2. 4 Introduction 1 2 3 5 Carrier-sensing Range Network Model Distributed Data Collection Simulation 6 Conclusion 2

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4.  Centralized algorithms vs. Distributed algorithms in WSNs  Challenges in Distributed and asynchronous data collection  C1: only local information is available  C2: how to avoid disadvantages introduced by time asynchronization  C3: how to theoretical analyze a distributed and asynchronous data collection algorithm  Contributions  Derive a under the generalized physical interference model for each node  Propose a scalable and order-optimal Distributed Data Collection (DDC) algorithm  Simulations are conducted to validate DDC 4

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6.  n sensor nodes, s i (1≤i≤n), i.i.d. in a square area  transmission radius r,  Single radio, one common channel with bandwidth W bits/second   Interference model 6

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8.  R 0 -feasible state: a set of active nodes, s.t. all the nodes in this set can conduct data transmission simultaneously and each has a data transmitting rate no less than R 0.  R-set (S R ): assume R is the carrier-sensing range. An R-set S R is any maximal subset of V s.t. any two nodes in S R has a distance of at least R.  R 0 -Proper Carrier-sensing Range (R 0 -PCR): the carrier-sensing range R is a R 0 -PCR if for any R-set S R, it is always a R 0 -feasible state. 8

9.  How to decide the proper carrier-sensing range? . (Theorem 1) 9

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11.  Connected Dominating Set (CDS)-based data collection tree 11

12.  Distributed Data Collection (DDC) algorithm 12

13.  Analysis  Any node with data for transmission can transmit at least one data packet within time. (Theorem 2)  The time consumption of collecting all the data packets at to is upper bounded by. (Corollary 2)  After time, it takes DDC at most time to collection all the data to the sink. (Theorem 3)  The lower bound of data collection capacity achieved by DDC is, which is scalable and order optimal. (Theorem 4) 13

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15.  Network setting  Power = 1, data packet size = 1, time slot = 1, and ……  Multi-Path Scheduling (MPS) algorithm [7] 15

16.  Capacity 16

17.  Scalability 17

18.  We study the distributed data collection problem in asynchronous WSNs  We propose a Distributed Data Collection (DDC) algorithm, which is scalable and order-optimal  Simulations are conducted to validate the performance of DDC 18