RFID Tag Coverage Optimization using Particle

RFID Tag Coverage Optimization using Particle Swarm Optimization (PSO) Algorithm and Design of Experiments (DOE) Technique Azli bin Nawawi Assoc. Prof. Dr. Khalid bin Hasnan Assoc. Prof. Dr. Sh Salleh bin Sh Ahmad Doctor of Philosophy (PhD) Faculty of Mechanical & Manufacturing Engineering Universiti Tun Hussein Onn Malaysia (UTHM)

RFID Tag Coverage Optimization using Particle Swarm Optimization

(PSO) Algorithm and Design of Experiments (DOE) Technique

Tag Coverage Optimization using Particle Swarm Optimization


Radio Frequency IDentification (RFID) • Use radio wave to operate

• More efficient asset management system • Various industries: Manufacturing, retail, supply chain, construction, automotive, health care and airline.

RFID reader

RFID reader Computer

RFID tag


RFID tag


Detecting RFID Tags..

RFID tag


Detecting RFID Tags..

RFID tag


RFID tag

RFID reader Computer RFID Tag Detected Tag ID: 123xyz

RFID tag

RFID reader

RFID tag

RFID reader

RFID tag

RFID reader

20 meter

20 meter

30 meter 30 meter

30 meter 30 meter

Important Questions • How to detect all tags/as many tags as possible? • How to avoid the interference between readers? • How to get the right amount of readers? • How to make sure all readers have the equal load balance?

RFID Network Planning (RNP)

RNP Optimal Tag Coverage

Readers Interferences

Economic Efficiency

Good Load Balance

RFID Network Planning (RNP)

RNP

Optimal Tag Coverage

Readers Interferences

Economic Efficiency

Good Load Balance



RFID using Particle Swarm Optimization (PSO) Algorithm and Design of

Experiments (DOE) Technique

Nature Inspired Algorithms 1. Genetic Algorithm (GA). 2. Particle Swarm Optimization (PSO). 3. Bacteria Foraging Optimization(BFO).

Nature Inspired Algorithms 1. Genetic Algorithm (GA).

2. Particle Swarm Optimization (PSO). 3. Bacteria Foraging Optimization(BFO).

Nature Inspired Algorithms 1. Genetic Algorithm (GA).

2. Particle Swarm Optimization (PSO). 3. Bacteria Foraging Optimization(BFO). Simple and fast Very established Effective on various problems Fewer parameters

No. of publications related to PSO 25000

No. of publications

20000

15000

10000

5000

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Year of publication




(

) Algorithm and Design of Experiments (DOE) Technique

PSO • No parameters settings of PSO that fits all. • More significant: PSO is used for solving complex optimization problem. • Any variation made to PSO parameters  huge difference. • Parameter Tuning.

Parameter Tuning: A Weakness Area • Parameter tuning weakness area for most algorithm developers (Dobslaw, 2010). • The weak tuning of PSO parameters has the potential to generate a set of low quality results with lack of accuracy (Beielstein, Parsopoulos and Vrahatis, 2002; El-Gallad et al., 2002; Kramer, Gloger and Goebels, 2007).

• Literatures suggest the application of Design of Experiments (DOE).




(PSO) Algorithm and (

) Technique

Design of Experiment (DOE) • A tool to understand a particular process. • All important parameters are included in the analysis. • Identify key factors quality improvement. • Identify significant parameters and their optimum settings. • Quantitative effect of each parameter.

Related Works Researcher(s)

Contribution/Outcome

(Beielstein, Parsopoulos DOE for PSO parameter tuning. Used on a and Vrahatis, 2002) very simple function (El-Gallad et al., 2002)

DOE for PSO parameter tuning. Function with one scenario only

(Tamizharasan, Barnabas and Ahamed, 2002)

Comparing 3 optimization processes. PSO and DOE are not combined

(Kramer, Gloger and Goebels, 2007)

Fractional factorial used on PSO. Only one scenario

(Chen and Zhu, 2011)

PSO variants for solving RNP. The objective function is very flexible.

