The Hybrid Mobile Wireless Sensor Networks for Data Gathering

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The Hybrid Mobile Wireless Sensor Networks for
Data Gathering

• Biao Ren, Jian Ma, Canfeng Chen
• Proceeding of the 2006 international conference
  on Communications and mobile computing IWCMC '06
• 
  July 2006

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Outline

• Introduction
• Models and Related works
• Analyses
• Simulations
• Conclusions

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Introduction

• Traditional wireless sensor networks extension
  based on pure ad-hoc networks ,where the dense
  distribution of sensor nodes and multi-hop
  transmission over the whole network are their
  outstanding characteristics.
• Some disadvantages still exist, such as poor
  scalability, weak energy balance, as well as low
  network lifetime.
• Hybrid wireless sensor network is usually
  comprised of some kinds of heterogeneous devices,
  which mainly act as sinks responsible for
  gathering and forwarding data from sensor nodes.

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Introduction(cont.)

• Some of them are energy-rich or rechargeable,
  some are capable of communication with better
  capability and some are mobility enabled. These
  features can not only improve the network
  performance, but also extend the potential
  applications and make commercial implementation
  easy.
• The paper investigates the impacts of the number,
  velocity, transmission radius and gathering mode
  of mobile sinks on large-scale and sparse
  wireless sensor networks.

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Models and Related works

• Network Model
• Mobility Model
• Traffic Model

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Network Model

• The hybrid mobile sensor network consists of
  numerous static sensor nodes and some number of
  mobile sinks. The location of the static nodes
  are fixed and distributed uniformly at random.
• The mobile sinks are randomly distributed at
  initial time. At later time their position and
  velocities are given by the mobility model.
• In order to comparison, we also consider the
  fixed sinks where the velocity of mobile sinks is
  set to zero. Each fixed sink is arranged at a
  grid point to optimize the performance.

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Mobility Model

• Each of the total m mobile sinks pick a direction
  uniformly at random from (0, 2p and moves in
  that direction for a distance d at speed v ,
  where d is a exponentially distribution.
• If the sink hits the boundary of the sink, it is
  reflected at the boundary (The positions of
  mobile nodes are independent of each other). The
  direction of the mobile node is also uniformly
  distributed in (0, 2p all the time.

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Traffic Model

• In dense sensor network, data gathered from
  environment is forwarded to sink node in
  multi-hops fashion. Although it can provide low
  data delivery delay, the energy consumption per
  bit is much higher due to the multi-hop
  forwarding of one packet.
• We adopt the limited k-hop scheme for data
  gathering from sensor nodes. That is, the data
  transmission of a sensor node will not happen
  until at least one mobile sink approach to it
  within at most k-hop distance.

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Analyses

• Delay Analysis
• Velocity Impact on Delay

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Delay Analysis(/)

• The end to end data delivery delay is dominated
  by the duration during the sensor nodes are
  waiting for a sink to approach. So waiting
  duration is our primary concern.
• It is related to the number and the velocity of
  mobile sinks as well as the transmission radius
  of sensor nodes.

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Delay Analysis(/)

• Theorem Given a sensor node S. Let m denotes the
  number of mobile sinks, r is the range of
  transmission and v is the velocity of mobile
  sinks. With high probability, the average
  duration D until which a mobile sink first enters
  the field of sensor node S is

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Delay Analysis(/)

• M some mobile sink at a distance l from the
  static sensor node S.
• The probability that M enters the neighborhood of
  S is equal to the angle subtended by the
  neighborhood of S at M.
• cr scaling factor angle
• Ri rings with width m-1/2 (consists of points
  which are at a distance between i -1/ m-1/2 and
  i / m-1/2 from S)
• Let Xi,jbe a random variable such that Xi,j1 if
  the i th mobile sink is in Rj and it will enter
  neighborhood of S.
• Xi,j 0, otherwise. (The event Xi,j 2cr/m )
• The probability that Mi ? Rj is
  2j m-1/2

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Delay Analysis(/)

• Define
  
• d the number of rings far from S.
• Use the second Chernoff bound Equation
• gt
  d4f logm / cr
• a sensor node can take time less than
• to wait for a mobile sink reaching it with
  high probability.
• The service probability that a sensor is within
  the coverage of at least one mobile sink

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Velocity Impact on Delay(1/3)

• High velocity can increase the probability for
  the sensor and mobile sink meet with each other,
  but mobile sinks passing through the effective
  region of a sensor node so fast that there is no
  adequate time to perform continuous transmission.
  
• Increasing velocity will increase the service
  probability whereas decrease the service duration
  of each time.

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Velocity Impact on Delay(2/3)

• The average travel distance through the region is
  equal to
• The available time for message transmission is
  proportional to
• L message length w channel bandwidth
• gtL/w the number of time slots required
  to transmit a
• message
• p service probability
• service time µ

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Velocity Impact on Delay(3/3)

• The average message delivery delay
• ?1 gt

? average arrival rate
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Simulations

• Evaluate three different performance metrics
  which are crucial to improve the QoS and prolong
  the lifetime of sensor network.
• 1.The average data delivery delay
• - duration from data generation to data
  reception
• 2.The data success rate
• - the ratio of the number of message
  generated by sensor
• nodes to it received by mobile
  sinks
• 3.The lifetime of the network
• - minimal remaining energy (1000)

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Simulations(cont.)

• Totally 1500 sensor nodes are deployed uniformly
  at random in the determined area.
• Varying transmission radius is chosen properly to
  assure some degree of connectivity of network.
• Mobile sinks move according to mobility model.
• The data generation of each sensor nodes follows
  a Poisson process with an average arrival
  interval of 1s.

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Conclusions

• Choosing appropriate number, transmission range,
  velocity as well as gathering fashion of mobile
  sinks can significantly guarantee lower
  end-to-end data delivery delay and achieve better
  energy conservation.
• A promising direction for future work is to
  explore the use of cooperative mobility, find an
  effective data dissemination protocol, and
  improve the throughput capacity of hybrid sensor
  network.