[ieee 2013 12th annual mediterranean ad hoc networking workshop (med-hoc-net) - ajaccio, france...

1

Energy-Efficient Rate-Adaptive Passive TrafficSensing using Smartphones

Raphael Frank, Foued Melakessou, German Castignani, Thomas Engel

Interdisciplinary Centre for Security, Reliability and TrustUniversity of Luxembourg, 1359, Luxembourg

[email protected], [email protected], [email protected], [email protected]

Abstract—Participatory mobile sensing is becoming increas-ingly popular. However, the challenges to make such applicationssuccessful are multiple. The most important are the reduction ofthe energy consumption of the service and the automation ofthe sensing procedure in order to increase the user acceptance.In this paper we are focusing on vehicular traffic monitoringusing embedded sensors in modern smartphones. We presentENRAPT, an Energy-Efficient Rate-Adaptive Passive TrafficSensing system that autonomously detects the users’ context.If “Driving” is detected, a location monitoring procedure isinvoked that periodically sends relevant traffic metrics to a trafficmanagement server. We show that ENRAPT accurately detectsthe driving context and provides relevant information on thereal time traffic conditions encountered while driving. Further,we show that ENRAPT allows to significantly extend the batterylifetime compared to other sensing schemes.

Index Terms—Mobile Computing, Community Sensing, TrafficMonitoring, Context Detection, Energy Efficiency

I. INTRODUCTION

Individual mobility is an important aspect of our daily lives.

However, increasing vehicular traffic causes more and more

congestion on our roads generating billions of dollars worth

of economic damage [1]. Urbanization is accelerating and the

existing road network cannot be easily extended to meet the

growing traffic demands. Traditional traffic monitoring infras-

tructures such as inductive loops, cameras and radars have a

high maintenance cost and are prone to errors. New means

of traffic management have to be elaborated to overcome this

issue.

One solution is to rely on crowdsourcing applications to

retrieve relevant information on the real time vehicular traffic

conditions. Such participative applications also known as

community sensing are becoming increasingly popular due

the growing penetration of always-connected mobile smart-

phones. Being able to reconstruct real time traffic conditions

using user-contributed data is a first step towards new traffic

services such as smart routing, car pooling and multimodal

transportation services. However, the challenges to make such

applications successful are multiple. First, it has to be ensured

that the sensing service that runs on the smartphones does

not compromise the daily usage of the smartphone due to

high-energy demands that would drain the battery in a few

hours. Next, the interaction with the user has to be limited in

order to encourage user acceptance and thus increase active

participation.

In this paper we present ENRAPT, an Energy-Efficient Rate-

Adaptive Passive Traffic Sensing algorithm for smartphones.

It allows autonomously detecting when a user is driving and

send relevant traffic information to a central traffic server for

further processing. We show that ENRAPT accurately detects

the context of the user. We use a two-dimensional classification

scheme that solely relies on statistical data obtained from

the embedded accelerometer sensor. Further, we define an

efficient location retrieval procedure that limits the access to

the location services. Using ENRAPT, we are able to extend

the battery lifetime up to eight times compared to other sensing

schemes.

The remainder of this paper is organized as follows. Section

II provides an overview of the challenges and opportunities of

mobile traffic sensing and discusses the sensing capabilities

of modern mobile phones. In Section III we present ENRAPT

and provide details on the different components. The results

of the experimental campaign are presented in Section IV.

Related work is discussed in Section V. Finally, in Section VI

we conclude the paper and provide directions for the future

work.

II. BACKGROUND

Mobile phones are becoming increasingly powerful in

terms of processing power but also in terms of mobile

sensing capabilities. By default, modern smartphones are

now equipped with a large range of MicroElectroMechanical

Systems (MEMS), to measure acceleration, orientation and

position of the device. The available network interfaces (e.g.

WiFi, Bluetooth, 3G/4G) allow to make the data harvested by

the different sensors available to remote systems, enabling a

plethora of new applications and services. Participatory traffic

sensing relies on this paradigm to collect and aggregate user-

generated data to provide a community service. There are

several challenges that have to be met in order to provide

an accurate and seamless service:

• Number of users: a critical mass of participants is

needed in order to provide a good service.

