a dynamic query-tree energy balancing protocol for sensor networks h. yang, f. ye, and b. sikdar...

A Dynamic Query-tree Energy Balancing Protocol for Sensor Networks

H. Yang, F. Ye, and B. SikdarDepartment of Electrical, Computer and systems Engin

eering

IEEE WCNC 2004Speaker: Hao-Chun Sun

Outline

Introduction Energy Consumption Model Dynamic Query-tree Energy Balancing

Algorithm (DQEB) Simulation Results Conclusion

Introduction -background-

Sensor network Use Querying to collect information across the

whole sensor network. A set of sensors is specifically asked to report

information of interest.

Query

Sink

Sensor network


Basic Sensor Network Query Procedure flooding


Sensor network characteristics Limited power Lower processing ability Limited bandwidth Smaller memory


Optimal Sensor Network Query Procedure Broadcast Tree Protocols Minimum energy broadcast tree problem is

NP-complete. Many approximate algorithms is proposed.

Introduction -motivation-

These protocols are useful only for the development of static trees.

These protocols are un-weighted protocols whose assumptions are valid only when the nodes are first deployed.

Query Broadcast Tree

Non-leaf Nodes

Leaf Nodes

Sink

Introduction -motivation-

Focus on Developing techniques to improve the

querying procedure to minimize energy consumption and maximize the sensor network lifetime.

Distributed the broadcast load evenly on nodes so that the energy distribution is balanced.

DQEB update the query tree structure to avoid uneven energy depletion.

Energy Consumption Model

Sensor network query tree Sink Non-leaf nodes Leaf nodes

Query Broadcast Tree

Non-leaf Nodes(Receive, Forward)

Leaf Nodes(Receive)

Sink

Energy Consumption Model -weight-

Every node is associated with a weight. ω: [0, 1], nodes weight is initialized to 0. , β is the power attenuation factor and

determines the rate at which depleting power affects the weight of a node.

P is its remaining battery lifetime. Desire to increase the weight faster as the

remaining battery becomes lower.

)1( P

Energy Consumption Model -energy cost-

The energy cost of broadcast depends on the number of leaf and non-leaf nodes in query tree as well as the amount of remaining batter power at a node.

, Tx power=λ× Rx power (γ)

iLiLi

iC

1

LLi Li

iiC )(

Minimum

Dynamic Query-tree Energy Balancing Algorithm (DQEB) Assumptions

A cluster structure Only involves the cluster heads

Nodes have uniform hardware, software and battery capacity.

Nodes are energy-aware. A query tree is assumed to exist with the sink

node as the root and all nodes in the network being either leaf or non-leaf nodes of the tree.

Dynamic Query-tree Energy Balancing Algorithm (DQEB) Overview

UC triggers DQEB algorithm. Neighborhood Information Synchronization (NIS) algorithm Designated parents (DP) selection from alternative parents (AP)

9(0.1) 1(0.1)

7(0.3)8(0.3)

2(0.3)

3(0.5)

4(0.2) 6(0.4)

5(0.6)Update

Coordinator(UC)

Dynamic Query-tree Energy Balancing Algorithm (DQEB) Neighborhood Information Synchronization

Algorithm Neighborhood Information Table (NIT)

State information of neighbors ID, route to root, state,….

Periodic “Hello” message to detect node failure.

If a node’s state information changes in the interval between hello messages, the hello message is substituted by the changed information.

Dynamic Query-tree Energy Balancing Algorithm (DQEB) A Greedy algorithm for parent selection

Set-Covering problem

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={ }C={3,4,5,7}A={2,3,4,6,8,9}

(w5/d5)



i

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151

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122

13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9}C={3,4,5,7}A={2,3,4,6,8,99}

(w5/d5)



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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,8}C={3,4,5,7}A={2,3,4,6,88,99}

(w5/d5)



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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,8,6}C={3,4,5,7}A={2,3,4,66,88,99}

(w5/d5)



i

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14

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6

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151

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122

13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,8,6,3}C={3,4,5,7}A={2,33,4,66,88,99}

(w5/d5)DP={9,8,6,3}


Disconnected Problem

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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,6}C={3,4,5,7}A={2,3,4,66,8,99}

(w5/d5)



i

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14

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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,6}C={3,4,5,7}A={2,3,4,66,8,99}

(w5/d5)DP={9,6,3}

Dynamic Query-tree Energy Balancing Algorithm (DQEB) Differentiating APs

Sibling AP Offspring AP Independent AP

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13(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w8/d8)

(w5/d5)Sibling AP

Offspring AP

Independent AP



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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={ }C={3,4,5,7}A={2,3,4,6,8,9}

(w5/d5)C={3,4,5,6,7}A={2,8,9}



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151

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122

13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={ }C={3,4,5,6,7}A={2,8,9}

(w5/d5)



i

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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

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1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9}C={3,4,5,6,7}A={2,8,99,3}

(w5/d5)



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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

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(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,8}C={3,4,5,6,7}A={2,88,99,3,7}

(w5/d5)



i

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151

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13C={3,4,5,7}A=A1={2,4,9}A2={3,4}A3={6}A4={6,8}

m

iAi

1

(w9/d9)

(w2/d2)(w3/d3)

(w4/d4)

(w6/d6)

(w7/d7)

(w8/d8)

K={9,8,7}C={3,4,5,6,7}A={2,88,99,3,77,6}

(w5/d5)

Dynamic Query-tree Energy Balancing Algorithm (DQEB) Resolving Update Conflicts

Two nodes trigger DQEB concurrently. Lock mechanism

Once the UC triggers all its children for their APs’ information, all children will freeze themselves.

Any UC that does not get a response from some of its children waits for a given time.

Simulation Results

Network Size: 600 m x 600 m Nodes number: 1000 Nodes positions: Uniformly distributed Network is connected and Query tree has

been constructed initially. λ=3 β=3 , w=(1-P) β

Simulation Results

Power Distribution Balance v.s Time

Simulation Results

Life Time v.s Death Rate threshold

Simulation Results

Power STD v.s Connectivity

Simulation Results

Life Time v.s Connectivity

Conclusion

We proposed an energy aware, distributed protocol to dynamically update query tree structures in sensor network.

Decisions are taken at each non-leaf node to locally minimize the cost of the broadcast tree by switching a non-leaf node with low remaining power to a leaf node so that its energy depletion rate is decreased.

a dynamic query-tree energy balancing protocol for sensor networks h. yang, f. ye, and b. sikdar...

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