Continuous Residual Energy Monitoring i n Wireless Sensor Network s

1Department of Computer ScienceCity University of Hong Kong

Department of Computer Science

City University of Hong Kong

Continuous Residual Energy Monitoring

in Wireless Sensor Networks

Song Han and Edward Chan

Department of Computer Science, City University of Hong Kong83 Tat Chee Avenue, Kowloon, HONG KONG

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Continuous Residual Energy Monitoring in Wireless Sensor Networks

Agenda

Introduction Objective Related Work System Model Methodology Performance Analysis Conclusion

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Introduction

Features of Wireless Sensor Network (WSN) Large scale Static nodes Limited resources

Residual Energy Monitoring (REM) Get WSN’ s energy information Maintain the WSN active Accurate vs. Approximate monitoring

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Objective

To propose an approach for monitoring residual energy information continuously in the WSN

Scalability

Accuracy

Maximized lifetime & Minimized message cost

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Related Work

Energy Consumption Prediction by Nath et al. Energy dissipation model Probabilistic prediction scheme

Residual Energy Scan by Zhao et al. Notion of energy map In-network aggregation Abstract representation of energy graph

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System Model

Base Station

m

Communication Range Rm

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Methodology

Topology Discovery Divide the WSN into several clusters Construct a monitoring tree

Residual Energy Monitoring Abstracted Representation of Energy Graph Determining the Local Energy Graph In-Network Aggregation of Energy Graphs

Topology Maintenance

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Topology Discovery

Step 1: A “Topology Discovery Request” is initiated from the base station and propagates through controlled flooding.

Step 2: WSN is divided into clusters based on TopDisc algorithm by Nath et al.

A simple greedy log (n)-approximation algorithm. Communication range is reduced to R/2.

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Topology Discovery (cont.)

Base Station

Topology Discovery Request

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Base Station


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Base Station


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Base Station


Become Black after an interval

And broadcast the request again

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At the end of this phase, Monitoring tree is constructed (Figure.2)

Consists of black nodes and grey nodes:

Black node: Cluster head

Grey node: Bridge between two heads

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Ordinary Node

Cluster Boundary

Cluster Head

Delivery Node

Figure.2. Monitoring Tree

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Residual Energy Monitoring

Abstracted Representation of Energy Graph

Structure of the message

Structure of the polygon information

Sender ID Receiver ID Energy Range Polygon Information

Part 1 Part 2 Part 3 Part 4

Outside contour

Hole 1

Hole 2

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Residual Energy Monitoring (cont.)

Determining the Local Energy Graph

1) Divide sensors according to energy range

2) Get the convex contour for each energy range

3) Perform Boolean Computing on the set of polygons

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Residual Energy Monitoring (cont.)

In-Network Energy Graph Aggregation

Scheme:

Forward energy information along the monitoring tree from leaf to root.

Non-leaf node merges two polygons if they are in the same energy range and adjacent physically.

Vertex number and communication cost are reduced.

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Topology Maintenance

Node Selection Criteria Residual energy Proximity to lower energy range

X Y

After Selection Initial State

X Y

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Topology Maintenance (cont.)

Static topology maintenance schema

Parent cluster selects a new head; Child cluster selects new head and deliver node; Both new head nodes broadcast the change in

their clusters.

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Performance Analysis

Performance Metrics:

Residual reachable nodes Fidelity Total Message Cost

Methods to compare: Continuous Residual Energy Monitoring (CREM) Centralized Collection Static Clustering

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Cost ratio CREM vs. Centralized Collection

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Fidelity vs. Network Size

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CREM Fidelity vs. Monitoring Cycles

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Residual reachable nodes vs. Monitoring Cycle

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Methods Comparison:Fidelity vs. Monitoring Cycles

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Message Cost: CREM vs. static clustering

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Conclusion

In this paper, we proposed a hierarchical structure for energy monitoring, in the monitoring process, we use the in-network graph aggregation and node selection schema to reduce the message cost, expand the lifetime of the WSN and at the same time, we maintain the accuracy of the result energy graph.

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References [1] A. F. Mini, Badri Nath and Antonio A. F. Loureiro, “Prediction-

based Approaches to Construct the Energy Map for Wireless Sensor Networks”, Proc. 21st Brasilian Symposium on Computer Networks, Natal, RN, Brazil, May 19-23, 2003.

[2] J. Zhao, R. Govindan, and D. Estrin, “Computing aggregates for monitoring wireless sensor networks”, Technical Report 02-773, USC, September 2003.

[3] B. Deb, S. Bhatangar, and B. Nath, “A Topology Discovery Algorithm for Sensor Networks with Applications to Network Management”, Proc. IEEE CAS Workshop on Wireless Communications and Networking, Pasadena, USA, Sept. 2002.

[4] Michael V. Leonov, Alexey G. Nikitin, “An Efficient Algorithm for a Closed Set of Boolean Operations on Polygonal Regions in the Plane”, Preprint 46, Novosibirsk, A. P. Ershov Institute of Informatics Systems, 1997.

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Continuous Residual Energy Monitoring i n Wireless Sensor Network s