International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 677–689, Article ID: IJMET_10_01_069
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=1
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
COMPARATIVE EVALUATION OF THE
GRADIENT-BASED CUCKOO SEARCH (GBCS)
AND (MC-GPSO) TECHNIQUES FOR OPTIMAL
RFID NETWORK PLANNING
Nihad Hasan Talib, Khalid bin Hasnan, Azli bin Nawawi, Haslina Binti Abdullah And
Adel Muhsin Elewe
Faculty of Mechanical and Manufacturing Engineering. University Tun Hussein Onn
Malaysia (Batu- Pahat, Johor, Malaysia)
ABSTRACT
Large-scale, complex, and dynamic radio frequency identification network planning
(RNP) problem has been proven to be an NP-hard case. Meta-heuristic algorithms
provide efficient techniques to resolve the problems of RFID Network Planning
optimization that are not possible with the traditional techniques. The gradient of the
RFID Network Planning objective function was recently used to improve the precision
of global optimal solutions. This work presents a comparative study between the
Gradient-Based Cuckoo Search (GBC Search) and Multi-Colony Global Particle
Swarm (MC-GLOBAL PSO) in complex, and dynamic RNP network, Experiments are
conducted on two standard RFID sets of benchmark data which consist of random
topologies. The results of this comparison investigated the performance of the algorithm
in terms of (1) maximum of tag coverage, (2) required number of readers, (3) and
minimum interference between readers. The present method specifies the combined
performance of the reader propagation area based on the evaluation of the tag density
and location by using the Gradient-Based to manage the input representation of the
Cuckoo Search .Simulation outcomes demonstrate the (GBC Search) technique
outperforms the reference algorithms for designing RFID networks, in terms of
optimization efficiency and calculation robustness, indicating that the GBC Search is
suitable for solving huge dimension RNP problems.
Keywords: RFID system, RNP hard-problem, Meta-heuristic algorithms (GBCS) and
MC-GPSO.
Cite this Article: Nihad Hasan Talib, Khalid bin Hasnan, Azli bin Nawawi, Haslina
Binti Abdullah,And Adel Muhsin Elewe, Comparative Evaluation of the GradientBased Cuckoo Search (Gbcs) and (Mc-Gpso) Techniques for Optimal Rfid Network
Planning , International Journal of Mechanical Engineering and Technology, 10(01),
2019, pp. 677-689.
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Nihad Hasan Talib, Khalid bin Hasnan, Azli bin Nawawi, Haslina Binti Abdullah,
And Adel Muhsin Elewe
1. INTRODUCTION
Radio frequency identification (RFID) system as a unique inventory tracking technology that
has produced significant development in various practical industrial situations where much big
application potential has been realized and many are being explored. In various real-world
applications of RFID, such as manufacturing, Tracking of tools, Library Systems, railway,
logistics, maintenance, and warehouse,[1] a sufficient number of RFID readers are used in
order to give full coverage of the RFID tags in the given space [2] In recent years, RFID
system is applied to construct up an “Internet of Things” .a network that combines physical
things to the Internet, making it possible to access remote sensor information and to monitor
the physical world without any physical contact [3] This raises some issues in the deployment
of an RFID network for the control and management of the large-scale Internet of Things
purposes, such as good tag coverage, quality of service , and cost [4]. This results in some
important questions to be considered in the case of avoiding reader to reader interference [5][6]
such as (1) how many readers are required; (2) where readers should be installed; (3) what the
suitable parameter is setting for each RFID reader [6]. In addition, considering the cost-efficient
for the radio frequency identification system, the system should meet the objects with the
smallest number of readers and highest tags coverage [5] In general, we described that the
network planning of RFID aims to optimize a set of targets ( the least cost of the reader,
maximum coverage of tags, good load balance, interference, etc.), by setting the control
variables (the coordinate of the reader, readers number, the parameters of antenna, etc.) of the
system [7].
