Distributed Spectrum Sensing Utilizing
Heterogeneous Wireless Devices and Measurement
Equipment
Junichi Naganawa, Hojun Kim, Shunsuke Saruwatari, Hisashi Onaga, and Hiroyuki Morikawa
Research Center for Advanced Science and Technology,
The University of Tokyo
5th floor, 3rd building, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan.
{naga, hojun, saru, onaga, mori}@mlab.t.u-tokyo.ac.jp

Abstract—A suitable spectrum policy is essential to allow
efficient use of the radio spectrum. The Japanese government
currently employs a Command and Control (C&C) regime, but
measures must be taken to speed up governmental decisions.
The first step is to obtain spectrum utilization data which canform the basis of such decisions. This paper describes the design,
implementation and evaluation of a distributed spectrum sensing
system that continually measures spectrum utilization at multiple
locations. The system has an architecture that enables it toutilize a wide range of existing wireless devices and measurement
equipment, which can be used as sensing nodes to rapidly expand
the coverage area. By focusing on the way to easily expand
the measurement coverage, our work complements previous
spectrum measurements, which have been conducted with highaccuracy in the context of cognitive radios.

I. I NTRODUCTION
Due to recent rapid developments in wireless communication technology, the radio spectrum has become a precious
resource, and appropriate spectrum management policies are
required to allow innovation of existing industries and creationof new ones. Market mechanisms and spectrum commons
have been proposed as alternative spectrum policy regimes tothe conventional Command and Control (C&C) regime [1]–[5]. However, these alternatives have several shortcomings
such as coexisting with public or non-profit systems and“The Tragedy of the Commons” [6], in which the spectrum
band is devastated due to the selfish behavior of users. The
C&C regime therefore continues to play an important role indetermining the efficient use of the radio spectrum.
C&C, however, has a problem in that governmental decisions, such as approval of new systems and retrieval of unused
spectrum bands, are delayed. In order to speed up the C&C
process, it is essential to continually collect detailed spectrum
utilization data which is the basis for governmental decisions.
At present, the Japanese government obtains spectrum utilization information by requesting license holders to fill out aquestionnaire [7]. In addition, in order to obtain more realistic
data, spectrum measurements have been conducted worldwide[8]–[35]. These previous measurement have been conductedwith high accuracy and played an important role in the context
of cognitive radios.

In this paper, we describe a distributed spectrum sensing
system which complements these related studies by focusing
on the way to easily expand the measurement coverage.
The contributions of this paper are as follows: First, wedemonstrate the necessity of distributed spectrum sensing forthe efficient use of the radio spectrum. Second, we show thatthe key to implementing a distributed spectrum sensing system
is to utilize heterogeneous wireless devices and measurement
equipment as sensing nodes. Third, we define the interface
between software components based on an abstraction of
the spectrum utilization into binary information. Also, we
propose a new file format named the Tiny Spectrum Format(TSF) which enables binary information to be stored at ahigh compression ratio. Finally, we verify the feasibility ofthe system through implementation and measurement using 3
types of sensing nodes: a spectrum analyzer, a WLAN module,
and a software defined radio (SDR) front-end.
The remainder of the paper is organized as follows: in
Section II, we demonstrate the necessity of speeding up the
C&C process. Then, we describe the importance of collecting
spectrum utilization data and the problems that still remain tobe solved. Based on an analysis of previous studies, we present
our approach and describe the system architecture that utilizes
heterogeneous wireless devices and measurement equipmentin Section III. Implementation of the system is described in
Section IV. In Section V, we perform an evaluation of the
proposed system using several types of measurements. Finally,
we summarize this paper in Section VI.

ServerVisualization Web Service

Sensing NodeSpectrum Policy
Stakeholders

Fig. 1.

Overview of system

II. E FFICIENT USE OF RADIO SPECTRUM
A. Spectrum policy
Due to rapid developments in wireless communication technology, the radio spectrum resource has become a precious
resource. However, recent measurements have revealed theexistence of licensed but unused spectrum bands. These facts
highlight the need for more efficient management of the radio
spectrum.
In Japan, the radio spectrum is under the control of theJapanese government. Thus, government policy has a large
impact on how efficiently the spectrum is utilized. At present,
the government employs the Command and Control (C&C)
regime, under which the government determines spectrum
allocation and a licensed band is exclusively utilized bya license holder. In the C&C regime, relatively long-term
discussions take place before the government makes a decision
on issues such as the usage of a particular band and thelicensee, in order to guarantee fairness and the validity ofthe decision. In reality, these delays lead to inefficient useof the radio spectrum. For example, it is difficult to quickly
assign a spectrum band to a new wireless system emerging
from the development of wireless technology. Also, it is timeconsuming to make decisions concerning retrieval of unused
spectrum bands.
To address the problem, two alternative spectrum policy
regimes have been proposed to date. The first regime involves
market mechanisms, an example of which is a spectrum
auction [1]. A government announces a new assignment ofa spectrum band, operators who wish to use that band bid
for it, and the highest bidding operator acquires it. The
introduction of an auction system allows the government tochoose operators who have sufficient funding and businessmotivation to use the spectrum band.
Another example of a market mechanism is to allow secondary trading of spectrum bands [2], [3]. Under the statusquo, the Japanese government prohibits license holders fromtransferring their license to others, which leads to low liquidityof the spectrum bands. If secondary trading was allowed,
unused spectrum bands could be easily transferred to morecompetitive operators. As a result, more efficient use of theradio spectrum is expected.
Market mechanisms, however, can not be adopted for all
spectrum bands because of problems such as auction costs
that might exhaust operators, the difficulty in keeping spectrum
bands for public safety networks and non-profit systems, thepotential danger of a monopoly being created due to the buying
up of a large number of spectrum bands, and an unfairness
to new operators in comparison to existing operators who
acquired spectrum bands before the start of the auction system.
Another alternative spectrum policy regime is referred toas spectrum commons [4]. Spectrum commons abolishes the
spectrum licensing system entirely. Because anyone can thenfreely use the radio spectrum, new operators or manufacturers
can more easily enter the market, thereby increasing theefficiency of the radio spectrum use.

