Energy-Efficient Upload Engine for Participatory Sensing
Takahiro Yamamoto∗† , Shunsuke Saruwatari∗ , Hiroyuki Morikawa∗∗ Research Center for Advanced Science and Technology, University of Tokyo, Japan† CORE Research and Development Institute, Japan

Abstract—Participatory Sensing enables us to build a large
platform for wide-area sensing by utilizing cellphones as sensor
nodes. One of the main problems in the participatory sensing
is the high power consumption on a cellphone because of thelimitation of its battery. In this paper, we propose a low power
data transport protocol, which is called as Piggyback Transport
Protocol (PBTP). The PBTP utilizes the user think time to upload
sensor data. In the user think time, a user watches the display
and thinks about what should he or she do next. The PBTP
uploads sensor data in the inactivity timer period on the user
think time. The inactivity timer period represents the cellphone
has connection to a base station, but does not communicate to
the base station. The paper evaluates the PBTP with simulation,and the result shows PBTP reduces power consumption comparedto the previous work.

I. I NTRODUCTION
Sensor network enables us to retrieve real-space informationand has renovated a lot of research until now. However, whencollecting the wide-area information, such as urban sensing
and habitat monitoring, sensor network faces problems oflarge installation cost and large operation load[1]. Participatory
sensing[2] and people-centric sensing[3] can collect the widearea information at low cost and with small operation load
by utilizing the user’s mobile phone with the various kindsof sensors such as GPS, camera, microphone, accelerometer, gyroscope and so on. The mobile phone senses theenvironment around the user and uploads sensor data via
wireless network periodically. Participatory sensing is also
currently under study in fields, such as air monitoring[4],[5], noise monitoring[6], [7], road monitoring[8], personalmonitoring[9], social network tool[10], [11] and so on.
Since mobile phone runs on battery, participatory sensing
will require a new power-saving technique. The existing research in sensor network and participatory sensing reduces
power consumption by minimizing the active time of sensing
device and communication device under a single sensing
application environment[12], [13], [14]. However, multiple applications run on a mobile phone at the same time. Therefore,
participatory sensing needs an energy-saving technique which
is applicable to various sensor environments and realized atcommunication protocol level.
In mobile network, a cellphone decreases connection latency
and overheads by maintaining a connection for a certain period
after data transfer ends. If no data transfer occurs for thisperiod, then the inactivity timer[15], [16] expires and themobile phone terminates its wireless connection. In this paper,
we define the period from the completion time of the lasttransfer to the expiration time of inactivity timer as inactivity

timer period. If its wireless connection is established every
time a request occurs, all the connections have inactivity timer
period and the energy consumption increases. To tackle thisproblem, TailEnder[17] shortens the total time of the inactivity
timer periods by buffering requests until their deadline andsending them in groups at the earliest deadline. However, whenbeing applied to participatory sensing, TailEnder degrades theuser operability because the user’s request and the sensor
data upload occur at the same time and the communication
throughput decreases.
We focus on the periods when user thinks about the next action during his or her operation and propose PBTP (Piggyback
Transport Protocol) which uploads sensor data during the user
think time. By improving the utilization efficiency of communication device, PBTP reduces the energy consumption. PBTP
buffers sensor data and uploads them during the unused timeof the communication device which happens during the user’s
web browsing operations. When detecting the generation of
the user request or the termination of the user operation, PBTP
stops uploading the sensor data.
The main contributions of this paper are as follows. First,
we show the power consumption can be reduced by uploading
sensor data during the user think time. Second, we model thepower consumption of the data transfer from the connectedstate and the disconnected state based on the measurement
results with the smartphone and the state transition mechanismin the wireless communication. Third, we show PBTP can
reduce more energy than TailEnder by simulation.
The rest of the paper is structured as follows. Section II reviews the related research about energy conservation techniquein the wireless module and outlines our contribution. Section
III presents PBTP protocol which achieves low power transfer
of sensor data in participatory sensing. Section IV describes anenergy model of data transfer and shows the energy-reduction
effect of PBTP by simulation. Finally, Section V concludes
this paper and presents the future work of this research.
II. PARTICIPATORY S ENSING
Participatory sensing realizes a large-scale data collection
by building sensor node functions on the mobile phone which
senses and uploads the environmental information periodically.
For example, Maisonneuve, et al.[6] gather noise data using
the microphone of the mobile phone. Since mobile phone runs
on a battery, the repetitive sensing and uploading operations
require a new power-saving technique.
A new energy-reduction technique for participatory sensing
needs to meet the following three requirements. First, it can

