The Effect of Unwanted Internet Traffic on Cellular Phone Energy
Consumption
Ismo Puustinen
Nokia Research Center
Helsinki, Finland
[email protected]
Abstract—Unwanted Internet background traffic (often called
―Internet background radiation‖) is received to every public IPv4
address. This is a problem for cellular phones with an always-online
GPRS connection, because receiving the unwanted sporadic data
traffic will cause the cellular radio to be used, which in turn drains
the device battery. We present measurements of the effect of
unwanted Internet traffic to cellular phone energy consumption, and
simulate different 3G fast dormancy timeout values to find out a way
to reduce the time the radio is active. The simulations demonstrate
that the use of a well-chosen static fast dormancy timeout value
enables large power savings without causing too much extra 3G state
transition signalling between the cellular phone and the 3G base
station.
Index Terms—energy consumption, power saving, cellular
networks, unwanted traffic
1. INTRODUCTION
As the data connection capabilities of mobile phones
improve they increasingly resemble other Internet connected
devices. Like personal computers, cellular phones can use
services, such as voice over IP or instant messaging, which
require an always-online Internet presence. Continuous
connectivity also improves the user experience by reducing
the latency of server access, e.g. for web page downloads.
While continuous Internet connectivity provides phone
users with access to new solutions and developers with new
innovation opportunities, it also has its drawbacks.
Unsolicited traffic resulting e.g. from worms, denial of service
attacks, or misconfigured devices is steadily increasing in the
Internet [1]. If cellular phones are working like regular
Internet hosts they will also experience such unwanted traffic.
This causes a number of problems for the user. First, since
part of the unwanted traffic is malicious, it may try to exploit
possible vulnerabilities in the mobile phone. Second, the
traffic may cause monetary costs especially if the user does
not have an unlimited data plan. Finally, incoming data
packets require the activation of the radio connection between
the mobile phone and the base station, which is expensive in
terms of energy consumption. In 3G, connection activation
and, especially, deactivation are slow and power hungry
operations. Even if the amount of transferred data was small,
each separate communication event would still require
considerable amount of energy.
Jukka K. Nurminen
Nokia Research Center Helsinki, Finland
Dept. of Computer Science and Engineering
Aalto University School of Science and Technology,
Finland
[email protected]
In this paper we study the last problem to understand how
harmful the unwanted traffic is to the energy consumption of
the mobile phone and look for ways to improve the situation.
Our key contributions are that
 We present measurement results of the effect of the
unwanted traffic on the cellular phone battery life
(section 3). The results show that phones are likely to
experience significant energy drain from the unwanted
traffic.
 We analyse how faster deactivation of radio connections
would influence the energy consumption of unwanted
traffic. For this we simulate the energy cost of captured
traffic traces with different timeout values (section 4).
 We discuss the practical implementation of faster
connection deactivation with existing proprietary
solutions and upcoming 3GPP fast dormancy standard.
Furthermore, we briefly review alternative solutions to
reduce the energy consumption of unwanted traffic, such
as the use of a firewall in either the device or within the
cellular operator network (section 5).
2. CELLULAR PHONE ENERGY CONSUMPTION
The energy needed to handle a data transfer event consists
of three components: head energy for connection activation,
transfer energy for actual data transfer, and tail energy for
connection deactivation. In 3G, the phone spends a
considerable amount of time and energy in high-power states
after the data transfer has been completed making tail energy
the dominant energy consumer when the amount of
transferred data is small. E.g. when the transferring 50 kB of
data, an amount that is much higher than in a typical unwanted
traffic event, the share of tail energy can be 60%, while the
remaining 40% is roughly equally split between head and
transfer energies [2].
The network activity of the 3G cellular modem is governed
by a set of states that the Radio Resource Controller manages
[3]. An idle phone is typically in a low power PCH state.
When the need for data transfer arises, the phone is usually
transferred to DCH state for the transmission of the data. After
the data has been sent the cellular network starts a timer (T1).
When this timer expires, the cellular network transfers the
phone to FACH state and a second timer (T2) is started. When
T2 expires, the phone is transferred back PCH state.
Figure 1 illustrates how the amount of power consumed by
the 3G cellular data communication depends on the cellular
state that the device is in. The figure clearly shows how the
arrival of a very small data packet (the magnified green spike)
forces the phone to shift from PCH to DCH state and how
long it takes after the data transfer to return back to PCH via
FACH state. The figure also clearly shows that power
consumption increases dramatically when the phone is in
DCH state. When moving to FACH state from DCH, the
power consumption drops to half but is still much higher than
in PCH.
public internet-routable IPv4 address where the operator does
not block any ports.
