Project Report on Security in Cognitive Radio Networks
1. Project Report
on
Security in Cognitive Radio Networks
submitted in partial fulfillment of the requirement
for the award of the Degree of
Bachelor of Engineering
in
Electronics & Telecommunication Engineering
by
Rishabh Hastu
Parag Jagtap
Abhishek Shukla
under the guidance of
Prof. D.D. Ambawade
Department of Electronics & Telecommunication Engineering
Bharatiya Vidya Bhavan’s
Sardar Patel Institute of Technology
Munshi Nagar, Andheri-West, Mumbai-400058
University of Mumbai
April 2014
2. Certificate
This is to certify that the Project entitled “Security in Cognitive Radio Networks” has
been completed successfully by Mr. Rishabh Hastu, Mr. Parag Jagtap and Mr. Abhishek
Shukla under the guidance of Prof. D.D. Ambawade for the award of Degree of Bachelor of
Engineering in Electronics & Telecommunication Engineering from University of Mumbai.
Certified by
Prof. D.D. Ambawade Dr. Y. S. Rao
Project Guide Head of Department
Dr. Prachi Gharpure
Principal
Department of Electronics & Telecommunication Engineering
Bharatiya Vidya Bhavan’s
Sardar Patel Institute of Technology
Munshi Nagar, Andheri(W), Mumbai-400058
University of Mumbai
April 2014
3. Project approval Certificate
This is to certify that the Project entitled “Security in Cognitive Radio Networks” by Mr.
Rishabh Hastu, Mr. Parag Jagtap and Mr. Abhishek Shukla is approved for the award of
Degree of Bachelor of Engineering in Electronics & Telecommunication Engineering from
University of Mumbai.
External Examiner Internal Examiner
(signature) (signature)
Name: Name:
Date: Date:
Seal of the Institute
4. Statement by the Candidate
We wish to state that the work embodied in this project titled “Security in Cognitive Ra-
dio Networks” forms our own contribution to the work carried out under the guidance of
Prof. D. D. Ambawade at the Sardar Patel Institute of Technology. We declare that this
written submission represents our ideas in our own words and where others’ ideas or words
have been included, we have adequately cited and referenced the original sources. We also
declare that we have adhered to all principles of academic honesty and integrity and have
not misrepresented or fabricated or falsified any idea/data/fact/source in our submission.
(Candidate’s Signature)
Rishabh Hastu Parag Jagtap Abhishek Shukla
Roll No.:
5. Acknowledgement
This project owes its existence to the profound help given to us by Prof. Dayanand Am-
bawade. We are obliged to our Principal, Dr. Prachi Gharpure, for giving us the opportunity
to implement this project and our Head of Department, Dr. Y. S. Rao, for helping us outper-
form our own selves. We would like to express our sincere gratitude to the faculty members
and staff of Electronics and Telecommunication Department of Sardar Patel Institute of
Technology for giving their continuous guidance throughout the project.
i
6. Abstract
Cognitive Radio Networks are envisioned to drive the next generation wireless networks
that can dynamically optimize spectrum use. Recent advancement in wireless technology is
creating a spectrum shortage problem on a daily basis. Cognitive radio, a novel technology,
attempts to solve these problems by dynamically using the free spectrum in wireless com-
munication. It is a wireless technology which is aware of its environment and uses a certain
methodology by changing its operational parameters to complete two important objectives:
highly reliable communication and efficient utilization of the radio spectrum. Cognitive radio
networks (CRNs), can be formed using cognitive radios by extending the radio link features
to network layer functions.
These CRNs are entitled to achieve the result by means of sensing, understanding, making
decisions and adapting to the environment. CRNs are more flexible and exposed to Wireless
Networks compared with other traditional radio networks. However, there are many secu-
rity threats to CRNs because of its special characteristics, such as intelligence functionality
and dynamic spectrum access application. Securing communication, while exploiting the
flexibilities offered by Cognitive Radio still remains a daunting challenge. Some of the chal-
lenges and threats to CRNs can be found in Spectrum sensing, Spectrum decision, Spectrum
sharing and Spectrum mobility. This project aims to tackle such challenges and threats by
making a Security Model to make CRNs more secure from the malicious attacks.
This solution is brought about in two stages. The 1st stage being the efficient spectrum
sensing, using eigenvalue based energy detection and the 2nd stage being the detection of
unauthorized malicious user using security algorithm. Encryption algorithm is used in the
2nd stage and as the malicious user doesn’t have the secret key, it fails to decrypt the
Identification Tag (IT), and hence is detected as a malicious user.
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10. Chapter 1
Introduction
There is an ever-increasing demand for spectrum for emerging wireless applications and
there is a spectrum shortage for the wireless applications. In view of this, the Federal
Communications Commission (FCC) has considered making the licensed spectrum available
to unlicensed users. This will allow unlicensed users to use the empty spectrum, provided
they cause no interference to licensed users. Most radio systems today are aware of the radio
spectrum. Cognitive radio is a new research area for wireless communication in which either
a network or a wireless node is able to change its transmission or reception parameters to
communicate efficiently by avoiding interference with licensed or unlicensed users. Basically,
the parameters that are used in CRNs are based on the active monitoring of several factors,
either in the external or internal radio environment, such as radio frequency spectrum, user
behaviour and network state. A cognitive radio senses available spectrum, occupies it and
can vacate the spectrum on sensing the return of the primary user (PU).
Efficient spectrum sensing (SS) is the key step in the operation of CRs using DSA. In
the process of SS, various channel effects (e.g., shadowing, multi path fading etc.) play
very crucial roles. To mitigate all these effects, collaborative or distributed SS has been
proposed. Collaborative SS incorporates spatial diversity to improve its performance. In
collaborative SS, a number of CRs form a network and the final decision regarding the
availability of spectrum opportunity for the CR network is based on the information received
from all the CRs. Information collection from the participating CRs depends on the nature
of the CRN. In this paper, we consider infrastructure based CRNs which are similar to the
classical parallel data fusion model in distributed detection (DD). In this model, each CR
in the network forwards its processed observation to the central entity which is called the
fusion centre (FC). The FC then makes the final decision about the state of nature based
on all the information received from the participating CRs. Recently, SS for CRNs has
attracted the attention of many researchers. But the issue of security in CRNs has not
been considered in detail. Like all other networks, CRNs are also vulnerable to various
security issues. Collaborative SS process itself is subject to various security threats. Two of
these attacks have been defined as: 1) Incumbent Emulation (IE) attacks and 2) Spectrum
Sensing Data Falsification (SSDF) attacks (i.e., Byzantine attacks). In IE attacks, some of
the participating CRs or some outsiders try to mimic the transmission of the incumbent
(primary user) to disrupt the SS process. The presence of IE attackers makes the FC
decide that the spectrum band under consideration is not available and the CRN holds its
transmissions which provides an opportunity to IE attackers to exploit the spectrum holes.
