Hai Liu, Pengjun Wan, ChihWei Yi, Siaohua Jia,

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Maximal Lifetime Scheduling In Sensor
Surveillance Networks
Hai Liu, Pengjun Wan, Chih-Wei Yi, Siaohua Jia,
Sam Makki and Niki Pissionou Infocom 2005
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Contents
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• Introduction
• System model and problem statement
• Solutions
• Experiments and simulations
• Conclusions

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Introduction
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• Sensor surveillance networks
• Given a set of sensors and targets in a Euclidean
  plane, all targets should be watched by sensors
  at any time
• A sensor can watch only one target at a time
• Lifetime of surveillance
• Length of time until there exists a target j such
  that all sensors in S(j) run out of energy
• One important characteristic of sensor networks
• Stringent power budget of wireless sensor nodes

? prolong the lifetime in sensor surveillance
networks
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System model and problem statement

• How to get long lifetime
• To find a schedule for sensors to watch the
  targets
• Switch on/off modes for sensor nodes

S the set of sensors, T the set of targets n
S the number of sensors, m T S(j) the
set of sensors able to watch target j,j1,,
m T(i) the set of targets within the
surveillance range of sensor i, i1,,n Ei
initial energy reserve of sensor i, i 1,,n
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Solutions
Computing the upper bound on the maximal life
time of the system and a workload matrix of
sensors
Decomposing the workload matrix into a sequence
of schedule matrices
Obtaining a target watching timetable for each
sensor
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Find maximal lifetime

• Find the upper bound on the life time and total
  time sensor i watching target j from computing
  above LP formulation 12

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Decompose workload matrix

• The lifetime of the surveillance system can be
  divided into of a sequence of sessions
• In each session, a set of sensors are scheduled
  to watch their corresponding targets

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Decompose workload matrix

• A special case nm

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Decompose workload matrix

• Generate Case ngtm

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Decompose workload matrix
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Decompose workload matrix

• Represent the filled workload matrix as a
  bipartite graph where one side are sensors and
  the other are targets.

Sensor(i)


0
Ci
Xij
We compute a perfect matching in the Bipartite
graph, which has exactly n edges. This operation
is repeated until there is no perfect matching
can be found.

Target(j)
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Obtain schedule timetable

• Sensor can switch on/off and switch to watch
  other targets asynchronously from each other
• We simply take the i-th row of all the schedule
  matrices and combine the time of the consecutive
  sessions that it watches the same target
• Sensor can cooperate correctly according to the
  timetable to achieve the maximal network lifetime

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Experiments and simulations (1/5)
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Surveillance range of sensor 20
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Fig. 1. An example system with 6 sensors and 3
targets.
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Experiments and simulations (2/5)
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Experiments and simulations (3/5)
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Experiments and simulations (4/5)

• Growth of decomposition steps in linear

? Decomposition steps is linear to the size of
system in real runs
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Experiments and simulations (5/5)

• Comparison with a greedy method

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Conclusion

• Maximal lifetime scheduling in sensor
  surveillance networks
• Solution
• Optimum in sense
• More advantage in the situation that senses are
  densely deployed or sensors have larger coverage
  ranges

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