(Dobslaw, 2010)

Weak parameter tuning  suboptimal optimization result.

Related Works Researcher(s)

Contribution/Outcome

(Wang et al., 2011)(a)

PSO is used to optimize the mathematical model generated from DOE.

(Wang et al., 2011)(b)

PSO is used to optimize the mathematical model generated from DOE.

(Gong et al., 2012)

PSO variant used as comparison

(Packianather et al., 2013)

DOE methodology used as a reference

(Chen and Zhu, 2008)

PSO variant used as comparison

DOE for PSO’s parameter tuning is common

2 DOEs for PSO’s parameter tuning

Research Objectives Obj.1 Obj.2 Obj.3

• Construct the objective function of the RFID tag coverage optimization -> exposes the direct correlation between the parameters of RFID tag coverage and the solutions of PSO. • Justify the optimum and general settings of PSO parameters for all RNP scenarios generated by the 1st DOE session. • Evaluate the performance of the combination of PSO and DOE method against other PSO variants.

Research Scopes 1. RFID Passive tag. 2. RFID application: Asset tracking & management. 3. Original version of PSO (most stable and established). 4. Matlab (programming). 5. Minitab (DOE and statistical analysis).

Research Scopes The upper and lower ranges of each parameter of interest NO.

PARAMETER

LOWER RANGE

HIGHER RANGE

1

Number of iterations

50 iterations

200 iterations

2

Number of swarms

50 swarms

200 swarms

3

Inertia weight value

0.5

3

4

Correction factor value

0.5

3

5

Number of readers

1 reader

10 readers

6

Number of tags

10 tags

100 tags

7

Working space area

5m x 5m

30m x 30m

Research Methodology Obj.1

Construct the objective function

Obj.2

Optimum & general settings of PSO parameters

Obj.3

Evaluate the performance against other PSOs



Obj.2


Obj.3




Obj.2


Obj.3




Obj.2


Obj.3




Obj.2


Obj.3




Obj.2


Obj.3




Obj.2


Obj.3


Results of Objective 1

C

Coverage Optimization

(x, y) Coordinate of Reader

NT

Number of Tags

(a, b) Coordinate of Tag

Pt

Power of Reader

Ae

Effective Apperture

C

Coverage Optimization

(x, y) Coordinate of Reader

NT

Number of Tags

(a, b) Coordinate of Tag

Pt

Power of Reader

Ae

Effective Apperture

Factors used for generating RNP scenarios using DOE RNP Parameter

Low Level

High Level

Number of readers

1 reader

10 readers

Number of tags

10 tags

100 tags

Working space area

5m x 5m

30m x 30m

Sc.1

Sc.5

Sc.2

Sc.6

Sc.3

Sc.7

Sc.4

Sc.8



Obj.2


Obj.3



Sample of Results (Scenario 2) Pareto Chart of the Effects

(response is Obj. func. value, Alpha = 0.05) 59.81 F actor A B C D

B CD A BC A BD

Term

A BCD AB C AD BCD A CD D A BC AC BD

0

10

Lenth's PSE = 23.2687

20

30

40 Effect

50

60

70

80

N ame N o. of iterations N o. of sw arms Inertia w eight C orrection factor

Sample of Results (Scenario 4) Normal Plot of the Effects

(response is Obj. func. value, Alpha = 0.05) 99 D

95 90

F actor A B C D

Percent

80 70 60 50 40 30 20 10 5 1

Effect Ty pe Not Significant Significant

B

-15

-10

Lenth's PSE = 3.60277

-5

0 Effect

5

10

N ame N o. of iterations N o. of sw arms Inertia w eight C orrection factor

Sample of Results (Scenario 6) Main Effects Plot for Obj. func. value Data Means

No. of iterations

No. of swarms

1950 1900

Mean

1850 50

200

50

Inertia weight

200 Correction factor

1950 1900 1850 0.5

3.0

0.5

3.0

Optimum Settings for Each Scenario Scenario No. of Iterations

No. of Swarms

Inertia Weight Correction Value Factor Value

1

50 (min)

