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
Landmark-Based Information Storage and Retrieval in Sensor Networks Qing Fang Department of Electrical Engineering, Stanford University Jie Gao Department.
Jan 12, 2016
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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
Outline
Introduction
Related Work
Landmark-Based Data Centric
Simulation
Conclusion
Background
sink
Background
sink
Background
sink
Geographical Hash Table (GHT)
lion
Disadvantage of GHT
No distance-sensitive
producer
lion
consumer
Disadvantage of GHT
No distance-sensitive
Communication bottleneck
Disadvantage of GHT
No distance-sensitive
Communication bottleneck
Bad for queries the cross-type data
GHT with Structured Replication
quad-tree
d = 1
41 = 4
d = 2
42 = 16
GHT with Structured Replication
d = 1
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
GLIDER
a
b
c
t
s
d
Overview
producer
h
consumer
T1
T2
T3
T4
replication pathretrieval path
Replication Path
producer
h
La
Lc
Ld
a
Le
Lf
b
Lg
cconsumer
No distance-sensitiveLb
Distance-sensitive
Optimal Principle
a → b → c → d → e → f → g
c → d → e → f → g
Optimal Principle
producer
h
La
Lc
Le
Lf
Lg
consumer
Lb
Ld
Finger Tree
producer
h
La
Lc
Le
Lf
Lg
consumer
Lb
Ld
Finger Tree
La
Lc
Le
Lf
Lg
Lb
Ld
s
Replication Path
producer
h
La
Lb
Lc
Le
Lf
Lg
a
consumer
Ld
Replication Path
producer 1
h
La
Lb
Lc
Le
Lf
Lg
a
producer 2
Ld
Simulation
Network Enviroments
– 316m × 316m sensing field
– 2000 nodes
– 11m communication range
– 6.2 degrees
– 23 landmarks
– Simulated with C++
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
Simulation
Compare producer cost with GHT
Simulation
Compare consumer cost with GHT
Simulation
Retrieval Path Length
GHT 100%
Landmark-based 70.2%
Simulation
Load distribution oflandmark-based
Load distribution of GHT
Conclusion
A location-free, landmark-based information brokerage scheme for sensor networks
– Distance-sensitive
– Load-balanced
Thank You !
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