On the other hand, under SSDF (Byzantine) attacks some of the CRs introduce false sensing
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11. information in the fusion process to disrupt the SS process.Fig. 1.1 shows a Cognitive Radio
Network. [1]
Figure 1.1: Cognitive Radio Network
1.1 Motivation
The need for higher data rates is increasing as a result of the transition from voice-only com-
munications to multimedia type applications. Given the limitations of the natural frequency
spectrum, it becomes obvious that the current static frequency allocation schemes cannot
accommodate the requirements of an increasing number of higher data rate devices. As a
result, innovative techniques that can offer new ways of exploiting the available spectrum
are needed. For this purpose, the Federal Communications Commission (FCC) has approved
the use of licensed bands by unlicensed users, and thus a novel network named CRNs with
cognition ability emerges.
1.2 Objectives
The aim of having chosen this project is to provide security in cognitive radio networks.
The Federal Communications Commission (FCC) has made the licensed spectrum available
to unlicensed users. This will allow unlicensed users to use the empty spectrum, provided
they cause no interference to licensed users. This will lead to the development of spectrum
sensing techniques. Thus, we aim to provide an efficient way for spectrum sensing using
energy detection method. But this can lead to unauthorized and unlawful usage of spectrum
creating various challenges and threats. This creates a demand for a secure way to carry
2
12. on the functioning of the system. Here, we propose a security model for ensuring secure
communication between any two entity in the network.
1.3 Layout of the Report
A brief chapter by chapter overview is presented here
Chapter 2: In this chapter literature review of different techniques and advancements in
cognitive radio is discussed along with the attacks and challenges in implementing the CRN
Chapter 3: In this chapter we mention the Problem Statement we would be working on
Chapter 4: In this chapter, we will discuss the basics, advantages and applications of CRN’s
Chapter 5: Here we mention the feasibility and economic justification of the project
Chapter 6: In this chapter the software and hardware used in the project are extensively
discussed
Chapter 7: Here we discuss our proposed algorithm
Chapter 8: In this chapter we mention how we implemented the above mentioned algorithm
in the given environments
Chapter 9: Here the simulation results are discussed
Chapter10: The conclusion and future works are discussed
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13. Chapter 2
Literature Review
We have done an extensive research on the topic and refered many papers to understand
the concept. Different papers give different solutions and adopt different approaches. Some
papers provide solutions with number of assumptions, limiting it from reaching the practical-
ity. While some papers only proposed theoritical solutions without any experimental base.
Keeping the Objective in mind, all the required solutions and sources have been described
in the coming sections.
2.1 Overview
CRN was first described by Mitola in his Ph.D Thesis, Cognitive radio: Integrated agent
architecture for software defined radio in 2000. Radioscene analysis, Channelstate estimation,
predictive modeling, Transmit power control, dynamic spectrum management, described
extensively by[15] Simon Haykin, tells us the basic idea of cognitive spectrum sensing. Also
it discusses the basic requirements of performing efficient methods for spectrum sensing
and the challenges associated with it. They provided the hypothesis to find probability of
detection and probability of false alarm, but failed to mention the concept of security model
while sensing.
Cognitive Radios (CR) has the capability to adapt to the communication parameters.
As Akyildiz et. al explains about the re-configurability and the cognitive capability. The
radio should sense the environment constantly, based on the result; it needs to change the
parameters giving birth to a cognitive cycle. Fig. 2.1 shows a basic cognitive cycle. Based on
the environmental parameters, namely battery life, occupancy information, noise power, etc.,
the CR can change the transmission parameters like carrier frequency, power, modulation
method and index, bandwidth, symbol rate, etc., for a proficient usage of the spectrum
According to Akyildiz et.al, the four basic functions of the cognitive radios for enabling
DSA are as follows:
• Sensing of Spectrum: Cognitive radio need to sense unused spectrum for secondary
usage without interfering primary user.
• Management of Spectrum: Cognitive radio need to find the best available spectrum
for optimizing the communication requirements.
• Mobility of Spectrum: Cognitive radio need to seamlessly transition the spectrum used
for communication, when needed to leave the currently used spectrum
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14. • Sharing of Spectrum: Cognitive radio need to fairly share the available spectrum among
the coexisting secondary users.
By enabling secondary utilization of the spectrum, cognitive radios can help in efficient usage
of the spectrum.
Figure 2.1: Cognitive Cycle
Cases assumed by [11] (Muhammad Usman and Insoo Koo, The scientific world journal
volume2014), [12] (Ankit Singh Rawat, Priyank Anand, Hao Chen, IEEE transaction 2011)
supports more number of reliable users than unreliable secondary users while collaborative
cognitive spectrum sensing. Their hypothesis may not stand well for the Sybil attacks where
number of malicious users is more than honest users. Different types of security threats
and how to tackle them was discussed by [13] (Shameek Bhattacharjee, Shamik Sengupta,
Mainak Chatterjee, 2013), [14] ( Sazia Parvin , Farookh Khadeer Hussain , Omar Khadeer
Hussain, Song Han , Biming Tian , Elizabeth Chang, 2013). They covered all the types of
attacks which secondary user can incur, but they didnt provide sufficient information for
counter measures. They provided solutions for individual Byzantic as well as Emulation
attacks but didnt specify solution for Sybil attacks in details.
[3] (Rozeha A. Rashid) describes the whole scenario in detail. It covers the hardware
requirements for secured collaborative spectrum sensing and also explains the software test
bed which is required for generating algorithm using python script in order to prevent ma-
licious attacks of secondary user. Thus, they explain the overall procedure of spectrum
sensing, detecting and security with the help of USRP and GNU radio and also its practical
implementation.
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15. 2.2 Challenges in Dynamic Spectrum Access Environ-
ment
Cognitive Radios facilitate in the secondary usage of the licensed spectrum (when not in
use). When in use by a primary user, the secondary user cannot use it. Therefore, accurate
spectrum occupancy information needs to be maintained by a secondary user. This minimizes
the interference. A malicious user can try to falsify the spectrum occupancy information,
which may cause interference. [4]
In dynamic spectrum access environment, the following security threats are prominent:
• Primary User Emulation Attacks (PUEA): Primary User Emulation Attacks (PUEA)
are attacks in which the malicious nodes emit signals whose signal power and waveform
characteristics are almost similar to the licensed primary transmitter. PUEA can be
divided into different sub-genres based on impacts the adversary wants to achieve.
– Denial PUEA: An attacker emits spurious signals in absence of primaries, so that
the ‘radios believe that a primary is present and thus refrains from using the
spectrum. This is an immediate/short term attack, where the radios are denied
immediate use of the available channels as sensors are manipulated with faulty
sensory inputs of the RF environment.
– Induce PUEA: Here a malicious user in the vicinity of a secondary can mask the
primary signal by raising the noise floor, or it may transmit at low power masking
signals if close to the secondary. With a higher noise floor, or equivalently a less
Signal to Noise Ratio (SNR), a secondary will erroneously infer that a primary
is not present and try to use the spectrum. This is a violation of spectrum
regulations and sooner or later the radio may be banned.
• Jamming disruption attacks in DSA: Jamming is transmitting a signal to the receiving
antenna on the same frequency as that of an authorized transmitter, thus hindering
the legitimate reception by the receiving antenna. In the context of cognitive radios,
jamming is done during the data transmission. The difference between PUEA and
jamming in DSA networks is the emission of primary like signals in the sensing slot
in an effort to manipulate the sensors; while in jamming, disruption is realized in the
data transmission slot, Channel aggregation, fragmentation and bonding allow support
of more users, increase spectrum utility and provide improved bandwidth if necessary.