• Energy consumption: The application needs to be

energy-efficient in order to avoid compromising the daily

usage of the mobile devices.

• Accuracy: a good sensing policy needs to be defined in

order to provide an accurate service.

978-1-4799-1004-5/13/$31.00 ©2013 IEEE

2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)

67

2

-120

-110

-100

-90

-80

-70

-60

-50S

igna

l Str

engt

h [d

Bm

]

Time

IndoorOutdoor

Fig. 1. Variation of the GSM signal strength.

• Usability: The service used to collect the data and make

it available to the community should require only minimal

or no user interaction.

How to satisfy the first item is out of the scope of this paper.

However as it has already been shown in the past, participatory

traffic services are of great interest to the public [2], [3]. In

this work we are going to propose a new sensing scheme that

provides the best trade-off between energy efficiency, accuracy

and usability.

We start by defining two modes of monitoring. In the

Active Sensing mode, the user proactively starts and stops the

monitoring service. One example of the active sensing mode

is a turn-by-turn navigation system that is used on a mobile

phone and monitors traffic conditions in the background [4].

Once the user arrives at the destination, the application and the

monitoring service are usually stopped. In the Passive Sensingmode, the service continuously runs in the background even if

the user is not actively interacting with the mobile phone. In

other words, even once the primary application has been closed

(e.g., navigator) the service will continue to run passively in

the background. It is evident that active and passive monitoring

have different characteristics and scopes. As an example, for

the active traffic sensing, it can be assumed that the devices

will be connected to a power-supply in the vehicle and thus

do not have any energy restrictions, which is not the case

for the passive sensing service. The most important difference

however is that the passive sensing mode has to deal with

different contexts. In other words, the monitoring service has

to autonomously detect if a user is driving or not and only

send traffic information while driving.

The big advantage of the passive traffic sensing is that

it allows to fill the gaps between the active usage periods

providing a better (i.e. more continuous) overall service and

coverage. Before describing our approach, we first discuss

which of the available mobile phone sensors are suitable to de-

tect the context of the user, with the additional constraint that

the detection should be energy efficient. The latter disqualifies

the use of GPS, which on average consumes around 270mW(value obtained on a Google Nexus One using PowerTutor [5])

0

2

4

6

8

10

12

14

16

18

Mag

nitu

de V

ecto

r [m

/s2 ]

Samples

DrivingWalkingRunning

Fig. 2. Magnitude of the accelerometer for different profiles.

and thus will drain the battery in several hours (see section

IV-C). A recent publication [6] suggests that the GSM signal

strength is a good metric to detect if a user is inside or outside

a building. It provides a first step towards identifying a road

user. In Fig. 1, we show the GSM signal strength variation

over time while walking in a urban area. During this runs

we switched four times between indoor (red line) and outdoor

(blue line). No explicit conclusions can be drawn based on the

signal if the phone is inside or outside a building. This trend

has been confirmed by multiple runs. The main reasons for this

are the complexity of urban environments and the topology of

the mobile network. In particular, the topology changes with

the user’s location and thus makes it difficult to rely solely

on the received signal strange. More complex algorithms have

been proposed in [6] and [7] that combine multiple sensors to

reduce the risk of false positives with more or less success.

In this work we try to find the best trade-off between energy

efficiency and accurate context detection. The sensor that is the

most suitable for this purpose is the accelerometer. Compared

to the 270mW energy consumption of the GPS receiver, it

only consumes on average around 1mW at full rate [8]. The

high sampling rate of mobile phone accelerometers (ranging

from 25 − 100Hz depending on the model) can be used to

provide valuable information about the context of the user. The

magnitude force vector Amag can be obtained by combining

the measurements from all three axis as follows:

Amag =√(Ax)2 + (Ay)2 + (Az)2 (1)

The magnitude vector has the advantage that we can assume

an arbitrary orientation of the mobile phone. For continuous

computation over time, the magnitude vector provides valuable

information regarding the context of the user. Fig. 2 depicts

the acceleration profiles for Driving, Walking and Runningactivities. One can clearly see that the recorded profiles

have different characteristics. In the following section, we

will investigate how the data from the accelerometer can be

exploited in order to detect in particular the driving context.


