Modeling of Wireless Sensor Networks for Localization and Mobile Targets Tracking ?????????????????????

1
Modeling of Wireless Sensor Networks for
Localization and Mobile Targets
Tracking?????????????????????

• Prasan Kumar Sahoo
• Dept. of Information Management
• Vanung University
• ???
• ????????????
• Present by C.T. Lee
• 2007 / 4 / 16, 30

2

• Educational Background
• ?????????????
• Ph.D. in Mathematics from Utkal University, India
  with advisor from Department of Mathematics,
  Indian Institute of Technology (IIT), Kharagpur,
  India, April, 2002.
• Master of Technology M. Tech in Computer
  Science from Indian Institute of Technology
  (IIT), Kharagpur, India.
• Master of Science M. Sc. in Mathematics from
  Utkal University, India.

3
Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

4
Introduction

• Target detection and tracking can be classified
  into four different categories.
• The first category is to find out the trajectory
  of the target.
• The second category is to wake up the sensors by
  using predictive strategy in order to keep track
  with the target.
• The third category is to use the predictive
  strategy to reduce the transmitted data between
  the sink and each sensor node.
• The last category is to obtain more accurate
  information of the target.

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Introduction

• In this report
• Authors propose the boundary node selection
  algorithms.
• They also propose a target detection protocol to
  track the entry and exit of the single target.
• Design of an extended linear feedback model
  taking binary exponential backoff mechanism of
  IEEE 802.15.4 CSMA/CA based wireless sensor
  network to analyze the energy consumption issues
  of the one hop sensors.

6
Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

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Boundary node Selection and TargetDetection
Protocols

• In this work, it is assumed that all sensors are
  randomly and densely deployed over the monitoring
  region.
• The sensing range is variable, which may be
  larger or smaller than the communication range.

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Boundary node Selection and TargetDetection
Protocols

• e.g. Cover nodes ? 6

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Boundary node Selection and TargetDetection
Protocols

• A and D are BNs after initial phase
• B and C are BNs after second phase
• e.g. B and C communication range x 2 ? cover 2
  BNs (A and D)
• Pruning phase is developed to reset the redundant
  BNs to Non-BNs

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Boundary node Selection and TargetDetection
Protocols

• The BN X, first detects the target at time Td,
  and it broadcasts the Detect X message to its
  neighbors.
• Besides, it checks and finds its recording table
  is empty, and then sends the Entering Time (Td,
  X) to the sink.
• After the target leaves the BN X's sensing range,
  it broadcasts the Leave X packet and checks its
  recording table again.
• Non-BN Y has already sent the Detect Y packet to
  the BN X. So, BN X finds a non-empty field in its
  recording table and therefore does not transmit
  the Leaving Time (Tn, X) to the sink.
• Thus, there is collaboration among nodes X, Y and
  Z to detect the entry or exit of a mobile target.

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Boundary node Selection and TargetDetection
Protocols

• Sam Phu Manh Tran and T. Andrew Yang, OCO
  Optimized Communication Organization for Target
  Tracking in Wireless Sensor Networks,
  International Conference on Sensor Networks,
  Ubiquitous, and Trustworthy Computing, IEEE,
  2006.

12
Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

13
Analytical Model

• Energy Efficiency Modeling and Analysis in
  Wireless Sensor Networks, published in the Proc.
  of IEEE, AusWireless Conference, March, 2006,
  Sydney, Australia.
• Either completed successfully or rejected owing
  to the retransmission limit, a backlogged device
  can immediately switch back to the thinking state.

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Analytical Model

• Authors consider a homogeneous WSNs that consists
  of N number of nodes where nodes may be in the
  thinking or backlogged state, alternatively.
• Let B0, B1,. . . ,BL represent those backlogged
  states.
• Nodes in thinking state may generate new packets
  with probability g.
• It remains in backlogged state if the medium
  sensed by it is busy due to the data transmission
  by other nodes of the network or due to collision
  of its packets with others.
• L 1 number of backlog states are considered,
  where L is the retry limit which is application
  oriented or set as default value as per IEEE
  802.15.4 standard.

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Analytical Model

• Let, W0 be the initial size of the contention
  window.
• The contention window of the r-th retransmission
  is defined as Wr W0 x 2r.
• Backoff Time INT(CW x Random()) x Slot Time

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Analytical Model

• Let, i0, i1,. . . ,iL are the number of
  backlogged nodes present within the backlogged
  states B0, B1,. . . ,BL respectively and Xt
  denotes the total number of backlogged nodes
  present within all those backlogged states Br,
  .
• So,

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Analytical Model

• The transition from state i to state j (i ? j)
  means that there are some thinking terminals
  entering to the backlogged state. Similarly, the
  transition from state i1 to state i represents
  that there is a successful packet transmission.