200 (max)

0.5 (min)

0.5 (min)

2

50 (min)

200 (max)

0.5 (min)

0.5 (min)

3

200 (max)

200 (max)

0.5 (min)

0.5 (min)

4

50 (min)

200 (max)

0.5 (min)

0.5 (min)

5

50 (min)

200 (max)

3.0 (max)

0.5 (min)

6

50 (min)

200 (max)

3.0 (max)

0.5 (min)

7

50 (min)

200 (max)

0.5 (min)

0.5 (min)

8

50 (min)

200 (max)

0.5 (min)

0.5 (min)

General Settings for All Scenarios

PSO

Number of

Number of

Inertia weight

Correction

Parameter

iterations

swarms

value

factor value

General

setting

50 (min)

200 (max)

0.5 (min)

0.5 (min)



Obj.2


Obj.3



Settings for PSO parameters for each variant of PSO PSO variant

Number of

Number of

Inertia

Correction

iterations

swarms

weight value factor value

PSO & DOE

50 (min)

200 (max)

0.5 (min)

0.5 (min)

Chen and

200

50

0.729

2.05

200

50

0.9 to 0.4

2.0

Zhu (2008) Gong, et al. (2012)

(Decreasing

value)

Sample of Results (Scenario 1) Individual Value Plot of PSO & DOE, Chen & Zhu 2008, Gong, et al. 2012 900 800 700

Data

600 500 400 300 200 100 PSO & DOE

Chen & Zhu 2008

Gong, et al. 2012

Sample of Results (Scenario 1) Boxplot of PSO & DOE, Chen & Zhu 2008, Gong, et al. 2012 900 800 700

Data

600 500 400 300 200 100 PSO & DOE

Chen & Zhu 2008

Gong, et al. 2012

Results of Objective 3 • The proposed method managed to generate lower objective function values compared to other PSO variants. • Lower value = Better result. • The fastest: PSO used in Gong, et al. (2012) due to the application of the gradually reduced the inertia weight value.

Conclusion Implementation of 2 DOE sessions on the RFID tag coverage optimization using PSO algorithm

Conclusion General setting of PSO parameters for RFID tag coverage optimization PSO

Number of

Number of

Inertia weight

Correction

Parameter

iterations

swarms

value

factor value

General

setting

50 (min)

200 (max)

0.5 (min)

0.5 (min)

Conclusion The general settings are applicable for the following scopes: NO.

PARAMETER

LOWER RANGE

HIGHER RANGE

1

Number of iterations

50 iterations

200 iterations

2

Number of swarms

50 swarms

200 swarms

3

Inertia weight value

0.5

3

4

Correction factor value

0.5

3

5

Number of readers

1 reader

10 readers

6

Number of tags

10 tags

100 tags

7

Working space area

5m x 5m

30m x 30m

Conclusion The proposed method (PSO and DOE

combination) can be considered as a robust and efficient optimization system because it manages to generate high quality results in overall RNP scenarios.

Contribution (Obj.1) Useful for beginners in the field of RFID tag

coverage optimization  offers a useful reference for initiating RNP optimization.

Contribution (Obj.2) Suggests the optimum setting for PSO

parameters  optimization process on every RNP scenario.

Contribution (Obj.3) Shows that the proposed method

performs better than the other PSO variants  proof of the usefulness.

Research Continuity • 5 FYPs (Final Year Projects). • Research Group: – RFID Network Planning (ISE: Industrial Systems Engineering). – RFID for Warehouse Management (MES: Mechatronics and Embedded Systems).

• Industrial Case Study (Unijoh Sdn. Bhd.)

Thank you! For your precious time..