Energy-Efficient Sensor Network Design Subject to Complete Coverage and Discrimination Constraints - PowerPoint PPT Presentation

Loading...

PPT – Energy-Efficient Sensor Network Design Subject to Complete Coverage and Discrimination Constraints PowerPoint presentation | free to view - id: 208078-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Energy-Efficient Sensor Network Design Subject to Complete Coverage and Discrimination Constraints

Description:

Energy-Efficient Sensor Network Design Subject to Complete Coverage and ... The detection radius of sensor is 1 ... Pjk. Qj. Solution Procedure ... – PowerPoint PPT presentation

Number of Views:20
Avg rating:3.0/5.0
Slides: 36
Provided by: SYHU
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Energy-Efficient Sensor Network Design Subject to Complete Coverage and Discrimination Constraints


1
Energy-Efficient Sensor Network Design Subject to
Complete Coverage and Discrimination Constraints
  • Frank Y. S. Lin, P. L. Chiu
  • IM, NTU
  • SECON 2005

Presenter Steve Hu
2
Outline
  • Problem Description
  • Problem Formulation
  • Solution Procedure
  • Computational Results
  • Conclusion

3
Problem Description
  • The detection radius of sensor is 1
  • A complete coverage/discrimination sensor field
    with 3 by 5 grids

4
Problem Description
  • Completely discriminated unique power vector for
    each grid point
  • Ex lt1,0,0,0,0,0gt for grid point (1,3)
  • lt0,0,1,1,0,0gt for grid point (3,2)

5
Problem Description
  • If we want to prolonged life time K times, two
    options
  • (1) Deploy K duplicate sensor networks on a
    sensor field
  • (2)No duplicate sensor networks, but divide the
    network in K covers

6
Overall placement
Cover 1
Cover 2
Cover 3
7
Problem Description
  • No duplicate but divide in 3 covers
  • Total sensor number 14

Duplicate 3 times Total sensor number 6 3 18
8
Problem Description
  • Lemma 1
  • Gr the number of covering grids

9
Problem Description
  • Lemma 2 A grid point can be covered by a set of
    sensors. The maximum cardinality of the set
    exactly equals the number of covering grid points
    of a sensor that is allocated in the grid point.

10
Problem Description
  • Lemma 3 On rectangular sensor field with a
    finite area, the upper bound of the number of
    covers, Ur, is

11
Problem Description
  • By Lemma 3

12
Problem Formulation
  • Given Parameters
  • A 1,2,,m The set of indexes for candidate
    locations where sensor can be allocated.
  • B 1,2,,n The set of the indexes for grid
    points that can be covered and located by the
    sensor network, m lt n
  • K The number of covers required (with upper
    bound regards to radius)
  • aij Indicator which is 1 if grid point i can be
    covered by sensor j, and 0 otherwise
  • cj Cost function of sensor j

13
Problem Formulation
  • Decision Variables
  • Xjk 1 if sensor j is designated to cover k of
    sensor network, and 0 otherwise
  • Yj Sensor allocation decision variable, which
    is 1 if sensor j is allocated in the sensor
    network and 0 otherwise

14
Problem Formulation
  • Objective function

15
Problem Formulation
  • Constraints

(Coverage Constraint)
B every grid point in the field aij 1 if grid
point i can be covered by sensor j, and 0
otherwise
A candidate sensor location
16
Problem Formulation
  • Constraints

(Discrimination Constraint)
17
Solution Procedure
  • Lagrangean Relaxation
  • a method for obtaining lower bounds (for
    minimization problems)
  • Ex

18
Solution Procedure
  • Lagrangean Relaxation ( )
  • with

19
Solution Procedure
  • Lagrangean Relaxation ( )
  • This (19.5) is the smallest upper bound we can
    found by Lagrangean Relaxation.

20
Solution Procedure
  • Original LR

21
Solution Procedure
  • Since there are two decision variable(Xjk, Yj)
  • gtdivide into two subproblem

22
Solution Procedure
Pjk
Qj
23
Solution Procedure
  • After optimally solving each Lagrangean
    relaxation problem (by subgradient method), a set
    of decision variables can be found, but may not
    feasible
  • Propose a heuristic algorithm for obtaining
    feasible solutions

24
Solution Procedure
25
Solution Procedure
26
Solution Procedure
27
Computational Results
  • algorithm tested on 10 by 10 sensor area

28
Computational Results
  • algorithm tested on 10 by 10 sensor area

29
Computational Results
  • algorithm tested on 10 by 10 sensor area

0.80 / 3 26.7
30
Computational Results
  • algorithm tested on 10 by 10 sensor area

80 / 40 2
31
Computational Results
  • algorithm tested on 10 by 10 sensor area

32
Computational Results
  • algorithm tested on 10 by 10 sensor area
  • The solution time of the algorithm is below 100
    seconds in all cases. The efficiency of the
    algorithm thus can be confirmed.

33
Computational Results
  • algorithm tested on different size of sensor area

34
Conclusion
  • Proposed algorithm is truly novel and it has not
    been discussed in previous researches
  • Prolong the networking lifetime almost to
    theoretical upper bound

35
Conclusion
  • My opinion and what I learned here
  • Algorithm description is too rough
  • An example to formulate a problem into integer
    programming
  • Use Lagangean Relaxation to obtain lower bounds
    for minimization problems
About PowerShow.com