Title: Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks (CRAHNs) : A Multi-Layer Approach
1Spectrum 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
2Literature 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
3Multi-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
4Results
- 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
5Future Work
- Lateral Application Areas
- Cloud Networking Smart Grids
6Open Issues
- Provision of Common Control Channel
- Integration of all the layers
- Security Related Issues
- Byzantine attacks
- Primary User Emulation
- Attacks
- Trustworthiness/
- Authentication
7Back up slides
8- Cognitive Radios Secondary Users (SUs)
- Dynamic Spectrum Access ?
- Spectrum Sensing ? Local Collaborative
- Spectrum Allocation
- Spectrum Mobility
9Application 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
10Two 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
11Adaptive Threshold based on Group Decisions
Adaptive Threshold
Confidence
12Estimation of optimal number of CRs required for
sensing for targeted accuracy
Group SNR-gt Pd_av, Pf_av-gt K
13Game 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
14Adaptive Proactive Implementation Model
Centralized Architecture
Utility Function
15Decentralized Architecture
16Papers 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) -