Spectrum commons, however, is prone to a potential danger
known as “The Tragedy of the Commons” [6] by which theradio spectrum may be devastated by the selfish behavior ofusers. Even though a method of self regulation by users has
been proposed [5], public safety networks such as police,
fire department, and military need to operate under perfectly
guaranteed spectrum bands.
As explained above, neither market mechanisms nor spectrum commons can act as a perfect substitute for the conventional C&C regime. The coexistence of the market mechanism,
the spectrum commons, and the C&C is a practical solution.
Therefore, it is critical for efficient spectrum use to speed up
the C&C process.
The first step is to provide a means for the government
to continually obtain detailed spectrum utilization data. Currently, the Japanese Ministry of Internal Affairs and Communication conducts surveys of spectrum utilization using questionnaires [7]. In a survey, the ministry requests each license holder
to answer questions including the type of wireless station being
operated, the number of wireless stations, their availability andoperating hours. Such surveys are, however, conducted only
once every 3 years, and more frequent updates are necessary.
There are also three additional problems.
First, only registered wireless systems are surveyed. Wireless systems that can operate without a license or registration
are not surveyed because the ministry can not send therequest. In the case where a wireless system operates undera comprehensive license, which allows multiple terminals to
operate with a single license, the spectrum utilization of eachterminal can not be surveyed.
Second, the actual spectrum occupancy can not be determined because only the operation hours is surveyed. For
example, even in the case of sparse transmissions with alow spectrum occupancy, the questionnaire will indicate total
utilization of the spectrum band as long as a wireless device
is turned on. Waiting time is also classified as spectrum
utilization.
Third, there is a possibility of false answers in the questionnaires because the survey depends on self-enumeration.
An operator who is afraid of blame for inefficient spectrum
use might report operation hours that are longer than the actual
utilization.
B. Related Work
In order to obtain continuous and detailed spectrum utilization data, measurements based on radio signal observations
in real environments are necessary. This would also avoid thetime cost of sending, collecting and evaluating questionnaires.
Also, such an approach could reveal the spectrum utilizationof unregistered wireless systems and also vacancies due tosparse transmissions and receiver waiting time.
The spectrum utilization have been conventionally studied in the context of cognitive radios which is a part ofspectrum policy issues [8]–[35]. These related studies haveplayed an important role in the technology developments andthe policy discussions by providing the in-depth analysis of

the actual spectrum utilization. Our work complements these
related studies by focusing on the way to easily expand themeasurement coverage.
A large number of spectrum utilization studies have already been carried out based on the installation of a single
measurement system in a single location [8]–[15]. In most ofthese studies, the measurement system comprised an antenna,a spectrum analyzer or a receiver, and a PC. Measurementsites were chosen to be representative of a typical radio
environment. Bacchus et al. [15] installed a system consistingof antennas, a spectrum analyzer, a radio scanner, and a PC on
a rooftop of a building with a downtown view. They measured
the spectrum utilization of land mobile radios and public
safety networks over several months. The results identified
seasonal/event-driven variations such as weekdays, weekends,
New Year holidays, and Christmas holidays.
A single site measurement using a single system, however, isinsufficient because spectrum utilization varies by location. For
example, low-energy devices operating indoors or undergroundcan not be detected by a measurement system installed outside.
There are also cases in which buildings or mountains blockradio propagation so that radio signals can not reach themeasurement system. In addition, artificial noise from vehicles
can strongly affect the measurement results, especially nearroadsides.
In order to obtain more accurate spectrum utilization data,
multi-site measurements and a comparison among measurement sites are important. One way to achieve this is tosequentially perform measurements in different locations [16]–[26]. McHenry [18] conducted sequential measurements in
6 U.S. locations using a system consisting of an antenna, aspectrum analyzer, and a laptop. The results showed that the
maximum occupancy was 13.1%, the minimum occupancy
was 1%, and the average occupancy was 5.2% over the 6
locations.
The systems used in [16]–[26], however, can not carry out
measurements simultaneously in the time and space domains.
If measurements are conducted at different locations, themeasurement time per location must be shortened. Conversely,
if the measurement time per location is long, it is more difficult
to perform measurements at many locations. In addition, comparisons among different locations are not entirely accurate
because each measurement must be conducted at a differenttime.
To simultaneously obtain temporal and spatial data allowing
accurate comparisons among locations, multi-site measurement using multiple systems is necessary [27]–[35]. Qaraqe
et al. [27] installed measurement systems at 4 locations in an
urban area and found that spectrum utilization varied between
4% and 15% across these locations. While a similar trend wasfound for all 4 locations, a location-specific event that was a
jamming signal in this case was also partially observed.
The coverage areas of the measurement systems used in[27]–[35], however, can not be easily extended due to costproblems. These systems use an antenna, a spectrum analyzer,
and a laptop, of which the spectrum analyzer is most expen-

sive, and usually costs from thousands of dollars to hundredsof thousand of dollars.
Because of the cost, a spectrum analyzer performs beyond
the essential requirement for spectrum utilization measurement. For example, the output is exactly calibrated, the linearity is guaranteed, and a large number of functions are available.
Also, recent wireless systems have the capability to correctly
decode received signals with very low SNRs thanks to an
achievement of coding gain or improvements in modulation
techniques. This means that the signal level that will berecognized as spectrum occupancy is quite low. Consequently,
a more sensitive spectrum analyzer will be required if spectrum
occupancy is determined only by signal strength.
III. D ISTRIBUTED S PECTRUM S ENSING S YSTEM
Although previous studies have mainly used the power
spectrum as an indicator of spectrum utilization, it is sufficient
to measure only binary information, which means occupancy
or vacancy, for the purposes of supporting governmental
decision making. The most important governmental decision
is an assignment of a spectrum band. Such assignment isbased on evaluations such as an abandoned spectrum bandthat needs to be re-allocated, the necessity of an additionalband assignment due to heavy congestion, the possibility of coexistence of different wireless systems in the same band, and apossible regional license based on geo-locational white-spaces.
These are derived from statistical factors such as the spectrum
utilization ratio (or duty cycle) which can be calculated using
only binary information.
Not only a spectrum analyzer but also various type of
wireless devices and measurement equipment can produce
binary information. For instance, a WLAN module embedded
on a laptop can measure spectrum utilization at 2.4 GHz
band, and a DTV tuner can measure spectrum utilization at
TV bands. Therefore, our system employs an architecture thatutilizes not only a spectrum analyzer but also heterogeneous
wireless devices and measurement equipment.
This allows us to include existing devices and measurement
equipment owned by research organizations, industrial companies, and individuals to form a wide-area spectrum measurement system. Purchase of expensive spectrum analyzers
can also be minimized. In addition, we can collect data frommany types of locations as wireless terminals move aroundwith their users.
A. Overview of System
Figure 1 depicts an overview of the distributed spectrum
sensing system. The system consists of numerous sensing
nodes and a server. We utilize heterogeneous wireless devices
and measurement equipment as sensing nodes. Each sensing
node measures the spectrum utilization at that location, andthen sends this data to the sever.
Figure 2 depicts the software architecture of the system.
The server sends commands to each sensing node for remotecontrol, and receives the measurement data coming from thesenodes. It also provides data search and visualization functions

