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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Mingyuan yan shouling ji and zhipeng cai presented by mingyuan yan

Mingyuan Yan, ShoulingJi, and ZhipengCai

Presented by: Mingyuan Yan

Time efficient Data aggregation scheduling in cognitive radio networks


Outline
Outline

  • Introduction

  • System model and problem formulation

  • Scheduling under the UDG/ PHIM model

  • Experimental Results

  • Conclusion & future work



Motivation
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
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
System Model and Problem Formulation


Network model
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 model1
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
Definitions

  • Logical link

  • SU-PU collision

  • SU-SU collision


Problem formalization
Problem formalization

  • Minimum Latency Data Aggregation Scheduling (MLDAS)


Scheduling under the udg phim model
Scheduling under the UDG/PHIM Model


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)










Conclusion future work1
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 shouling ji and zhipeng cai presented by mingyuan yan1

Mingyuan Yan, ShoulingJi, and ZhipengCai

Presented by: Mingyuan Yan

Time efficient Data aggregation scheduling in cognitive radio networks


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