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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

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

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

  2. Literature survey Our proposal proactive, dynamic, LRT based (better immunity against Byzantine attacks) meeting sensing requirements for emergency networks

  3. 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

  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. Common Control Channel Security Spectrum Allocation Co-operative Spectrum Sensing Optimized Link State Routing Time synchronization Cognitive Radio Ad hoc Network Open Issues • 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 SU SU Coordinator SU 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

  9. 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

  10. Two levels of optimization PU Usage pattern Frequency of sensing From other (K-1) SUs Sensing time Number Of Sensing SUs Fusion Rule Risk SNR Channel Model Qdk Local decisions, Pdi , Pfi From ith SU Sensing Mechanism Qfk Ik Threshold Level 2 Optimization Level 1 Optimization PHY LINK Performance Metrics

  11. Adaptive Threshold based on Group Decisions Adaptive Threshold Confidence

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

  13. 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

  14. Adaptive Proactive Implementation Model: Centralized Architecture UtilityFunction

  15. Decentralized Architecture

  16. 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?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 • 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)

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