Mingyuan yan shouling ji and zhipeng cai presented by mingyuan yan
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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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Mingyuan Yan, ShoulingJi, and ZhipengCai

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

  • Conclusion & future work


Introduction


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


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


System Model and Problem Formulation


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


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


Definitions

  • Logical link

  • SU-PU collision

  • SU-SU collision


Problem formalization

  • Minimum Latency Data Aggregation Scheduling (MLDAS)


Scheduling under the UDG/PHIM Model


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)


DA Hierarchy


UDSA Scheduling


PDSA Scheduling


Experimental Results


Experimental Results

  • UDSA


Experimental Results

  • UDSA


Experimental Results

  • PDSA


Conclusion & Future Work


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


Mingyuan Yan, ShoulingJi, and ZhipengCai

Presented by: Mingyuan Yan

Time efficient Data aggregation scheduling in cognitive radio networks


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