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Time efficient Data aggregation scheduling in cognitive radio networks

Mingyuan Yan, Shouling Ji , and Zhipeng Cai Presented by: Mingyuan Yan. Time efficient Data aggregation scheduling in cognitive radio networks. Outline. Introduction System model and problem formulation Scheduling under the UDG/ PHIM model Experimental Results

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Time efficient Data aggregation scheduling in cognitive radio networks

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  1. Mingyuan Yan, ShoulingJi, and ZhipengCai Presented by: Mingyuan Yan Time efficient Data aggregation scheduling in cognitive radio networks

  2. Outline • Introduction • System model and problem formulation • Scheduling under the UDG/ PHIM model • Experimental Results • Conclusion & future work

  3. Introduction

  4. Motivation • CRNs • a promising solution to alleviate the spectrum shortage and under-utilization problem • Unicast, broadcast, multicast have been investigated, no data aggregation • Data aggregation • An effective strategy for saving energy and reducing medium access contention • Widely investigated in wireless networks • Has a broad potential in CRNs • Existing works can not be intuitively applied to CRNs • Links are not symmetric • Interference is more complicated

  5. Contributions • Data aggregation scheduling in CRNs with minimum delay • Formalize the problem • Scheduling under UDG interference model • Scheduling under PHIM interference model • Performance evaluation based on simulations

  6. System Model and Problem Formulation

  7. Network model • Primary network • N randomly deployed Pus, P1 , P2 , ..., PN • K orthogonal parallel licensed spectrums –{C1, C2, …, CK} • Transmission radius R • Interference radius RI • PU is either active or inactive in a time slot • test

  8. Network model • Secondary network • Dense with n randomly deployed Pus, S1 , S2 , ..., SN • Base station Sb • Each SU is equipped with a single, half-duplex cognitive radio • Transmission radius r • Interference radius rI • Channel accessing probability • test

  9. Definitions • Logical link • SU-PU collision • SU-SU collision

  10. Problem formalization • Minimum Latency Data Aggregation Scheduling (MLDAS)

  11. Scheduling under the UDG/PHIM Model

  12. UDG/PHIM Model • UDG Interference Model • Under this model, the interference range and transmission range of wireless devices are denoted by equally likely disks. That is, R = RI and r = rI . • Physical Interference Model (PhIM) with Signal to Interference Ratio (SIR)

  13. DA Hierarchy

  14. UDSA Scheduling

  15. PDSA Scheduling

  16. Experimental Results

  17. Experimental Results • UDSA

  18. Experimental Results • UDSA

  19. Experimental Results • PDSA

  20. Conclusion & Future Work

  21. Conclusion & Future Work • Conclusion • we investigate the minimum latency data aggregation problem in CRNs • Two distributed algorithms under the Unit Disk Graph interference model and the Physical Interference Model are proposed, respectively • Future work • solution with theoretical performance guarantee • improving the performance of data gathering in conventional wireless networks with cognitive radio capability

  22. Mingyuan Yan, ShoulingJi, and ZhipengCai Presented by: Mingyuan Yan Time efficient Data aggregation scheduling in cognitive radio networks

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