to various users interested in spectrum policy: government,commercial companies, and citizens.
A sensing node consists of 3 components: the Web Interface,
Data Manager, and Device Controller. The design principles of
the interface of each component are based on abstractions of
the spectrum utilization into binary information with respect
to 3 axes such as location, time, and frequency, as shown in
Fig. 3.
To achieve remote control, the Web Interface receivescommands from the server and transfers them to the Device
Controller. To achieve data collection, the Web Interface
uploads the data files written by the Data Manager to theserver. In addition, the Web Interface is aware of the locationof a sensing node based on a positioning system such as GPS.
This means that the sensing node location in physical world
is abstracted into just location information by Web Interface.
The Web Interface transfers this location information to the
Data Manager.
The Data Manager receives outputs from both the Device
Controller and the Web Interface and saves this data in storage.
Because it is important to reduce consumptions of storage andnetwork capacity especially in resource-limited devices suchas smartphones, we employ Tiny Spectrum Format (TSF). TSF
achieves a high compression ratio by leveraging the abstractionof spectrum utilization into binary information.
The Device Controller provides two abstractions to allow
the use of heterogeneous wireless devices and measurement
equipment. First is an abstraction of both the time and frequency axes, which is achieved in the form of a common
command set to control a wireless device or measurement
equipment. Second is an abstraction of the spectrum utilization
into binary information, which is achieved by generating
binary information based on various forms of data acquired
from a wireless device or a measurement equipment.

Frequency
Spectrum
Utilization

Time
LocationFig. 3.

Abstraction of the 3 axes and spectrum utilization.
ServerAdmin

Admin Interface

User

Visualization Service

Data Base
File System

Sensing Node Interface

Sensing Nodes

Fig. 4.

Server software components

Visualization Web Service, and Sensing Node Interface. Eachcomponent exchanges the necessary information through the
database and file system.
The Admin Interface provides a user interface to issuecommands to control the sensing node. The command is
temporarily stored in the database until it is read out whena sensing node accesses the server. The Visualization Web
Service allows search and visualization of the data based onuser inquiries on location, time, and frequency. The Sensing
Node Interface communicates with sensing nodes to sendcommands and receive measurement data. The received data
is registered in the database and stored in the file system.

ServerAdmin
Users

C. Web Interface

ServerInternet

Processing
DeviceWeb Interface
Data
Manager

Positioning
DeviceorDevice Controller
Other
Nodes

Wireless Device / Measurement Equipment

Other
Nodes

Sensing NodeFig. 2.

System architecture

B. Server
The server provides functions to issue commands, collectdata, and visualize data. Figure 4 depicts the structure ofserver components. The server consists of the Admin Interface,

The Web Interface operates on a sensing node to provide
a communication channel for command retrieval and data uploading using HTTP POST based polling. In the first polling,
the Web Interface sends a HTTP POST request with sensing
node information set in the HTTP POST arguments. Table I
lists the sensing node information. It consists of informationon hardware organization and measurement performance suchas hardware type, model, and frequency range. The server
registers the sensing node information in the database, andthen issues an ID number. In the second and later pollings,
the sensing node sends a HTTP POST request in which the ID
number is set in a HTTP POST request argument. The server
replies with a HTTP Response that holds the commands thatare issued by the administrator. Uploading of measurement
data is achieved by attaching a measurement data file to a
HTTP POST argument.
HTTP POST based polling has two advantages. First,
sensing nodes can be installed in any network where HTTP
is available. Even if the network is inside a firewall andLocation Section（latitude，longitude，altitude）
Binary Information Section

１
1
1
1
１
１

0
0
0
0
0
１

１
1
1
１
0
１

0
0
0
0
0
0

1
1
0
0
0
1

Frequency
0
0
0
１
1
１

(a) TSF Writing Direction

Fig. 5.

Time

Frequency Section（lower，upper，resolution）

Frequency

2010/9/23 14:07:00
2010/9/23 14:08:00
0000/0/00 00:00:05
800.00 MHz
900.00 MHz
300.00 kHz
35.64748,,,,35.64733,,,,139.62433,,,,139.62383,,,,72.00,,,,72.00,,,,100010010011111111110000000000

TimeTime Section（start，end，resolution）

１
1
1
1
１
１

0
0
0
0
0
１

１
1
1
１
0
１

0
0
0
0
0
0

1
1
0
0
0
1

0
0
0
１
1
１

(b) Normal Writing Direction

TSF (Tiny Spectrum Format)
Fig. 7.

Writing direction of binary 2D array

１
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0
１

１
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0
0
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１
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0
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1
5

１ １ １ 1 0 1 １ 1 0 0 1 1 ・・・

10
15
20

Time 1Time 2Time[sec]

Time

Frequency

25
30
35
40
45

Fig. 6.

Binary 2D array written as a single line

50
55
59
100

connections from outside to inside are prohibited, a sensing
node can communicate with the server. Second, on the serverside, we can employ load balance techniques developed in web
service domains such as DNS round-robin and load-balancer
in order to mitigate traffic congestion or overloading.
The location information is acquired using a positioning
system such as GPS. In addition, the Web Interface supports
input from a sensing node owner if the sensing node is a fixed
type and rarely moves. The acquired location information istransferred to the Data Manager.
D. Data Manager
The Data Manager receives the output of the Web Interface
and Device Controller to save measurement data in a TSF file.
Figure 5 shows an example of the contents of a TSF file. It
has 4 information sections that are time, frequency, location,and binary information. In the time section, the start time,end time, and time resolution are written. In the frequency
section, the lower bound frequency, upper bound frequency,
and frequency resolution are written. In the location section,
samples of latitude, longitude, and altitude are written in a timeseries. When the value of the location does not vary since theprevious sample, only a comma is written, omitting the value.
In the binary information section, a 1 is written for occupancy,
and a 0 is written for vacancy.
The TSF has 3 features that allow it to achieve a highcompression ratio. The first is that binary information is
written in a single line. When the binary information has astart point, end point, resolution on the time and frequency
axes, it can be handled as a 2D array as shown in Fig. 6. Thus,
we don’t have to write all the values of time and frequency
corresponding to the binary information samples. Also, writingout the binary information in a single line removes line feed
codes which degrades the compression performance of runlength and dictionary compression algorithms.
The second feature is that the binary information is written
out in the time direction as shown in Fig. 7(a). The real

200

400

600

Fig. 8.

