/kWh [15], this translates to $16.08 of
are capturing the Video Gaming market [10, 11]. In
energy savings. For the digital TV with set-top box
term of energy savings smart devices outperform
this energy gap is more significant: 520 [kWh] vs.
top game consoles [5] Nintendo Wii 27 [kWh],
0.000
25
Microsoft’s Xbox 112 [kWh] and Playstation 3 118
[kWh] to 0.3052 [kWh], annually.
A household which includes a Notebook PC,
two Digital TVs w/ Digital Set-top boxes, a digital
A typical home in the United States has 40
camera, an MP3 player, two clock radios, a modem,
products continuously drawing power [13], and a
two cordless phones, an answering machine, and a
typical household includes a desktop computer and
game
LCD monitor, two TVs, a DVD or VCR, and an
consumption by 1300-1400 [kWh], or up to $160.00
allocation
per year by using smart devices as their primary
for
“other”
devices
that
include
telephones, consumer electronics, and home office
console
could
reduce
their
power
consumer electronic device.
equipment [14].
TABLE VIII
POWER CONSUMPTION OF DEVICES COMPARED TO A SMART DEVICE [2-5]
Smart
Power
Device
Notebook PC [4]
Device
Smart
Power Device
Differen
Power ce
in
Power
Consumption
Consumption
Consumption
Consumption
(kWh/yr)
(Wh)
(kWh/yr)
(kWh/yr)
59
0.1054
0.2888
58.7112
139.741
Analog TV [5]
Digital
TV
140
0.0943
0.2584
6
w/
Digital Set-top box [5]
519.741
520
0.0943
0.2584
Digital Camera [4]
7.2
0.0985
0.2699
6.9301
MP3 player [4]
5.6
0.0763
0.2090
5.3910
Nintendo Wii [6]
27
0.1114
0.3052
26.6948
Microsoft
112
0.1114
0.3052
111.694
Xbox
6
26
360 (2007 Model) [6]
8
Sony Playstation 3
117.694
(2007 Model) [6]
Clock Radio [5]
118
0.1114
0.3052
8
15
0.0852
0.2334
14.7666
53
0.0957
0.2623
52.7377
Modem (Cable &
DSL) [4]
Answering
25.8665
Machines [7]
26
0.0487
0.0487
6
25.8665
Cordless Phones [7]
Average
28
0.0487
0.0487
92.5666
0.09010
0.23276
6667
8333
From our research the following conclusions can be
drawn:
6667
6
92.1531
1
(4) single-function products, and some multifunction products, are losing market share to
smart devices [10-12].
In a recent IDC report [12], it was estimated 92.1 million
(1) the convergence of products could save energy
computers and over 100.9 million smartphones were sold in 2010. Other
[10-12]
(2) people would rather have one device that serves
reports indicate that the total market share of smartphones and tablets
has exceeded that of computers for the first time [9]. Smartphone
platforms continue to grab share of the portable gaming market [10],
several functions than a number of devices that
[11].
have only one function or limited functions [1012]
(3) consumers are interested in the energy
The results from this study demonstrate how smart
efficiency of their products, and do take that
devices are transforming our society by providing
into account when purchasing [2],[10-12].
superior functionality at a lesser cost in energy. It is
27
anticipated technologies will continue to be
integrated into smart devices, and will continue to
V. ACKNOWLEDGMENT
The authors would like to thank the
consume significantly less power than single-
Environmental Protection Agency (EPA) and the
function devices.
Florida Institute of Technology (FIT) for the use of
Consumers can save a substantial amount of money
their facilities and resources during the execution of
by utilizing a smart device in replacement of single-
this research. In addition the authors would like to
function consumer electronics.
thank the anonymous reviewers who evaluated this
research for technical accuracy.
VI. REFERENCES
Century. Berkeley, Lawrence Berkeley National
Laboratory, 2001.
[1] Horowitz N., May-Ostendorf P. Tuning in to
Energy Efficiency: Prospects for Energy Savings in
[6] N., Horowitz, et al. Lowering the Cost of Play:
TV Set-top Boxes. EOC Consulting, 2008.
Improving teh Energy Efficiency of Video Game
[2] M., Kurlyandichik. Google's mobile OS now
Consoles. NDS, Novemeber2008. NDS Paper.
dominates 36 percent of the national smartphone
[7] Karen B. Rosen, Alan K. Meier, and Stephan
market. DailyTech, June, 2011. Article.
