A Time-Varying Opportunistic Approach to Lifetime Maximization of Wireless Sensor Networks IEEE Transaction on Signal Processing, Oct. 2010 Kobi Cohen, Amir Leshem School of Engineering, Bar-Ilan University, Ramat-Gan, Israel - PowerPoint PPT Presentation

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A Time-Varying Opportunistic Approach to Lifetime Maximization of Wireless Sensor Networks IEEE Transaction on Signal Processing, Oct. 2010 Kobi Cohen, Amir Leshem School of Engineering, Bar-Ilan University, Ramat-Gan, Israel

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Title: A Time-Varying Opportunistic Approach to Lifetime Maximization of Wireless Sensor Networks IEEE Transaction on Signal Processing, Oct. 2010 Kobi Cohen, Amir Leshem School of Engineering, Bar-Ilan University, Ramat-Gan, Israel


1
A Time-Varying Opportunistic Approach to Lifetime
Maximization of Wireless Sensor NetworksIEEE
Transaction on Signal Processing, Oct. 2010
Kobi Cohen, Amir LeshemSchool of Engineering,
Bar-Ilan University,Ramat-Gan, Israel
2
Talk overview
  • Transmission scheduling problem over energy
    limited wireless sensor networks (WSN)
  • Network model and lifetime definition
  • Overview of existing distributed protocols
  • Introducing Time-varying Opportunistic Protocol
    (TOP)
  • Simulation examples
  • Conclusions

3
Sensor networks with a mobile access point (Tong
et al.)
  • Each sensor transmits its measurement directly to
    an access point (AP) through a fading channel
  • AP powerful hardware unit
  • Sensors - low-power nodes
  • Small-scale fading - random variable channel
    gain
  • Exploiting channel diversity significantly
    affects the transmission energy and network
    lifetime

4
Network model
  • Transmission energyetr,n denotes the
    transmission energy consumed by sensor n during a
    single data collection- Selecting sensor with
    better channel gain reduces the transmission
    energy etr,n

5
Network model
  • Wasted energyWe define the wasted energy as
    the total unused energy in the network when it
    dies

where ew,n is the residual energy at sensor n
when the network dies.
6
Lifetime definition
  • The network lifetime is defined as the number of
    data collections until the first sensor dies
    (i.e. its residual energy drops below threshold
    energy, eth).
  • Based on previous work of Chen and Zhao, we can
    show that the expected network lifetime is given
    by
  • - Eetr is the expected transmission energy
  • - EEw is the expected wasted energy

7
Design principle for lifetime maximization
  • Chen and Zhao Have developed the design
  • principle for lifetime maximization
  • We should reduce the transmission energy whenthe
    network is young (l is small)
  • We should reduce the wasted energy as l
    increased (the network is old)

8
Distributed access via opportunistic carrier
sensing (Zhao et al.)
  • Each sensor in the network calculates an
    energy-efficiency index and maps it to a
    back-off time based on decreasing common
    function
  • Each sensor listens to the channel and if no
    other sensor transmits before its back-off time
    expires, the sensor is allowed to transmit.
  • Goal Decide during each data collection which
    sensor should transmit in a distributed fashion
    by exploiting local CSI and local REI, in order
    to maximize the network lifetime.

9
The transmission scheduling problem
  • The chosen sensor, , is determined by solving
    the
  • following transmission scheduling problem
  • We denote this constraint as local survivability
    condition.
  • Our goal is to find an adequate strategy for
    obtaining
  • in order to maximize the network
    lifetime.

10
Overview of existing protocols
  • Pure opportunistic protocols
    - The performance of the pure
    opportunistic protocol in terms of network
    lifetime is extremely poor.
  • Dynamic Protocol for Lifetime Maximization
    (DPLM)- DPLM implementation via carrier
    sensing is much more complicated.

11
Time-varying Opportunistic Protocol (TOP)
  • By implementing TOP we require
  • Opportunistic strategy when the network is young,
    while less opportunistic and more conservative
    strategy when the network is old
  • Simple implementation via opportunistic carrier
    sensing
  • Approaching the pure opportunistic protocol as

12
Time-varying Opportunistic Protocol (TOP)
  • Justification to the third requirement
  • It can be shown that selecting the sensor with
    the best channel for transmission is generally
    preferred over other distributed protocols in the
    case where
  • Then, we consider the realistic case where It
    can be shown that selecting the sensor with the
    best channel for transmission as long as the
    sensor has sufficient energy for current
    transmission plus the predicted energy loss, is
    generally preferred over other distributed
    protocols

13
TOP algorithm
Step 1 The predicted energy loss is estimated by
where the network lifetime is estimated by
- The transmission energy is estimated by
averagingover previous data collections
- The wasted energy is estimated by
14
TOP algorithm
Step 2 Each sensor updates its corrected
residual energy by
Hence, each sensor transmits in the following
scheme
where
We denote the corrected constraint as long term
local survivability condition
15
Main properties of TOP
  • TOP strategy
  • - As long as the network is young, the long term
    local survivability condition is not valid
  • - Consequently, the chosen sensor is
    determined according to the best channel
    during each data collection
  • - When the network is old, the long term
    local survivability condition is valid
    for some sensors
  • - Consequently, sensor which has better
    channel gain may not transmit, although it has
    sufficient energy for current transmission

16
Main properties of TOP
  • Asymptotic optimality of TOP
  • The relative performance loss of TOP compared to
    the optimal protocol decreases as the initial
    energy across the sensors increases
  • Explicitly, we have
  • - Lopt and LTOP denote the network lifetime
    achieved by the optimal protocol and TOP,
    respectively

17
Simulation examples
  • Block Rayleigh fading channel, i.i.d across data
    collections and across sensors
  • The channel gain mean was set to 1
  • The channel bandwidth was normalized to 1
  • The SNR was set to 3dB
  • The normalized energy required for CSI
    acquisition was set to ece0.01

18
Simulation examples
Scenario 1 ein10
19
Simulation examples
Scenario 1 ein10
20
Simulation examples
Scenario 2 ein100 Scenario 3
ein10, ece0
21
Conclusions
  • TOP algorithm prioritizes sensors with better
    channels when the network is young, while
    prioritizes sensors with more residual energies
    when the network is old
  • TOP simplifies the implementation via
    opportunistic carrier sensing as compared to
    other distributed MAC protocols
  • TOP is asymptotically optimal The relative
    performance loss of
  • TOP compared to the optimal protocol
    decreases as the initial energy across the
    sensors increases
  • Simulation results have shown that TOP achieves
    significant performance gain over other
    distributed protocols that have been proposed
    recently

22
Related work
  • K. Cohen and A. Leshem, A Time-Varying
    Opportunistic Approach to Lifetime Maximization
    of Wireless Sensor Networks, IEEE Trans. on
    Signal Process., 2010
  • L. Tong and Q. Zhao and S. Adireddi, Sensor
    networks with mobile agents, in proc. 2003 Intl.
    Symp. Military Communications.
  • Y. Chen and Q. Zhao, On the lifetime of wireless
    sensor networks, IEEE Commun. Lett., 2005.
  • Q. Zhao and L. Tong, Opportunistic carrier
    sensing for energy-efficient information
    retrieval in sensor networks, EURASIP J.
    Wireless commun. Netw., 2005.
  • Y. Chen and Q. Zhao, An integrated approach to
    energy aware medium access for wireless sensor
    networks, IEEE Trans. Signal Process., 2007.
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