800

1000
1200
Frequency[MHz]

1400

1600

1800

2000

Real spectrum utilization data.

spectrum utilization tends to have a series of identical samples
in the time sequence within a frequency band as shown in
Fig. 8, so that writing the binary information in the time
direction helps run-length compression work more effectively.
If the normal method was used, and the binary information
was written at each sampling, i.e., in the frequency direction
as shown in Fig. 7(b), this would degrade the compression
performance.
The third feature is to separate the binary information
section from the location section as different lines. Normally,
it is natural to write both binary information and the location
in a single line at each sampling. However, since the location
data contains characters other than 1 and 0, the mixture ofthese characters degrades the compression performance. On
the other hand, TSF separates the binary information from thelocation information to improve the compression performance.
E. Device Controller
The Device Controller provides a common command set
to control wireless devices and measurement equipment. The
command set specifies a start point, an end point, and a
resolution on the time and frequency axes. Table II lists
the common command set. Each command has a command
name and an argument. The argument is the value that an
administrator wishes to set. For example, to measure thefrequency range from 100 MHz to 1 GHz, the issued command
would be FREQ_LOWER 100M and FREQ_UPPER 1G.
When executing the commands, the Device Controller transforms each command into unique operations depending on
the wireless device or the measurement equipment that thesensing node is equipped with. This command transformation
totally depends on the type and model of the wireless device orTABLE IS ENSING NODE INFORMATION
Parameter Namefrequency:min
frequency:max
frequency:resolution:min
frequency:resolution:max
time:resolution:min
time:resolution:max
equipment:typeequipment:model
source:typesource:available
source:decision_method
antenna:typeantenna:model
antenna:directional
antenna:azimuth
antenna:elevation
antenna:height
positioning:typepositioning:model

Meaning
Lower bound of measurable frequency[Hz]Upper bound of measurable frequency[Hz]Minimum frequency resolution [Hz]Maximum frequency resolution [Hz]Minimum time resolution [sec]Maximum time resolution [sec]Device or equipment type
Device or equipment modelType of raw data
Availability of raw data
Method to judge spectrum occupancy
Antenna type
Antenna model
Antenna directionality
Antenna azimuth
Antenna elevation
Antenna height [m]Method to get location information
Positioning device model

measurement equipment. We show example implementationsin Section IV.
As a result of command execution, the Device Controller
obtains information related to spectrum utilization in various
forms. Based on this information, the Device Controller judges
the spectrum occupancy and outputs the binary information.
The techniques to be used to judge the spectrum occupancy
depend on the type of wireless device or measurement equipment. We present an overview of the judgment strategy here,
and example implementations are described in Section IV.
Figure 9 shows the classification of the information generated from wireless devices and measurement equipment. There
are two major categories of information: logical informationand physical information.
Logical information is that obtained only after reading
decoded signals. Logical information includes content suchas audio, images, and data, channel quality factors, and the
payload of a packet or a frame. The Device Controller canjudge the spectrum occupancy based on whether the logicalinformation is available or not.
Physical information is that obtained without any decoding.
Physical information includes frequency domain informationsuch as the power spectrum, and time domain informationsuch as the base band signal, RF signal, and IF signal. The
Device Controller can judge the spectrum occupancy based on
physical information by applying spectrum sensing techniques
pertaining to cognitive radio such as energy detection, featuredetection, and matched filter detection [36].
IV. I MPLEMENTATION
A. Server
Server software is implemented using MySQL, PHP5,
HTML/CSS, JavaScript, Gnuplot [37], and Google Maps API[38]. Figure 10 is a screenshot of the command terminal for
administrators provided by the Admin Interface. Figure 11 isa screenshot of the Visualization Web Service. Each sensing
node is represented as a icon, and the list of measurement

Range of Value or Example Valuee.g., 100M
e.g., 3.0G
e.g., 100ke.g., 100ke.g., 0.1
e.g., 60.0
spectrum_analyzer, sdr_frontend, tuner, ....
e.g., USRP2, R&S FSL 6
spectrum, iq, sound, channel_quality, ...
1, 0
decodability, threshold, ...
discone, logperiodic, ...
e.g., DS-3100
1, 0
0,...,360 or rotate
0,...,90
e.g., 5.0
GPS, user, ...
e.g., SONY GPS-CS1

Fig. 10.

Fig. 11.

Screenshot of the Admin Interface.

Screenshot of the Visualization Web Service.

data appears when a user clicks the icon. Selecting the data,
the user can see the plot of the spectrum utilization ratio.
B. Sensing Node Hardware
To verify that our architecture can utilize heterogeneous
wireless devices and measurement equipment as sensing
nodes, we implemented 3 types of sensing node using aspectrum analyzer Rohde & Schwartz FSL 6, a WLAN moduleIntel 5300 embedded on a Lenovo X200, and a SDR frontend USRP2. Figure 12, Figure 13, and Figure 14 show photosof the sensing nodes. In each node, we used a Lenovo X200
for data processing and hardware control. We used a disconeTABLE II
C OMMON COMMAND SET.
Command Name
FREQ_LOWER
FREQ_UPPER
RBW
TIME_RESOLUTION
START
STOP

Meaning
Set the upper bound for measurement frequency range
Set the lower bound for measurement frequency range
Set frequency resolution
Set time resolution
Start a measurement
Stop a measurement

Example Device

Contents

SoundsAmateur Radio Receiver
Walkie-Talkie
Wireless Microphone

ImagesTV tuner

Data

RF-ID Reader
Time Signal

RSSI
LogicalInformation

Channel Quality

Noise Level

Measuring Receiver

PER, BER etc.

WLAN Module
Data Communication Module(WiMAX etc.)
Sensor Network Module

PayloadAcquiredInformation

Base Band Signal
PhysicalInformation

Time Domain

IF Signal
RF Signal

Frequency Domain

Fig. 9.