Zandelin, Energy Use of Set-top Boxes and
[3] Shunsuke Kobayashi, Shigeo Mikoshiba,
Telephony Products in the U.S., Environmental
Sungkyoo Lim, Editors, LCD Backlights,Wiley
Energy Technologies Division, Lawrence Berkeley
Series in Display Technology, 2009
National Laboratory, University of California,
[4] Roth K., McKenney K. Energy Consumption by
Berkeley, California 94720, June 2001,
Consumer Electronics in U.S. Residences.
http://eetd.lbl.gov/ea/reports/45305/45305.pdf
Cambridge, MA, TIAX LLC, 2007.
[8] Whole Sale Solar Inc. [Online] 2003.
[5] Rosen K., Meier A. Energy use of U.S.
http://www.wholesalesolar.com/pdf.folder/Downloa
Consumer Electronics at the End of the 20th
d folder/Power-table.pdf.
28
[9] Power. www.edho.com. [Online]
[14] IDC Report, Worldwide Top 10 Mobile
http://www.edho.com/power/.
Phone Chipset and Connectivity Semiconductor
[10] Digital Voice Recorders. us.sanyo.com.
4Q10 Vendor Shares, 2011
[Online] 2011. http://us.sanyo.com/Digital-Voice-
[15] Meier, A., et al., Low Power Mode
Recorders/SANYO-ICR-FP700D-Digital-Stereo-
Energy Consumption in California Homes, 2008,
MP3-Voice-Recorder-with-expandable-SD-card-
California Energy Commission, Public Interest
memory-slot.[9] Weintruab, S.; Industry first:
Energy Research Program, Report No. CEC-500-
Smartphones pass PCs in sales, CNNMoney,
2008-035: Sacramento, Calif.
Technology. 2011.
[16]
[11] E., Slivka. Smartphone Platforms Continue to
Grab Share of Portable Gaming Market.
Typical
House
memo,
Lawrence
Berkeley National Laboratory, 2009
[17] 2011 U.S. Electric Rate: Energy
touchArcade, April, 2011.
Information
Administration,
Annual
Energy
[12] Farango, Peter. Apple and Google Capture
Outlook 2012 (Early Release) edition. (converted
U.S. Video Game Market Share in 2010. Flurry,
from 2010 to 2011 dollars).
April 15, 2011. Web Blog.
VII.
Paul Karaffa was born in Richmond, Virginia in
VIII. BIOGRAPHIES
1985. He received his BSci & MSci degrees from George
Mason University in Fairfax, Virginia. He worked in the
Veton Këpuska was born in Vushtrri, Republic of
fields of sustainability, water ecology, and environmental regulation at the
Kosova, on November 7, 1957. He graduated from the
beginning of his career; and later continued environmental work in the field of
Technical Faculty of University of Prishtina, Prishtina,
energy conservation. He began working with EPA’s ENERGY STAR in 2010
Republic of Kosova. He continued his education by obtaining his MSci and
to manage mobile technology interests and the development of several
PhD degrees at Clemson University, Clemson SC, USA, and completed the
technical specifications.
Post-Doctoral Research study at the Swiss Federal Institute of Technology in
Zürich Switzerland. He worked for over 10 year in industry as Speech
Recognition Scientist and has been issued two patents. He joined academia in
2003 in part to pursue independently his Wake-Up-Word Technology that he
has invented over a decade ago.
29
OF SLEEP DISORDER DIAGNOSIS AND
TREATMENT.
Guinevere Shaw was born in Washington
D.C. on October 4, 1989. She is a current undergraduate
at Florida Institute of Technology studying in Solar Earth
and Planetary Sciences. She interned for the Department
of Energy and Environmental Protection Agency during the Summer of 2011.
She also interned during the Fall of 2011 for the Department of Energy at the
E.O. Lawrence Berkeley National Laboratory. She plans to graduate Fall of
2012 and hopes to pursue a Ph.D at the University Tennessee in Knoxville for
Energy Science and Engineering program
IX. JACOB ZURASKY WAS BORN
ON JANUARY 3, 1984, IN SAN
ANTONIO, TEXAS. HE
GRADUATED SUMMA CUM LAUDE FROM
FLORIDA INSTITUTE OF TECHNOLOGY WITH
A B.S. IN ELECTRICAL ENGINEERING IN
2010. HE IS CURRENTLY PURSUING A
MASTER'S DEGREE IN THE FIELD OF SPEECH
PROCESSING / RECOGNITION. HE ALSO HAS
A COUPLE PATENTS PENDING IN THE AREA