The measurements were done with a Nokia N900 mobile
phone. The device model or type should not influence the
unwanted traffic experienced by the mobile as long as the
phone supports "always online" -type GPRS IPv4 connections.
By default, all ports on N900 (software release PR1.2) are
closed on non-loopback network interfaces, meaning that the
phone does not provide any services to Internet.
N900 uses a proprietary fast dormancy mechanism for
reducing the tail energy. Since this mechanism is proprietary
and does not conform to 3GPP specifications, we disabled it
for the measurement.
We did the measurements by capturing all network traffic
on the GPRS network interface using 'tcpdump' tool [4]. At
the same time we used Nokia Energy Profiler [5] to capture
power consumption and data traffic statistics. We postprocessed the collected traces to remove network traffic
originating from the phone, such as automatic checking for
software updates.
We collected data from several weekdays and weekends at
weeks 38/2010 and 39/2010. We performed detailed energy
consumption analysis for a 16-hour measurement from
October 6, 2010. For traffic analysis we aggregated all
network traffic we captured totalling 133 hours of data.
3.2. Energy Measurement Results
Table 1 shows a summary of the energy measurements. Idle
row shows how much energy N900 consumes in the test
period in the absence of unwanted traffic. We measured this
by filtering out the communication event related energy
consumption from the trace. Data Transfer means how much
energy the actual data transfer of the unwanted traffic required
and Head & Tail indicates the sum of head and tail energies
caused by the unwanted traffic.
TABLE I
ENERGY CONSUMPTION OF UNWANTED TRAFFIC IN A
16H MEASUREMENT INTERVAL
Measured energy (J)
Idle
Data
Transfer
Head &
Tail
Total
Figure 1: The arrival of a single ICMP ECHO packet over 3G cellular data
connection, measurement with Nokia Energy Profiler on N900
3. THE EFFECT OF UNWANTED TRAFFIC ON ENERGY
CONSUMPTION
3.1. Measurement Setup
The measurements were done in the network of Finnish
cellular operator Elisa using a standard 3G subscription with
voice and unlimited data capped to 1 Mb/s download speed.
Elisa offers by default (via access point name ―internet‖) a
Energy in 24h (J)
Share
6303
62
9504
92
78.77%
0.77%
1637
2468
20.46%
8002
12065
100.00%
It is clearly evident that unwanted traffic is an important
consumer of energy. As expected, the actual data transfer is
only in a minor role because unwanted traffic consists of very
small messages. However, head and tail energies related to
separate communication events are major consumers of
energy. In fact, the energy to receive the unwanted traffic and
to respond is less than 4% of the total communication energy.
It is important to note that the absolute numbers of energy
consumption are location and operator dependent. First, if the
cellular radio field strength is good, cellular modem data
transfer may require only one third of the power compared to
a phone with a bad cellular reception [6]. In our case we
measured 0.51 W for FACH state and 0.95 W for DCH state.
We also measured that an idle N900 consumes 0.11 W when
running tcpdump and Nokia Energy Profiler.
Second, different operators are free to choose different
values for the timers. In our case we measured the timers to be
T1=8s and T2=6s. For the tail energy our measurements gave
10.6 J. The average latency from idle to the start of the data
transfer was 2.25 seconds resulting in 1.8 J head energy. In
comparison to measurements in US [2] our head energies are
slightly higher and our tail energies slightly smaller, but as
said there are context and operator specific differences in the
absolute values.
3.3. Traffic Analysis Results
During our complete trace of 133 hours we recorded 4268
unsolicited incoming packets. Around half of their interarrival
times, shown in Figure 2, were less than four secondsError!
Reference source not found.. The other half of the
interarrival times was rather evenly distributed with the
longest interval of 3701 seconds.
25%
20%
15%
10%
5%
0%
0
0.5
1
1.5
2
2.5
3
3.5
4
Figure 2: Incoming packet interval distribution (seconds)
As Figure 2 shows, there are two main clusters of
interarrival times. A detailed analysis of the data shows that
some attackers send a burst of packets in quick succession,
which explains the cluster in 0.1 seconds. The other wider
cluster around 0.7 seconds is likely to be a result of the
attacker trying again after receiving a reply (TCP RST or
ICMP ―Destination unreachable‖) from the mobile. We
suspect the Gaussian shape of the interarrival times around 0.7
seconds could be a result of the roundtrip time variance
between the mobile and the attacker and the processing time
of the attacker.
The incoming packets were sent from 189 unique IP
addresses. The protocols used were TCP (79%), UDP (16%),
and ICMP (5%). The traffic mixture corresponds roughly to
the results of Wustrow et al. in their large-scale Internet
background radiation study [1] confirming the expectation
that mobile devices are similar to regular devices.
