landmark-based information storage and retrieval in sensor networks qing fang department of...

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Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, St anford University Jie Gao Department of Computer Science, Stony Br ook University Leonidas J. Guibas Department of Computer Science, Stanford University INFOCOM 2006

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Page 1: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Landmark-Based Information Storage and Retrieval in Sensor Networks

Qing FangDepartment of Electrical Engineering, Stanford UniversityJie GaoDepartment of Computer Science, Stony Brook University Leonidas J. GuibasDepartment of Computer Science, Stanford University

INFOCOM 2006

Page 2: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Outline

Introduction

Related Work

Landmark-Based Data Centric

Simulation

Conclusion

Page 3: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Background

sink

Page 4: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Background

sink

Page 5: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Background

sink

Page 6: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Geographical Hash Table (GHT)

lion

Page 7: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Disadvantage of GHT

No distance-sensitive

producer

lion

consumer

Page 8: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Disadvantage of GHT

No distance-sensitive

Communication bottleneck

Page 9: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Disadvantage of GHT

No distance-sensitive

Communication bottleneck

Bad for queries the cross-type data

Page 10: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

GHT with Structured Replication

quad-tree

d = 1

41 = 4

d = 2

42 = 16

Page 11: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

GHT with Structured Replication

d = 1

Page 12: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Related Work

Title– GLIDER: Gradient Landmark-Based Distributed Routing for

Sensor Networks

Author– Qing Fang, Jie Gao, Leonidas J. Guibas, Vin de Silva

From– INFOCOM 2005

Page 13: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

GLIDER

a

b

c

t

s

d

Page 14: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Overview

producer

h

consumer

T1

T2

T3

T4

replication pathretrieval path

Page 15: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Replication Path

producer

h

La

Lc

Ld

a

Le

Lf

b

Lg

cconsumer

No distance-sensitiveLb

Page 16: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Distance-sensitive

Page 17: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Optimal Principle

a → b → c → d → e → f → g

c → d → e → f → g

Page 18: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Optimal Principle

producer

h

La

Lc

Le

Lf

Lg

consumer

Lb

Ld

Page 19: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Finger Tree

producer

h

La

Lc

Le

Lf

Lg

consumer

Lb

Ld

Page 20: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Finger Tree

La

Lc

Le

Lf

Lg

Lb

Ld

s

Page 21: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Replication Path

producer

h

La

Lb

Lc

Le

Lf

Lg

a

consumer

Ld

Page 22: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Replication Path

producer 1

h

La

Lb

Lc

Le

Lf

Lg

a

producer 2

Ld

Page 23: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Network Enviroments

– 316m × 316m sensing field

– 2000 nodes

– 11m communication range

– 6.2 degrees

– 23 landmarks

– Simulated with C++

Page 24: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Data structure and storage requirement

– The landmarks

– Neighborhood distances to its reference landmarks

– The hash function

– A bit to record it’s on boundary or not

– The IDs of its neighboring sensors

Page 25: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Compare producer cost with GHT

Page 26: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Compare consumer cost with GHT

Page 27: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Retrieval Path Length

GHT 100%

Landmark-based 70.2%

Page 28: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Simulation

Load distribution oflandmark-based

Load distribution of GHT

Page 29: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Conclusion

A location-free, landmark-based information brokerage scheme for sensor networks

– Distance-sensitive

– Load-balanced

Page 30: Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department

Thank You !