A Durable Sensor Enabled Lifeline Support for Firefighters

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A Durable Sensor Enabled Lifeline Support for
Firefighters
Hady Abdel Salam Syed Rizvi Scott
Ainsworth Stephan Olariu Computer Science
Department, Old Dominion University, Norfolk,
VA, USA
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Smart Environments SE

• Sensor-based smart living environments is
  emerging as a hot research area.
• In a SE, sensors are deployed in a building or a
  facility to provide services to an intended class
  of users, for instance,
• In a SE for Handicapped Assistance, sensors can
  guide a blind person to his destination.
• In Emergency Networks, they can provide a
  lifeline for firefighters or rescue men during
  their mission.
• Durability is a key issue for the success of such
  networks.

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Motivation

• Sensors have very limited non-renewable energy
  budget.
• Many energy efficient protocols have been
  proposed for different research areas in sensor
  networks
• MAC
• Routing
• Localization
• Data Aggregation
• others

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Motivation (cont.)

• Tasking sensors unwisely may result in uneven
  consumption of their energy due to excessively
  tasking a group of sensors more than others.
• Eventually, this may reduce network density,
  create energy holes, and affect network
  reliability and durability.
• In this work, we propose a tasking protocol that
  overcomes these problems in critical emergency
  networks by recruiting sensors based on their
  energy.

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Network Model

• Sensors are anonymous devices that work
  unattended.
• Sensors are tasked by an Aggregation Node AN
  (that is mounted on the helmet of a firefighter).

• The AN and the sensors are within the
  transmission range of each other.
• Each task requires a workforce of w sensors, and
  consumes 1 unit of energy of each sensor.

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Tasking Model

• A task starts, when the AN sends a sequence of
  CTW message to get the attention of enough number
  of sensors.
• After that, a contention based workforce
  selection mechanism is used to select required
  workforce.
• Task execution starts immediately after
  collecting the workforce.
• Sensors send their results to the AN in the
  order that was determined during workforce
  selection

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Workforce Selection
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Parameter Estimation
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Parameter Estimation (cont.)

• We used a bottom-up approach to estimate the
  values (k, s) such that the number of bidding
  rounds r is minimum (r 1).
• The required workforce, w, will be selected from
  the sensors that were the only bidder in their
  bidding slots (single bidder slots).

Given B bidders and S slots, the expected number
of good slots ,G w, is given by Gmax occurs
when B S, and equals Hence, S is chosen such
that
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Parameter Estimation (cont.)

• Now, the AN has to send k CTW messages to draw
  the attention of B bidders, where B is given by
  (1).
• Given N number of sensors, P the probability a
  sensor responds to a CTW message (by
  participating in bidding), we showed that the
  expected number of responding sensors is given by

Hence, k is given by
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Participation Probability P
It is Awake PAwake
Its energy allows this participation Pe
A sensor responds to a CTW Message

• Participation Probability can be expressed as,
• If a sensor alternates between sleeping for a
  random amount of time from s to S and stay waking
  up for another random amount of time from l to L,
  then the probability that a sensor is awake is
  given by,

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• When a sensor hears a CTW message, how does it
  decide whether to participate in this task or not
  ?

• Any CTW message contains AN estimation of Emax,
  the current maximum energy among sensors.
• The AN estimates Emax from the energy values it
  receives in good slots during bidding.
• Based on Emax and Es, sensor current energy, a
  sensor can decide whether to participate or not.

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Simulation Results
Figure 3. Network Longevity vs. Number of Sensors
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Simulation Results (cont.)
Figure 4. Average Spread in Energy Levels
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Conclusion

• We showed how Energy-Neutral workforce selection
  strategies can reduce the durability of the
  network.
• We also proposed a tasking protocol to maximize
  the longevity of lifeline support sensor network.
• We provided a simple analytical model to estimate
  the parameters of the proposed tasking model.
• Simulation results show that with our approach
  the durability of the network increases by
  keeping sensors energy within three consecutive
  levels for most of the time.