There were a few interesting anomalies in the data. Some
certain ports were the targets of a large amount of connection
attempts. The amount and content of the spurious Internet
traffic is known to vary a lot based on the IP address and time
due to the implementation details of several Internet worms.
4. REDUCING THE EFFECT OF TAIL ENERGY
Because the tail energy is in such a dominant role the
obvious solution is to try to deactivate the radio connection
faster. However, if this is done too fast the connection may
need to be reactivated again. Unnecessary reactivation (PCH>DCH state transfer) causes extra signalling burden for the
cellular operator and — as the head energy — additional
energy consumption for the mobile phone.
To study these contradicting goals we created a model of
the 3G radio states and their energy consumptions. We
implemented the model in a simulator, which allows us to
experiment with different rules and timeouts to move between
radio states. The simulator uses the traces we collected with
tcpdump as the input and gives an estimate of the overall
energy cost of the traffic as the output.
Initially, we used the values of T1, T2, head and tail
energies that we measured (see section 3.2) to calibrate the
simulator. The simulator results calibrated in this way
deviated only 1.4% of the measured energy consumption. The
simulation software used as the download speed value 80 kB/s
and as the upload speed value 60 kB/s. However, since the
traffic consists mostly of small packets (such as SYN cookies,
ICMP ECHO packets etc.), the timer values proved to have
more effect on the overall energy consumption than the actual
data transfers. We got also the power costs for the simulation
from the Nokia Energy Profiler measurements. We only
simulated the increase of power that data traffic causes, so we
set the idle power at 0 W. Therefore, the power used for data
transfer in DCH state was 0.84 W. The amount of energy used
for PCH->DCH state transition was 1.55 J, and the amount of
energy used for FACH->DCH state transition was 1 J. All
other state transitions were considered to be free in energy
terms.
We did the baseline simulation with the actual Elisa T1 and
T2 timer values to confirm that the simulation is in line with
the measured results. The simulation result was that the
amount of energy data the unsolicited traffic consumes during
this 133-hour period was 19574 J. Of this energy, 13% was
spent on network signalling during the PCH->DCH state
transfers, and the rest of the energy was spent on the data
transmission itself and during the timer periods after the data
traffic. There were 1695 PCH->DCH state transfers due to the
unwanted traffic.
We then analysed the effect of deactivating the connections
faster. We examined a model where the data channel was
released after a static timeout whenever a packet transfer was
completed. If more packets were received during the timeout
period, the timer was started from the beginning. The energy
costs and the number of PCH->DCH transitions associated
with different timeout values are presented in Figure 3. The
results indicate that a static timeout value of 800 milliseconds
gives approximately 73% reduction in cellular energy costs
with only 22% increase in PCH->DCH transitions. This result
has a strong correlation with the packet arrival distribution
cluster around 0.7 seconds in Figure 2.
If minimizing the number of unnecessary PCH->DCH
transitions is important for the cellular network operator the
timeout value of 3.5 seconds would still allow around 50%
energy savings for the phone user with less than 10% extra
PCH->DCH transitions.
100%
80%
60%
40%
20%
0%
Transfer + tail energy
Saving
Head energy
Extra PCH->DCH transfers
Figure 3: Required energy and extra signalling with different cellular timeouts
(milliseconds)
5. RELATED WORK
The energy consumption of unwanted traffic is a challenge
if we want cellular phones to be always online and able to
offer full Internet access to present and future services. So far
most of the research of unwanted traffic on cellular networks
has focused on explicit attacks to either bring down the mobile
device [7,8] or to harm the operation of the cellular network
[9]. Our work provides a complementary perspective by
investigating how ―normal‖ Internet traffic, sometimes called
background radiation, harms the user by increasing the phone
energy consumption.
Studies (e.g. [1]) that measure the background radiation
show that it varies over time but on a longer perspective is on
the increase. Because mobile devices — unless they are
behind firewalls — are regular Internet hosts, there should be
no major difference between the traffic seen by the mobile
devices and by other connected devices.
A number of papers have investigated the problem of tail
energy consumption. As observed in [10], sporadic
communication can be a significant drain on mobile devices
because the tail energy cost is incurred after every
communication episode. The effect of unwanted traffic is thus
two-fold. In the first place the unwanted traffic causes extra
communication and processing by the mobile device.
Secondly, unwanted traffic causes a lot of communication
episodes with their tail energies.
One solution, used by [2] and [11], is to try to do as much
useful work as possible when the radio connection has been
activated. In case of unwanted traffic this approach is difficult
because most of the unwanted traffic happens when the phone
is idle and does not have any pending communication
activities.