Connection Status
Disconnected

Connected

DisconnectedtUser Traffict

Inactivity Timer

Fig. 1.

Start

RestartExpireThe behavior in wireless communication of mobile phone

reduce energy consumption in any monitoring environment.
Second, it can work on the existing mobile network because
participatory sensing utilizes the user’s mobile phone. Third,
it does not interfere with the user’s communication.
Wang, et al.[13] and Musolesi, et al.[14] propose energysaving methods for participatory sensing. These proposals
optimize the sensing and uploading operations depending on
the application and achieve low power consumption. Wang,et al.[13] reduce power consumption on the sensing devices
by limiting running sensors based on the context estimation. Musolesi, et al.[14] achieve low energy consumption
by performing the context estimation on both the server andthe mobile phone to reduce the number of communications.
However, since these techniques strongly depend on the application, they cannot be applied to the participatory sensing
applications which use other sensors. In order to achieve
low power consumption regardless of the application, a new
power-reduction technique is necessary at the communication
protocol level.
A lot of research explores low energy-communication protocols for the wireless devices, especially in the field of sensor
network[12] and wireless LAN[18], [19]. These proposals
achieve low power consumption by controlling sleep andwakeup time of the communication device on MAC layer.
For example, Krashinsky, et al.[18] realize low latency andlow energy consumption by changing the polling intervalof power-saving mode in accordance with the user’s trafficcharacteristics. However, these techniques are not applicableto the mobile phone because mobile communication operators
have strictly regulated the MAC layer protocol.
Balasubramanian, et al.[17] and Nurminen, et al.[20] reduce
communication energy with the existing mobile phones. The
communication device on the mobile phone consumes energy
not only in transmitting data but also in keeping its wirelessconnection with the base station. Fig.1 shows the behaviorof wireless communication in mobile phone. By maintaining
its wireless connection with the network for some seconds
after data transfer, a mobile phone reduces communication
latency in successive requests. When no data transfer occurs
during this period, inactivity timer expires and a mobile
phone terminates its wireless connection. We define the period
from the end of the data transmission to the expiration of
the inactivity timer[15], [16] as the inactivity timer period.

When intermittent communications happen, this mechanism
leads a problem that the total time of inactivity timer periods increases. Balasubramanian, et al.[17] propose TailEnder
protocol and shorten total time of inactivity timer periods bybuffering communication requests and sending them together.
However, when we apply TailEnder to participatory sensing,
the throughput during user’s operations decreases. Therefore,
user operability becomes degraded. Nurminen, et al.[20] decrease the communication energy by sending data during theuser’s voice call. However, the average number of voice calls
is 1.4 times per day in 2008 in Japan and tends to decrease
year by year. Therefore, energy-reduction effect using their
method is very limited
III. P IGGYBACK T RANSPORT P ROTOCOL
To tackle the energy problems mentioned in Section.II,
we leverage the wireless connection which the user keeps
for using a communication application like web-browser. We
focus on the inactivity timer period during the user’s operations when the wireless connection is maintained but the datatransfer does not occur. We achieve low energy consumption
by sending sensor data during this period and call this energyefficient protocol Piggyback Transport Protocol (PBTP).
A. Inactivity timer period
A cellular network allocates an uplink channel when amobile phone requests to transfer data. When no data transfer
occurs, mobile phone performs discontinuous reception (DRX)
operation. DRX shortens the active time of the communication
device and reduces energy consumption. The communication
device changes the behavior by performing the state transition
according to RRC (Radio Resource Control) protocol as shownin fig.2. In CELL DCH (Dedicated Channel) state, mobile
phone occupies a dedicated uplink channel. In CELL FACH
(Forward Access Channel) state, mobile phone uses a shared
uplink channel. In this paper, we define CELL DCH and
CELL FACH as the connected states. In CELL PCH (Paging
Channel) state, mobile phone has no uplink channel andperforms DRX operation. In Idle state, mobile phone does nothave RRC connection. In this paper, we define CELL PCH
and Idle as the disconnected states. RRC protocol controls thestate transitions based on the report of traffic volume fromthe mobile phone and the inactivity timer. The number of the
supported states and the timer values T1 , T2 , T3 depend on theHigh