Optimization is a process for developing functional procedures such as locating the
maximum or minimum of a function in order to greatest achievable performance under the
given limitations, Artificial Intelligence (AI) methods offer an interesting application in
engineering. Optimization techniques represent a robust set of tools which can be employed to
determine optimal solutions for many kinds of problems [8] In recent years, a class of RFID
Network Planning optimization based on SI techniques has been developed, including (EA)
Evolutionary Algorithms and (SI) Swarm Intelligence. [9] [10] SI includes five different
techniques, namely (ACO) Ant Colony Optimization, (ABC) Artificial Bee Colony, (PSO)
Particle Swarm Optimization, (BFO) Bacterial Foraging Optimization, and Firefly Algorithm
(FA) [11]. Recently, cuckoo search algorithms have been applied in wide function optimization
domains such as feature selection, engineering optimization, scheduling, planning, and realworld applications [12]. The Cuckoo Search (CS) is a unique nature- inspired stochastic
optimization approach.). Fateen et al.in 2014 allowed the CS algorithm to apply the gradient information to
increase the performance and reliability of the algorithm [13]. GBCS proved to be a strong algorithm candidate
for solving difficult optimization problems. Jaballah and Meddeb in 2017 presented a Self-Adaptive
Cuckoo (SACS) algorithm [14] The SACS technique is a helpful method to solve real RFID
network planning cases. The empirical results observed optimal solutions for the problem of
RFID network planning. [14]. This paper compares the state of the art PSO developed
algorithm, known as the MC-GPSO technique presented by Hasana in 2015 with GradientBased Cuckoo Search (GBCS). The aim of this study is to determine a proper algorithm in order
to employ the RFID in a large and complex area. The simulation results showed the superiority
of the suggested algorithm (GBCS). The rest of the paper is organized as follows. Section 2:
gives a review of the RNP Optimization. Section 3: describes the implementation of the
proposed approach based on GBCS Section 4: gives description of the MC-GPSO algorithm.
Section 5: defines the Mathematical Model and Problem Formulation, Section 6: algorithm
simulation results and Section 7: outlines the conclusions.
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2. RFID NETWORK PLANNING OPTIMIZATION METHODS
RFID Network Planning (RFID-NP) is defined as a method of network-synthesis based on
multi-objective of RFID technology. RNP is aimed to ensure that the service of the network
can satisfy the requirements of the contributors as well as operator. RFID technology Planning
optimization assists to maximize network structure performance. due to of the complex
engineering problems such as variables, dimensions and time. Nature inspired techniques are
created to optimize numerical benchmark functions and solve NP-hard obstacles for a huge
number of dimensions, variables, etc. Therefore, RNP is enhanced by employing different
algorithms.
The (PSO) algorithm is one of the state-of-art methods used in this area. PSO algorithm is
considered a population - based stochastic optimization method. This algorithm is inspired via
social behavior as demonstrated by fish and birds. PSO is a fast operation speed algorithm based
on few parameters that need to be adjusted. Researchers offered a new development for tuning
the parameters and hybridized the algorithm. Chen et al. in 2011 presented the PS20 method,
which increases the single population and improves PSO update equations. The researchers
presented the idea of a reader collision problem. They observed a good optimization effect for
weight and speed [11] Gong et al. in 2012 updated the PSO method via neglecting readers
during the search process by the Tentative Reader Elimination operator. The proposed method
improved overall RFID network performance for the number of readers by reducing the number
of used readers correlated with interference and maximizing tag coverage [15] [16] Feng (2013)
developed a new optimization method using ellipse propagation patterns to solve the problems
of multi-objective nonlinear optimization of difficult large working space of RFD network
planning. The multi-community Global-PSO method applies the mutation approach and genetic
selection to develop particle swarm dynamic controls for RPN applications. The algorithm
working process is to partition the single population of the standard PSO into a multi-swarm
[17] Gong et al. in 2013 used the adaptive small-world network design to develop a new local
topology system called (AWPSO). This system enables each particle to interact with its
neighbors, and via chance communicates by small-world randomization with some remote
particles. The ASWPSO method was tested using thirteen benchmark functions, and the results
verified the robustness and high power of the introduced adaptive small-world topology [18].
Nawawi et al. in 2015 improved PSO using (DOE) Design of Experiment. The present method
was able to choose the excellent setting of Particle Swarm parameters. They managed high
quality results in overall RNP [19]
In 2015 Hasnsn et al. applied a robust method for RFID system using (MC-GPSO) a MultiColony Global Particle Swarm Optimization approach. The procedure in this method is to
participate the swarm into multi-colonies to find the smallest readers number and the min
interference impact that covers RFID tags in a large-area. This method exhibited a strong and
effective RNP technique [9]. Therefore, it was compared with the Firefly Algorithm (FA) in
2017 based on large scale RNP [18]. All the presented algorithms were applied in a specific
area that does not exceed 80 meters square. However, in real applications, the use of larger area
such as in railway stations requires more efficient methods. This guides us to test the GradientBased Cuckoo Search (GBCS) Algorithm for this purpose.