68

3

Fig. 3. Decision Tree (DT) of the sensing procedure.

III. ENERGY-EFFICIENT RATE-ADAPTIVE PASSIVE

TRAFFIC SENSING

In this section, we present ENRAPT, an Energy-Efficient

Rate-Adaptive Passive Traffic Sensing system. We first de-

scribe the Decision Tree (DT) used to define the hierarchical

sensing policy. Then, we show how the classification mecha-

nism works, followed by the description of the energy efficient

localization procedure.

A. Decision Tree

In this section we present the DT of the ENRAPT system.

Based on several environmental factors, it takes the decision

on the sensing strategy. Its purpose is to limit the usage

of energy hungry sensors if no specifically required by the

application. The resulting overall procedure is depicted in Fig.

3. We define the Sensing Interval (SI) as the rate at which the

procedure is repeated. The algorithm first starts by setting the

regular sensing interval SIR and by checking the local time.

We assume that such a traffic sensing service should only

run during daytime as most of the traffic problems happen

0.1

1

10

Driving Walking Running

Mag

nitu

de V

ecto

r [m

/s2 ]

MedianMean

Standard Deviation

Fig. 4. Training dataset evaluation.

during rush hours. We therefore specify a validity interval for

which the procedure is executed. Next, we check if the phone

has a mobile data connection. As this service is intended to

provide a real time traffic service, it is important to ensure

the timely delivery of the data once it has been collected. If

both conditions are met, the context detection is initiated. The

detailed explanation of this step is provided in Section III-B.

If the analyzed mobility pattern is identified as Driving, the

location service stack is invoked to retrieve the position and

velocity information. If a GPS fix is successfully retrieved,

the SID is set to increase the sensing rate while driving. The

last operation consists in sending the traffic metrics to the

traffic management server. The procedure is then repeated at

the defined SI from the previous round.

B. Context Detection

The core feature of ENRAPT is the context detection

scheme that solely relies on the accelerometer. We start by

explaining how the training data has been collected and eval-

uated followed by the detailed description of the classification

procedure.

1) Training Set: In order to correctly fingerprint the Drivingactivity, it is important to collect and analyze training data. For

completeness sake, we extended the activity detection to three

classes: Driving, Walking and Running. The training data has

been collected by a group of volunteers with the following

requirements; the traces record the magnitude vector Amag

at maximum sampling rate of the accelerometer (60Hz in

our case). The minimum duration of the monitored activity

is ten minutes. The Driving and Walking activities have been

subdivided in two categories; mobile phone in the pocket and

in a handbag as those being the most common locations [9].

For all the activities that have been recorded, at least ten traces

have been collected and evaluated. Fig. 4 shows the Median

x̃, the Mean x̄ and the Standard Deviation σ of Amag per

training set.

We can see that the obtained values are very distinctive for

each of the monitored activities. Especially the x̃ and σ pa-

rameters provide distinctive values that can be used to profile


69

4

an activity. In fact, the distribution of x is not symmetric and

it presents a longer tail than a Normal distribution. Then, the

median value becomes a more accurate statistic to describe

such time series.

2) Classification: After evaluating the training set, the next

step is to specify a procedure to accurately detect the context of

the user. As previously indicated we rely on x̃ and σ to perform

a two-dimensional classification. However, the difficulty is to

choose the right parameters to allow a real time and accurate

context detection. As opposed to the large training sets, the

classification procedure has to rely on a small number of

samples to perform the statistical analysis. To our advantage,

the activities that we would like to detect have recurring

patterns (e.g., walking) that provide distinct statistical data

even for small samples. As a result, the sampling rate faof the accelerometer and the window size WS, i.e., the

number of samples, are crucial for the performance of the

classification procedure. A low fa will not capture enough

details of the users’ context and a too large window might

capture activity transitions resulting in a high error rate. The

best trade-off is to choose a fast sampling rate (fa = 60Hz)

and a relatively low window size (WS = 100), which allows

us to perform a classification in less than two seconds. The

training data sets have been processed according to those

parameters. The obtained values for x̃ and σ are illustrated

in the two-dimensional classification graph depicted in Fig. 5.