CELL_DCH

Connected

T1
CELL_FACH

T2

energyconsumption

CELL_PCH

Disconnected

T2

T3
Idle

Fig. 2.

RRC state transition

LowState Transition
Disconnected

Data Inactivity Timer
Transfer
Period2.0)(W 1.5
n
o
ti
p
m 1.0
u
s
n
o 0.5
C
gyr
e
n
E 0.0

128(byte)+10(KB)

2.0

Connected

Disconnected

)
(W
n 1.5
io
t
p
m 1.0
su
n
o
C 0.5
yg
r
e
n
E 0.0

128(byte)

002Fig. 3.

4

6
8Time (sec)

10

2

3

4
Time (sec)

5

6

7

8

12

Fig. 5.

The difference between single and continuous transmission

Power consumption in sending 10 KB data

implementation and configuration of the wireless network. For
example, Haverinen, et al.[21] set the inactivity timer values
as followings. The default values of T1 were 5 seconds for
8-32 kbits/s, 3 seconds for 64 kbits/s and 2 seconds for 128
kbits/s and faster. T2 was 2 seconds and that for T3 was several
minutes or even ten of minutes.
In this paper, we define the period when the communication
device keeps its wireless connection as the inactivity timer
period. Fig.3 shows the power consumption in sending 10
KB data. In the measurement of this paper, we used Android
Dev Phone 1[22], whose hardware components are the samewith the commercial handset T-Mobile G1 and measured thepower of the shunt resistance between the mobile phone andthe battery with DL750[23]. The energy which is consumed
through RRC state transition on the mobile phone contains not
only the energy for data transfer but also the communication
overheads caused by uplink channel assignment/release andthe inactivity timer period. We measured the power for sending
10 KB data from the disconnected and the connected stateswith Android Dev Phone 1. We found that the communication
overhead accounts for 4.40 J of the total power consumptionof 6.22 J. This result shows that when a mobile phone sends
sensor data every time getting environmental information, thecommunication overheads give a large impact on the energy
resources in participatory sensing.
B. User think time
Crovella, et al.[24] clarify that the user’s traffic has theuser think time in which a user watches the screen and thinks
about the next action on PC. Fig.4 shows the characteristicof the user’s communication traffic on PC. Even in the user’s
Data Transfer

Inactivity Timer Period

Connecting

Disconnected1

Connecting

Connected

User Think Time
User AccessFig. 4.