3. GRADIENT– BASED CUCKOO SEARCH (GBC SEARCH)
ALGORITHM
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This section will introduce modified CS algorithm based on the objective gradient function. In
the present method, the present modification did not change the stochastic nature of the
processes which reduce the performance of the algorithm. The CS operation was created from
cuckoo species based on the obligate type parasitism. It lays their eggs in the host bird nests.
Each egg is considered as a solution to a RFID reader position. The cuckoo egg is considered
as a new solution (i.e. new reader position). The aim of the algorithm processes is to employ
the best solution (cuckoo) in order to develop the weak solutions in the nests. The independent
rules of CS are [10] [13]:
i. The cuckoo places one egg at time randomly in a chosen nest.
ii. A high-quality solution presents the best nest and carries over to the next step of
generation.
iii. The number of independent host nests will be fixed, and the alien egg can be found by
the host based on the probability of ϵ [0,1].
Based on the presented rules, the process of finding the optimal solution will be either by
throwing the egg away or leaving the nest to generate a new nest in new position. In order to
facilitate the processes in the algorithm operation, the final assumption will approximate the
fraction pa of the n nests. The CS working flow can be reviewed in the pseudo code in Figure
1 below [20]
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Figure 1 Pseudo code of the CS algorithm [9]
L´evy flight is considered as the best random exploration model and has been helpful in
stochastic simulations of random real events. To create a fresh egg in original CS, an L´evy
flight is performed utilizing the coordinates of an egg chosen randomly. This stage can be
described by: [9] [20]
𝑥𝑖𝑡+1 = 𝑥𝑖𝑡 +⊗ 𝛼Levy(λ)
(1)
In present formula the (⊗ ) means multiplications of entry-wise. The Lévy flight is
considered as
Step-lengths based on the following probability distribution:
𝐿𝑒𝑣𝑦 𝑢 = 𝑡 −𝜆 ,
𝑤ℎ𝑒𝑟𝑒 𝑡 < 𝜆 ≤ 3
(2)
The real application of this algorithm it to observe the cuckoo’s behavior, It shows that if
the cuckoo egg is similar to the host’s eggs, the process of random walk will be less likely in
order to discover the cuckoo egg. Therefore, the fitness will be considered as a difference in
solution [10] The modification presented by Fatten in 2014 used the local random walk based
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on the fraction (1-pa) of the replaced nests [13] The nest fraction (1-pa) which is selected at
random process is considered as abandoned and changed new positions based on new ones via
local random walks. The formula: [9] [20] of local random walk can be described as
𝑥𝑖𝑡+1 = 𝑥𝑖𝑡 +∝ (𝑥𝑗𝑡 + 𝑥𝑘𝑡 )
(3)
where 𝑥𝑗𝑡 and 𝑥𝑘𝑡 are two different solutions. These solutions generated and selected
randomly by the factor represents a random number intended by the regular distribution. In the
original algorithm, the value and the direction of the step are both random in walk depending
on the new nests when they are generated from the replaced nests. The modification in the
present algorithm, the researcher resaved the randomness of the magnitude of the step.
However, direction is calculated based on the gradient sign of the objective function. When the
gradient is negative, the direction of step will be positive. If the gradient is positive, the direction
of step will be negative. Based on the present sequence, new nests will be generated randomly
from the worst nests while in the direction of the minimum number of old nests. Thus, (Eq. 1)
is renewed by:
i)
𝑠𝑡𝑒𝑝𝑖 = 𝛼(𝑥𝑗𝑡 − 𝑥𝑘𝑡 ), xit+1 = xit + stepi ⨂sign (−step
df
(4)
i
where sign function involves the sign of its argument and dfi is the objective function
gradient at each variable, that is, f/ i, ∂f/∂ i [15] This simple adjustment does not renew the
building of the CS algorithm but offers major usage of the available data about the gradient of
the objective function.
4. MC-GPSO ALGORITHM
Meta-heuristic [MC-GLOBAL PSO] technique is an improved Particle Swarm Optimization.
