A Novel Framework for EnergyConserving Data Gathering in Wireless Sensor Networks

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A Novel Framework for Energy-Conserving Data
Gathering in Wireless Sensor Networks
Wook Choi and Sajal K. Das
INFOCOM 2005

• 10. 13. 2005
• Jinsung Lee

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Outline

• Introduction
• Motivation
• Problem definition
• A Novel Framework for Energy-Conserving Data
  Gathering
• System Model
• Desired Sensing Coverage
• Data Reporter Selection
• Connectivity of Selected Sensors
• Sensor Scheduling
• Performance Evaluation
• Conclusion

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Introduction

• Sensor networks
• Sense their vicinity (called sensing coverage)
• Deliver to a data gathering point (called sink)
• Thru a single- or multi-hop path
• The data gathering model
• Continuous
• Event-driven
• On-demand
• Hybrid
• Due to high node density
• Redundant data transmission
• Significantly reduce the network lifetime

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Motivation

• Energy conservation can further be enhanced while
  meeting the users following requirements
• Data delivery latency
• Sensing coverage of a monitored area

Data gathering based on trade-off between
coverage data reporting latency while meeting
the DSC
The entire monitored area can be sensed after two
consecutive data reporting rounds!!
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Problem Definition
Monitored area Q
SR
Desired Sensing Coverage aQ, where 0ltalt1
A minimum of k sensors are chosen such that
, for each round j (1j d)
If Q has to be covered within a fixed delay
T, are required
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A Novel Framework for Energy-Conserving Data
Gathering Key Idea

• A simple case

Data Gathering Point

• Evaluation of the minimum k satisfying DSC
• Selection of randomized k-sensor for each round
• Formation of a DGT with connectivity
• Reporting their sensed data to data gathering
  point

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System Model

• Undirected connected graph G(V,E)
• Multihop routing path to the base station
• Each sensor has specific radio (sensing) range
• A circular area with radius r
• Assumptions
• Homogeneous sensors are uniformly deployed
• The coordination scheme such as GPS may not be
  available

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Desired Sensing Coverage

• A trade-off factor between coverage and data
  reporting latency
• A probabilistic percentage of covering any point
  in Q
• Inversely proportional to both energy
  conservation rate data reporting latency
• The question is
• In order to meet DSC, how many sensors do we need
  to select at each data reporting round?
• Definition

A probabilistic sensing coverage, ?, is the prob.
of any point in Q being covered by circular
sensing range of at least one of selected k
sensors residing in ? (DSC)
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Randomized (approximately) k-Sensor Selection

• Reporting cycle, round and data reporting group
• Each sensor belongs to exactly one group
• So, each sensor reports only in one round
• Reporting sequence

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Connecting k-Selected Sensors

• Data gathering tree (DGT) construction
• To minimize the number of additional sensors to
  connect the selected k sensors
• Example

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Sensor Scheduling

• Basic method
• Off-duty sensors turn their transceivers off
  except serving as an additional sensor
• Immediate Data Reporting
• Due to a specific event detection
• Affected by the distribution of neighbors RS in
  the range

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Performance Evaluation

• Metrics
• Data reporting latency
• Immediate data reporting capability
• Energy conservation capability
• Simulation parameters
• Note that
• Implementation does not include any MAC and
  wireless channel characteristics

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Simulation Results

• DGT construction
• Energy Conservation Capability

• Periodic Reporting Latency for 100 Coverage

Note that the maximum latency of DSC is
always smaller than (d-1)?t
Energy conservation can be significantly increased
with a small trade-off when ?0.8
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Immediate Reporting Capability and Latency
n is the number of sensors which successfully
report what they detected without forwarding delay
This latency pertains to the immediate reporting
failure case only
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Conclusion

• Proposed A novel framework for energy-conserving
  data gathering in WSNs
• Based on the desired sensing coverage
• Presented a randomized k-disjoint-sensor
  selection scheme and a probabilistic model to
  estimate the connectivity of the selected k
  sensors
• Considered routing and scheduling so as to
  maximize the network lifetime
• Future work
• Enhancement of the randomized sensor selection
  scheme