The considered activities (i.e., driving, walking and running)

are represented as distinct non-overlapping 2D areas. We

excluded the extreme values by applying the two-sigma rule.

In other words, we first generated the gravity center of each

classification region and performed basic statistics on the raw

data. Thereafter we removed all points that are located at a

distance larger that two times the standard deviation from the

centroid. This process permits to remove the effect of these

points on the real gravity center position. Next, we calculate

the centroid coordinates CA(x,y) for each activity (A) as follows:

CA(x,y) =

(1

n

n∑i=1

x̃Ai ,

1

n

n∑i=1

σAi

)(2)

For each activity check, we first compute the statistical val-

ues in respect with the previous approach. Next, we calculate

the euclidian distance between the freshly obtained values and

the centroid coordinates for each activity. The activity with the

shortest distance to the measurement data is used as results of

the classification procedure.

C. GPS Data Retrieval

If the driving mode is detected, the next step is to efficiently

retrieve the GPS metrics. We define the Search Time (ST) as

the time the GPS receiver is active. In [10] we studied the

trade-off between ST and the related accuracy. We found that

for an ST of ten seconds, we are able get a successful GPS fix

for over 90 percent of all the attempts with an average position

error of less than ten meters. Please note that, those tests have

been performed during relatively clear weather and the mobile

phone was carried inside the drivers pocket to account for

realistic conditions.

Fig. 5. Two-dimensional classification.

Based on those findings, we use the same parameters for the

ENRAPT algorithm. In order words, setting ST to 10 seconds

means that we keep the GPS active only during this time

interval. If we get the first fix after five seconds we go on

searching up to ten seconds. The last fix that is successfully

retrieved will be then sent to the server. In general, the longer

the ST the better the accuracy [10]. The traffic metrics that are

sent to the server include the position coordinates (longitude

and latitude), position accuracy, the velocity and the bearing.

For all the other operations performed by ENRAPT, the GPS

remains disabled.

IV. EVALUATION

In this section we evaluate the performance of the ENRAPT

system. We first propose an evaluation of the classification pro-

cedure in a controlled environment. Next, we show the results

for a real world experiment and discuss the outcome. Finally,

we demonstrate the energy savings that can be achieved using

ENRAPT compared to other sensing schemes. For all tests,

we used HTC Wildfire S smartphones with Android version

2.3.5.

A. Classification Results

In order to identify the general performance of the context

detection procedure, we conducted several validation runs

in a controlled environment. For our validation study, seven

volunteers (four male and three female) have been asked to

perform the following activities:

• Driving: Record a ten minutes driving activity (with a

private vehicle) having the mobile phone in the pocket for

the male volunteers and in the handbag on the passenger

seat for the female volunteers.

• Walking: Record a ten minutes walking activity having

the mobile phone in the pocket for the male volunteers

and in the handbag for the female volunteers.

• Running: Record a ten minutes running or jogging

activity having the mobile phone in the pocket.

For those validation runs, we only invoke the context de-

tection procedure and not the entire ENRAPT algorithm. The


70

5

Detected

Still Driving Walking RunningA

ctua

lStill 100 0 0 0

Driving 1.86 96.28 1.86 0

Walking 0 14.01 85.99 0

Running 0 0 6.45 93.55

TABLE ICONFUSION MATRIX OF THE CLASSIFICATION RESULTS (IN %).

parameters have been chosen according to the results described

in Section III-B. The sampling rate fa of the accelerometer

has been set to 60Hz and the window size WS to 100 values,

which result in a classification in less than two seconds. The

procedure is repeated every five seconds resulting in about 120classifications per volunteer and activity.

