mingyuan yan, shouling ji, and zhipeng cai presented by: mingyuan yan
TRANSCRIPT
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TIME EFFICIENT DATA AGGREGATION SCHEDULING
IN COGNITIVE RADIO NETWORKS
Mingyuan Yan, Shouling Ji, and Zhipeng CaiPresented by: Mingyuan Yan
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Outline
Introduction System model and problem formulation Scheduling under the UDG/ PHIM model Experimental Results Conclusion & future work
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Introduction
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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
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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
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System Model and Problem Formulation
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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
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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
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Definitions
Logical link
SU-PU collision
SU-SU collision
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Problem formalization
Minimum Latency Data Aggregation Scheduling (MLDAS)
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Scheduling under the UDG/PHIM Model
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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)
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DA Hierarchy
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UDSA Scheduling
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PDSA Scheduling
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Experimental Results
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Experimental Results UDSA
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Experimental Results UDSA
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Experimental Results PDSA
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Conclusion & Future Work
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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
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TIME EFFICIENT DATA AGGREGATION SCHEDULING
IN COGNITIVE RADIO NETWORKS
Mingyuan Yan, Shouling Ji, and Zhipeng CaiPresented by: Mingyuan Yan