However, there is a potential vulnerability introduced by these features. This is because
the fragmented channels are no longer orthogonal, and the energy leakage increases.
An attacker exploits the correlation between the non-orthogonal fragments, and causes
a disruptive denial of service similar to jamming attacks. The key difference between
jamming and disruption due to fragmentation is that an attacker can attack a different
channel if, by spoofing power on another channel j which may be legally acquired by
the attacker by capitalizing on the loss of orthogonality. In this case there might not
be a total denial of service disruption but certainly would cause impaired QoS, loss in
channel capacity, and decreased throughput.
• Secondary spectrum data falsification (SSDF) or Byzantine attacks: A Byzantine fail-
ure in secondary networks may occur when radios are unable to correctly determine
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16. the presence of primaries due to attackers who modify spectrum sensing data. This at-
tack exploits the cooperative nature of spectrum sensing where an attacker sends false
spectrum data to the fusion centre or data collector, thus inducing erroneous decisions
on spectral usage. There are two ways in which a Byzantine attack can be launched.
– Denial SSDF: The adversary may advertise 0 (not occupied) as 1 (occupied) thus
causing the fusion/channel allocation centre to believe that primary is present,
thus restricting channel access. This attack comes under both short term and
denial attack, as interpreting empty spectrum as occupied means that a radio
cannot use the spectrum with immediate effect.
– Induce SSDF: The adversary may advertise 1 as 0 thus causing harmful interfer-
ence to primary incumbent. Repeated occurrence of such breach of policies may
cause the radio to be barred temporarily or banned permanently from the net-
work. Since repeated occurrence of this instance is necessary, it is a long term or
induced attack. This is distinct from the previous case which was a denial attack
and is achieved quickly.
• Sybil attacks: A number of Sybil based malicious nodes with multiple unique counter-
feit identities may spoof incorrect channel occupancy information and render incorrect
spectrum decision. This type of attack spoofs an illusion, that there are nodes that
have sensed a channel, when in reality there are no such nodes. Of course the occu-
pancy information advertised by different logical Sybil interfaces have to be the same
on a particular sensing cycle in order to mislead the entity deciding on the spectrum
availability. A malicious Sybil node can out vote the honest users. In case a channel
is allocated to the counterfeit node, it reduces spectrum utilization.
These four attacks stated above causes the network to work inefficiently resulting in
an unfair outcome. As a part of the conclusion, it is always wise not to decide upon
the spectrum allocation and utilization using information from a misbehaving user.
2.3 Misbehaving User detection
In cooperative scenario, the primary user shares the spectrum occupancy information with
the legitimate secondary user to improve the reliability and better spectrum utilization. In
this scenario, a misbehaving user (node) can masquerade to be a primary user and send
fallacious spectrum occupancy information to the secondary users, thus disproving the dy-
namic spectrum theory. It is very important to detect the misbehaving user and disregard
the information and requests sent by them. Our research on identifying a misbehaving user
led to pleasant surprises. Chen et.al proposes a transmitter verification technique using the
signal characteristics and more importantly the location of the primary user. The below
Fig. 2.2 shows a flowchart for verifying whether a transmitter is a malicious or misbehaving
user, or a secondary user or a legitimate primary user (Chen, 2008). [6]
This technique makes use of the location information to verify the transmitter. As the
location information may also be known to a malicious user, he may masquerade the location
information and pretend to be a primary user, thereby negating the goal of the dynamic
spectrum access.
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17. Figure 2.2: Flowchart to detect a malacious user
2.4 Eigen Value Based Energy Detection
In Fig. 2.3 the main parts of the method are showed: the sampled signal comes from the
System Interface to build the covariance matrix or the Hankel matrix; the eigenvalues of the
matrix are calculated with a specific algorithm to make the ratio maximum-minimum; with
the user’s settings the threshold is defined and the comparison with the eigenvalues ratio
detects the signal presence. The discussion is divided in three subsections to highlight the
main parts of the method: why the eigenvalues ratio can be used to detect signals, how to
find the eigenvalues with a single source and a single receiver, how to define the threshold.[18]
Eigenvector is a vector which if multiplied with covariance matrix results into scalar
multiple of that vector. Here scalar multiple is known as Eigenvalue. Thus, any change in
sampled signal value with noise will not affect eigenvector and it will be invariant, this will
be the significance of eigenvalue. Also Buccardo considered a matrix for a series of sampled
signal x(n), where n=0,1,2,3 .. N. Matrix is filled with N - L + 1 rows and L columns.
Thus with the help of Singular Values Decomposition (SVD) maximum eigenvalues max
and minimum eigenvalues min is being calculated. Depending upon that, max min is
found.Now, ratio is compared with the threshold value which is being calculated by Pfa.
2.5 Summary
From our extensive literature survey, we conclude that the cognitive radio is definitely a
promising solution to achieve dynamic spectrum access and alleviate the inefficient spectrum
utilization. In dynamic spectrum access networks, mutual sharing of the spectrum occupancy
information helps accomplish the goal to use the spectrum efficiently and minimize the
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18. Figure 2.3: Eigen Value Based Energy Detection
interference to the primary users. However, it is also important to detect and conclude the
information is from the trustworthy user and the information is not compromised, so that
the spectrum sharing objective is accomplished.
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19. Chapter 3
Problem Statement
We know that most of the frequency spectrum band has already been licensed and the
licensed spectrum is not being utilized efficiently. Cognitive radio helps to efficiently utilize
the spectrum band when the primary user is not using it. The main objective of the cognitive
radio is to identify the unoccupied spectrum for the secondary usage without interfering with
the primary licensed holders. When primary user is using the spectrum band then secondary
user cannot use that band at that time. When a primary user starts using the spectrum
band, secondary users have to stop using the spectrum band as soon as possible for avoiding
any interference to the primary users. Primary users hold the exclusive rights to the licensed
spectrum. This can be classified as a core business problem as the primary user has to
acknowledge a few compromises and deal with it as well.
There are two scenarios in spectrum sharing: Cooperative and Non-Cooperative sce-
narios. In the cooperative scenario, the primary users and secondary user may share the
spectrum occupancy information thereby maximizing the spectrum utilization and minimiz-
ing the interference. However, the source of spectrum occupancy information needs to be
verified to be from the primary user. In this case, malicious user can claim to be as a pri-
mary user and might cause interference with the primary user. So, a security model should
be maintained in the network to avoid such situations by detecting the malicious users and
discarding any information they send.