SDR Front-end
Oscilloscope
Spectrum Analyzer

Classification of information generated from various wireless devices and measurement equipment.

antenna DS-3100 with the spectrum analyzer and SDR frontend.
The spectrum analyzer, the WLAN module, and the SDR
front-end respectively produce the frequency domain signal inphysical information, the channel quality in logical information, and the time domain signal in physical information asshown in Fig. 9.
C. Web Interface
We implemented the Web Interface in PHP 5. It is independent of the type of wireless device or measurement equipment
so that it is common to the 3 types of sensing node. We
used the PHP extension pecl http for HTTP POST. We also
implemented inter-process communications using UDP for
data exchange with the Data Manager and Device Controller.
D. Data Manager
We implemented the Data Manager in C. Since it is independent of the type of wireless device or measurement equipment,
it is also common to the 3 types of sensing node. The
Data Manager receives binary information from the Device
Controller and location information from the Web Interface via
UDP. It stores the received data in a TSF file and compresses
the file using libbzip2. One TSF file is generated per minute.
The Data Manager also monitors the storage capacity of thesensing node, and deletes the oldest file if it detects a shortfall
in capacity.

E. Device Controller
Implementation of the Device Controller depends on thetype of wireless device or measurement equipment that a sensing node is equipped with. For references, we implemented 3
versions of the Device Controller, for the spectrum analyzer,
the WLAN module, and the SDR front-end.
Spectrum Analyzer R&S FSL 6 : Figure 15(a) shows aschematic circuit of a typical spectrum analyzer. A spectrum
analyzer consists of local oscillators, mixers, filters, and an
energy detector. The spectrum is obtained by detecting thepower of the signals that are down-converted from RF to baseband or IF, while sweeping the local oscillators’ frequency.
The Device Controller can judge the spectrum occupancy
based on the strength of the spectrum.
In a spectrum analyzer, FREQ_LOWER and FREQ_UPPER
corresponds to the start and end of the sweep, respectively.
TIME_RESOLUTION corresponds to the time taken for asingle sweep. RBW corresponds to the bandwidth of the bandpass filter in front of the energy detector. RBW also determines∆FLO , which is the increment of the frequency when sweeping. RBW and ∆FLO are usually independent of each other.
However, in the case that RBW is wider than ∆FLO , a gap
where the signal is not measured appears as shown in Fig.
15(b). To prevent this, the Device Controller automaticallychooses a ∆FLO that satisfies ∆FLO <= RBW.
To generate the binary information, we utilize the energy
detector which compares the power of each spectrum bin to apredetermined threshold.
We connected the R&S FSL 6 and the Lenovo X200

Fig. 12.

Fig. 13. WLAN module Intel 5300 (embedded on
Lenovo X200)

Spectrum Analyzer R&S FSL6

Fig. 14.

SDR front-end USRP2

20MHz

DET
RBW

OUT

LO

(a) Circuit
Power

Fig. 16.
FLO

ΔFLO

Measurement using a WLAN module

Frequency(b) Gap

Fig. 15.

ON

2.412GHz

gap

RBW-35dBmPower

IN

Channel 1(Frequency 2.412GHz)
Signal Level -35dBm
Noise Level -87dBm

Measurement using a spectrum analyzer

via an Ethernet connection. We implemented the Device
Controller using C and a library named RsSpecAn provided
by R&S [39]. In RsSpecAn, the sweep start and sweep
end are set via the ConfigureFrequencyStartStop
function. The bandwidth of the band-pass filter is set
via the ConfigureSweepCoupling function with the
COUPLING_RBW flag on. The time for a single sweep is
set via the ConfigureSweepCoupling function with the
COUPLING_SWEEP_TIME flag on. ∆FLO can not be directlyset; instead, we set the number of measurement points that isdetermined by (FREQ_LOWER − FREQ_UPPER)/RBW via theConfigureSweepPoints function.
WLAN module Intel 5300 : The WLAN module embedded on the laptop measures channel quality factors such as
RSSI. For example, the iwlist command available in Linuxprovides the signal strength and noise level of all channels.
We can obtain the occupancy of the spectrum based on theavailability of such channel quality information.
IEEE 802.11 defines each channel to have a 20 MHz
bandwidth and a 5 MHz interval. For example, if a WLAN
module detects a channel that has a -35 dBm signal strength
at 1ch (2.412 GHz) as shown in Fig.16, the Device Driver
determines the occupation of the 20 MHz bandwidth centeredat 2.412 GHz which spreads from 2.402 GHz to 2.422 GHz.
FREQ_LOWER and FREQ_UPPER determine the measurement channel. RBW is fixed to 5 MHz which is the channelinterval defined in IEEE802.11. TIME_RESOLUTION is defined as the measurement interval.
We implemented the Device Controller for the WLAN module in C using iwlib. iwlib is provided by Wireless Extension[40], the typical WLAN device driver API, and used in theiwlist command.
SDR front-end USRP2 : Although it is possible to determine the spectrum occupancy based only on IQ signals
acquired from USRP2, we embedded FFT in the Device
Driver in order to improve the frequency resolution. Because
the bandwidth of the spectrum is limited by the sampling
frequency FS , the Device Controller periodically sweep thecenter frequency FC and calculate the FFT as shown in Fig.
17(a). The Device Driver can judge the spectrum occupancy
based on the signal strength of each FFT bin.
FREQ_LOWER(FL ) and FREQ_UPPER(FU ) determine FCL
and FCU which are the lower and the upper bounds of thesweep, respectively, as expressed in the following formulas.
FCL
FCU

FS
= FL +
2 ⌉⌈FU − FLFS=· FS −+ FLFS
2

RBW affects the number of FFT points as FS /RBW.
TIME_RESOLUTION determines Tseg which is the measurement time per spectrum segment as follows.
Tseg =

TIME_RESOLUTION⌈(FU − FL )/FS ⌉To generate the binary information, we utilize the energy
detector which compares a predetermined threshold and the
maximum power of each spectrum bin per unit time.

FREQ_UPPER

FREQ_LOWER

Power

FS

Frequency

FC
FCL
Fig. 17.