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Analytical Model
19
Analytical Model
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Analytical Model
Thinking state may generate new packets with
probability g,

• Authors denote R as the state transition matrix
  for the last idle slot t I.
• Authors specify the transition probability matrix
  R S F, where the (i, k)-th element of S and F
  are defined as

state i -- gt state k and transmission successful
state i -- gt state k and transmission failed
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Analytical Model

• For any t t I 2 (tI1) t I T,
  authors define the one-step transition
  probability matrix Q
• If the transmission is successful, the busy
  period's length is T slots and if it is
  unsuccessful, its length is C slots. So the
  transmission matrix P, is expressed as

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Analytical Model

• where S, F, and Q are defined as follows

??????
In backlogged state ??1??????,???? 1????,
thinking state??
??????
??????
In backlogged state 1?????,????(?thinking state )
In backlogged state??1????thinking state1????
???thinking state ??traffic????
In backlogged state ??
??????
In thinking state?2?(?)?????
??????
??????
In busy period ????node???
In thinking state N-i?node ??k-i?node ??traffic
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Analytical Model

• where J represents the fact that a successful
  transmission decreases the backlog by 1. So its
  (i, k)-th entry is defined as follows

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Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

25
Energy Consumption Analysis

• Let, be the expected successful
  probability of the r-th retransmission of
  transmission attempts, for .
• be the expected successful probability
  of the first transmission.
• be the total energy consumption of the
  successful transmission attempt with r number of
  retransmissions.
• be the total energy consumption of the
  failed transmission attempts with r number of
  retransmissions.

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Energy Consumption Analysis

• Then the expected energy consumption for any
  transmission attempts, due to L-retransmission
  attempts can be estimated as follows

?1??????
?1L??????
??????
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Energy Consumption Analysis

• The expected successful probability of the r-th
  retransmission of the transmission attempts as
  follows
• where pi is the probability that the system
  status Xt equals to i. Ps(r, i) is the successful
  probability of the r-th retransmission of the
  transmission attempt while there are i nodes in
  the backlogged state.

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Energy Consumption Analysis
thinking state 1?????,????(?backlogged state)
In backlogged state 1?????,????(?thinking
state)(and r0 )
In backlogged state 1?????,????(?thinking state)
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Energy Consumption Analysis

• Generalizing for any retry limit r, the total
  energy consumption is given by

???????? (Clear Channel Assessment,CCA) ??????????
????(Busy) ??????(Idle)?? ???MAC Layer?????????
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Energy Consumption Analysis

• The energy consumption while the backoff counter
  is decreasing ( )
• The energy consumption while the backoff counter
  is halted ( ) due to the busy
  medium

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Energy Consumption Analysis
32
Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

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Simulation Results
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Simulation Results

• effective energy consumption means the energy
  consumption due to successful transmission
  attempts

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Simulation Results
36
Simulation Results
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Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

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Experimental Setups
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Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

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Implementation Strategies
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Implementation Strategies

1. Implementation at the Mobile Mote

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Implementation Strategies

1. Implementation at the static nodes (MICAz)

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Implementation Strategies

• Implementation at the SINK
• Upon receiving the RSSI values from different
  static MICAz, The sink compares the RSSI values
  with corresponding ID of the MICAz.

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Implementation Strategies

• Implementation at the Database (Notebook)
• In order to store the position of the mobile
  target, authors execute XListen.exe in the
  notebook with a SQL server. Once the
  XListen.exe is executed, the raw data is saved
  to DBTest.txt. Then authors use JAVA SDK to read
  those raw data and save it to the SQL database.
  This JAVA code estimates the position of the
  target if it is nearer or farther to any static
  MICAz.

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Outline

• Introduction
• Boundary node Selection and TargetDetection
  Protocols
• Analytical Model
• Energy Consumption Analysis
• Simulation Results
• Experimental Setups
• Implementation Strategies
• Conclusion and Future Work

46
Conclusion

• Performance analysis show that the energy
  consumption of packet transmission in wireless
  sensor networks is increased with the
• increment of contention window
• traffic load
• network population
• An optimal contention window can be derived from
  the use of fixed contention window to achieve
  the best effective energy consumption.

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Future Work

• Multi-layer boundary nodes problem
• Set cover problem
• Maximizesubject to1. Energy constraint2.
  Coverage constraint
• Tradeoff
• Energy consumption vs. Successful delivery ratio

48
References

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49
References (Authors)

• 9 Power Control Based Topology Construction
  for the Distributed Wireless Sensor Networks,
  accepted for publication in Computer
  Communications (SCI), September, 2006.
• 10 Energy Efficiency Modeling and Analysis in
  Wireless Sensor Networks, published in the Proc.
  of IEEE, AusWireless Conference, March, 2006,
  Sydney, Australia.
• 11 Boundary Node Selection and Target
  Detection in Wireless Sensor Network, under
  review of IEEE, ICITA, China, 2007.
• 12 A Routing Protocol for the Bluetooth
  Scatternet published online in Wireless Personal
  Communications (SCI), September, 2006.

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51

• Energy 1J 1NM 1 QV
• Power 1W1J/s 1 VA

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The desired device lifetime is one year, the
average power dissipation must be less than
Source Wang, A. and Chandrakasan,
A., Energy-efficient DSPs for wireless sensor
networks, Signal Processing Magazine, IEEE
8x0.010.008x0.99 0.08792
0.2369 / 3600 0.0000658 65.8 (µ W)
2000/(0.2369x12x30x24) ?0.9775 (years)
3000/(0.2369x30x24) ?17.59 (months)
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Q A

• Thank You for Your Attention.