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20. Chapter 4
Cognitive Radio Networks
4.1 What is cognitive radio ?
Haykinss definition of cognitive radio (Haykin,2005) [15]: Cognitive radio is an intelligent
wireless communication system that is aware of its surrounding environment (i.e., the out-
side world), and uses the methodology of understanding-by- building to learn from the
environment and adapt its internal states to statistical variations in the incoming RF stim-
uli by making corresponding changes in certain operating parameters (e.g., transmit power
,carries-frequency ,and modulation strategy) in real time, with two primary objectives in
mind: highly reliable communication, whenever and wherever needed, and efficient utiliza-
tion of the radio spectrum. The authors in Chenetal stated that whenever cognitive radios can
find opportunities for communication using the spectrum holes, cognitive radio transports
packets on top of cognitive radio links in order to successfully facilitate useful applications
and services. A mobile terminal with cognitive radio capabilities can always sense the com-
munication environments (e.g. spectrum holes, geographic location, available wire/wireless
communication system or networks, and available services), analyze the environment and
learning formation from the environments with the users requirements and reconfigure itself
by adjusting system parameters to conform to certain policies and regulations.
4.2 Spectrum Sharing Scenarios in Cognitive Radio
There are two different types of spectrum sharing scenarios. They are:
• Cooperative scenario
• Non-cooperative scenario.
In cooperative scenario, a primary user provides secondary users with all information regard-
ing the occupancy of the spectrum and about the unused spectrum so that the secondary
users make use of that unused spectrum and keep away from the occupied spectrum. In the
non-cooperative scenario, a secondary user needs to sense the spectrum for the unused spec-
trum and use that spectrum band without causing any interference to the primary user. In
the cooperative scenario, a malicious user can masquerade as the primary user and provide
false information to the secondary user regarding the occupancy of the spectrum, such as the
spectrum is unoccupied and the secondary user can use though the primary user occupies the
spectrum. With the information provided, the secondary user tries to occupy the spectrum
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21. and as a result, interference takes place between the primary user and secondary user. In
some cases, the malicious user informs the secondary user as the spectrum is occupied even
though the spectrum is free and as a result the spectrum is not utilized either by primary
user or by secondary user. Because of these issues, a secondary user must make sure that the
information regarding the occupancy of the spectrum is provided by a legitimate primary
user. In this project, we propose a trust model to identify the legitimate users, the infor-
mation is provided by a legitimate primary user there by maximizing the utilization of the
spectrum and avoiding the interference between the primary users and secondary users.[1]
4.3 Types of Cognitive Radio
Depending on the available network side information and the regulatory constraints there
are three different classes of Cognitive radio paradigms. They are:
• Underlay
• Overlay
• Interweave
The Underlay cognitive radio paradigm is used when the interference between the cog-
nitive users and non-cognitive users is below a certain threshold. In Overlay cognitive radio
paradigm communication is provided by sophisticated signal processing. The Interweave
cognitive radio paradigms opportunistically exploit the white spaces without causing any in-
terference to the other transmissions. Generally, Interweave cognitive radio system is used.[1]
There are four broad inputs in a Cognitive radio. They are:
• Understanding the working environment in which it operates
• Understanding the users requirements for a better communication
• Understanding all regulatory policies that applies to it
• Understanding its own capabilities i.e. spectrum sensing, spectrum management, spec-
trum mobility and spectrum sharing.
4.4 Advantages Of Cognitive Radio
The main purpose of using a cognitive radio over a primitive radio is because of the following
advantages:
• Senses the radio frequency environment for the presence of white spaces
• Manages the unused spectrum
• Increases the efficiency of the spectrum utilization significantly
• Improves the spectrum utilization by neglecting the over occupied spectrum channels
and filling the unused spectrum channels
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22. • Improves the performance of the overall spectrum by increasing the data rate on good
channels and moving away from the bad channels
• Use the unused spectrum for new business propositions, such as providing high speed
internet in the rural areas and high data rate network applications like video confer-
encing can be made.
A cognitive radio makes use of the available spectrum efficiently, solves the spectrum scarcity
issue and thus can save millions of dollars.
4.5 Applications Of Cognitive Radio
The applications of CRNs in different fields are described as follows and depicted in:
• Emergency management or disaster recovery: According to the Federal Emergency
Management Agency (FEMA), a disaster is defined as the abnormal occurrence of any
situation. Cognitive radio can resolve problems in disaster situations. CR can adapt
its operating parameters to provide an ad hoc connection in times of need.
• Search and rescue: In a typical search and rescue scenario, the person in distress can
send information about his location by creating visual signs such as smoke or fire from
a flare gun. The GPS capability of CR can come in handy to appropriately detect the
position of the person in need of rescue. At the same time, if a special channel over
the available spectrum hole is used for short range signalling, this channel can work
like a beacon for the distressed person.
• Mining: A mining accident can occur any time. In the event of a mining accident, CR
can choose an appropriate waveform and apply other techniques to establish a clear
signal between the adverse environment in the mine and the outside world.
• Traffic control: Traffic is a major problem, especially during the rush hours in the
mornings and in the evenings. In such situations, the local traffic control can transmit
to the mobile user the location of congested traffic, the predicted traffic delay and an
alternate route. Cognitive intelligence can be applied to traffic signals themselves as
well, to determine how long the red or green signal remains on, depending on the traffic
volume in each direction.
• Medical applications: The application of cognitive radio can bring improvements in
areas of medical and bio-medical engineering. In a hospital environment, a new-born
baby needs to be identified by its mother. A cognitive radio tag can inform the mother
intelligently if the baby is carried outside of a designated premise, such as outside the
hospital baby ward. In relation to adult patients, each may be provided with a personal
cognitive ID tag, which can record the vital signs of the patient and intelligently inform
the respective authority if an abnormality is detected. Cognitive radio can enable
intelligent detection of abnormal tissues or blood cells in a human body and notify the
doctor.
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23. Chapter 5
Economic Analysis and Project
Justification
In this project, we primarily focus on the security in cognitive radio which is one of the needs
for new technologies requiring spectrum bands. As we are developing an efficient technique
for determining the legitimacy of the spectrum occupancy information, we do not focus on
the economic analysis. We are not going to make any device or software. We will be using
GNU radio and Network Simulator 2 as simulation software for our project and Universal
Software Radio Peripheral (USRP) as our hardware for interfacing the virtual environment
with the spectrum in the real world. We know that the entire spectrum band has already
been assigned to the licensed users. Dynamic spectrum access enables the primary user to
share their spectrum band with secondary user when primary users are not using at that time.
So, cognitive radio helps to sense the spectrum band and find out which band is available
and let the secondary users know that the band is available and use it if you want. As a
finite amount of spectrum band is available and cannot be manufactured, spectrum band is
priceless. So, we focus on justification of the project rather than on economic analysis. We
justify our project with a need of security and trust on cognitive radio.
Chen et. [6] al have proposed a technique called transmitter verification technique which
is used for the location and characteristics of signal of primary user. Transmitter will verify
who is primary user, secondary user, or malicious user. Authors combine their technique
with existing technique to determine spectrum occupancy and develop the trustworthiness
of the occupancy information. However, in this technique, users need to know the signal
characteristics of primary and secondary users. As the location information and signal
characteristics are assumed to be known, the malicious user also knows the primary signal
characteristics and he can pretend to be a primary user.
Even though several techniques have been proposed in the literature for evaluating trust
of a user in cognitive radio environment, they suffer from several shortcomings. In this
project we propose a technique to maintain a reliability queue for all the users and detect
the malicious user to make the system more secure.
