2. 3 Introduction 1 2 4 System Model Distributed Data Collection Simulation and Analysis 5 Conclusion 2

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4.  Cognitive Radio Networks (CRNs)  The utilization of spectrum assigned to licensed users varies from 15% to 85% temporally and geographically (FCC report)  Unlicensed users (Secondary Users, SUs) can sense and learn the communication environment, and opportunistically access the spectrum without causing any unacceptable interference to licensed users (Primary Users, PUs) 4

5.  Why Distributed Algorithms?  CRNs tend to be large-scale distributed systems  CRNs are dynamic Systems  Spectrum opportunities are dynamic with respect to time and space  Challenges  How to guarantee secondary network activities do not hurt primary network activities?  How to make decision based on only local information?  How to overcome problems induced by lack of time synchronization?  How to theoretically analyze the performance of distributed algorithms? 5

6.  Contributions  Derive a Proper Carrier-sensing Range (PCR) under the physical interference model for Secondary Users (SUs)  Propose an order-optimal Asynchronous Distributed Data Collection (ADDC) algorithm  Simulations are conducted to validate ADDC 6

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8.  Primary Network  N independent and identically distributed ( i.i.d. ) PUs  Locally finite property  Working power  Network time is slotted with slot length  During each time slot, each PU transmits data with probability 8

9.  Secondary Network  n SUs and one base station  Maximum transmission radius of SUs is r  The secondary network can be represented by graph  Conditions on communication between two SUs 9

10.  Data Collection  At a particular time slot t, every SU produces a data packet of size B  The set of all the n data packets produced by SUs at time t is called a snapshot  The task of gathering all the n data packets of a snapshot to the base station without any data aggregation is called a data collection task  The data collection delay is the time consumption to finish a data collection task  The data collection capacity is the average data receiving rate at the base station during a data collection process 10

11.  Interference Model  Physical interference model  For PUs  For SUs 11

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13.  Data Collection Tree  Proper Carrier-sensing Range (PCR)  Data Collection Algorithm  Performance Analysis 13

14.  Connected Dominating Set (CDS) based Data Collection Tree 14

15.  Objectives  The secondary network does not cause unacceptable interference to the activities of the primary network  All the SUs transmitting data simultaneously are interference-free  The carrier-sensing range is as small as possible, which implies SUs can obtain more spectrum opportunities 15

16.  Concurrent Set: a set of active nodes s.t. all the nodes in this set can conduct data transmission simultaneously.  :  Proper Carrier-sensing Range (PCR): the carrier-sensing range R is a PCR if for any R- set, it is a concurrent set. 16 sisi

17.  How to decide the proper carrier-sensing range (PCR)?  In a R-Set, to guarantee SUs will not cause unacceptable interference to PUs, it is sufficient to have (Lemma 2)  In a R-Set, to guarantee SUs can transmit data simultaneously and interference-freely, it is sufficient to have (Lemma 3)  We can set the PCR, where 17

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19.  Asynchronous Distributed Data Collection (ADDC) algorithm 19

20.  The number of dominators and connectors within the PCR of an SU is upper bounded by, where is a function on x with (Lemma 5)  The number of SUs within the PCR of an SU is upper bounded by, and with probability 1.(Lemma 6)  The expected time for an SU to obtain a spectrum opportunity is where. (Lemma 7)  Any SU having data for transmission can transmit at least one data packet to its parent within time. (Theorem 1) 20

21.  The delay induced delay by the proposed Asynchronous Distributed Data Collection (ADDA) algorithm is upper bounded by This implies the achievable data collection capacity of ADDC is which is order-optimal. (Theorem 2) 21

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23.  Network setting  An i.i.d. primary network  An i.i.d secondary network  Please refer to the paper for detailed settings  Compared algorithm  Coolest (ICDCS 2011): the path with the most balanced and/or the lowest spectrum utilization by PUs is preferred for data transmission 23

24.  Data Collection Delay vs. Network Size ( n and N ) 24

25.  Data Collection Delay vs. and 25

26.  Data Collection Delay vs. Transmission Power 26

27.  We study the distributed data collection problem in CRNs  We propose an Asynchronous Distributed Data Collection (ADDC) algorithm for CRNs, which is order-optimal  Simulations are conducted to validate the performance of ADDC 27