Snapshot/Continuous Data Collection Capacity for Large-Scale Probabilistic

Wireless Sensor NetworksShouling Ji

Georgia State UniversityZhipeng Cai and Raheem BeyahGeorgia Institute of Technology

2

OUTLINE

4

Introduction1

2

3

5

Network Partition

Network Model

Snapshot Data Collection

Continuous Data Collection

6 Simulation

Conclusion7

3

Introduction

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

Introduction

5

Deterministic network model

Transitional region phenomenon

Probabilistic network model

ContributionsA 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

Introduction

6

Network Model

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

Network Model

8

The number of transmission times satisfies the geometric distribution with parameter

Promising transmission threshold probability A modified time slot Data collection capacity

Network Model

9

Network Partition

10

Cell-based network partitionThe 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)

Network Partition

11

Zone-based network partitionCompatible Transmission Cell

Set (CTCS)

Let

then the set

is a CTCS. (Theorem 1)

Network Partition

12

Snapshot Data Collection

13

Data collection treeSuper node, super time slot

Snapshot Data Collection

14

Cell-based Multi-Path Scheduling (CMPS)Phase I: Inner-Tree

Scheduling. Schedule CTCSs orderly.

Phase II: Schedule

.

Snapshot Data Collection

AnalysisIt 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)

Snapshot Data Collection

15

AnalysisThe 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)

Snapshot Data Collection

16

17

Continuous Data Collection

18

Continuous Data Collection Compressive Data Gathering

+ pipeline Zone-based Pipeline

Scheduling (ZPS) algorithm Inter-Segment Pipeline

Scheduling.

Intra-Segment Scheduling.

Continuous Data Collection

19

AnalysisTo collection N continuous snapshots, the achievable network capacity of

ZPS is

in the worst case, and

in the average case. (Theorem 3)

Continuous Data Collection

20

Simulation

21

Network Setting Parameters [17]

CMPSPS [4], MPS [8][9]

ZPSPSP (PS + pipeline) [PS], CDGP (CDG + pipeline) [15], PSA [8][9]

Simulation

22

Performance of CMPS

Simulation

23

Performance of ZPS

Simulation

24

Performance of CMPS and ZPS in deterministic WSNs

Simulation

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

Conclusion

THANK YOU!

Snapshot/Continuous Data Collection Capacity for Large-Scale Probabilistic

Wireless Sensor NetworksShouling Ji and Zhipeng Cai

Georgia State UniversityRaheem Beyah

Georgia Institute of Technology