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24. Chapter 6
Hardware and Software Description
6.1 Software Defined Radio
With the exponential growth in the ways and means by which people need to communicate
data communications, voice communications, video communications, broadcast messaging,
command and control communications, emergency response communications, etc. modifying
radio devices easily and cost-effectively has become business critical. Software defined radio
(SDR) technology brings the flexibility, cost efficiency and power to drive communications
forward, with wide-reaching benefits realized by service providers and product developers
through to end users. A number of definitions can be found to describe Software Defined
Radio, also known as Software Radio or SDR. The SDR Forum, working in collaboration with
the Institute of Electrical and Electronic Engineers (IEEE) P1900.1 group, has worked to
establish a definition of SDR that provides consistency and a clear overview of the technology
and its associated benefits.
Simply put Software Defined Radio is defined as: ”Radio in which some or all of the
physical layer functions are software defined” A radio is any kind of device that wirelessly
transmits or receives signals in the radio frequency (RF) part of the electromagnetic spec-
trum to facilitate the transfer of information. In today’s world, radios exist in a multitude of
items such as cell phones, computers, car door openers, vehicles, and televisions.Traditional
hardware based radio devices limit cross-functionality and can only be modified through
physical intervention. This results in higher production costs and minimal flexibility in
supporting multiple waveform standards. By contrast, software defined radio technology
provides an efficient and comparatively inexpensive solution to this problem, allowing mul-
timode, multi-band and/or multi-functional wireless devices that can be enhanced using
software upgrades.
SDR defines a collection of hardware and software technologies where some or all of the
radios operating functions (also referred to as physical layer processing) are implemented
through modifiable software or firmware operating on programmable processing technologies.
These devices include field programmable gate arrays (FPGA), digital signal processors
(DSP),general purpose processors (GPP), programmable System on Chip (SoC) or other
application specific programmable processors. The use of these technologies allows new
wireless features and capabilities to be added to existing radio systems without requiring
new hardware.
15
25. 6.2 GNU Radio
6.2.1 Introduction
GNU Radio is a free open-source software development toolkit that provides signal pro-
cessing blocks to implement software radios. It can be used with readily-available low-cost
external RF hardware to create software-defined radios, or without hardware in a simulation-
like environment. GNU Radio performs all the signal processing. You can use it to write
applications to receive data out of digital streams or to push data into digital streams, which
is then transmitted using hardware. GNU Radio has filters, channel codes, synchronization
elements, equalizers, demodulators, encoders, decoders, and many other elements (in the
GNU Radio jargon, we call these elements blocks) which are typically found in radio sys-
tems. More importantly, it includes a method of connecting these blocks and then manages
how data is passed from one block to another. Extending GNU Radio is also quite easy; if
you find a specific block that is missing, you can quickly create and add it.
Since GNU Radio is software, it can only handle digital data. Usually, complex baseband
samples are the input data type for receivers and the output data type for transmitters.
Analog hardware is then used to shift the signal to the desired centre frequency. That
requirement aside, any data type can be passed from one block to another - be it bits,
bytes, vectors, bursts or more complex data types. GNU Radio applications are primarily
written using the Python programming language, while the supplied, performance-critical
signal processing path is implemented in C++ using processor floating point extensions,
where available. Thus, the developer is able to implement real-time, high-throughput radio
systems in a simple-to-use, rapid-application-development environment. [17]
6.2.2 Using GNU Radio
Important thing to remember about GNU radio:
• GNU radio comes with a large variety of tools and programs which can be used out of
the box.
• Programming is very important while using GNU radio. All the flow graphs are written
in python.
• All signal processing in GNU Radio is done through flow graphs.
• A flow graph consists of blocks. A block does one signal processing operation, such as
filtering, adding signals, transforming, decoding, hardware access or many others.
• Data passes between blocks in various formats, complex or real integers, floats or
basically any kind of data type you can define.
• Every flow graph needs at least one sink and source.
We have another option to create a flow graph other than programming the entire thing.
The other alternative is GNU radio companion.
16
26. 6.2.3 GNU Radio Companion
GNU Radio Companion (GRC) is a graphical tool for creating signal flow graphs and gen-
erating flow-graph source code. It contains predefined blocks of various functions. The
properties of all the blocks can be set as per the requirements and dragged and drop to
create the flow graph. [17]
The typical opening screen of GNU Radio Companion is shown in Fig. 6.1 It shows 4
regions in total.
• The centre region is the work space where we make flow graphs.
• The bottom region is the message box. All kinds of messages like error messages,
success messages, etc. are displayed here.
• The right most region is the place where we can find all kinds of functional blocks.
Blocks like ’FFT Sink’, ’Random Signal Source’, etc. are present. We can either drag
the block to the centre region or double click to work on it.
• The top most region is the tool bar. All the actions to be done in GRC are present
here.
Figure 6.1: Opening Screen Of GNU Radio Companion
Some tips for using GRC:
• Add a block: double click on a block in the block selection window.
• Connect blocks: click on the port of one block, then click on the port of another block.
• Remove a connection: click on the connection, press delete, or drag the connection to
remove.
• Edit block parameters: double click on a block in the flow graph.
• Select a block, hit up/down for quick type change.
17
27. • For more short cuts, see the hot keys in the menu.
• Flow graphs that are completely simulation (without audio or usrp blocks) will consume
100
6.2.4 USRP
The current wireless communication system use high frequencies to communicate, down
converting must use to sample and transfer those high frequencies the SDR implementa-
tion. Universal Software Radio Peripheral (USRP) is such a family of hardware by computer
hosted.Simultaneously, a flexible and low-cost platform for SDR developed by matt Ettus
used to create the connection between the RF-world (radio frequency) and the PC. USRP
composed by USRP motherboard, along with a variety of daughter board and the corre-
sponding antenna. The individual blocks of a typical USRP product consists of two parts:
one motherboard with high- speed signal processing FPGA, and one or more daughter boards
which cover different frequency ranges and can be swapped. Combine them to achieve the
bit stream data from the antenna to the host computer as a receiver, or from the host com-
puter to the antenna as a transmitter. In a variety of daughter boards, USRP series covers
the entire range from DC to 5.95GHz, which include all frequencies from AM radio to over
IEEE 802.11 standard. The USRP is constructed out of the different components, which are
described in detailed below:
• USB 2.0 Controller
• ADC (Analog to Digital Converter)
• DAC (Digital to Analog Converter)
• PGA (Programmable Gain Amplifier)
• Daughter Boards
• FPGA (Field Programmable Gate Array) enditemize
Figure 6.2: NI-2921 USRP
18
28. The URSP (N2921) which we are using is a product of National Instruments of the
series N29XX. The range of this product lies in the ISM band. It can transmit and
receive over the range 2.4GHz - 2.5GHz and 4.5GHz - 5.9GHz. It has two Half-duplex
channels for communication through the antenna. The Fig. 6.2 is a snap of one of the
USRPs used in this project.
6.2.5 Interfacing GNU Radio and USRP
GNU Radio can be interfaced with the USRP to create a software defined radio system.
