Mingyuan Yan, Shouling Ji, and Zhipeng Cai Presented by: Mingyuan Yan

 Introduction  System model and problem formulation  Scheduling under the UDG/ PHIM model  Experimental Results  Conclusion & future work

Introduction

 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

 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

 Primary network N randomly deployed Pus, P 1, P 2,..., P N K orthogonal parallel licensed spectrums –{C 1, C 2, …, C K } Transmission radius R Interference radius R I PU is either active or inactive in a time slot test

 Secondary network Dense with n randomly deployed Pus, S 1, S 2,..., S N Base station S b Each SU is equipped with a single, half-duplex cognitive radio Transmission radius r Interference radius r I Channel accessing probability test

 Logical link  SU-PU collision  SU-SU collision

 Minimum Latency Data Aggregation Scheduling (MLDAS)

Scheduling under the 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 = R I and r = r I.  Physical Interference Model (PhIM) with Signal to Interference Ratio (SIR)

Experimental Results

 UDSA

 PDSA

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 Yan