Table I shows the confusion matrix containing the averaged

results of the campaign. For completeness, we added Still,referring to the motionless state of the mobile phone, which

can be easily detected and thus does not show any false

positives. The Driving mode has been detected with a success

rate of over 96% even considering the two locations for the

mobile phones i.e. pocket and handbag. Walking has the lowest

success rate with almost 86%, the remaining 14% identified

as Driving. This is mainly due to the fact that walking with

the phone in the pocket and in the handbag have slightly

different patterns. Thus it is not always possible to clearly

classify the motion pattern. Finally the Running mode has been

correctly identified in almost 94% of the attempts. Overall,

the classification mechanism used by ENRAPT provides good

detection results. The Driving context which is the most

important for the traffic sensing application provides the best

overall results. Next, we perform a real world experiment

to evaluate the performance of the ENRAPT algorithm on a

typical use case scenario.

B. ENRAPT Experimental Run

In this section we evaluate the ENRAPT algorithm over a

period of four hours to observe the overall performance on a

typical use case. The scenario spans from 8 AM in the morning

until noon and includes a half an hour driving segment, i.e.,

the way to work. The reminder of the time our tester performs

typical office activities, which can mainly be summarized as

sitting at the desk and occasionally walking around.

Two mobile phones ran the ENRAPT algorithm in parallel,

one located in the trouser pocket of the tester and the second in

a handbag. It can be noted that the handbag, once arrived at the

office, is not carried around for the reminder of the experiment.

The parameters used for the algorithm are summarized in

Table II.

We choose 60 seconds for the regular sensing interval,

which is small enough to timely capture a change of activity.

Once Driving has been detected and a successful GPS fix has

been retrieved, the interval is reduced to 20 seconds to increase

the amount of collected traffic sensing data. Those values have

Paramter Value

Start Time 06h00

End Time 22h00

Regular Sensing Interval (SIR) 60s

Driving Sensing Interval (SID) 20s

Accelerometer Sampling Rate (fa) 60Hz

Window Size (WS) 100

GPS Search Time (ST ) 10s

TABLE IIENRAPT PARAMETERS.

been chosen to validate the proof of concept. A more detailed

study on how those values reflect on the quality of the traffic

information service can be found in [10].

Fig. 6 shows the activity detection results for both scenarios.

As indicated, the driving window starts at time t1 = 0.45hand ends at t2 = 0.9h from the beginning of the experiment.

For the scenario where the mobile phone is carried in the

handbag (see Fig. 6(a)), the overall correct detection rate

was close to 100%, which is mainly due to the fact that the

handbag and the phone remained motionless most of the time.

Looking at the driving window, the correct driving detection

rate reached 95.2%. The more complex scenario where the

mobile phone is carried inside the trousers pocket is depicted

in Fig. 6(b). The correct detection rate during the driving

window is the same as the one for the handbag scenario.

However, during the remaining time of the experiment, the

false positive rate is significantly higher. The incorrect Drivingactivities that have been detected amount to 28.8% of the

total activity checks, after the end of the driving window. It is

clear that under those circumstances, our approach that solely

relies on the accelerometer will not be able to detect all the

activities correctly. Office activities such as sitting at the desk

and moving around cannot be easily modeled due to a high

amount of randomness. This can be tolerated, as the main

purpose of our algorithm is to have a good detection rate while

driving. Further, as our tester was located indoor, the incorrect

driving detection did not lead to send any data to the traffic

management server as no valid GPS fix could be retrieved.

On the other hand, the correct detection during the driving

window leads to successful GPS fixes for all attempts. Fig.

7 shows traffic map resulting from the experiment. The green

color indicates a fluid traffic scenario, which corresponds to the

conditions encountered while driving. Further, the parameters

we chose for the ENRAPT algorithm allow us to reconstruct

the traffic flows on the monitored road segment without

requiring a high data rate (SID = 20s). More information

on how the data is processed and displayed can be found in

[3].

C. Energy efficiency

In order to evaluate the energy efficiency of ENRAPT, we

tested how the battery discharges over a period of 24 hours.

We compared ENRAPT to two other sensing schemes. The


71

6

0 0.5 1 1.5 2 2.5 3 3.5 4

Act

ivity

Time [h]

DrivingWindow

StillDriving

Walking

(a) ENRAPT Bag.

0 0.5 1 1.5 2 2.5 3 3.5 4

Act

ivity

Time [h]

DrivingWindow

StillDriving

Walking

(b) ENRAPT Pocket.

