Spectrum Sensing in Emergency Cognitive Radio Ad
Hoc Networks (CRAHNs) : A Multi-Layer Approach
Requirements of Emergency CRAHNs:
•Accuracy
•Resource
•Low
Frequency
of sensing
Fusion
Rule
efficiency
latency in the delivery of packets,
•Adaptive
to varying number of SUs,
•Adaptive
to varying SNR conditions,
•Uniform
Sensing
time
battery consumption
•Resilience
Sensing
Mechanism
Local
decisions,
accuracy
Number
Of
Sensing
SUs
,
Threshold
to Byzantine attacks
SNR
Global
decisions,
accuracy
,
PHY
Sasirekha GVK,
,Supervisor: Prof. Jyotsna Bapat, IIIT Bangalore
LINK
Performance
Literature survey
Collaborative spectrum
sensing
1. Amir Ghasemi and Elvino S. Sousa,
Cognitive Radio Ad hoc
Networks
Ian F. Akyildiz, Won-Yeol Lee, Kaushik R. Chowdhury,
Emergency Networks
IEEE Standards
2. Wei Zhang, Rajan K. Mallik, Khaled Ben Letaief
3.Clancy
4. L. Chen, J. Wang, S. Li,
5. Yunfei Chen
Adaptive Ad-hoc Free Band Wireless Communications
IEEE 802.22 (Shell Hammer)
Static/Reactive
methods using ‘OR’
based fusion,
Civilian Networks
Considering only
some parameters
for optimization
Protocol stack,
routing, transport
and high level
architecture
Requirements in
general
Regional Area
Networks in TV
band
Our proposal proactive, dynamic, LRT based (better immunity against Byzantine
attacks) meeting sensing requirements for emergency networks
Multi-Layer Framework
Cognitive Radio
Receiver
Front End
Blind/
Semi-blind
Spectrum
Sensing
Rx_Signal
Threshold
Adaptive
Thresholding
Physical Layer
Group Decision
Averaging
And
Final
Decision
Logic
Decision
Confidence
Sensing
Scheduler
Data Fusion
with opt. K
Estimator
Focus of the research
Link Layer
Soft/Hard
Decision
from other users
Being a Multi-Layer Multi-Parameter optimization problem tackled as 2 levels
•Level 1: Local Optimization: Spectrum sensing method, time, frequency
•Level 2: Global Optimization: Data Fusion, Optimal number of Sensing CRs
•Cross Layer: Adaptation of local sensing threshold based on Global Decisions
Results
•
Qd actual versus Qd desired for various sensitivites
Variance of energy spent,Payoff Qd, probability of sense of an SU
with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000
1.05
1
0.9
Normalized value of variance / probability
•
Estimation of smallest number of sensing CRs for a targeted accuracy.
Algorithm for adapting the number of sensing SUs in changing
environments; i.e. network size and SNR. Proposed for centralized and
distributed spectrum sensing.
Algorithm for adapting threshold for local energy detection based on global
group decisions.
Application of evolutionary game theory for behavioral modeling of the
network.
1
Qd actual
•
•
0.95
reference
-3%
+3%
(Pd,Pf)=0.4,0.1
(Pd,Pf)=0.5,0.15
(Pd,Pf)=0.6,0.25
(Pd,Pf)=0.76,0.4
(Pd,Pf)=0.85,0.5
0.9
0.85
0.9
0.91
0.92
0.93
0.94
0.95
0.96
Qd desired
0.97
0.98
0.99
Normalized variance of energy spent across SUs
Probability of detect of fused data
Probability of sense of an SU
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
0
0.2
0.4
0.6
0.8
1
1.2
iterations
1.4
1.6
1.8
2
4
x 10
Sample Results on the Estimation of minimal no. of CRS and Adaptation of CRs
Future Work
Lateral Application Areas
Cloud Networking
Smart Grids
Open Issues
Spectrum Allocation
Co-operative Spectrum Sensing
Optimized Link State Routing
Time synchronization
Cognitive Radio Ad hoc
Network
Common Control Channel
Security
•Provision of Common Control
Channel
•Integration of all the layers
•Security Related Issues
•Byzantine attacks
•Primary User Emulation
Attacks
•Trustworthiness/
Authentication
Back up slides
SU
SU
SU
SU
Coordinator
SU
SU
SU
SU
SU
Centralized Architecture
Distributed Architecture
Cognitive Radios : Secondary Users (SUs)
Dynamic Spectrum Access 
•Spectrum Sensing  Local & Collaborative
•Spectrum Allocation
•Spectrum Mobility
Application Scenarios
•Military Networks
•Disaster Management
Features:
• Nomadic Mobility
• Group Signal to Noise Ratio
• Collaborative Spectrum Sen
PU
PU
PU
[fr-2 fr-1]
[f1 f2]
[f3 f4 f5 f6]
[fr]
PU
Scenario
Mobile CRAHN
Two levels of optimization
Frequency
of sensing
Sensing
time
SNR
Channel
Model
Sensing
Mechanism
PU
Usage
From other (K-1) SUspattern
Number
Of
Fusion
Sensing
Rule
SUs
Local
decisions, From ith SU
Pdi
, Pfi
Threshold Level 1 Optimization
Level 2 Optimization
PHY
LINK
Risk
Qdk
Qfk
Ik
Performance Metrics
Rk  C F Q fk  CDQdk  C
I k  1  Rk
J k  αI k   1  α  ηk
0  α  1,ηk 
N k
N
Adaptive Threshold based on Group
Decisions Adaptive Threshold
Confidence
zt  f Yt  λt  
1
1 e
- β(Yt  λt )
λt 1
 
