Title: Modeling of Wireless Sensor Networks for Localization and Mobile Targets Tracking ?????????????????????
1Modeling 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.
3Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
4Introduction
- 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.
5Introduction
- 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.
6Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
7Boundary 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.
8Boundary node Selection and TargetDetection
Protocols
9Boundary 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
10Boundary 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.
11Boundary 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.
12Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
13Analytical 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.
14Analytical 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.
15Analytical 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
16Analytical 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,
17Analytical 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.
18Analytical Model
19Analytical Model
20Analytical 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
21Analytical 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
22Analytical 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
23Analytical 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
24Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
25Energy 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.
26Energy Consumption Analysis
- Then the expected energy consumption for any
transmission attempts, due to L-retransmission
attempts can be estimated as follows
?1??????
?1L??????
??????
27Energy 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.
28Energy Consumption Analysis
thinking state 1?????,????(?backlogged state)
In backlogged state 1?????,????(?thinking
state)(and r0 )
In backlogged state 1?????,????(?thinking state)
29Energy Consumption Analysis
- Generalizing for any retry limit r, the total
energy consumption is given by
???????? (Clear Channel Assessment,CCA) ??????????
????(Busy) ??????(Idle)?? ???MAC Layer?????????
30Energy 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
31Energy Consumption Analysis
32Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
33Simulation Results
34Simulation Results
- effective energy consumption means the energy
consumption due to successful transmission
attempts
35Simulation Results
36Simulation Results
37Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
38Experimental Setups
39Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
40Implementation Strategies
41Implementation Strategies
- Implementation at the Mobile Mote
42Implementation Strategies
- Implementation at the static nodes (MICAz)
43Implementation 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.
44Implementation 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.
45Outline
- Introduction
- Boundary node Selection and TargetDetection
Protocols - Analytical Model
- Energy Consumption Analysis
- Simulation Results
- Experimental Setups
- Implementation Strategies
- Conclusion and Future Work
46Conclusion
- 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.
47Future Work
- Multi-layer boundary nodes problem
- Set cover problem
- Maximizesubject to1. Energy constraint2.
Coverage constraint - Tradeoff
- Energy consumption vs. Successful delivery ratio
48References
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49References (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,
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Detection in Wireless Sensor Network, under
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Scatternet published online in Wireless Personal
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50(No Transcript)
51- Energy 1J 1NM 1 QV
- Power 1W1J/s 1 VA
52The 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)
53Q A
- Thank You for Your Attention.