Fig. 6. Results of the experimental run.

Fig. 7. Traffic Map resulting from the experimental run.

first is the simple approach where the GPS receiver is always

on. The second approach used the same policy than ENRAPT

but without the activity detection. In other words the GPS

location service is invoked once every minute (SI = 60 s) for

a duration of 10 seconds (ST = 10 s). As a result, the GPS

receiver is active 1/6 of the total time. In the reminder of the

document we refer to this policy as the Alternated scheme.

Further, if at least one position update has been successfully

retrieved within one sensing interval, the data is sent to the

central server via the mobile network. This has been done

to ensure a fair comparison between the sensing schemes,

knowing that the data transmission over a 3G mobile network

consumes a significant amount of energy, on average around

1W [11]. For the evaluation of ENRAPT, we choose the same

parameters and scenarios as in the previous subsection, i.e.,

one mobile phone in a handbag and one mobile phone in the

trousers pocket.

All the test have been performed during regular workdays

under similar conditions (e.g., time spend on the road and at

the office). The results are depicted in Fig. 8. By taking the

trivial approach of having the GPS always on, we drain the

battery completely in about seven hours. Reducing the access

to the location service using the alternated approach allows

extending the autonomy up to 13 hours. Using the ENRAPT

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20 22 24

Bat

tery

Lev

el [%

]

Time [h]

GPS Always OnAlternated

ENRAPT BagENRAPT Pocket

Fig. 8. Battery drain over a 24h experimental run for different sensingpolicies.

algorithm allows to have to service running over the entire

24h period and have a remaining charge level of 42% and

59% respectively. As expected, ENRAPT Bag has a higher

end charge as ENRAPT Pocket. This is mainly due to the

fact, that the location services are more frequently invoked

when the mobile phone is carried in the pocket as explained

in section IV-B. One can also note that during night time (from

t = 14h to t = 22h), the service is completely interrupted. It

is interesting to see that the slope for the idle activity is very

similar to the slope while the service was active. Of course

this behavior depends on the amount of time spent on the road.

Finally, compared to the other tested policies, ENRAPT pro-

vides on average up to eights time more autonomy compared

to the GPS always on scheme and around four times compared

to the Alternated scheme.

V. RELATED WORK

Participatory mobile sensing has first been introduced by

Burke et al. [12]. In this paper the authors first describe

the opportunities of having ubiquitous mobile sensors and

provide example applications for different practical areas.

More recently Lane et al. [13], presented a survey of mobile


72

7

phone sensing. In this paper, they discuss the emerging sensing

paradigms, formulate an architectural framework and point out

a number of open issues and challenges emerging in the new

area of mobile phone sensing research.

A concrete platform for collaborative traffic sensing using

smartphones has been proposed in [14]. They conducted a field

experiment including 100 vehicles carrying a GPS-enabled

mobile phones driving loops on a ten mile highway stretch.

They concluded that even a low (i.e., 2 − 3%) penetration

of cell phones in the driver population is enough to provide

accurate traffic flow information. We recently proposed a

similar approach in [3]. The main difference here is that

the mobile application is freely available for download. The

proof of concept has been successfully validated by several

hundred volunteers during a campaign that lasted six months.

We concluded that in order to increase user acceptance, the

sensing service needs to be energy efficient and autonomous

i.e. minimize user interaction.

Energy efficient mobile sensing methods are presented in

[15], [6] and [16]. In [15], the authors present a novel

sensor management scheme for mobile devices that operates

sensors in a hierarchical manner. The idea to reduce energy

consumption by monitoring the activity of the user with a

minimum usage of sensors (in this case the accelerometer and

microphone) and only activating the GPS location services

when required by the sensing policy. An energy-efficient rate-

adaptive GPS-based positioning system for smartphones is

proposed in [6]. The idea here is to reduce the amount of

energy spent by the positioning system while still providing

sufficiently accurate position information. To do this, other

sensors and environmental parameters are evaluated (e.g., cell

tower information, accelerometer, bluetooth). The results show

that the energy consumption can be reduced by a factor of 3.8relative to the case when the GPS is always on. The paper also

points out that relying on too computational complex methods

to determine context or location might consume more energy

than having the GPS location services always on.