The USRP fulfils the RF and IF functions of an SDR, while GNU Radio performs
all baseband functions and reconfigures the USRP. GNU Radio controls the USRP
through the Universal Hardware Driver (UHD). The UHD provides a host driver and
an application programming interface (API) for the USRP. GNU Radio uses the UHD
to modify user-specified parameters such as RF centre frequency, antenna selection,
gain and interpolation or decimation parameters. These parameters are set by the
user in Python and the lower level C++ passes the data to the UHD API which then
translates the data for the USRP FPGA. With the USRP and GNU Radio one can
create a transmitter, a receiver or a transceiver system. A block diagram of a USRP-
based SDR transmitter built with a 9 GNU Radio flow graph is shown in Fig. 6.3, and
a receiver block diagram is shown in Fig. 6.4 GNU Radio also offers several graphical
sink blocks that can be used to visualize the data throughout the flow graph in the
time or frequency domains. The Command we use to create a virtual network in the
PC to get into the network of USRP NI-2921 having IP Address of 192.168.10.2 is :
sudo ifconfig eth0:1 192.168.10.50 255.255.255.0 up
Figure 6.3: Transmitter Architecture. Dashed Lines implemented in GNU Radio
Figure 6.4: Receiver Architecture. Dashed Lines implemented in GNU Radio
6.3 Network Simulator
6.3.1 Introduction
A network is a piece of software that predicts the behavior of a network, without an
actual network being present.NS or the network simulator (also popularly called the
19
29. network simulator, in reference to its current generation) is a discrete event network
simulator. It is popular in academia for its extensibility (due to its open source model)
and plentiful online documentation. NS is popularly used in simulation of routing and
multicast protocols, among others, and is heavily used in ad-hoc research. NS supports
an array of popular network protocols, offering simulation results for wired and wireless
networks alike. NS is a discrete event simulator targetted at networking research. NS
provides substantial support for simulation of TCP, routing, and multicast protocols
over wired and wireless (local and satellite) networks. [16]
6.3.2 Otcl Linkage
NS is an object oriented simulator, written in C++, with an Otcl interpreter as a
frontend. The simulator supports a class hierarchy in C++ (also called the compiled
hierarchy), and a similar class hierarchy within the Otcl interpreter (also called as
interpreter hierarchy). The root of this hierarchy is the class TclObject.NS meets both
of this need with two languages, C++ and Otcl. C++ is fast to run both slower to
change making it suitable for detail protol implementation. Otcl runs much slower but
can be changed very quickly making it ideal for simulation configuration. NS proviedes
glue to make objects and variables appear on both languages.
– Otcl is to be used:
∗ For configuration, setup and one-timer stuff
∗ If you can do what you want by manipulating existing C++ objects
– C++ to be used:
∗ If you are doing anything that requires processing each packet of a flow
∗ If you have to change the behavior of an existing C++ class in ways that
werent anticipated
20
30. Chapter 7
Algorithm for Security
CASE I
Consider N number of secondary users, comprising both honest and malicious
user with a fusion center(FC). Initially, on the call of fusion center each
secondary user will send their local decision value of sensing to the FC. Fusion
center assigns random reliability for each secondary user and will collect all
the local decision value and calculates the global decision, on the basis of
the local decision value of individual SU along with their reliability, which
is different at different time slot. This random reliability is also used as
IT(Information Tag) communicated with each users in encrypted form. Now,
each authorized secondary user will decrypt the identification tag and send
that decrypted tag with their next local decision. Here malicious user, which
is not authorized will not be able to decrypt properly and it will send wrong
decrypted tag with its local sensing information. This will help FC to detect
malicious user and it will discard that user for next chain of sensing. [2]
CASE II
Suppose, malicious user is somehow able to decrypt an identification tag, then
reliability of each is calculated and later depending upon its wrong informa-
tion reliability is reduced. Let Ui be the reliability of each secondary user
then after its each decision:
here Z and yi is 1 bit global and local decision. Hence, if global and local
decision is different than ex-or value will be 1 and reliability will decrease and
for same decisions with ex-or value 0, reliability will increase. Reliability of
each secondary user will start decreasing depending upon its local decision
with respect to global decision. We will keep one particular threshold, if value
of Ui becomes less than that, secondary user will be declared as a malicious,
and will be stopped from sending reports to FC. [11] Fig. 7.1 is the Flowchart
of our proposed Security Model.
21
32. Chapter 8
Implementation of the Idea
8.1 Creating Environment in GNU Radio
8.1.1 Installing GNU Radio
We are using Ubuntu 14.10 as the Operating system for our project. Ubuntu
is an open source operating system and GNU radio (version 3.7.2.1) is well
supported in it. Installing steps:
· Step 1: First we need to install all the dependencies which are needed to
compile gnuradio in ubuntu
sudo apt-get -y install git-core cmake g++ python-dev swig pkg-config
libfftw3-dev libboost1.55-all-dev libcppunit-dev libgsl0-dev libusb-dev libsdl1.2-
dev python-wxgtk2.8 python-numpy python-cheetah python-lxml doxy-
gen libxi-dev python-sip libqt4-opengl-dev libqwt-dev libfontconfig1-dev
libxrender-dev python-sip python-sip-dev
· Step 2: For gnuradio we need to install three packages using command
sudo apt-get install gnuradio gnuradio-dev gnuradio-doc
This will install all the packages required for running real time examples
in gnuradio using gnu radio companion.
8.1.2 Creating the Energy Detector block
Following are the steps required for creating an Energy Detector Block :
· STEP 1: Here, we will use an out-of-tree module called howto. The first
step is to create this module with gr-modtool
gr-modtool newmod howto
Creating out-of-tree module in ./gr-howto... Done.
Use ’gr-modtool add’ to add a new block to this currently empty module
· STEP 2: Jump straight into the gr-howto module and see what it’s made
up of.
gr-howto ls
apps cmake CMakeLists.txt docs examples grc include lib python swig
It consists of several subdirectories. Anything that will be written in
23
33. C++ (or C, or any language that is not Python) is put into lib. For
C++ files, we usually have headers which are put into include (if they
are to be exported) or also in lib (if they’re only relevant during compile
time, but are not installed later, such as impl.h files.Of course, Python
stuff goes into the python/ directory. This includes unit tests (which are
not installed) and parts of the Python module which are installed.GNU
Radio blocks are available in Python even if they were written in C++.
This is done by the help of SWIG, the simplified wrapper and interface
generator, which automatically creates glue code to make this possible.
SWIG needs some instructions on how to do this, which are put into the
swig/ subdirectory. Unless doing something extra clever with your block,
you will not need to go into the swig/ directory; gr-modtool handles all
of that for us. If we want our blocks to be available in the GNU radio
companion, the graphical UI for GNU Radio, you need to add XML
descriptions of the blocks and put them into grc. [17] For documentation,
docs/ contains some instructions on how to extract documentation from
the C++ files and Python files (we use Doxygen and Sphinx for this) and
also make sure they’re available as docstrings in Python. Of course, we
can add custom documentation here as well. The apps/ subdir contains
any complete applications (both for GRC and standalone executables)
which are installed to the system alongside with the blocks. The directory,
examples/ can be used to save examples, which are a great addendum to
documentation, because other developers can simply look straight at the
code to see how your blocks are used. The build system brings some
baggage along, as well: the CMakeLists.txt file (one of which is present
in every subdirectory) and the cmake/ folder. The CMakeLists.txt files
need to be edited a lot in order to make sure your module builds correctly.