Connected

Connected

User Think Time

User Access

User AccessDisconnected

User Think TimeOperation End

The characteristic of user’s traffic

consecutive operations, the communication device has the time
when it processes communication and the time when it doesn’t.
Also on the mobile phone, the user think time is expected tohappen during user’s browsing operations.
While a user browses websites, the communication device
has the periods in which the wireless connection is maintained but data are not transferred. Fig.5 shows the power
consumption when mobile phone uploads 128 bytes and when
it uploads 128 bytes and 10 KB successively. The differenceof these two waveforms is so little and the power consumption
without data transfer is almost the same with that with datatransfer. That is, if a mobile phone sends data during theinactivity timer period of the user think time, it can reduceenergy consumption.
C. PBTP algorithm
Piggyback Transport Protocol (PBTP) achieves the energyefficient upload by detecting inactivity timer period and theuser think time. Fig.6 shows PBTP algorithm. PBTP has
three states, namely ”ON”, ”OFF” and ”SLEEP”, which thevariable s represents. ”ON” is the state when mobile phone
is processing the user’s communication request. ”OFF” isthe state when mobile phone is not processing the user’s
request. ”SLEEP” is the state when the user does not operate
a communication application. By changing upload operations
according to the state s, PBTP reduces the power consumption.
The variable q represents the size of sensor data in queue tobe transmitted.
When sensor data occurs, PBTP waits for the generationof the user’s communication request. When the user’s request
occurs, the state s transits to ”ON”. Afterward, when user’s
q: the number of sensor data to be transmitteds: the state of user communication
D: the deadline of transmission
while (true)
wait (q > 0)
repeatswitch (s)case ”ON”: stop uploadcase ”OFF”: upload datacase ”SLEEP”:
if (t < D) stop upload
else upload data
until q == 0
Fig. 6.

PBTP algorithm

request is completed, the state s transits to ”OFF” and mobile
phone starts uploading sensor data. When the user’s request
occurs again, the state s transits to ”ON” and mobile phone
stops uploading sensor data. With this operation PBTP does
not degrade the user’s operability. PBTP continues the above
operations until q = 0. When detecting the end of the user
operation, the state s transits to ”SLEEP” and the mobile
phone stops uploading sensor data. PBTP reduces the increaseof the energy consumption through this operation.
If a mobile phone uploads sensor data only during the user
think time and the user think time does not happen for along time, the transmission delay increases. To resolve thisproblem, each sensor data has their deadline D. Each sensing
application can set the deadline D to upload sensor data
depending on the requirement. If the user’s request does not
happen for a long time and the current time t exceeds D,
PBTP establishes a new wireless connection and upload all
the data in queue.
We can achieve this PBTP algorithm by the following
implementations. A mobile phone can transfer the user’s
request prior to the sensor data by the priority control method.
For example, in the case of Android Dev Phone 1, we can set
the traffic control conditions by adding the QoS function to
the linux kernel and using tc command. A mobile phone candetect the occurrence of the user’s request by monitoring thepacket in the communication protocol stack. In the case ofAndroid Dev Phone 1, the network device layer of the socket
module can detect the occurrence of the user’s request because
it requests the network device driver to send the packets.
A mobile phone can detect the end of the user’s operation
by monitoring the running applications or the state of LCD
backlight. In the case of Android Dev Phone 1, we can detect
the termination of the current application by using getRunningTasks() of ActivityManager and the LCD backlight off byreceiving ACTION_SCREEN_OFF event from PowerManager.
IV. E VALUATION
A. Energy model
To evaluate the energy efficiency of PBTP, we model
the energy consumption of the wireless data transfer in thissection. We model the energy consumption with the linear
equation which is often used for energy modeling.
The energy consumption in each upload can be represented
with the energy consumption einit in establishing wirelessconnection, esend (x) in sending data, einactive caused by theinactivity timer period and ef inal in terminating the wirelessconnection.
e(x) = einit + esend (x) + einactive + ef inal
x is the data size to be sent in each upload. Tinactive is the timeof the inactivity timer period. Since Tinactive is independentof the data size, we can consider it as a constant. That is, we
can express einactive as follow. Won is the energy consumed
when the wireless communication device is ”ON”.
einactive = Won Tinactive

TABLE IT HE PARAMETERS OF ENERGY MODEL IN A NDROID D EV P HONE 1

parameter
einit
c0
c1
Wsend
Won
TinactiveR
ef inalvalue
1.295 J
0.025 J
0.134 sec
0.808 W
0.618 W
4.686 sec
5.123 KB/sec
0.302 J

