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Efficient Event Boundary Approximated Suppression Algorithm in Wireless Sensor Networks Longjiang Guo Heilongjiang University [email protected]

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EBAS: An Energy-Efficient Event Boundary Approximated Suppression Algorithm in Wireless Sensor Networks. Longjiang Guo Heilongjiang University [email protected]. Outline. Introduction EBAS Algorithm Experiment And Result Analysis Conclusion. Introduction. - PowerPoint PPT Presentation

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Page 1: Longjiang Guo Heilongjiang University  longjiangguo@gmail

EBAS: An Energy-Efficient Event Boundary Approximated

Suppression Algorithm in Wireless Sensor Networks

Longjiang GuoHeilongjiang University [email protected]

Page 2: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Outline

Introduction

EBAS Algorithm

Experiment And Result Analysis

Conclusion

Page 3: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Introduction

Event Boundary Approximated

Page 4: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Example

Forest fire alarm system

Page 5: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Challenge

Sink has to collect information from all the nodes that lie in field boundaries.

Therefore:A lot of the number of sending messagesMuch more energy consuming The higher the message packets collision rate

Page 6: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Resolving 

Based on the above challenge, we propose a novel energy-efficient algorithm (EBAS).

In EBAS, sink do not need all the information from the nodes in field boundaries.

EBAS supports a suppression scheme that conservers energy by reducing the number of sending message.

Page 7: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Outline

Introduction

EBAS Algorithm

Experiment And Result Analysis

Conclusion

Page 8: Longjiang Guo Heilongjiang University  longjiangguo@gmail

EBAS algorithm

EBAS is composed of three parts

Key node automatically selection in-network

Transportation

Event boundary rebuilt

Page 9: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Key node selection

Definition 1 :[Slope] We define the slope of a sensor node i which lies in an event boundary as following:

Given the coordination of sensor node i as (Xi, Yi) and the coordination of the neighbor j of sensor node i as (Xj, Yj), we calculate the slope of sensor node i with its neighbor j as following:

ij

ijij

ij

ji

XXwhen

XXwhenXX

YY

K ,

,,

Page 10: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Key node selection

Definition 2. [Verge Node] A node is called verge node if it has only one neighbor on an event boundary.

Definition 3. [Key Node] A sensor node m is called Key node if and only if it satisfies one of the following 2 conditions:

(1) Difference of slopes with its two neighbor nodes i and j outrages the predefined threshold, ε. i.e.

(2) It is a verge node in an event boundary. || ,, jmim KK

Page 11: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Slope: K23, 24=-2, K23, 22=0.5. Node 1 and node 27 is Verge Node.When ε=0.5 , | K23, 24 -K23, 22|>0.5 ,so node 23 is a Key Node

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1 0 1 1 1 2

1 3

1 4

1 5 1 6 1 7

1 8

1 9

2 0

2 1

2 2

2 3

2 4

2 52 6

2 7

( 0 ,0 )

Page 12: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Key node selection

Initialization Phase:Individual behavior :Each node lies in an event

boundary broadcasts its node ID and coordination (ID, X, Y) to its neighbors. If a node just gets one message. Definition 2 If a node just gets two messages. Definition 3 if a node receives more than 2 messages, for each received

node id, calculate the distance between the two nodes using Euclidean distance, then sort these distances, pick out the 2 nodes with least distances. Then apply Definition 3.

Page 13: Longjiang Guo Heilongjiang University  longjiangguo@gmail

EBAS algorithm

EBAS is composed of three parts

Key node automatically selection in-network

Transportation

Event boundary rebuilt

Page 14: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Transportation

Building aggregation tree Transportation key node information Suppression Strategy

Page 15: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Building aggregation tree

There are two types of message package in building aggregation tree: one is Tree-building package: (PackageType=1, NodeID, level) (PackageType=2, ParentNode, NodeID).

Initialization sink node broadcasts the Tree-building package (1, sink’s NodeID, 0) and set the local level with 0.

Individual Behavior Each node in senor networks maintains three variables: parent, child and level which are initialized with null, null, and . parent, child and level indicate the parent, children and level of sensor node in aggregation tree respectively.

Page 16: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Example

s ink

(null,0 ,0)

(null,1 ,+ )

(nu ll,3 ,+ )(nu ll,4 ,+ )

(nu ll,2 ,+ )

s ink(null,0 ,0)

(0 ,1 ,+ )

(1 ,3 ,+ )(2 ,4 ,+ )

Page 17: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Transportation key node information

Given a set of key nodes Mi={di1, di2, di3, …} located on sensor node i. Sensor node i will send Mi to his parent node. Suppose j is a parent node in aggregation tree. i1, i2 …ik are j’s children. When sensor node j receives Mi1, Mi2 …Mik, sensor node j will unite Mi1, Mi2 …Mik as following operation:

(1) MjMi1Mi2 …Mik..

(2) Sensor node j sends Mj to his parent.

Page 18: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Suppression Strategy

Given a set of key nodes M= {di1, di2, di3 …}, the FM sketch of M, denoted S (M), is a bitmap of length k. The entries of S (M), denoted S (M) [0… k-1], are initialized to zero and are set to one using a random binary hash function h applied to the elements of M. Formally,

.}1),(|min{ s.t. 1])[( ijdhjMdiffiMS

Page 19: Longjiang Guo Heilongjiang University  longjiangguo@gmail

EBAS algorithm

EBAS is composed of three parts

Key node automatically selection in-network

Transportation

Event boundary rebuilt

Page 20: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Event Boundary Rebuilt

Transportation over,Since we know exactly all key node ID, we can put event boundary rebuilt easy.

Page 21: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Outline

Introduction

EBAS Algorithm

Experiment And Result Analysis

Conclusion

Page 22: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Experiment and result analysis

We have completed EBAS implementation in the TinyOS2.x TOSSIM simulator.

The accuracy error is defined as follows: 22 )ˆ()ˆ(

1Error iiii yyxx

n

0 1 2 3 4

0

2

4

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10

Err

or

Slop threshlod

Centralized EBAS

This Figure shows that the accuracy of EBAS mainly depends on the predefined threshold, ε. The smaller the predefined threshold is, the smaller the accuracy error of the recovered event boundaries is.

Page 23: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Message quantity with slope threshold varying

0 1 2 3 4

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Network size is 30

Me

ssa

ge

Slope threshlod

Centralized EBAS

0 1 2 3 4

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Network size is 40:

Me

ssa

ge

Slope threshold:

Centralized EBAS

Page 24: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Outline

Introduction

EBAS Algorithm

Experiment And Result Analysis

Conclusion

Page 25: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Conclusion

In this paper, we present a novel energy-efficient algorithm EBAS to solve event boundaries transportation problems.

The entire idea can be divided into 4 parts: Key node generation;Key nodes set suppression;Transportation; Decompression.

Our experiment results confirmed the correctness and effectiveness of our algorithm.

Page 26: Longjiang Guo Heilongjiang University  longjiangguo@gmail

Thanks