FCU

Measurement using the SDR front-end

Command

Measurement
Parameters

Command
Interface
Process

Binary Information

udp_sink_with_detection
complex_to_mag_squared

fft_vcc

Measurement
ProcessFig. 18.

frequency range was 100 MHz – 2.0 GHz, and the frequency
resolution was 300 kHz. We wrote the real data in TSF, Binary,
CSV, and XML formats, and then compressed them usingbzip2.
Figure 19 shows the file sizes for each format as the number of samples increases. All formats, including Compressed
XML, Compressed CSV, Compressed Binary, and Compressed
TSF linearly increase in file size as the number of samples
increases, but Compressed TSF has the smallest file size.
If we denotes the number of samples (×10000) as N , thefile sizes can be approximated as SizeTSF (N ) = 0.193N +0.726 [kByte] and SizeBinary (N ) = 0.293N + 1.320 [kByte].
If we assume that the number of samples is sufficiently
large, the ratio of the file sizes of Compressed TSF andCompressed Binary is

stream_to_vector

SizeTSF (N )usrp2_source_32fc

N →∞ SizeBinary (N )IQ Signal from USRP2

Process and GNU Radio signal processing block diagram

We used Python and GNU Radio [41], which is an SDR
development environment. Figure 18 shows the implemented
processes and the signal processing blocks using GNU Radio.
udp_sink_with_detection is a newly implemented
original signal processing block.
To sweep the center frequency, the Device Controller sets the center frequency of USRP2 through the
set_center_freq function of the signal processing block
usrp2_source_32fc every Tseg sec. The number of FFT
points is set as an initialization parameter in the signal processing block fft_vcc. The threshold for spectrum occupancy
judgment is set through udp_sink_with_detection as
an initialization parameter.

lim

=

0.193
= 0.659
0.293

Thus, Compressed TSF achieves a file size approximately 66%
that of Compressed Binary.
In addition to the real data, we also made a file size
comparison using random data. Comparison using random data
represents the worst case scenario because TSF is not effective
anymore. Figure 20 shows the file sizes for each format. The
file sizes for TSF are the same as those for Compressed XML
and Compressed CSV. Compressed TSF shows larger file sizes
than those of compressed Binary. The approximations of thefile size are SizeTSF (N ) = 1.601N + 0.1228 [kByte] andSizeBinary (N ) = 1.255N + 0.406 [kByte].
If we assume that the number of samples is sufficiently
large, the ratio of the file sizes for Compressed TSF andCompressed Binary is
SizeTSF (N )1.601=
= 1.28N →∞ SizeBinary (N )
1.255
lim

Even in the worse case, the file size of Compressed TSF is
only 1.28 times that of Compressed Binary.

F. Development cost of new Device Controller

B. Measurement using heterogeneous sensing nodes

The Web Interface and Data Manager are independentof the type of wireless devices or measurement equipment.
Thus, when implementing a sensing node with a different
configuration, only the Device Controller needs to be newly
implemented. Table III shows the program size of the implemented software components. The Web Interface, DataManager, and Device Controller have roughly the same size.
This means that our architecture reduced the development costfor supporting new hardware to 1/3 of the value for a verticallyintegrated architecture.

In order to verify the ability to measure the spectrum utilization using heterogeneous wireless devices and measurement
equipment, we measured the spectrum utilization of the 2.4
GHz band in laboratory environment using the 3 types ofsensing nodes.

V. E VALUATION
A. Performance of Tiny Spectrum Format
In order to confirm the effectiveness of the TSF, we compared the file size with that of other formats using real dataacquired using the spectrum analyzer R&S FSL 6. The time
resolution was 1 sec, the measurement time was 2 min, theTABLE IIIP ROGRAM SIZE
Component
Web Interface
Data Manager
Device Controller- Spectrum Analyzer R&S FSL 6- WLAN module Intel 5300
- SDR front-end USRP2
Server Software

Language
PHP 5
C

Lines
1399
1699

C
C
C++
PythonHTML/CSSJavaScript
PHP 5

958
1212
730
559
266
205
1170

30

0
Compressed XML
Compressed CSV

10Compressed Binary

20

Time [sec]File Size [KB]

25

Compressed TSF
15
10

20
30
40

5

50
0
0

10

Fig. 19.

20

30
40
50
60
The Number of Samples [x10000]

70

60
2.40

80

Comparison of file sizes for real data

Fig. 21.

2.42

2.44
Frequency [GHz]

2.46

2.48

Measurement results for the spectrum analyzer

120Compressed XML

0Compressed CSV

10Compressed Binary

80

Compressed TSF

Time [sec]File size [kB]

100

60
40
20

20
30
40
50

0
0

10

Fig. 20.

20

30
40
50
60
The Number of Samples [x10000]

70

80

Comparison of file sizes for random data

60

2.40

Fig. 22.

2.42

2.44
Frequency [GHz]

2.46

2.48

Measurement results for the WLAN module0

C. Multi-site measurement
In order to verify the ability to collect measurement datafrom multiple locations, we conducted multi-site measurements using the USRP2 sensing nodes at 4 locations inTokyo. The measurement sites were 1. University of Tokyo(Komaba), 2. University of Tokyo (Hongo), 3. Tokyo Instituteof Technology, and 4. Residential area (Soshigaya). Figure 24
shows a map of the measurement sites. The measurement time
period was 24 hours from 16:00 Oct. 4, 2010 – 16:00 Oct. 5,
2010. The frequency range was from 100 MHz – 2.0 GHz, andthe frequency resolution was 390 kHz. The time resolution was
20 sec.
Figure 25 shows the average spectrum utilization ratio overthe 24 hour period. Heavy usage is seen for 100 MHz, 200
MHz, 500 MHz, which are used for TV broadcasting, and 800
MHz ,which is used for mobile phones. Frequencies above 900

10Time [sec]One-minute snapshots captured by the spectrum analyzer,
WLAN module, and SDR front-end are shown in Fig. 21, Fig.
22, and Fig. 23, respectively. Heavy utilization of 1ch (2.402
GHz – 2.422 GHz) and 11ch (2.452 GHz – 2.472 GHz) is
observed in all the snapshots. As for other channels, while
the snapshots of the WLAN module shows the utilization of
7ch (2.432 GHz – 2.452 GHz) only between 4 and 10 sec,
the snapshots of the spectrum analyzer and the SDR frontend shows scattered utilization of 7ch during the whole time
period.
From these results, the spectrum utilization ratio is calculated as 31.2 % for the spectrum analyzer, 49.4 % for the
WLAN module, and 32.6% for the SDR front-end.

20
30
40
50
60
2.40

Fig. 23.

2.42

2.44
Frequency [GHz]

2.46

2.48

Measurement results for the SDR front-end

MHz are somewhat less utilized than those below 900 MHz.
Because all of the 4 locations are in the urban area of Tokyo,
the overall trend is similar at every location. However, somedifferences exist in spectrum utilization between the sites, suchas the distinct utilization of the 900 MHz band at Universityof Tokyo (Komaba).
D. Route measurement
In order to verify the ability to measure spectrum utilization
using a mobile sensing node, we conducted measurements
along a route using a WLAN module embedded on a Lenovo
X200. Figure 26 shows the measurement route. The route is
7.8 km long and runs partly beside a railway line through a
residential area in the western part of Tokyo. The frequency
range was from 2.402 GHz to 2.478 GHz which the WLAN
module is able to measure.
Figure 27 shows the spectrum utilization observed alongFig. 25.