Activity detection mechanisms using mobile phones has

been addressed by several works [17], [18] and [7]. In [17]

the authors propose an application that implements indoor

and outdoor mobility classifiers that determine the users

transportation mode. An adaptive GPS strategy allows the

phone to save power. A physical activity recognition method

using mobile phones is introduced in [18]. The sensor data

is collected by built-in accelerometer to measure orientation-

independent motion intensity. They found that using a decision

tree achieves the best performance among several static clas-

sifiers. An extensive study on activity detection using mobile

phones has been presented in [7]. The authors first start by

a literature review pointing out different classifiers and data

collection techniques. They introduced a novel classification

system that consist of a decision tree followed by a first-order

discrete Hidden Markov Model. The results show that the

system has an accuracy level of 93.6% on the tested dataset.

To the best of our knowledge, there currently exist no

works that combine an energy efficient location sensing and

autonomous context detection applied to vehicular traffic mon-

itoring.

VI. CONCLUSIONS

In this paper we presented ENRAPT, an Energy-Efficient

Rate-Adaptive Passive Traffic Sensing system for smartphones.

ENRAPT makes use of a Decision Tree (DT) to decide

whether or not to run a specific task based on environmental

factors such as the time of the day and network conditions.

ENRAPT implements a novel context detection procedure that

relies solely on the accelerometer to identify the activity of

the user. We first performed a validation study in a controlled

environment to test the accuracy of the context detection

algorithm. The obtained results show that we are able to

accurately identify different activities (e.g. driving, walking

and running) with a low error rate. Further, compared to the

other tested schemes, ENRAPT provides in average up to eight

times more autonomy compared to having the GPS always on.

Next, we tested the performance of ENRAPT under realistic

conditions, carrying two mobile phones simultaneously in the

pocket and in a handbag. Our results show that the driving

context is correctly identified for 95% of the attempts. Further,

we showed that the data sent to the traffic management server

can be use to accurately reconstruct the traffic flow information

on the monitored road segments.

Future work will consist in investigating the performance

of ENRAPT on different types of mobile devices (e.g. brands

and operating systems) and propose a solution to efficiently

calibrate the devices.

ACKNOWLEDGMENT

The authors would like to thank the National Research

Fund of Luxembourg (FNR) for providing financial support

through the CORE 2010 MOVE project (C10/IS/786097). This

work was partially supported by the European FP7 projects

BUTLER, under the contract no. 287901, and IoT6 under the

contract no. 288445.

REFERENCES

[1] Schrank, D. and Lomax, T. and Eisele, B., “2011 Urban MobilityReport,” URL: http://tti.tamu.edu/documents/mobility-report-2011.pdf,Last Accessed in July 2012.

[2] J. C. Herrera, D. B. Work, R. Herring, X. J. Ban, Q. Jacobson, andA. M. Bayen, “Evaluation of traffic data obtained via gps-enabledmobile phones: The mobile century field experiment,” TransportationResearch Part C: Emerging Technologies, vol. 18, no. 4, pp. 568 –583, 2010. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X09001430

[3] A. Kovacheva, R. Frank, and T. Engel, “LuxTraffic: A CollaborativeTraffic Sensing System,” in Proceedings of the 19th IEEE InternationalWorkshop on Local and Metropolitan Area Networks (LANMAN 2013),2013, (to appear).

[4] Waze Mobile, “Traffic Jam News, Updates from the Road - Waze LiveMap,” URL: http://www.waze.com/livemap/, 2013, Last Accessed inMarch 2013.