We can represent esend (x) with the power Wsend consumed
when the wireless communication module is ON and with thesize x to be transmitted.
x
esend (x) = Wsend ( + c1 ) + c0
R
R is the communication throughput for data transfer c0 is theoverhead of the upload time which occurs during the datatransmission process. c1 is the overhead of data size like apreamble in the data frame. We express the energy consumption edisconnect (x) in transmitting data from the disconnectedstate as follow.
x
edisconnect (x) = einit + Wsend ( + c1 ) + c0
R
+Won Tinactive + ef inal
(1)
The energy consumption econnect (x) in sending data fromthe connected state does not contain the overheads by uplinkchannel assignment, inactivitiy timer period and uplink channel release. Therefore, we can express econnect (x) as below.
xeconnect (x) = (Wsend − Won )( + c1 ) + c0 + Won t (2)R
t varies depending on the relation among the user think time
length Tthink , inactivity timer length Tinactive and the datax
+ c1 .
transmission time R⎧
0Tthink < Tinactive
(3)
⎪
⎪
⎪
⎪
⎪
T
−
T
T
≤
T
<
T
inactiveinactivethinkinactive⎪⎨ think
x
+
+
c
(4)
1
t=
R
⎪
x
⎪
x
⎪
⎪
+ c1
Tinactive + R
+ c1
⎪
⎪
⎩ R
≤ Tthink(5)
TailEnder sends all the requests in queue when a wireless connection is established. According to the equations (3), (4) and(5), the energy consumption varies depending on how muchdata x is transmitted and when the data is transmitted. For thelarge x, the energy consumption increases in proportion to x
from the equations (4) and (5). Therefore, PBTP controls thetransfer data size and the timing in order to make the most of
the user think time. In this way PBTP controls the upload toincrease the time represented in (3).
Table I shows the parameters which were obtained by themeasurement in Android Dev Phone 1. einit , einactive and
ef inal , which are the overheads in establishing the wirelessconnection, are 1.295 J, 2.896 J, 0.302 J respectively. c0 , c1 ,

180
Energy consumption (J)Energy consumption (J)

1600
1550
1500
1450
1400

NormalTailEnder
PBTP

1350

10

20

160
NormalTailEnder
PBTP

150
140
130
120
110

1300
0

170

30

40

100

50

0

10

20

30

40

50

User Connection (times/day)User Connection (times/day)Fig. 7.
Energy consumption versus user’s communication number(xsensor = 100)

Fig. 8.
Energy consumption versus user’s communication number(xsensor = 10)

Wsend , Won and R, which relate to the energy consumption
for data transfer, are 0.025 J, 0.134 sec, 0.808 W, 0.618 W,
5.123 KB/sec respectively.

Fig.7 shows the relationship between the energy consumption and the user connection frequency raccess where thesensor data size xsensor is 100 KB. This result indicates that as
raccess increases, the energy consumption for upload decreases
because PBTP can send sensor data during the user think time.
TailEnder also can reduce the energy consumption as raccessincreases, but its energy-reduction effect is smaller than thatof PBTP.
Fig.8 shows the relationship between the energy consumption and the user connection frequency raccess where thesensor data size xsensor is 10 KB. Although the energyreduction effect is smaller than the case of xsensor = 100,
PBTP provides the energy-reduction effect. In the large raccessarea, the energy-reduction effect is small. This is because thenumber of the user access is large enough and both PBTP
and TailEnder transfer almost the entire data during the user
think time. When the number of the user’s communication
is extremely small (raccess = 1), the energy-reduction effectof PBTP is smaller than that of TailEnder. PBTP tries to

