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 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


Introduction

Introduction


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)


Da hierarchy

DA Hierarchy


Udsa scheduling

UDSA Scheduling


Pdsa scheduling

PDSA Scheduling


Experimental results

Experimental Results


Experimental results1

Experimental Results

  • UDSA


Experimental results2

Experimental Results

  • UDSA


Experimental results3

Experimental Results

  • PDSA


Conclusion future work

Conclusion & Future Work


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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