TIME EFFICIENT DATA AGGREGATION SCHEDULING

IN COGNITIVE RADIO NETWORKS

Mingyuan Yan, Shouling Ji, and Zhipeng CaiPresented by: Mingyuan Yan

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

TIME EFFICIENT DATA AGGREGATION SCHEDULING

IN COGNITIVE RADIO NETWORKS

Mingyuan Yan, Shouling Ji, and Zhipeng CaiPresented by: Mingyuan Yan