IPCCC’11 1

Assessing the Comparative Effectiveness of Map Construction

Protocols in Wireless Sensor Networks

Abdelmajid Khelil, Hanbin Chang, Neeraj Suri

IPCCC 2011

IPCCC’11 2

Maps

Maps are an intuitive data representation technique provide a visual representation of an attribute in a certain area; street map, typographic map, world map, etc.

Maps for Wireless Sensor Networks (WSN) applications help users to understand sensed physical phenomena help users to make a decision

Sensor location Sensor value(112, 209) 145(218, 163) 163(617, 783) 158(530, 745) 163(477, 625) 165(936, 423) 157(745, 817) 155(653, 237) 168... ...

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IPCCC’11 3

Sink

Map Construction in WSN

Naive approach for map construction

Energy-efficient approachesfor map construction

Data collection and processing

centrally at sink in-network

Energy efficiency(Comm. complexity on sensor nodes)

high comm. overheadLower comm. overhead

Map accuracynode-level accuracy, may

decrease because of comm. failures

may lose detailed information of each in

dividual node

Naive Approach Example of Available Approaches

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Problem statement and Objectives

Several approaches have been proposed. However,

Evaluation in carefully selected application scenarios

No assessment of the comparative effectiveness of existing approaches:

Which is outperforming in Which application/scenario

for Which network configuration?

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Outline

Motivation

Classification of Existing Map Construction

Approaches

Performance Comparison in a Wide Range

Scenarios

Conclusions

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Data Collection Scheme

Classification of Map Construction Approaches

Map construction approaches for WSN

Region Aggregation

Data Suppression

Tree-based data

collection

eScan [9]

Isobar [8]

Iso-node based data collection

Cluster-based data collection

Isolines [14]

Iso-map [10,11]

Contour Map [18]

CME [19]

Cluster-based data collection

CREM [7]

Multi-path data

collection

INLR [16]

In-network Processing Technique

IPCCC’11 7

Region Aggregation Class

Basic idea Sensor nodes are ordered hierarchically (clusters, tree ..) Every sensor reports to a dedicated node (cluster head,

parent ..) Dedicated node aggregates adjacent similar data to regions

3 Phases:Region Segmentation At each sensor Non-overlapping polygons Vertex representation

Data Collection Aggregator determination

Region Aggregation At aggregator Regions formation Aggregation function, e.g. average

m m+1 m+2

Tree-based Cluster-based Ring-based

36 37

37 3837

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Basic idea A subset of sensor nodes (iso-

nodes) report their value to the sink suppress similar data to be reported

2 PhasesIso-node Identification what is an iso-node?

• has a neighbor with different value how to identify?

• broadcast • snoop

Isoline Report Generation iso-node based

• generated at Iso-node• routed directly to the sink

cluster based• generated at cluster-head• Iso-node reports to cluster-head• a local map

Data Suppression Class

38 42 43

36 41 42

37 41 45

Isoline

Nodes report to the sink

Nodes suppress reports to the sink

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Data Collection Scheme

Classification of Map Construction Approaches

Map construction approaches for WSN

Region Aggregation

Data Suppression

Tree-based data

collection

eScan [9]

Isobar [8]

Iso-node based data collection

Cluster-based data collection

Isolines [14]

Iso-map [10,11]

Contour Map [18]

CME [19]

Cluster-based data collection

CREM [7]

Multi-path data

collection

INLR [16]

In-network Processing Technique

IPCCC’11 10

Selected Map Construction Algorithms

The eScan approach [9] Nodes ordered as an aggregation-tree Polygon regions Aggregation function: Average

The Isoline approach [14] Local flood to label border nodes Each iso-node reports to the sink Map constructed at the sink

[9] Y. Zhao et al. Residual Energy Scan for Monitoring Sensor Networks. In IEEE WCNC, 2002.[14] I. Solis and K. Obraczka. Isolines: Energy-efficient Mapping in Sensor Networks. In ISCC, 2005.

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Outline

Motivation

Classification of Existing Map Construction

Approaches

Performance Comparison in a Wide Range

Scenarios

Conclusions

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Evaluation Framework: Methodology Selected map construction protocols

Region aggregation class: eScan Data suppression class: Isoline

Simulations using OMNet++ Network

• Area : 300 x 300 m²• Topology: Grid or random

Tree-based routing protocol

Performance metrics Map accuracy: The ratio of false classified sensors to all

sensor nodes. Energy efficiency: Network traffic

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Evaluation Framework: Comparative Studies

Compare for a wide range of parameters:

Impact of physical phenomena properties Hotspot effect range : limited vs. diffusive Hotspot number : 1 vs. n

Impact of protocol parameters Sensor value range [0, 60], classes: [0, GV[, [GV, 2GV[ ...

• Signal discretization (Granularity value: GV) GV=5…25

Impact of network properties Node density N=256(16x16)...1225

(35x35) Communication failures BER=0…10-2

Communication range CR=60m

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Granularity increases # Isolines and #Iso-nodes decrease

-> lower msg overhead Region size increase -> lower msg

overhead Accuracy

Isoline always outperforms eScan Efficiency

Isoline outperforms eScan for lower granularities

50 40 30 20 10

50 25

(b) Step value = 25 unit

Comparison: Impact of Granularity

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Acc

ura

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Granularity Value

BER=1E-4, N=256, CR=60m

eScan_grideScan_random

Isoline_gridIsoline_random

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ork

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ffic

[byte

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Granularity Value

BER=1E-4, N=256, CR=60m

eScan_grideScan_random

Isoline_gridIsoline_random

(a) Step value = 5 unit

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Comparison: Impact of BER

BER increases Loss of messages -> lower

msg overhead Overhead reduction is

higher for eScan

Higher BER decreases map accuracy Loss of messages -> gaps in

the map• Higher accuracy drop for

eScan

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Comparison: Impact of Node Density

Node density increases #Iso-nodes increases ->

higher msg overhead #Region and “region border

information” increase -> higher msg overhead

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ffic [

byte

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#Nodes

BER=1E-4, CR=60m, GV=5

eScan_grideScan_random

Isoline_gridIsoline_random

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Accura

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#Nodes

BER=1E-4, CR=60m, GV=5

eScan_grideScan_random

Isoline_gridIsoline_random

Node density has low impact on map accuracy Region border precision

increases -> provide a more detailed map

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Conclusions

Region aggregation class Data suppression class

＋High accuracy with reliable comm.

- Less suitable for less reliable comm.

＋high accuracy for reliable comm.

＋performs also well for less reliable comm.

＋accuracy increases with increasing granularity value

＋Small granularity value

＋Low density network

- Small granularity value

＋ low density network

Acc

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ffici

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IPCCC’11 18

Thanks for Your Attention!