It consists of a swarm of [Birds or fish] placed at a location in the exploration area. The particles
travel up the space area based on path and velocity, which is known as particle speed. Any
particle is dependent based on the excellent location and the good solution compared with its
neighbors. All the created random numbers will converge to the best places and tests the fitness
of each particle. Total the numbers will repeat into PSO equations. Equation 5 will be
modernized by adjusting the speed (t) in order to renew the location value which is shown in
Equation 6[21]
(𝑡 + 1) = 𝑣𝑤(𝑡) + (𝑃(𝑡) − 𝑋(𝑡))𝑅(𝑐1 ) + (𝑔(𝑥) − 𝑋(𝑡))𝑅(𝐶2 )
(5)
𝑋(𝑡 + 1) = 𝑋(𝑡) + 𝑣(𝑡 + 1)
(6)
2015, Hasnsn et al. introduced a robust method for RFID system using a Multi-Colony
Global Particle Swarm (MC-GPSO) approach. The procedure in this method is to shear the
swarm into multi-colonies in order to detect the minimum number of RFID readers that covers
all tags on a working area and the minimum overlapping effect. This method presented a strong
and useful technique of RFID –network design [21].
The procedure of MC-G Particle Swarm is defined as follows:
Stage 1. Initialize random position and velocity of all RFID readers.
Stage 2. Evaluate the fitness of all RFID readers and the evaluation of the fitness function will
register each swarm “personal Best” and then determine the “global Best”.
Stage 3. Update the location and speed by using equations (5) and (6).
Stage 4. The fitness will be compared with the previous good fitness speed and location.
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Stage 5. If global best and maximum value has been achieved by achieving the global good
location, then end procedure; otherwise, move to stage 3.
5. RNP MATHEMATICAL MODEL AND PROBLEM FORMULATION
The mathematical form of the RNP involved two issues [21]. The first issue is to define the
parameters of reader. The parameters of the (RNP) problem defined in each optimization
methods were changed to improve the Solution Quality [SQ][22] These parameters are shown
in Table 1
Table 1 Parameters of the (RNP) problem.
S. No.
Symbol
s
Parameters
S. No.
Symbol
s
Parameters
1.
𝑃𝑟
Power input at receiving
antenna
14.
𝑥𝑡 , 𝑦𝑡
Coordinate of RFID tags
2.
𝑃𝑡
Power output at
transmitting antenna
15.
𝑥, 𝑦
Coordinate of RFID reader
3.
𝐺𝑡
Transmitting antenna
gain(reader)
16.
n
Path loss exponent (freespace)
4.
𝐺𝑟
Receiving antenna
gain(tag)
17.
5.
𝜆
Wave length
18.
𝐶𝑜𝑣
Coverage of tags group in
TS range
6.
𝑟𝑚𝑎𝑥
Max propagation distance
in meter
19.
𝑁𝑖
Readers number
7.
𝐿𝑚
Path loss in meter
20.
𝑡
Tag
8.
𝐿𝑑𝑏
Path loss in decibels
21.
𝑅𝑆
The propagation domain
9.
𝑐
Speed of light (299792458
m/s)
22.
𝑇𝑆
The working domain
10.
𝑓
Frequency (Hz)
23.
𝑘
Number of groups
11.
𝑁𝑡
Total RFID Tags
24.
𝐶𝑜𝑣𝑖
Coverage rate of first group
of RFID tags
12.
𝐶𝑜𝑣𝑗
Coverage rate of second
group of RFID tags
25.
ri,rj
interference range of the
RFID readers i,j
13.
𝑟𝑡𝑑
Distance between RFID
tag and reader center.
26.
Ri,Rj
the position of the readers
𝑟𝑡𝑑
Distance between tag and
reader center
The values of these parameters were identified as follows: (1) Operating Frequency of UHF
RFID was 915 MHz (2) Reader of RFID adjustable Transmitting power range was (0.1 to 2
watts). (3) (Tt minimum) sensitivity thresholds of tags was (-10dBm). (4) Sensitivity thresholds of
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Readers (Tr) was (- 70dBm). (5) RFID Reader Antenna Gain (Gr) was (6.3 dBi). (6) RFID Tag
Antenna Gain (Gt) was (3.7dBi) [16] the proposed method (GBCS) algorithm is evaluated
against two RNP instances: [RRONDUM50] and [RRONDOM100] with random topologies and 50
and 100 tags respectively All instances are tested on a (80m × 80m) working area. For
comparison targets, these values and benchmark cases are the same as introduced by Hasnan in
[9].
