1. Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks (CRAHNs) : A Multi-Layer Approach Sasirekha GVK,,Supervisor: Prof. Jyotsna Bapat, IIIT Bangalore 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 SNR Threshold Sensing Mechanism Local decisions, accuracy, Fusion Rule Number Of Sensing SUs Sensing time Frequency of sensing PHYLINK Global decisions, accuracy, Performance

2. 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

3. Multi-Layer Framework Focus of the research Confidence Link Layer Blind/ Semi-blind Spectrum Sensing Averaging And Final Decision Logic Decision Rx_Signal Threshold Data Fusion with opt. K Estimator Soft/Hard Decision from other users Cognitive Radio Receiver Front End Physical Layer Adaptive Thresholding Group Decision Sensing Scheduler 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

4. 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

5. Future Work Lateral Application Areas Cloud Networking Smart Grids

6. Open Issues Cognitive Radio Ad hoc Network Time synchronization Optimized Link State Routing Co-operative Spectrum Sensing Common Control Channel Spectrum Allocation Security Provision of Common Control Channel Integration of all the layers Security Related Issues Byzantine attacks Primary User Emulation Attacks Trustworthiness/ Authentication

7. Back up slides

8. SU Coordinator Centralized Architecture SU Distributed Architecture Cognitive Radios : Secondary Users (SUs) Dynamic Spectrum Access  Spectrum Sensing  Local & Collaborative Spectrum Allocation Spectrum Mobility

9. Application Scenarios PU [f 1 f 2 ] [f 3 f 4 f 5 f 6 ] [f r-2 f r-1 ] [fr][fr] Mobile CRAHN Scenario model PU Military Networks Disaster Management Features: Nomadic Mobility Group Signal to Noise Ratio Collaborative Spectrum Sensing

10. PHY LINK Performance Metrics SNR Threshold Sensing Mechanism Channel Model Local decisions, P di, P fi Fusion Rule Number Of Sensing SUs Risk From i th SU From other (K-1) SUs PU Usage pattern Level 1 Optimization Level 2 Optimization Sensing time Frequency of sensing Q dk Q fk IkIk Two levels of optimization

11. Confidence Adaptive Threshold Adaptive Threshold based on Group Decisions

12. Group SNR-> Pd_av, Pf_av-> K Estimation of optimal number of CRs required for sensing for targeted accuracy

13. Behavioral Model Interaction between autonomous CRs modeled using game theory Policies Frequencies to sense Who should be the coordinator? Authenticate the entry into network Implementation (Protocols) Adaptive System Design Levels Of Abstraction Ref: http: //www.ir.bbn.com/~ramanath/pdf/rfc-vision.pdf Approaches of Analysis (Our Contributions) Iterative Game (pot luck party) ---- Penalty Evolutionary Game based on Replicator Dynamics --- Reward Public Good Game ---Reward How many should sense? ---- K Who should sense? Assuming proactive spectrum sensing in the period quiet period Game theoretical modeling

14. Adaptive Proactive Implementation Model: Centralized Architecture Utility Function

15. Decentralized Architecture

16. 1.Sasirekha GVK, Jyotsna Bapat, “ Adaptive Model based on Proactive Spectrum Sensing for Emergency Cognitive Ad hoc Networks”, CROWNCOM 2012, Stockholm, Sweden 2.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 3.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 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 (under review) 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) Papers Published on Research Topic