Demo Abstract: Radio Information Management for Distributed
Spectrum Sensing
Junichi Naganawa, Hojun Kim, Kosuke Nishimura, Shunsuke Saruwatari, Makoto Suzuki,
Masateru Minami, Hiroyuki Morikawa
RCAST, The University of Tokyo{naga, kimhojun, kosuke, saru, makoto, minami, mori}@mlab.t.u-tokyo.ac.jp
Abstract
Radio spectrum has turned into a precious natural resource from free goods due to the rapid development of wireless communication technology. In order to efficiently utilize
radio spectrum, a spectrum policy should be enough appropriate to create new industries and new technologies. From
this point of view, we have developed a distributed spectrum
sensing system which collects dense information of spectrum usage over large area. In this work, we discuss therequirements and the design of the distributed spectrum sensing system.

Categories and Subject Descriptors
C.2.4 [Distributed Systems]: Distributed Systems—Distributed applications

General Terms
Design, Performance

Keywords
Distributed Spectrum Sensing, Cognitive Radio, Sensor
Network, Radio Information

1 Distributed Spectrum Sensing
The distributed spectrum sensing system provides the visualization service to access the spectrum information collected at each distributed sensor node. Each sensor node
can be chosen among wide range of devices from a low-end
100-dollar spectrum analyzer to high-end spectrum analyzers which cost tens of thousands of dollars.
Figure 1 shows a screen shot of the system. When a user
accesses to the visualization service provided by the system,
the spectrum usage information is shown on the map. In Figure 1, the spectrum usage at Komaba campus of the University of Tokyo is shown with the graph in which the horizontal
axis corresponds to the frequency and the vertical axis corresponds to the time. The user is also able to access raw
data by clicking a sensor node on the map. This visualization service allows many applications such as finding whitespace and detecting radio disturbance. Even analyzing the
disturbance cause is possible by examining raw data, determining shadowing or illegal radio transmission. The spectrum sensing will also contribute to the emerging cognitive
radio technology [1].
Copyright is held by the author/owner(s).
SenSys’09, November 4–6, 2009, Berkeley, CA, USA.
ACM 978-1-60558-748-6

Figure 1. Screen shot.
This distributed spectrum sensing, however, can not beachieved by merely distributing many high-end spectrum analyzers and connecting them to the Internet. There are twotechnological issues: the first one is how to efficiently collectspectrum usage information over large area, and the second
one is how to manage enormous raw data.
For the first issue, to achieve efficient collection of spectrum usage information over large area, the distributed dataacquisition system should satisfy network constraints such as
network bandwidth. The distributed spectrum sensing system collects data from thousands of sensor nodes. Hence,
appropriate data format of spectrum usage information andtraffic management are essential for distributed spectrum
sensing system to accommodate such a large number of sensor nodes under the limited network resource.
For the second issue, to manage enormous amount of
raw data on a sensor node, the storage capacity managementthat satisfies storage resource constraints is necessary. For
example, in the case of 100MHz bandwidth signals to be
stored in a local storage, the storage resource is consumed
at 800Mbyte per a second because a single symbol has 8byte
of data amount. Obviously, no high-end spectrum analyzer
can store such a huge amount of data for ever.

2 System Design
2.1 System Architecture
Figure 2 shows the overall system architecture of the distributed spectrum sensing system. The system is composedof a management server and sensor nodes. The management server provides the visualization service and bridges
between sensor nodes and users. A sensor node is composedof a front-end, an acquisition function, a storage function,

Sensor NodeFrontendAcquisition
Function

HDD

Storage Function

Raw Data

Management ServerSpectrum Usage Information
USERHTTPWeb
Interface

Visualization
ServiceHDD

Figure 2. Distributed spectrum sensing system.
and web-interface. The front-end is a device to receive radio information. The received radio information is passed
to the acquisition function to generate the spectrum usage
information. Then, web-interface sends the spectrum usage
information to the server. If the sensor node is equipped witha storage function, the collected radio information is written
in the storage as raw data.

2.2 Traffic Management
Generating spectrum usage information is a essentialfunction of the sensor node. It is achieved by following
steps. First, the sensor node estimates spectrum every minutes. Then, the sensor node adds a time stamp, a center frequency, bandwidth, and existence of corresponding raw data
to the estimated spectrum in order to form spectrum usage
information. If the size of the generated spectrum usage information exceeds a maximum data size, the sensor node repeats to decimate odd line data until the data size becomes
below the maximum data size. The maximum data size isdecided depending on the load of the management serverand a number of sensor nodes, then the server notifies themaximum data size to all sensor nodes. For example, we assume that we have 10,000 sensor nodes and each node generates 5Kbyte data every minutes. In this case, the network
bandwidth should be at least 6.7Mbps, and the management
server should handle 500 sessions at a time under the assumption that a single session needs 3 seconds.

2.3 Storage Capacity Management
Storing raw data is a optional function of the distributed
spectrum sensing system, which only high-end sensor nodes
is equipped with. Because raw data consume huge storage
capacity, we introduce RSSI (Received Signal Strength Indicator) as a criterion for storing so that only important raw
data is stored. Storing raw data is achieved by followingsteps. First, a user set the threshold value of RSSI, which
is determined from an intended application type. Then, thesensor node stores raw data in a separated file every minutes.
Receiving and writing raw data, the sensor node calculates
RSSI at the same time. If the calculated RSSI in the minutes does not exceed the threshold, the corresponding raw
data file is deleted. Thus, unnecessary data is avoided to bestored. In addition, when the sensor node begins to run outof the available storage capacity, the storage function deletes
the old data and notifies the deletion to the server.

Apache and PHP. As a sensor node, we use spectrum analyzers such as PXI-5660 of National Instruments[3], USRP[4]with GNU Radio[5], and 100-dollar spectrum analyzers[2].
For the initial evaluation, we selected USRP2 with GNU Radio and measured the CPU utilization of tasks for the distributed spectrum sensing system. In the implementation,
we used laptops with 1GB memory, Core 2 Duo P8400 as aCPU, 1TB HDD connected via USB as a storage. The measurement result shows that receiving 200kHz bandwidth signals, writing to the storage, calculating RSSI, and generating
abstract data consumed 6.6%, 0.1%, 3.7%, and 0.4% of theCPU power respectively and 10.8% in total. In this case, raw
data is stably stored at 96Mbyte per a minute.

4 Demonstration
Figure 3 shows the demonstration setup. In the demonstration, we show measurement and visualization of the spectrum usage on the demonstration site. During the measurement, visitors can see the uploaded abstract data as a screen
shot in Figure 1 by using the visualization service. The visualization service mashes up Google Maps and the spectrum
usage information. Each sensor node is represented by anicon on the map. The visitors can also view the radio information from the sensor nodes located in several points inJapan.

5 Conclusions
In this paper, we discussed the requirements and themethod to manage spectrum information. Now we arepreparing to measure radio information over wide region using sensor node described in this paper.

6 Acknowledgments
This work is supported by Ministry of Internal Affairs andCommunications, Japan (Ubiquitous Service Platform).

7 References[1] J. Mitola III, et al., “Cognitive Radio: Making Software Radios More Personal,” IEEE Personal Communications, 1999.
[2] H. Kim, et al., “Design of a Low-cost Sensor Node forDistributed Spectrum Sensing,” In Proc. of SenSys, ’09,
Nov. 2009.
[3] National Instruments. http://www.ni.com/[4] Ettus Research LLC. http://www.ettus.com/[5] Gnu Radio. http://www.gnuradio.org/

3 Implementation
We implemented the management server and the sensor
nodes. The server is implemented as a web service with

Figure 3. Demonstration setup.