Spectrum utilization ratio at 4 locations

VI. C ONCLUSION AND F UTURE W ORK
2In this paper, we described the design, implementation andevaluation of a distributed spectrum sensing system which
continually measures the spectrum utilization in the spatial
domain. The system is aimed at speeding up the decision
making process of the government concerning spectrum policy
to allow the more efficient use of the spectrum. The proposedarchitecture can utilize heterogeneous wireless devices andmeasurement equipment as sensing nodes in order to rapidly
extend the measurement coverage. To verify the architecture,
we implemented 3 types of sensing nodes using a spectrum analyzer, a WLAN module, and a SDR front-end. We evaluatedthe system through measurements using heterogeneous sensing
nodes, multi-site measurements, and mobile measurements.
There are three possible approaches to further research.
First, we can extend the server-side architecture to achieve thescalability to accommodate more sensing nodes. A distributed
server architecture might be a candidate in which each server
manages its own regional division. Second, we can extend oursystem to guarantee the accuracy of collected data. Authentication mechanism of sensing node owners might be helpful
to distinguish the data coming from reliable owners suchas research organizations from anonymous data. Finally, we
should investigate a framework for administration and operation. Studies in voluntary computing such as SETI@home [42]and also Participatory Sensing [43] or People-Centric Sensing[44], in which numerous mobile smartphones are utilized as
sensor nodes, might be used as references.

1
4

3

Fig. 24.

Locations of sensing nodes

Fig. 26.

Measurement route

Frequency [GHz]

2.4
2.42
2.44
2.46
2.48
7

6

Fig. 27.

5

4
Distance [km]

3

2

1

0

Spectrum utilization along a routethe route. 1ch (2.402GHz – 2.422GHz), 6ch (2.427 GHz –2.447), 11ch (2.452 GHz – 2.472 GHz) are heavily utilized so
as to avoid interference due to channel overlap. The overall
spectrum utilization is calculated to be 73.6%. This utilization
may correspond to home networks because the measurement
route is in a residential area.

ACKNOWLEDGMENT
This work was supported by the Ministry of Internal Affairs
and Communications, Japan (Ubiquitous Service Platform).
R EFERENCES[1] R. Coase, “The Federal Communications Commission,” Journal of Law
and Economics, 2(1), pp. 1-40, 1959.
[2] H. Yoon, J. Hwang, and M.B.H. Weiss, “Research on Secondary Spectrum Trading Mechanisms Based on Technical and Market Changes,”2010 IEEE Symposium on New Frontiers in Dynamic Spectrum AccessNetworks (DySPAN 2010), Singapore, Singapore, Apr. 2010.