In participatory sensing, the upload data size varies with thesensor data type like text or image, and the frequency of sensor
data occurrence varies depending on the application. The
frequency of the user’s wireless communication, the numberof user access during each connection and the user think time
are different depending on the user’s traffic pattern. In thissection, we evaluate the energy-reduction effect of PBTP in
the various sensor environments and the user traffic patterns
by using the model obtained in Section.IV-A.
To simulate the energy consumption, we model the user
traffic pattern and the monitoring environment as follows. For
the parameters representing a user’s traffic pattern, we define
the user connection frequency raccess times/day, the numberof user access during one wireless connection naccess times
and the user think time tthink sec. raccess obeys Poissonprocess. tthink and naccess obey Pareto distibutuion[25]. For
the parameters representing the monitoring environment, we
define the data generation frequency rsensor times/day and thesensor data size xsensor KB. rsensor obeys Poisson process
and xsensor is constant. We use the variable tdetect which
expresses the time to detect the end of user’s operation andevaluate the influence of tdetect on the energy-reduction effect.
In order to compare with the existing techniques, we simulate
the case which data transfer from the disconnected state
happens 1 time per day and the case of TailEnder. If theuser’s request occurs at the time which is less than ρTinactive ,TailEnder does not buffer the request and sends it to theserver at once. In this simulation, we set ρ = 0 which is
the lowest energy consumption case and the deadline D as
1 day in PBTP and TailEnder. About the parameters of theenergy consumption model obtained in Section.IV-A, we use
the variables of Table I.

35
Energy consumption (J)B. Simulation

30
25
20
15
10

NormalTailEnder
PBTP

5
0
0

10

20

30

40

50

User Connection (times/day)Fig. 9.
Energy consumption versus user’s communication number(xsensor = 1)

upload sensor data in some user’s connection. Therefore, when
the number of the user’s communication is extremely small,
PBTP establishes the extra wireless connections caused by thedeadline D and sends all the data in queue.
As is shown fig.9, when the sensor data size xsensor is 1 KB,
the difference between PBTP and TailEnder is small. Since
the transfer data is small compared to the capacity of the user
think time, both PBTP and TailEnder can transfer almost theentire data during the user think time. The energy consumption
increases as the user connection frequency raccess increases.
This is because the influence of the overhead c0 represented
as the equations (1) and (2) grows.
From the results above, PBTP provides a large energyreduction effect when the data size and the number of theuser’s connection is large. The data size to be uploaded tends
to grow in participatory sensing because the mobile phonecollects the data periodically. This is especially the case forusers with high browses and email applications as they require
lower energy upload protocol. Therefore, PBTP is effective forenergy-reduction in participatory sensing.
V. C ONCLUSION
In this paper we propose a low power data transport protocol
PBTP in participatory sensing. PBTP can upload sensor data
more energy-efficiently than the existing TailEnder protocol.
As such, we are now implementing PBTP protocol on theactual mobile phone.
R EFERENCES[1] A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, and R. A.
Peterson, “People-Centric Urban Sensing,” in WICON, ACM, vol. 220,
no. 18, Aug. 2006.
[2] J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy,
and M. B. Srivastava, “Participatory sensing,” in Sensys, ACM, Nov.
2006.
[3] A. T. Campbell, S. B. Eisenman, N. D. Lane, E. Miluzzo, R. A. Peterson,
H. Lu, X. Zheng, M. Musolesi, K. Fodor, and G. Ahn, “The Riseof People-Centric Sensing,” Internet Computing, IEEE, vol. 12, no. 4,
Jul/Aug. 2008.
[4] M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen,
E. Howard, R. West, and P. Boda, “PEIR: the personal environmental
impact report as a platform for participatory sensing systems research,”in MobiSys, ACM, Jun. 2009, pp. 55–68.
[5] E. Paulos, R. Honicky, and E. Goodman, “Sensing atmosphere,” in
SenSys, ACM, Nov. 2007.
[6] N. Maisonneuve, M. Stevens, M. E. Niessen, and L. Steels, “Noisetube:
Measuring and mapping noise pollution with mobile phones,” in ITEE,May. 2009, pp. 215–228.
[7] H. Lu, W. Pan, N. Lane, T. Choudhury, and A. Campbell, “SoundSense: scalable sound sensing for people-centric applications on mobile
phones,” in MobiSys, ACM, Jun. 2009, pp. 165–178.
[8] P. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell: Rich Monitoring of Road and Traffic Conditions using Mobile Smartphones,” in
SenSys, ACM, Nov. 2008, pp. 165–178.
[9] S. B. Eisenman, E. Miluzzo, N. D. Lane, R. A. Peterson, G.-S. Ahn,
and A. T. Campbell, “The Bikenet Mobile Sensing System for Cyclist
Experience Mapping,” in SenSys, ACM, Nov. 2007, pp. 87–101.
[10] E. Miluzzo, N. D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S. B.
Eisenman, X. Zheng, and A. T. Campbell, “Sensing meets mobile social
networks: The design, implementation and evaluation of the CenceMe
application,” in SenSys, ACM, Nov. 2008, pp. 337–350.
[11] M. Musolesi, E. Miluzzo, N. D. Lane, S. B. Eisenman, T. Choudhury,
and A. T. Campbell, “The Second Life of a Sensor Integrating RealWorld Experience in Virtual Worlds using Mobile Phones,” in HotEmNets, Jun. 2008.

