2. 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7

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4.  Capacity analysis in WSNs  Why?  Unicast, Multicast, and Broadcast capacity  Bits/Meter/Second  Data Collection Capacity  Snapshot Data Collection Capacity  Continuous Data Collection Capacity 4

5.  Deterministic network model  Transitional region phenomenon  Probabilistic network model  Contributions  A Cell-based Multi-Path Scheduling (CMPS) algorithm for snapshot data collection in probabilistic WSNs  A Zone-based Pipeline Scheduling (ZPS) algorithm for continuous data collection in probabilistic WSNs 5

6. 6

7.  n sensor nodes,,  i.i.d. deployed in a square area  The sink is located at the top-right corner of the square  Single-radio single-channel   Success probability of a link 7

8.  The number of transmission times satisfies the geometric distribution with parameter  Promising transmission threshold probability  A modified time slot  Data collection capacity 8

9. 9

10.  Cell-based network partition  The expected number of nodes in each cell. (Lemma 1)  It is almost surely that no cell is empty. (Lemma 2)  It is almost surely that no cell contains more than nodes. (Lemma 3) 10

11.  Zone-based network partition  Compatible Transmission Cell Set (CTCS)  Let then the set is a CTCS. (Theorem 1) 11

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13.  Data collection tree  Super node, super time slot 13

14.  Cell-based Multi-Path Scheduling (CMPS)  Phase I: Inner-Tree Scheduling. Schedule CTCSs orderly.  Phase II: Schedule. 14

15.  Analysis  It takes CMPS super time slots to finish Phase I. (Lemma 6)  Let be the number of super data packets transmitted by super node through the data collection process. Then, for, (Lemma 7)  Let be the number of super data packets at waiting for transmission at the beginning of Phase II and, then (Lemma 8) 15

16.  Analysis  The achievable data collection capacity of CMPS is in the worst cast and in the average case. In both cases, CMPS is order-optimal. (Theorem 2) 16

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18.  Continuous Data Collection  Compressive Data Gathering + pipeline  Zone-based Pipeline Scheduling (ZPS) algorithm  Inter-Segment Pipeline Scheduling.  Intra-Segment Scheduling. 18

19.  Analysis  To collection N continuous snapshots, the achievable network capacity of ZPS is in the worst case, and in the average case. (Theorem 3) 19

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21.  Network Setting  Parameters [17]  CMPS  PS [4], MPS [8][9]  ZPS  PSP (PS + pipeline) [PS], CDGP (CDG + pipeline) [15], PSA [8][9] 21

22.  Performance of CMPS 22

23.  Performance of ZPS 23

24.  Performance of CMPS and ZPS in deterministic WSNs 24

25.  We proposed a snapshot data collection algorithm CMPS for probabilistic WSNs, whose capacity is proven to be order-optimal  We proposed a continuous data collection algorithm ZPS for probabilistic WSNs, and analyzed its performance  Extensive simulations validated that the proposed algorithms can accelerate the data collection process 25