[3] C.E. Caicedo and M.B.H. Weiss, “The Viability of Spectrum Trading Markets,” 2010 IEEE Symposium on New Frontiers in Dynamic
Spectrum Access Networks (DySPAN 2010), Singapore, Singapore, Apr.
2010.
[4] M. Cooper, “The Economics of Collaborative Production in the Spectrum Commons,” First IEEE International Symposium on New Frontiers
in Dynamic Spectrum Access Networks (DySPAN 2005), Baltimore,
Maryland, Nov. 2005.
[5] W. Lehr and J. Crowcroft, “Managing Shared Access to a Spectrum
Commons,” First IEEE International Symposium on New Frontiers
in Dynamic Spectrum Access Networks (DySPAN 2005), Baltimore,
Maryland, Nov. 2005.
[6] G. Hardin, “The Tragedy of the Commons,” Science, Vol. 162, pp. 12431248, 1968,[7] Japanese Radio Act, Article 26-2.
[8] M.H. Islam, C.L. Koh, S.W. Oh, Q. Xianming, Y.Y. Lai, W. Cavin, L.
Ying-Chang, B.E. Toh, F. Chin, G.L. Tan, and W. Toh, “Spectrum Survey
in Singapore: Occupancy Measurements and Analyses,” 3rd International Conference on Cognitive Radio Oriented Wireless Networks andCommunications (CrownCom 2008), Singapore, Singapore, May 2008.
[9] M. Lopez-Benitez, A. Umbert, and F. Casadevall, “Evaluation of Spectrum Occupancy in Spain for Cognitive Radio Applications,” IEEE 69th
Vehicular Technology Conference (VTC Spring 2009), Barcelona, Spain,
Apr. 2009.
[10] V. Valenta, Z. Fedra, R. Marsalek, G. Baudoin, and M. Villegas, “Towards Cognitive Radio Networks: Spectrum Utilization Measurements
in Suburb Environment,” IEEE Radio and Wireless Symposium (RWS’09), San Diego, California, Jan. 2009.
[11] A. Martian, I. Marcu, and I. Marghescu, “Spectrum Occupancy in an
Urban Environment: a Cognitive Radio Approach,” Sixth Advanced International Conference on Telecommunication (AICT 2010), Barcelona,
Spain, May 2010.
[12] Y. Han, Y. Wen, W. Tang, and S. Li, “Spectrum Occupancy Measurement: Focus on the TV Frequency,” 2nd International Conference on
Signal Processing System (ICSPS 2010), Dalian, China, Jul. 2010.
[13] R. de Francisco and A. Pandharipande, “Spectrum Occupancy in the
2.36-2.4 GHz Band: Measurements and Analysis,” 2010 European
Wireless Conference (EW), Lucca, Italy, Apr. 2010.
[14] R.B. Bacchus, A.J. Fertner, C.S. Hood, and D.A. Roberson, “Long-term,
Wide-band Spectral Monitoring in Support of Dynamic Spectrum AccessNetworks at the IIT Spectrum Observatory,” 3rd IEEE Symposium on
New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2008),
Chicago, Illinois, Oct. 2008.
[15] R. Bacchus, T. Taher, K. Zdunek, and D. Roberson, “Spectrum Utilization Study in Support of Dynamic Spectrum Access for Public Safety,”2010 IEEE Symposium on New Frontiers in Dynamic Spectrum AccessNetworks (DySPAN 2010), Singapore, Singapore, Apr. 2010.
[16] O. Holland, P. Cordier, M. Muck, L. Mazet, C. Klock, and T. Renk,“Spectrum Power Measurements in 2G and 3G Cellular Phone Bands
During the 2006 Football World Cup in Germany,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum AccessNetworks (DySPAN 2007), Dublin, Ireland, Apr. 2007.
[17] T. Renk, C. Kloeck, F.K. Jondral, P. Cordier, O. Holland, and F. Negredo,“Spectrum Measurements Supporting Reconfiguration in Heterogeneous
Networks,” 16th IST Mobile and Wireless Communications Summit,
Budapest, Hungary, Jul. 2007.
[18] M.A. McHenry, “NSF Spectrum Occupancy Measurements Project
Summary,” Shared Spectrum Company Report, Aug. 2005.
[19] M. Wellens, J. Wu, and P. Mahonen, “Evaluation of Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of Cognitive
Radio,” 2nd International Conference on Cognitive Radio Oriented
Wireless Networks and Communications (CrownCom 2007), Orlando,
Florida, Jul. 2007.
[20] R.I.C. Chiang, G.B. Rowe, and K.W. Sowerby, “A Quantitative Analysis
of Spectral Occupancy Measurements for Cognitive Radio,” IEEE 65th
Vehicular Technology Conference (VTC Spring 2007), Dublin, Ireland,
Apr. 2007.
[21] V. Blaschke, H. Jaekel, T. Renk, C. Kloeck, and F.K. Jondral, “Occupation Measurements Supporting Dynamic Spectrum Allocation for
Cognitive Radio Design,” 2nd International Conference on Cognitive
Radio Oriented Wireless Networks and Communications (CrownCom2007), Orlando, Florida, Jul. 2007.
[22] M. Wellens, J. Riihijarvi, M. Gordziel, and P. Mahonen, “Spatial
Statistics of Spectrum Usage: From Measurements to Spectrum Models,”IEEE International Conference on Communications (ICC ’09), Dresden,
Germany, Jun. 2009.
[23] M. Biggs, A. Henley, and T. Clarkson, “Occupancy Analysis of the 2.4
GHz ISM band,” IEE Proc. Communications, Vol. 151, Issue 5, pp.
481-488, Oct. 2004.
[24] A. Petrin and P.G. Steffes, “Analysis and Comparison of Spectrum
Measurements Performed in Urban and Rural Areas to Determine the
Total Amount of Spectrum Usage,” 2005 International Symposium on
Advanced Radio Technologies, Boulder, Colorado, Mar. 2005.
[25] S. Pagadarai, A.M. Wyglinski, and R. Vuyyuru, “Characterization of
Vacant UHF TV Channels for Vehicular Dynamic Spectrum Access,”
First IEEE Vehicular Networking Conference (VNC 2009), Tokyo, Japan,
Oct. 2009.
[26] Y. Li, T.T. Quang, Y. Kawahara, T. Asami, and M. Kusunoki, “Building a
Spectrum Map for Future Cognitive Radio Technology,” ACM Workshop
on Cognitive Radio Networks (CoRoNet 2009), Beijing, China, Sept.
2009.
[27] K.A. Qaraqe, H. Celebi, A. Gorcin, A. El-Saigh, H. Arslan, and M.-s.
Alouini, “Empirical Results for Wideband Multidimensional Spectrum
Usage,” IEEE 20th International Symposium on Personal, Indoor andMobile Radio Communications (PIMRC 2009), Tokyo, Japan, Jul. 2009.
[28] J. Riihijarvi, P. Mahonen, M. Wellens, and M. Gordziel, “Characterization and Modelling of Spectrum for Dynamic Spectrum Accesswith Spatial Statistics and Random Fields,” IEEE 19th International
Symposium on Personal, Indoor and Mobile Radio Communications(PIMRC 2008), Cannes, France, Sept. 2008.
[29] D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li, “Mining Spectrum
Usage Data: a Large-scale Spectrum Measurement Study,” ACM 15th
Annual International Conference on Mobile Computing and Networking(MobiCom 2009), Beijing, China, Sept. 2009.
[30] S.W. Ellingson, “Spectral Occupancy at VHF: Implications forFrequency-agile Cognitive Radios,” IEEE 62nd Vehicular Technology
Conference (VTC Fall 2005), Dallas, Texas, Sept. 2005.
[31] S.D. Jones, E. Jung, X Liu, N. Merheb, and I. Wang, “Characterizationof Spectrum Activities in the U.S. Public Safety Band for Opportunistic
Spectrum Access,” 2nd IEEE International Symposium on New Frontiers
in Dynamic Spectrum Access Networks (DySPAN 2007), Dublin, Ireland,
Apr. 2007.
[32] Japanese Ministry of Internal Affairs and Communication,
http://www.soumu.go.jp/main content/000068577.pdf (in Japanese).
[33] D. Datla, A.M. Wyglinski, and G.J. Minden, “A Spectrum Surveying
Framework for Dynamic Spectrum Access Networks,” IEEE Transactions on Vehicular Technology, Vol. 58, Issue 8, pp. 4158-4168, Oct.
2009.
[34] F. Weidling, D. Datla, V. Petty, P. Krishnan, and G.J. Minden, “A
Framework for R.F. Spectrum Measurements and Analysis,” First IEEE
International Symposium on New Frontiers in Dynamic Spectrum AccessNetworks (DySPAN 2005), Baltimore, Maryland, Nov. 2005.
[35] A. Iyer, K. Chintalapudi, V. Navda, R. Ramjee, V.N. Padmanabhan,
and C.R. Murthy, “SpecNet: Spectrum Sensing Sans Frontieres,” 8th
USENIX Symposium on Networked Systems Design and Implementation(NSDI ’11), Boston, Massachusetts, Apr. 2011.
[36] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms
for Cognitive Radio Applications,” IEEE Communications Surveys andTutorials, Vol. 11, Issue 1, pp. 116-130, 2009.
[37] gnuplot homepage, http://www.gnuplot.info/[38] Google Maps API Family,
http://code.google.com/apis/maps/index.html[39] Rohde & Schwartz, Download Area - Drivers,
http://www2.rohde-schwarz.com/en/service and support/Downloads/Drivers/[40] Wireless Extensions for Linux,
http://linuxwireless.org/en/developers/Documentation/
Wireless-Extensions[41] GNU Radio, http://gnuradio.org/[42] SETI@home, http://setiathome.ssl.berkeley.edu/[43] J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, andM.B. Srivastava, “Participatory Sensing,” Workshop on World-SensorWeb (WSW 2006), Boulder, Colorado, Oct.-Nov. 2006.
[44] 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 Rise of
People-Centric Sensing,” IEEE Internet Computing, Vol. 12, No. 4, pp.
12-21, 2008.