4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data...
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Transcript of 4 Introduction 1 2 3 5 Network Partition Network Model Snapshot Data Collection Continuous Data...
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
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Introduction1
2
3
5
Network Partition
Network Model
Snapshot Data Collection
Continuous Data Collection
6 Simulation
Conclusion7
3
Introduction
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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
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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
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Network Model
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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
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The number of transmission times satisfies the geometric distribution with parameter
Promising transmission threshold probability A modified time slot Data collection capacity
Network Model
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Network Partition
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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
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Zone-based network partitionCompatible Transmission Cell
Set (CTCS)
Let
then the set
is a CTCS. (Theorem 1)
Network Partition
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Snapshot Data Collection
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Data collection treeSuper node, super time slot
Snapshot Data Collection
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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
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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
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Continuous Data Collection
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Continuous Data Collection Compressive Data Gathering
+ pipeline Zone-based Pipeline
Scheduling (ZPS) algorithm Inter-Segment Pipeline
Scheduling.
Intra-Segment Scheduling.
Continuous Data Collection
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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
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Simulation
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Network Setting Parameters [17]
CMPSPS [4], MPS [8][9]
ZPSPSP (PS + pipeline) [PS], CDGP (CDG + pipeline) [15], PSA [8][9]
Simulation
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Performance of CMPS
Simulation
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Performance of ZPS
Simulation
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Performance of CMPS and ZPS in deterministic WSNs
Simulation
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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