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A Contour Mapping Engine in
Wireless Sensor Networks
Yingqi Xu, Wang-Chien Lee and Mitchell, G.
The 28th International Conference on Distributed Computing Systems
17-20 June 2008, Page(s):133 - 140
Speaker：Shau-Shiang Hung and
Chia-Wei Chang
Date:2009/12/16
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Outline
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Introduction
Related Work
Design of Contour Mapping Engine
Experimental Results
Conclusions
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Introduction
• Environmental monitoring is one of the
killer applications for wireless sensor
networks
• Topographical contour map, also called
contour map in short
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Introduction
• A contour is a curve that connects points
with equal (or close) feature values.
• Adjacent contours have different values
separated by contour step
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Introduction
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Contour mapping that collects feature
readings from all the sensor nodes to a
sink node:
1. Communication overhead
2. Energy consumption
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Introduction
• For reduced communication and energy
overhead:
In-network processing techniques
We present a design of a contour mapping
engine (CME)
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Related Work
• J. Hellerstein et. al.(2005)
– Sensor networks based upon a routing tree
• W. Xue et. al. (2006)
– Use formed map as the means of event
detection
• X. Meng et. al. (2006)
– performs spatial and temporal data
suppression
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Related Work
• I. Solis et. al.(2005)
– The sink node without considering in-network
aggregation
– Moreover, the calculation of contours are not
discussed in their work
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3. Design of Contour Mapping
Engine
•F is the feature
•S is the contour step
•T is the lifetime of a contour map
CME method use multi-hop packet forwarding9
3.1 Contour Mapping
• To reduce communication overhead and
thus energy cost while building contour
maps:
– Selectively collect readings from certain
sensor nodes
– Push some computation of contour mapping
into the cluster heads
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3.1 Contour Mapping
• CME incorporates these two strategies in
two steps:
Sensor→Cluster head
Cluster head→Sink
step can be easily achieved with a multi-hop
forwarding protocol
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3.1.1 Contour Detection
• Contour detection aims at incurring as little
network communication cost
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3.1.2 Contour Formulation
• We adopt the nonlinear classification with kernel
techniques as our solution
Nonlinear Classification
 xi1   x j1 
K (  ,  )  ( xi1xj1  xi 2 xj 2) 2
 xi 2   x j 2 
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3.2 Configuration of Network
• The total energy consumption of CME is
mainly consumed by communication and
sensing operation.
• Communication cost can be further divided
– Sensor→Clusterhead cost
• exchange packets
• data packets
– Clusterhead→Sink cost
• contour packets
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3.2 Configuration of Network
Analysis of Cluster Size
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4. Default Experiment Settings
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4.1 Quality and Cost of Contour
Map
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4.2 Impact of Contour Step
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4.3 Impact of Network Size
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5. Conclusions
• We propose a novel and robust contour
map engine (CME)
• Our design minimizes the total amount of
communication cost
• In future, We plan to implement CME on
real sensor networks.
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