The main parameter is the propagation range of the reader (𝑟𝑚𝑎𝑥 )The propagation range of
the reader can be computed by Friis transformation below [23]
𝜆
2
1
𝑃𝑟 = 𝑃𝑡 × 𝐺𝑡 × 𝐺𝑟 × [(4×𝜋) × (𝑟𝑛 )]
(7)
𝑃𝑟𝑒𝑎𝑑𝑒𝑟 [𝑑𝑏𝑚] = 𝑃𝑡𝑎𝑔 [𝑑𝑏𝑚] + 𝐺𝑟𝑒𝑎𝑑𝑒𝑟 [𝑑𝑚] + 𝐺𝑡𝑎𝑔 [𝑑𝑚] − 𝐿[𝑑𝑚]
(8)
Where r is physical distance between the reader and the tag (n) Environmental factor called
(path loss exponent) is different for each environment. In the free-space it is equal to 2 and in
another environment varies from1.7 to 5 according to Table 2 below.
Table 2 Path loss exponent for different environment [23]
S. No.
Different Environment
Exponent
1.
Urban - zone
2.7- 3.5
2.
Suburban- zone
3-5
3.
Free- space
2
4.
Indoor system- (line of sight)
1.7 - 1.8
5.
Indoor system - (Non-line of sight)
3.5-5
Now we can calculate the max reader propagation range based on sensitivity thresholds of
tags
𝜆
𝑃 ×𝐺 ×𝐺
𝑡
𝑡
𝑟
𝑟𝑚𝑎𝑥 = 4×𝜋 × √Sensitivity thresholds
of tags
(9)
The consideration is fixed as the length and width of the space that contains the distributed
tags. The propagation range is computed in equation 9 and the present computations are subject
to the boundary conditions [9] [16] [23]:
𝑟𝑚𝑎𝑥 ≥ 𝑟𝑑
(10)
From the present formulas, the distance between reader and tag can be found from the
formula below:
𝑟𝑑 = √(𝑥 − 𝑥𝑡)2 − (𝑦 − 𝑦𝑡)2
(11)
The second issue in a mathematical pattern of the (RNP) is to define the objective functions
of RNP problems. And objective functions can be summarized as follows in three equations.
The first equation concerns the tag coverage (COV). [9] [16] [23]:
𝐶𝑂𝑉𝑖 = ∑𝑚𝑎𝑥
𝑡=𝑅𝑆(𝑟𝑚𝑎𝑥 − 𝑟𝑡𝑑 )
(12)
The second equation concerns the required number of readers (N) that covers all tags
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𝑁𝑖 = ∑𝑘𝑡=𝑇𝑆 𝐶𝑂𝑉𝑖
(13)
The rate of tags coverage in the specified network, which represents a highly important and
necessary objective function of all RFID systems, is defined as:
𝑓(𝐶𝑜𝑣) =
𝑑𝑒𝑡𝑒𝑐𝑡𝑒𝑑 𝑡𝑎𝑔𝑠
𝑡𝑜𝑡𝑎𝑙 𝑡𝑎𝑔𝑠
= ∑𝑚𝑎𝑥
𝑡=𝑇𝑆
1 𝑖𝑓
𝐶𝑜𝑣 = {
0
𝐶𝑜𝑣𝑖
𝑁𝑡
(14)
𝑟𝑚𝑎𝑥 ≥ 𝑟𝑡𝑑
}
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Third equation concerns the interference (I) between RFID readers which can be determined
by the following numerical equation [9]
𝑁
𝐼min = ∑𝑁−1
𝑖=1 ∑𝑗=𝑖+1(𝑟 𝑖 + 𝑟𝑗 ) − (𝑑𝑖𝑠𝑡(𝑅𝑖 , 𝑅𝑗 )
(15)
Where dist. (𝑅𝑖 , 𝑅𝑗 ) is function that computes the distance between readers. The
interference rate can be calculated by the formula:
𝑓(𝐼) =
𝐼𝑛𝑡𝑒𝑟𝑓𝑒𝑟𝑒𝑑 𝑡𝑎𝑔𝑠
𝑡𝑜𝑡𝑎𝑙 𝑡𝑎𝑔𝑠
=
∑𝑖,𝑗 𝐶𝑜𝑣𝑖 ∩𝐶𝑜𝑣𝑗
(16)
𝑁𝑡
The equations of each objective function such as maximum tags coverage, minimum
interference between reader and number of readers were placed into GBC-Search. GBCS
calculated the optimum solutions of network planning according to the priority of objective
function.