[12] G. Anastasi, M. Conti, M. Francesco, and A. Passarella, “Energy
conservation in wireless sensor networks: a survey,” Ad Hoc Networks,
vol. 7, no. 3, pp. 537–568, May. 2009.
[13] Y. Wang, J. Lin, M. Annavaram, Q. A. Jacobson, J. Hong, B. Krishnamachari, and N. Sadeh, “A Framework of Energy Efficient Mobile
Sensing for Automatic User State Recognition,” in MobiSys, ACM, Jun.
2009, pp. 179–192.
[14] M. Musolesi, M. Piraccini, K. Fodor, A. Corradi, and A. T. Campbell,“Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones,” in Pervasive, May. 2010.
[15] J. H. Yeh, J. C. Chen, and C. C. Lee, “Comparative analysis of energysaving techniques in 3gpp and 3gp2 systems,” Vehicular Technology,
IEEE, vol. 58, no. 1, pp. 432–448, Jan. 2009.
[16] S. R. Yang and Y. B. Lin, “Modeling UMTS discontinuous reception
mechanism,” Wireless Communications, IEEE, vol. 4, no. 1, pp. 312–
319, Jan. 2005.
[17] N. Balasubramanian, A. Balasubramanian, and A. Venkataramani, “Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications,” in SIGCOMM, ACM, Nov. 2009,
pp. 280–293.
[18] R. Krashinsky and H. Balakrishnan, “Minimizing Energy for WirelessWeb Access with Bounded Slowdown,” Wireless Networks, vol. 11, no.
1-2, pp. 135–148, Jan. 2005.
[19] G. Anastasi, M. Conti, E. Gregori, and A. Passarella, “802.11 Powersaving Mode for Mobile Computing in Wi-Fi Hotspots: Limitations,
Enhancements and Open Issues,” in Wireless Networks, vol. 14, no. 6,
Dec. 2008, pp. 745–768.
[20] J. K. Nurminen and J. Noyranen, “Parallel Data Transfer with VoiceCalls for Energy-Efficient Mobile Services,” in Mobiware, Apr. 2009.
[21] H. Haverinen, J. Siren, and P. Eronen, “Energy Consumption of AlwaysOn Applications in WCDMA Networks,” in VTC, IEEE, Apr. 2007, pp.
964 – 968.
[22] http://developer.htc.com/adp.html.
[23] http://tmi.yokogawa.com/products/oscilloscopes/scopecordersoscillographic-recorders/dl750dl750p-scopecorder/.
[24] M. Crovella and A. Bestavros, “Self-similarity in world wide web traffic:
evidence and possible causes,” Networking, IEEE, vol. 5, no. 6, pp. 835–
846, Dec. 1997.
[25] P. Barford and M. Crovella, “Generating representative web workloads
for network and server performance evaluation,” in SIGMETRICS, ACM,
Jun. 1998, pp. 151–160.