E et2
 λt  μ
λt
λt 1  λt  2 μet z t ( 1  z t )
Estimation of optimal number of CRs required
for sensing for targeted accuracy
Group SNR-> Pd_av, Pf_av-> K

K  min k  Qd  Qd _desired
~
~
Qd  f ( k , Pd , P f )
Qd actual versus Qd desired for various sensitivites
1.05
Qd actual
1
0.95
reference
-3%
+3%
(Pd,Pf)=0.4,0.1
(Pd,Pf)=0.5,0.15
(Pd,Pf)=0.6,0.25
(Pd,Pf)=0.76,0.4
(Pd,Pf)=0.85,0.5
0.9
0.85
0.9
0.91
0.92
0.93
0.94
0.95
0.96
Qd desired
0.97
0.98
0.99
1

Game theoretical modeling
Policies
Frequencies to sense
Who should be the coordinator?
Authenticate the entry into network
• How many should sense? ---- K
• Who should sense?
• Assuming proactive spectrum sensing
in the period quiet period
Behavioral Model
Interaction between autonomous CRs modeled
using game theory
Implementation (Protocols)
Adaptive System Design
Ref: http: //www.ir.bbn.com/~ramanath/pdf/rfc-vision.pdf
Levels Of Abstraction
Approaches of Analysis (Our Contributions)
• Iterative Game (pot luck party) ---- Penalty
• Evolutionary Game based on
Replicator Dynamics --- Reward
• Public Good Game ---Reward
Adaptive Proactive Implementation
Model: Centralized Architecture
Variance of energy spent,Payoff Qd, probability of sense of an SU
with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000
1
Normalized value of variance / probability
0.9
Normalized variance of energy spent across SUs
Probability of detect of fused data
Probability of sense of an SU
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.8
1
1.2
iterations
1.4
1.6
1.8
2
4
x 10
Utility Function

J Ps _ av  α I Ps _ av  1  α  1  Ps _ av

Decentralized Architecture
Computational Complexity Vs. N
5
10
Classical Iterative Algorithm
Proposed Algorithm
4
No. of Multiplications
10
3
10
2
10
1
10
0
10
0
10
20
30
K  Max( k )  J ( k )
J  (1   )I  
K'  Min( k )  J  ε
1 

C
J 
whereε is a constant 1
2
40
N
50
60
70


 DQd  CF Q f   N
80
Papers Published on Research Topic
1.
2.
3.
4.
5.
6.
7.
8.
9.
Sasirekha GVK, Jyotsna Bapat, “ Adaptive Model based on Proactive Spectrum Sensing for Emergency Cognitive Ad
hoc Networks”, CROWNCOM 2012, Stockholm, Sweden
Sasirekha GVK, Jyotsna Bapat , “Optimal Number of Sensors in Energy Efficient Distributed Spectrum Sensing”,
CogART 2010. 3rd International Workshop on Cognitive Radio and Advanced Spectrum Management. In conjunction
with ISABEL 2010. November 08-10, 2010, ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5702906
Sasirekha GVK, Jyotsna Bapat, “Optimal Spectrum Sensing in Cognitive Adhoc Networks: A Multi-Layer Frame Work”,
CogART 2011 Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
Article No. 31, ACM, ISBN: 978-1-4503-0912-7 doi>10.1145/2093256.2093287
Sasirekha GVK and Jyotsna Bapat, “Evolutionary Game Theory based Collaborative Sensing Model in Emergency
CRAHNs," Journal of Electrical and Computer Engineering, Hindawi Publishing Corporation, Special issue "Advances
in Cognitive Radio Ad Hoc Networks“, (accepted)
Sasirekha GVK ,George Mathew Tharakan, Jyotsna Bapat, “Energy Control Game Model for Dynamic Spectrum
Scanning”, IJAACS, Inderscience, 2012, DOI: 10.1504/IJAACS.2012.046280
Sasirekha GVK, Jyotsna Bapat, “Cognitive Radios: A Technology for 4G Mobile Terminals”, Third Innovative
Conference on Embedded Systems, Mobile Communication and Computing, 11th- 14th August, 2008, Infosys, Mysore,
India, http://www.pes.edu/mcnc/icemc2/
Rajagopal Sreenivasan, Sasirekha GVK and Jyotsna Bapat, “Adaptive Threshold based on Group Decisions for
Distributed Spectrum Sensing in Cognitive Adhoc Networks”, Wimone 2010
Rajagopal Sreenivasan, Sasirekha GVK and Jyotsna Bapat, “Adaptive Threshold based on Group intelligence”,
International
Journal of Computer Networks and Communications , AIRCC,May 2011
Sasirekha GVK, Jyotsna Bapat IGI-CRN Book Chapter # 4: “Spectrum Sensing in Emergency Cognitive Radio Ad Hoc
Networks”, Cognitive Radio Technology Applications for Wireless and Mobile Ad hoc Networks. IGI Global (under
review)