5.1. Fast dormancy
Fast dormancy is one way to implement the deactivation of
a radio connection prior to timer expiration. Currently
different phone vendors have proprietary solutions but the
feature has been standardized in the 3GPP Release 8 standard.
Most of the proprietary fast dormancy mechanisms
implemented by cell phone manufacturers have the cellular
phone send a message to operator simulating failure in cellular
connection [3]. This causes the cellular operator to move the
phone to the IDLE state without waiting for the T1 and T2
timers to expire, resulting in tail energy savings. There are two
main drawbacks using this approach.
First, the signalling overhead for the state transition from
IDLE to DCH state is big, meaning more resource usage for
the cellular operator and energy costs for the cellular phone.
Thus the fast dormancy is beneficent only if it doesn't cause
too many extra IDLE->DCH state transitions but still succeeds
in reducing the time spent in the DCH state. The background
radiation traffic proved to be very sporadic with long intervals
between packet bursts, suggesting that fast dormancy might be
useful.
Second, normally after T2 timer expires, the cellular
network transfers the phone to the PCH state. This is
conceptually close to the IDLE state, and consumes a very
low amount of energy. However, the PCH->DCH transition
requires much less signalling than IDLE->DCH transition, and
therefore is faster and consumes less energy.
3GPP Release 8 introduces a fast dormancy specification,
where the cellular phone can tell the cellular network that the
data transfer is over without indicating a real error case. This
means that the cellular phone can, in fact, request connection
release, and be transferred back to the PCH state. In our
simulation model, we used the PCH->DCH transition times
for the fast dormancy, implying the use of 3GPP Release 8
capable equipment in both the cellular phone and the cellular
operator.
A further challenge in using the fast dormancy approach is
to be able to detect when the phone is idle and when it is
performing some background task. A general mechanism to
detect the difference between unwanted traffic and useful
incoming packets is a topic for further investigation.
5.2. Firewalls
The network operator could run a firewall or NAT service,
which would filter out unwanted traffic headed to the mobile
phone. The benefit of this scheme is that no unwanted traffic
reaches the mobile phone. The drawback is that also no
wanted traffic is let through either, which can be a problem for
new innovative ideas. However, IETF has studied how to
allow operator firewall and NAT configuration by cellular
phones [12].
Another alternative is to run a firewall at the mobile device
where it would be easily controllable by the user. However,
this approach is much less efficient than the operator firewall
since the incoming messages would still require the energyhungry radio connection activation and deactivation. The
benefit gained by reduced processing at the phone and the
savings in outgoing traffic are likely to have negligible
influence on the energy consumption. However, this might
also have a secondary benefit of reducing the incoming traffic
if attackers focus their attempts to targets that give some form
of replies to their messages.
Finally, the energy consumption of PC computers that wake
up on LAN events has been previously studied (e.g. [13,14]).
Exiting the low-power ACPI sleep mode for LAN packet
processing causes extra energy costs to PCs. The problem has
some similar characteristics to the firewall question, since PCs
especially in enterprise domain need to listen to the network
traffic to be able to offer services, such as the capability to be
remotely administrated.
A network connectivity proxy is one solution to reduce the
energy consumption of PCs. The idea is that the proxy
analyzes the incoming traffic and reacts in one of the three
ways: discards the request, replies to it, or activates the PC
and forwards the request to it [14]. For discarded traffic the
operation is similar to a firewall, but because the proxy might
reply to some of the requests, the PC will not need to wake up
for every processed packet. This method can provide new
possibilities also for the cellular domain.
6. CONCLUSION
The amount of unwanted Internet traffic presents a problem
to those modern mobile phones whose 3G Internet connection
is always online. Based on our measurements, the energy
consumed by receiving and handling the traffic can be so high
that it alone is enough to drain a mobile phone battery in a few
days. The biggest reason for this is not the amount of data in
the unwanted traffic, but instead the energy costs associated
with activating the data connection and waiting before
deactivating the connection after the data transfer.
Solving the problem with unwanted data using a firewall
within the cellular operator network might not be an optimal
solution, if a requirement is to also retain the possibility for
running complex services on the mobile phone. Instead, we
focused on finding a suitable value for the 3G timeout before
deactivating the connection. We simulated the effect of
different timeout values on the energy consumption using the
previously collected unwanted Internet traffic data. The
simulations indicate that using a short static timeout might
decrease the wasted energy by 73% and only cause 22%
increase in the amount of signalling needed to make the
cellular state transitions from PCH to DCH state. The fast
dormancy mechanism introduced in 3GPP Release 8 is a
suitable technique for implementing the timeout, since it
allows the cellular phone to gracefully indicate to the cellular
operator that the data transfer is over.
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