· STEP 3: Creating our file howto-detect-ff and again, gr-modtool does the
job. On the command line, go to the gr-howto directory and enter
gr-modtool add -t general howto-detect-ff
GNU Radio module name identified: howto
Language: C++
Block/code identifier: howto-detect-ff
Enter valid argument list, including default arguments: float Pfa, int L,
int Samples
Add Python QA code? [Y/n]
Add C++ QA code? [Y/n]
Adding file ’howto-detect-ff-impl.h’...
Adding file ’howto-detect-ff-impl.cc’...
Adding file ’howto-detect-ff.h’...
Editing swig/howto-swig.i...
Editing python/CMakeLists.txt...
Adding file ’howto-howto-detect-ff.xml’...
Editing grc/CMakeLists.txt...
On the command line, we specify that we’re adding a block, its type is
’general’ (because we don’t know what block types are, yet) and it is
24
34. called howto-detect-ff. gr-modtool then asks you if your block takes any
arguments so we take Pfa, L and Samples as a arguments, whether or not
we want QA code for Python (no, we don’t right now) and for C++ (no,
we don’t right now) [17]
· STEP 4: Now we would make some changes in howto-detect-ff-impl.h and
howto-detect-ff-impl.cc files generated inside lib/ folder. howto-detect-
ff-impl.h file contain information related to header class of block while
howto-detect-ff-impl.cc contain actual logic behind the block. We will
also import static argument pfa, L and Samples in howto-swig.i file.
· STEP 5: Compilation:
mkdir build
cd build/
cmake ../
make
Now we have a new directory build/ in our module’s directory. All the
compiling etc. is done in here, so the actual source tree is not littered
with temporary files. If we change any CMakeLists.txt files, we should
re-run cmake ../ (although in truth, cmake detects these changes and
reruns automatically when you next run make). Next will be make test
command. When we run make test, we’re actually invoking the CMake
program ctest, which has a number of options we can pass to it for more
detailed information.
make test
Running tests...
Test project /home/braun/tmp/gr-howto/build
Start 1: test-howto
1/2 Test 1: test-howto ....................... Passed 0.01 sec
Start 2: qa-howto-detect-ff
2/2 Test 2: qa-howto-detect-ff ..................... Passed 0.38 sec
100 tests passed, 0 tests failed out of 2
Total Test time (real) = 0.39 sec
· STEP 6: Next is to make howto-detect-ff block available in GNU radio
companion. So once we are finished writing the block, gr-modtool has
a function to help you create the XML code for you. For the howto
example, you can invoke it on the howto-detect-ff block by calling
gr-modtool makexml howto-detect-ff
GNU Radio module name identified: howto
Warning: This is an experimental feature. Don’t expect any magic.
Searching for matching files in lib/:
Making GRC bindings for lib/ howto-detect-ff-impl.cc...
Overwrite existing GRC file? [y/N] y
Later, if we do a sudo make install and sudo ldconfig from the build
directory, we can use the block in GRC. If GRC is already running, you
can hit the ”Reload Blocks” button in the GRC toolbar; it’s a blue circular
25
35. arrow on the right-hand side. You should now see a ”HOWTO” category
in the block tree. [17]
8.2 Creating Environment in NS2
8.2.1 Installing NS2
Here are the steps to install NS2
· Step 1: Installation of Dependencies required for ns2:
sudo apt-get install autoconf
sudo apt-get install autoconf-doc
sudo apt-get install automake
sudo apt-get install g++
sudo apt-get install libx11-dev
sudo apt-get install libx11-doc
sudo apt-get install xorg-dev
sudo apt-get install xorg-docs
sudo apt-get install x11proto-core-dev
sudo apt-get install libxt-doc
sudo apt-get install libxt-dev
sudo apt-get install libxmu-dev
· Step 2: Installing NS2 in Ubuntu 14.04 Get ”ns-allinone-2.31.tar.gz” from
http://sourceforge.net/projects/nsnam/files/allinone/ns-allinone-2.31/ns-
allinone-2.31.tar.gz/download
then
tar -xvzf ns-allinone-2.31.tar.gz
cd ˜/ns-allinone-2.31/ns-2.31/linkstate
gedit ls.h
Now open the file named ”ls.h” and scroll to the 137th line. In that
change the word ”error” to ”this->error”
sudo cd /ns-allinone-2.31/./install
· Step 3: Edit bashfile
Edit bash file using following steps
$ sudo gedit /.bashrc
Add the following lines to the end of it:
# LD LIBRARY PATH
OTCL LIB=/opt/ns-allinone-2.31/otcl-1.13
NS2 LIB=/opt/ns-allinone-2.31/lib
X11 LIB=/usr/X11R6/lib
USR LOCALLIB = /usr/local/lib
exportLD LIBRARY PATH = $LD LIBRARY PATH : $OTCL LIB :
$NS2 LIB : $X11 LIB : $USR LOCAL LIB
#TCL LIBRARY
TCL LIB = /opt/ns − allinone − 2.31/tcl8.4.14/library
26
36. USR LIB = /usr/lib
exportTCL LIBRARY = $TCL LIB : $USR LIB
#PATH
XGRAPH = /opt/ns − allinone − 2.31/bin : /opt/ns − allinone −
2.31/tcl8.4.14/unix : /opt/ns−allinone−2.31/tk8.4.14/unix : /opt/ns−
allinone − 2.31/xgraph − 12.1/
NS = /opt/ns − allinone − 2.31/ns − 2.31/
NAM = /opt/ns − allinone − 2.31/nam − 1.13/
exportPATH = $PATH : $XGRAPH : $NS : $NAM
To make it run immediately:
source /.bashrc
Now, the installation has been completed. If we try:
$ ns
Then a % will appear on the screen
8.2.2 How to add a protocol in NS2
For the protocol to be added we require a systematic set of steps. This
algorithm is the best way to add a protocol to NS2. All the steps must be
performed in an orderly manner and not doing so will bring errors in the
procedure. The following are the steps followed in protocol addition to NS2.
It asumes that you have already created the .h and .cc file of the protocol
and only want to add it in the NS2 workspace for it to be used while writing
the TCL script. [16]
· Create a new header file Security packet.h for the protocol.
· Create Security packet.cc file which contains the required code to execute
the protocol.
· Add the protocol ID to the packet.h file of NS2. (PT Security packet)
· Edit enum packet t() and p info() files of packet.h. (name [PT Security packet]=
”Security packet”)
· Add the default value of the protocol to ns-default.tcl file.
· Add an entry for the new protocol packets in the file ns-packet.tcl.
· Add the file Security packet.o to the list of object files for NS in makefile.in
file.
· Recompile NS2 software
27
37. Chapter 9
Simulation and Result Analysis
9.1 GNU Radio Simulation
9.1.1 Working in an environment
In this section experimental activity in the real world is described. We created
one flowgraph for primary as well as for secondary user using GNU Radio
platform. Basically we performed eigenvalue based energy detetction at the
reciever side in order to check whether the signal at primary user’s channel is
efficiently detetcted. Benifits of eigenvaluebased method is, one can perform
detection with unknown source, unknown noise power and unknown channel.
