Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks (CRAHNs) : A Multi-Layer Approach

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Spectrum Sensing in Emergency Cognitive Radio Ad
Hoc Networks (CRAHNs) A Multi-Layer Approach
Sensing time

• Requirements of Emergency CRAHNs
• Accuracy
• Resource efficiency
• Low latency in the delivery of packets,
• Adaptive to varying number of SUs,
• Adaptive to varying SNR conditions,
• Uniform battery consumption
• Resilience to Byzantine attacks

Frequency of sensing
Fusion Rule
Sensing Mechanism
Local decisions, accuracy ,
Number Of Sensing SUs
Threshold
Global decisions, accuracy ,
SNR
PHY
LINK

• Sasirekha GVK,
• ,Supervisor Prof. Jyotsna Bapat, IIIT Bangalore

Performance
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Literature survey
Collaborative spectrum sensing 1. Amir Ghasemi and Elvino S. Sousa, 2. Wei Zhang, Rajan K. Mallik, Khaled Ben Letaief 3.Clancy 4. L. Chen, J. Wang, S. Li, 5. Yunfei Chen Static/Reactive methods using OR based fusion, Civilian Networks Considering only some parameters for optimization
Cognitive Radio Ad hoc Networks Ian F. Akyildiz, Won-Yeol Lee, Kaushik R. Chowdhury, Protocol stack, routing, transport and high level architecture
Emergency Networks Adaptive Ad-hoc Free Band Wireless Communications Requirements in general
IEEE Standards IEEE 802.22 (Shell Hammer) Regional Area Networks in TV band
Our proposal proactive, dynamic, LRT based
(better immunity against Byzantine attacks)
meeting sensing requirements for emergency
networks
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Multi-Layer Framework
Averaging And Final Decision Logic
Decision
Blind/ Semi-blind Spectrum Sensing
Cognitive Radio Receiver Front End
Confidence
Rx_Signal
Sensing Scheduler
Threshold
Adaptive Thresholding
Data Fusion with opt. K Estimator
Group Decision
Physical Layer
Focus of the research
Soft/Hard Decision from other users
Link Layer

• 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

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Results

• 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.

Sample Results on the Estimation of minimal no.
of CRS and Adaptation of CRs
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Future Work

• Lateral Application Areas
• Cloud Networking Smart Grids

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Open Issues

• Provision of Common Control Channel
• Integration of all the layers
• Security Related Issues
• Byzantine attacks
• Primary User Emulation
• Attacks
• Trustworthiness/
• Authentication

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Back up slides
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• Cognitive Radios Secondary Users (SUs)
• Dynamic Spectrum Access ?
• Spectrum Sensing ? Local Collaborative
• Spectrum Allocation
• Spectrum Mobility

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Application Scenarios

• Military Networks
• Disaster Management

• Features
• Nomadic Mobility
• Group Signal to Noise Ratio
• Collaborative Spectrum Sensing

PU
PU
PU
fr-2 fr-1
f3 f4 f5 f6
f1 f2

fr

PU
Scenario model
Mobile CRAHN
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Two levels of optimization
PU Usage pattern
Frequency of sensing
From other (K-1) SUs
Sensing time
Number Of Sensing SUs
Fusion Rule
SNR
Risk
Channel Model
Qdk
Local decisions, Pdi , Pfi
From ith SU
Sensing Mechanism
Qfk
Ik
Threshold
Level 1 Optimization
Level 2 Optimization
PHY
LINK
Performance Metrics
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Adaptive Threshold based on Group Decisions
Adaptive Threshold
Confidence
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Estimation of optimal number of CRs required for
sensing for targeted accuracy
Group SNR-gt Pd_av, Pf_av-gt K
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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-visi
on.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

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Adaptive Proactive Implementation Model
Centralized Architecture

Utility Function
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Decentralized Architecture
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Papers Published on Research Topic

• 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?arnumber5702906
• 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 doigt10.1145/2093256.2093287
• 4. 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)
• 5. Sasirekha GVK ,George Mathew Tharakan,
  Jyotsna Bapat, Energy Control Game Model for
  Dynamic Spectrum Scanning, IJAACS, Inderscience,
  2012, DOI 10.1504/IJAACS.2012.046280
• 6. 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/
• 7. Rajagopal Sreenivasan, Sasirekha GVK and
  Jyotsna Bapat, Adaptive Threshold based on
  Group Decisions for
• Distributed Spectrum Sensing in Cognitive
  Adhoc Networks, Wimone 2010
• 8. Rajagopal Sreenivasan, Sasirekha GVK and
  Jyotsna Bapat, Adaptive Threshold based on
  Group intelligence, International
• Journal of Computer Networks and
  Communications , AIRCC,May 2011
• 9. 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)