4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data...
-
Upload
rudolph-marshall -
Category
Documents
-
view
218 -
download
0
description
Transcript of 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data...
![Page 1: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/1.jpg)
Snapshot/Continuous Data Collection Capacity for Large-Scale Probabilistic
Wireless Sensor NetworksShouling Ji
Georgia State UniversityZhipeng Cai and Raheem BeyahGeorgia Institute of Technology
![Page 2: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/2.jpg)
2
OUTLINE
4
Introduction1
2
3
5
Network Partition
Network Model
Snapshot Data Collection
Continuous Data Collection
6 Simulation
Conclusion7
![Page 3: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/3.jpg)
3
Introduction
![Page 4: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/4.jpg)
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
![Page 5: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/5.jpg)
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
![Page 6: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/6.jpg)
6
Network Model
![Page 7: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/7.jpg)
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
![Page 8: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/8.jpg)
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
![Page 9: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/9.jpg)
9
Network Partition
![Page 10: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/10.jpg)
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
![Page 11: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/11.jpg)
11
Zone-based network partitionCompatible Transmission Cell
Set (CTCS)
Let
then the set
is a CTCS. (Theorem 1)
Network Partition
![Page 12: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/12.jpg)
12
Snapshot Data Collection
![Page 13: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/13.jpg)
13
Data collection treeSuper node, super time slot
Snapshot Data Collection
![Page 14: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/14.jpg)
14
Cell-based Multi-Path Scheduling (CMPS)Phase I: Inner-Tree
Scheduling. Schedule CTCSs orderly.
Phase II: Schedule
.
Snapshot Data Collection
![Page 15: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/15.jpg)
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
![Page 16: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/16.jpg)
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
![Page 17: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/17.jpg)
17
Continuous Data Collection
![Page 18: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/18.jpg)
18
Continuous Data Collection Compressive Data Gathering
+ pipeline Zone-based Pipeline
Scheduling (ZPS) algorithm Inter-Segment Pipeline
Scheduling.
Intra-Segment Scheduling.
Continuous Data Collection
![Page 19: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/19.jpg)
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
![Page 20: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/20.jpg)
20
Simulation
![Page 21: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/21.jpg)
21
Network Setting Parameters [17]
CMPSPS [4], MPS [8][9]
ZPSPSP (PS + pipeline) [PS], CDGP (CDG + pipeline) [15], PSA [8][9]
Simulation
![Page 22: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/22.jpg)
22
Performance of CMPS
Simulation
![Page 23: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/23.jpg)
23
Performance of ZPS
Simulation
![Page 24: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/24.jpg)
24
Performance of CMPS and ZPS in deterministic WSNs
Simulation
![Page 25: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/25.jpg)
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
![Page 26: 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data Collection 6 Simulation 2 Conclusion 7.](https://reader035.fdocuments.net/reader035/viewer/2022062503/5a4d1ae77f8b9ab059979942/html5/thumbnails/26.jpg)
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