Assessing the Comparative
Effectiveness of Map Construction
Protocols in Wireless Sensor Networks
Abdelmajid Khelil, Hanbin Chang, Neeraj Suri
IPCCC 2011
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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
1000
Y
800
600
400
200
0
0
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400
600
X
800
1000
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Sensor location
(112, 209)
(218, 163)
(617, 783)
(530, 745)
(477, 625)
(936, 423)
(745, 817)
(653, 237)
...
Sensor value
145
163
158
163
165
157
155
168
...
Map Construction in WSN
Naive approach
for map construction
Energy-efficient approaches
for map construction
Data collection and
processing
centrally at sink
in-network
Energy efficiency
(Comm. complexity on
sensor nodes)
high comm. overhead
Map accuracy
node-level accuracy, may
decrease
because of comm. failures
Lower comm. overhead
may lose
detailed information of each
individual node
Sink
Naive Approach
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Example of Available Approaches
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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Classification of Map Construction Approaches
Map construction
approaches for WSN
In-network
Processing
Technique
Region
Aggregation
Data
Suppression
Data Collection Scheme
Tree-based
data
collection
Clusterbased data
collection
eScan [9]
Multi-path
data
collection
CREM [7]
Isobar [8]
INLR [16]
Iso-node
based data
collection
Clusterbased data
collection
Isolines [14]
Iso-map [10,11]
Contour Map [18]
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CME [19]
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
m m+1 m+2
Data Collection
 Aggregator determination
Tree-based
Region Aggregation
 At aggregator
 Regions formation
 Aggregation function, e.g. average
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Cluster-based
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37
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Ring-based
37
Data Suppression Class
 Basic idea
 A subset of sensor nodes (iso-nodes)
report their value to the sink
 suppress similar data to be reported
Isoline
 2 Phases
Iso-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
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Nodes
report to
the sink
Nodes
suppress
reports
to the
sink
Classification of Map Construction Approaches
Map construction
approaches for WSN
In-network
Processing
Technique
Region
Aggregation
Data
Suppression
Data Collection Scheme
Tree-based
data
collection
Clusterbased data
collection
eScan [9]
Multi-path
data
collection
CREM [7]
Isobar [8]
INLR [16]
Iso-node
based data
collection
Clusterbased data
collection
Isolines [14]
Iso-map [10,11]
Contour Map [18]
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CME [19]
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
 Communication failures
 Communication range
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N=256(16x16)...1225 (35x35)
BER=0…10-2
CR=60m
Comparison: Impact of Granularity
BER=1E-4, N=256, CR=60m
Network Traffic [byte]
BER=1E-4, N=256, CR=60m
Accuracy
1
0.8
0.6
0.4
eScan_grid
eScan_random
Isoline_grid
Isoline_random
0.2
0
5
10
15
20
25
Granularity Value
40000
35000
eScan_grid
eScan_random
Isoline_grid
Isoline_random
30000
25000
20000
15000
10000
5000
0
5
10
15
20
Granularity Value
50
 Granularity increases
 #Isolines and #Iso-nodes decrease
-> lower msg overhead
 Region size increase -> lower msg
overhead
 Accuracy
 Isoline always outperforms eScan
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30
20
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(a) Step value = 5 unit
50
25
 Efficiency
 Isoline outperforms eScan for lower
granularities
(b) Step value = 25 unit
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Comparison: Impact of BER
 Higher BER decreases map
accuracy
 Loss of messages -> gaps in
the map
• Higher
eScan
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drop
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 BER increases
 Loss of messages -> lower msg
overhead
 Overhead reduction is higher
for eScan
Comparison: Impact of Node Density
BER=1E-4, CR=60m, GV=5
Network Traffic [byte]
BER=1E-4, CR=60m, GV=5
Accuracy
1
0.8
0.6
0.4
0.2
eScan_grid
eScan_random
Isoline_grid
Isoline_random
0
120000
100000
80000
60000
40000
eScan_grid
eScan_random
Isoline_grid
Isoline_random
20000
0
300 400 500 600 700 800 900 100011001200
300 400 500 600 700 800 900100011001200
#Nodes
#Nodes
 Node density has low impact
on map accuracy
 Region
border
precision
increases -> provide a more
detailed map
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 Node density increases
 #Iso-nodes
increases ->
higher msg overhead
 #Region and “region border
information”
increase
->
higher msg overhead
Conclusions
Accuracy
Region aggregation class
Data suppression class
＋ High accuracy with
＋ high accuracy for reliable
- Less suitable for less
reliable comm.
＋ performs also well for less
reliable comm.
comm.
reliable comm.
＋ accuracy increases with
Efficiency
increasing granularity value
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＋ Small granularity value
- Small granularity value
＋ Low density network
＋ low density network
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Thanks for Your Attention!
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