[5] L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M.Mao, and L. Yang, “Accurate online power estimation and automaticbattery behavior based power model generation for smartphones,” inProceedings of the eighth IEEE/ACM/IFIP international conference onHardware/software codesign and system synthesis, ser. CODES/ISSS’10. New York, NY, USA: ACM, 2010, pp. 105–114. [Online].Available: http://doi.acm.org/10.1145/1878961.1878982

[6] J. Paek, J. Kim, and R. Govindan, “Energy-efficient rate-adaptivegps-based positioning for smartphones,” in Proceedings of the 8thinternational conference on Mobile systems, applications, and services,ser. MobiSys ’10. New York, NY, USA: ACM, 2010, pp. 299–314.[Online]. Available: http://doi.acm.org/10.1145/1814433.1814463


73

8

[7] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava,“Using mobile phones to determine transportation modes,” ACM Trans.Sen. Netw., vol. 6, no. 2, pp. 13:1–13:27, Mar. 2010. [Online].Available: http://doi.acm.org/10.1145/1689239.1689243

[8] M. Gordon, L. Zhang, and B. Tiwana, “A Power Monitor for Android-Based Mobile Platforms,” URL: http://www.powertutor.org, Last Ac-cessed in Feburary 2013.

[9] C. Hon Lung, R. Vijay, S. Jeffrey, C. Romit Roy, K. Aman, and V. Bahl,“In-vehicle driver detection using mobile phone sensors.” in Proceedingsof the International Conference on Mobile Systems, Applications andServices, 2011.

[10] R. Frank, M. Mouton, and T. Engel, “Towards collaborative trafficsensing using mobile phones,” in Vehicular Networking Conference(VNC), 2012 IEEE, Nov., pp. 115–120.

[11] G. Castignani, N. Montavont, and A. Lampropulos, “Energy consid-erations for a wireless multi-homed environment,” in Energy-AwareCommunications, ser. Lecture Notes in Computer Science, R. Lehnert,Ed. Springer Berlin Heidelberg, 2011, vol. 6955, pp. 181–192.[Online]. Available: http://dx.doi.org/10.1007/978-3-642-23541-2 19

[12] J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy,and M. B. Srivastava, “Participatory sensing,” in Workshop on World-Sensor-Web (WSW06): Mobile Device Centric Sensor Networks andApplications, 2006, pp. 117–134.

[13] N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell,“A survey of mobile phone sensing,” Communications Magazine, IEEE,vol. 48, no. 9, pp. 140–150, 2010.

[14] J. C. Herrera, D. B. Work, R. Herring, X. J. Ban, Q. Jacobson, and

A. M. Bayen, “Evaluation of traffic data obtained via gps-enabledmobile phones: The mobile century field experiment,” TransportationResearch Part C: Emerging Technologies, vol. 18, no. 4, pp. 568 –583, 2010. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X09001430

[15] Y. Wang, J. Lin, M. Annavaram, Q. A. Jacobson, J. Hong,B. Krishnamachari, and N. Sadeh, “A framework of energy efficientmobile sensing for automatic user state recognition,” in Proceedings ofthe 7th international conference on Mobile systems, applications, andservices, ser. MobiSys ’09. New York, NY, USA: ACM, 2009, pp. 179–192. [Online]. Available: http://doi.acm.org/10.1145/1555816.1555835

[16] Z. Zhuang, K.-H. Kim, and J. P. Singh, “Improving energy efficiencyof location sensing on smartphones,” in Proceedings of the 8thinternational conference on Mobile systems, applications, and services,ser. MobiSys ’10. New York, NY, USA: ACM, 2010, pp. 315–330.[Online]. Available: http://doi.acm.org/10.1145/1814433.1814464

[17] J. Ryder, B. Longstaff, S. Reddy, and D. Estrin, “Ambulation: A toolfor monitoring mobility patterns over time using mobile phones,” inComputational Science and Engineering, 2009. CSE ’09. InternationalConference on, vol. 4, Aug., pp. 927–931.

[18] J. Yang, H. Lu, Z. Liu, and P. Boda, “Physical activity recognitionwith mobile phones: Challenges, methods, and applications,” inMultimedia Interaction and Intelligent User Interfaces, ser. Advancesin Pattern Recognition, L. Shao, C. Shan, J. Luo, and M. Etoh,Eds. Springer London, 2010, pp. 185–213. [Online]. Available:http://dx.doi.org/10.1007/978-1-84996-507-1 8


74

[ieee 2013 12th annual mediterranean ad hoc networking workshop (med-hoc-net) - ajaccio, france...

Documents