9.1.2 Transmitter- Primary User
Fig. 9.1 shows flowgraph of primary user’s which is transmitting a signal at
frequency 2.45 Ghz. Initially we generate a random signal from source which
is then ofdm modulated and transmitted to UHD: USRP Sink. Sink block is
used to connect our software platform with outside environment. It will send
signal sampled at 1M to real time environment through USRP.
Figure 9.1: Flowgraph of the Transmitter
28
38. 9.1.3 Receiver-Secondary User
Fig. 9.2 shows flowgraph of secondary user which is recieving a signal at
frequency 2.45 Ghz. Initially we have UHD-USRP Source, which is used
to collect sampled signal from external environment through USRP. This
sampled signal is decimated and converted into float using Complex to float
block. We add some noise in our sampled signal which is used to determine
threshold signal at detect ff block.
Here, we have made detect ff block using gr-howto method. We have defined
three default paraments: float pfa, int L, int samples. Samples are limited
number of sampled signal allowed to recieve. L is length of given sampled sig-
nal. We will calculate ratio of maximum eigenvalue and minimum eigenvalue
and then compared this with threshold. This threshold is calculated using
Pfa i.e Probability of false alarm. Thus, detected value using detect ff block
is tranfered to WX GUI Number Sink which will show whether the primary
user signal at particular channel is detected or not.
Figure 9.2: Flowgraph of the Receiver
9.1.4 Simulation
Fig. 9.3 shows a graph of amplitude and a frequency of sampled signal which
is being transmitted by the Primary User.
At reciever side we can efficiently detect whether Primary user’s signal is
flowing through particular channel or not. If primary user is detected then
GUI Number sink will show value 1.0 and if not it will show 0.0
Fig. 9.4 specifies 1.000 units, which states that at 2.45 Ghz frequency primary
user is transmitting its signal, and its energy detection is done efficiently.
29
39. Figure 9.3: Transmitted Signal
Figure 9.4: Shows that Primary Signal is Detected
9.2 NS2 Simulation
9.2.1 Secure communication
After sucessfully detecting the primary signal, the security aspect of the nodes
should now be addressed. After adding the new security protocol in NS2, we
make it available in the TCL script by the command :
set p0 [new Agent/Security packet]
Here p0 will act as a Security Agent which we will attach to a node by : $ns
attach-agent $n0 $p0
We attach more agents to other nodes to make them all communicate se-
curely.The Security Protocol helps the nodes to communicate in a secure way
by using hashing and encryption. The hashvalue of the message to be send
is calculated and the message is further sent for encryption. We are using
Cesar encryption here to encrypt the message. It is assumed that all the
security agents know the encryption before hand. Thus only those nodes
can communicate securely who know the encryption key before hand. The
packet containing the hash value and the encrypted message is sent over to
the receiving node. At the receiver, the security agent decrypts the encrypted
message using the known key. It then calculates the hash value of the de-
crypted message and compares it with the original hashvalue which was sent
within the packet. If the calculated hashvalue matches with the original hash-
value , then the data integrity is ensured and we have securely communicated.
In the other case, the data is modified because of a malacious user.
30
40. Figure 9.5: Topology for security simulation
Figure 9.6: Security implemented
This scheme corresponds to the encryption we require to send the Identifica-
tion Tag from the Fusion Centre to the Secondary Users assuming that the
SC’s know the encryption key before hand. Since the malacious user wont
have the encryption key , it will be unable to decrypt the message from the
FC and so when he sends the local sensing data for the second time along
with the Identification Tag, the FC will compare it with the IT tag it had
sent previously. Since the comparison will always be false, the malacious user
will be caught and further data from that user will not be taken.
9.2.2 Multicasting in NS2
Since in our simulation we will be having a centralised network unlike an ad-
hoc network, so we will be using multicast protocols available in NS2.The one
31
41. which we are using is DM (Dense Mode). Fig. 9.7 shows the implementation of
DM protocol in NS2 .The Dense Mode protocol (DM.tcl) is an implementation
of a dense-mode-like protocol. Depending on the value of DM class variable
CacheMissMode it can run in one of two modes. If CacheMissMode is set to
pimdm (default), PIM-DM-like forwarding rules will be used. Alternatively,
CacheMissMode can be set to dvmrp (loosely based on DVMRP [31]). The
main difference between these two modes is that DVMRP maintains parent-
child relationships among nodes to reduce the number of links over which
data packets are broadcast. The implementation works on point-to-point
links as well as LANs and adapts to the network dynamics (links going up
and down). Any node that receives data for a particular group for which
it has no downstream receivers, send a prune upstream. A prune message
causes the upstream node to initiate prune state at that node. The prune
state prevents that node from sending data for that group downstream to the
node that sent the original prune message while the state is active. The time
duration for which a prune state is active is configured through the DM class
variable, PruneTimeout. A typical DM configuration is shown below:
DM set PruneTimeout 0.3
DM set CacheMissMode dvmrp
$ns mrtproto DM
Figure 9.7: DM protocol for Multicast
32
42. 9.2.3 Secured Multicast in NS2
Now in mulcasting, we changed node1 configuration by adding a new Security
Agent i.e Security packet m to it. Due to this security agent, Node1 doesnt
have the same key to decrypt an identification tag with reference to sender
node i.e Fusion center, while the other nodes have same key. Hence this
configuartion helped us to detect unauthorized malacious user. Fig. 9.8 shows
that node 1 with different security agent modifies the data while other honest
nodes maintain the data integrity.
Figure 9.8: Malacious Node Detected in Multicast
33
43. Chapter 10
Conclusion and Future Scope
We see that allowing unlicensed user to use the empty spectrum has created a
new wave in the developments towards the efficient usage of spectrum. This
increases the importance of the techniques used for spectrum sensing and
we have created a block called detector block which we are using to sense
the spectrum and take corresponing decision based on the avalaibility of the
free specrum. We have used eigen value based energy detection method for
spectrum sensing since it requires least information to take decisions i.e. it
requires only the noise to detect the presence of primary user in the spectrum.
The only limitation in this part was that we have worked using only 2 nodes,
one primary user and one secondary user. The future enhancements in this
can be done by increasing the number of nodes participating in the spectrum
sensing i.e. by implementing collabarative sensing, where many secondary
users’ information is clubbed together for better and reliable decision.
Further, there is a demand of a secure communication free from any ma-
licious activity. This is possible if we make the communication safe using
cryptography. Here we have used a simple a Cesar’s algorithm to encrypt the
messages. This has been done in an multicast topology. We see that all the
reliable and legal users can decrypt the encrypted signals and send back ac-
knowledment. The CASE II of the stated algorithm can provide much better
security,if implemented. Further enhancement on the implementation can be
to use a better algorithm for encryption of signals.
In conclusion, Cognitive Radio is an evolving technology and we can expect
more advanments in security of the same.
34
44. Bibliography
[1] Tevk Yucek and Huseyin Arslan, ‘A Survey of Spectrum Sensing Algo-
rithms for Cognitive Radio Applications ,’ , IEEE COMMUNICATIONS
SURVEYS TUTORIALS, VOL.11, NO. 1, FIRST QUARTER 2009.
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