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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... ...
0 200 400 600 800 1000X
Y
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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
IPCCC’11 4
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?
IPCCC’11 5
Outline
Motivation
Classification of Existing Map Construction
Approaches
Performance Comparison in a Wide Range
Scenarios
Conclusions
IPCCC’11 6
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
IPCCC’11 8
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
IPCCC’11 9
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.
IPCCC’11 11
Outline
Motivation
Classification of Existing Map Construction
Approaches
Performance Comparison in a Wide Range
Scenarios
Conclusions
IPCCC’11 12
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
IPCCC’11 13
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
IPCCC’11 14
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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5 10 15 20 25
Acc
ura
cy
Granularity Value
BER=1E-4, N=256, CR=60m
eScan_grideScan_random
Isoline_gridIsoline_random
0
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10000
15000
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25000
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35000
40000
5 10 15 20 25
Netw
ork
Tra
ffic
[byte
]
Granularity Value
BER=1E-4, N=256, CR=60m
eScan_grideScan_random
Isoline_gridIsoline_random
(a) Step value = 5 unit
IPCCC’11 15
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
IPCCC’11 16
Comparison: Impact of Node Density
Node density increases #Iso-nodes increases ->
higher msg overhead #Region and “region border
information” increase -> higher msg overhead
0
20000
40000
60000
80000
100000
120000
140000
160000
300 400 500 600 700 800 900 1000 1100 1200
Netw
ork
Tra
ffic [
byte
]
#Nodes
BER=1E-4, CR=60m, GV=5
eScan_grideScan_random
Isoline_gridIsoline_random
0
0.2
0.4
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1
300 400 500 600 700 800 900 1000 1100 1200
Accura
cy
#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
IPCCC’11 17
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
ura
cyE
ffici
en
cy
IPCCC’11 18
Thanks for Your Attention!
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