6. RESUTS A ND DISCUSSIONS
In experiential tests, we applied two RNP situations: R50 and R100 with random topologies and
50 , 100 tags respectively as shown in Figure 3 (a and b).All situations are tested on a (80m ×
80m) working area as shown in Figure 2, where in each case 10 RFID Readers were initialized
and distributed in order to cover all the tags as shown in Figure 4(a and b). All simulation runs
were conducted with 15000 iteration and 25 independent runs. The special parameters of the
RFID problem are shown in section 4 Table 1. For comparison targets, these values are the
same as presented and developed by Hasnan in 2015 [9].
Figure 2 working area.
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Figure 3(a) Tags distribution [instance R100] Figure 3 (b) Tags distribution [instance R50]
Figure-4 (a) Reader initialization [instance R100] Figure-4 (b) Reader initialization [instance R50]
In all cases RFID reader positions were distributed in order to cover all the tags denoted as
plus signs” +”. The coordinates of the readers are shown as red stars “*”, and their interrogation
range as a red circle of dashed lines.
The experimental results of the GBCS algorithm observe a good solution for large scale
areas (80m2) based on the solution quality of the maximum tag coverage, minimum interference
between readers, and required number of readers. Figures (5 and 6) show the solution quality
for RNP large scale area.
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Case 1: R50 as shown in the simulation results in Fig (5a) the GBCS algorithm observes
robust solutions due to the required number of readers, the proposed method was able to employ
7 readers with minimum interference, .and it was able to achieve 94.56% coverage for 50 RFID
tags.
Case 2: R100 in proposed method (GBCS) algorithm only 6 RFID readers were able to
achieve 93.96% coverage for 100 tags, in the defined space with minimum interference as
shown in Figure 5(b).
Table 3 Analysis of the best results (GBCS)
Stochastic
optimization
technique
GBCS algorithm
RNP instances
No. of readers
Interference
Tags Coverage
Working area
R50
7
0.0006
94.56 %
80m2
R100
6
0.034
93.96 %
80m2
It can be observed from numerical results shown in Table (3) that GBCS can achieve
minimum number of readers due to optimal reader positions with good coverage. The useless
readers were removed from the space by GBCS algorithm using skip reader operator in order
to reduce the overall deployment cost and avoid reader-to-reader interference.
Table 4 Comparative results
Stochastic
technique
RNP instances
Gradient-Based Cuckoo Search
Multi-Colony G Particle Swarm
(GBCS)
MC-GPSO
No of
readers
Interference
http://www.iaeme.com/IJMET/index.asp
Coverage %
687
No of
readers
Interference
Coverage %
editor@iaeme.com
Nihad Hasan Talib, Khalid bin Hasnan, Azli bin Nawawi, Haslina Binti Abdullah,
And Adel Muhsin Elewe
R50
7
0.0006
94.56 %
9
0.053
95.09
R100
6
0.034
93.96 %
10
0.059
94.68
In Table 4 the comparative analysis shows that the GBCS algorithm provides robust
solutions due to the required number of readers with minimum interference, especially in large
data. In the case of R100 readers 6 readers were used while the (MC-GLOBAL PSO) algorithm
used 10 readers. And in case R50 GBCS 7 readers were used with minimum interference while
the (MC-GLOBAL PSO) algorithm used 9 readers. Also, the comparative analysis shows that
the GBCS the weak in tag coverage, as it achieved 94.56% of coverage area in case of 50 tags
and 93.96 % of coverage in case of 100 tags, while (MC-GPSO) covered 95.09% and 94.68
%respectively. It can be seen that the algorithm (MC-GPSO) was better than GBCS algorithm
in tag coverage. These results indicate that the GBCS algorithm can be used efficiently in largescale conditions with random big data. It can be a useful solution for RFID network planning.
This is because it uses the significantly fewer readers which is the next most crucial criterion
with minimum interference and good coverage area.
7. CONCLUSIONS
The influence of multi-objective RFID -NP design was analyzed and compared using CBCS
and MC-GPSO techniques. The suggested approach was experimented against Large-scale,
complex, and dynamic environment problem and implemented in two instances of tag
distribution which consist of random topologies in 80 × 80 meters square area. CBCS presented
greater capability than MC-GPSO algorithm in RFID-NP in terms of (1) required a number of
readers (2) interference between readers, but there were weaknesses in tag coverage. Simulation
outcomes observe the (GBC Search) technique improves the reference algorithms for designing
RFID networks, in terms of optimization efficiency and calculation robustness, indicating that
the GBC Search is suitable for solving huge dimension RNP problems.
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