Modeling%20In-Network%20Processing%20and%20Aggregation%20in%20Sensor%20Networks

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Modeling In-Network Processing and Aggregation in
Sensor Networks

• Ajay Mahimkar
• EE 382C Embedded Software Systems
• Prof. B. L. Evans
• May 5, 2004

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Sensor Networks

• Monitor physical environment from remote
  locations
• Challenges
• Battery is the most pressing
• Deployment of sensors in thousands
• No manual intervention
• Design protocols that extend network lifetime
• Network lifetime is the time at which first node
  dies

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In-Network Processing

• Why data aggregation???
• Individual sensor readings are of limited use
• Delivering large amount of data from all nodes to
  a central point consumes lot of energy
• Conserves limited energy and bandwidth
• Increases system lifetime

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Existing Approaches

• Directed Diffusion Intanagonwiwat, 2003

• LEACH (Low Energy Adaptive Clustering Hierarchy)
  Heinzelman, 2000
• Cluster-Head responsible for data aggregation

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Existing Approaches cont.

• PEDAP (Power Efficient Data gathering and
  Aggregation Protocol) Tan, 2003
• MST (Minimum Spanning Tree) based routing using
  energy as the metric
• Disadvantages
• Locally optimizes
• energy
• Increases end-to-end
• latency

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DEEPADS A Novel Approach

• Distributed Energy-Efficient Protocol for
  Aggregation of Data in Sensor Networks (DEEPADS)
• Novel approach that globally maximizes the energy
  and increases system lifetime

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C-DEEPADS

• Uses Clustering Approach
• Two Tier Methodology
• Sensors organize themselves into clusters, each
  cluster represented by a cluster-head
• Global energy metric similar to DEEDAP
• Cluster-head aggregates data and transmits to the
  base station
• Reduces end-to-end latency

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Simulation

• Using Ptolemy-II, VisualSense and Java
• Discrete Event Model
• Network Simulation
• Setup
• Environment
• 100 m x 100 m area
• Sensors location
• Uniformly distributed
• x and y random variables

Simulation Parameters
Battery Energy at Bootstrap 2.0 J
Energy Consumed during TX or RX 50 nJ/bit
Threshold Power 6.3 nW
Transceiver Maximum Range 50 m
Message Length 500 Bytes
Wavelength 0.325 m
Height of TX RX antenna 1.5 m
Gain of TX RX antenna 0 dB
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Sensor Node Model
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Performance Evaluation
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Performance Evaluation cont.
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Discussion

• Results
• DEEPADS C-DEEPADS perform much better than
  existing approaches
• Increase in the system lifetime
• Reduction in the total energy consumption
• Future Work
• Repeat experiments taking into consideration the
  sleep mode in sensors

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Questions ???Comments !!!