Enviar búsqueda
Cargar
Assignment of cells to switches using firefly algorithm
•
1 recomendación
•
676 vistas
I
iaemedu
Seguir
Denunciar
Compartir
Denunciar
Compartir
1 de 8
Descargar ahora
Descargar para leer sin conexión
Recomendados
call for papers, research paper publishing, where to publish research paper, ...
call for papers, research paper publishing, where to publish research paper, ...
International Journal of Engineering Inventions www.ijeijournal.com
Edd clustering algorithm for
Edd clustering algorithm for
csandit
3.a heuristic based_multi-22-33
3.a heuristic based_multi-22-33
Alexander Decker
E NERGY D EGREE D ISTANCE C LUSTERING A LGORITHM FOR W Sns
E NERGY D EGREE D ISTANCE C LUSTERING A LGORITHM FOR W Sns
ijwmn
Routing management for mobile ad hoc networks
Routing management for mobile ad hoc networks
IAEME Publication
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...
ijasuc
wireless power transfer
wireless power transfer
Mimar Sinan Saraç
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...
IJERA Editor
Recomendados
call for papers, research paper publishing, where to publish research paper, ...
call for papers, research paper publishing, where to publish research paper, ...
International Journal of Engineering Inventions www.ijeijournal.com
Edd clustering algorithm for
Edd clustering algorithm for
csandit
3.a heuristic based_multi-22-33
3.a heuristic based_multi-22-33
Alexander Decker
E NERGY D EGREE D ISTANCE C LUSTERING A LGORITHM FOR W Sns
E NERGY D EGREE D ISTANCE C LUSTERING A LGORITHM FOR W Sns
ijwmn
Routing management for mobile ad hoc networks
Routing management for mobile ad hoc networks
IAEME Publication
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...
ENERGY EFFICIENT ROUTING ALGORITHM FOR MAXIMIZING THE MINIMUM LIFETIME OF WIR...
ijasuc
wireless power transfer
wireless power transfer
Mimar Sinan Saraç
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...
An Improved Deterministic Energy Efficient Clustering Protocol for Wireless S...
IJERA Editor
Effect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routing
iaemedu
04 15029 active node ijeecs 1570310145(edit)
04 15029 active node ijeecs 1570310145(edit)
nooriasukmaningtyas
A survey on weighted clustering techniques in manets
A survey on weighted clustering techniques in manets
IAEME Publication
CDMA PARAMETER
CDMA PARAMETER
Tempus Telcosys
ENERGY EFFICIENCY IN AD HOC NETWORKS
ENERGY EFFICIENCY IN AD HOC NETWORKS
ijasuc
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
IJECEIAES
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
IRJET Journal
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
IOSR Journals
Effective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using grid
iaemedu
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
IJCSEIT Journal
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
chokrio
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
IJERA Editor
Og2423252330
Og2423252330
IJERA Editor
Mobile ad hoc networks and its clustering scheme
Mobile ad hoc networks and its clustering scheme
International Journal of Science and Research (IJSR)
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Alexander Decker
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
TELKOMNIKA JOURNAL
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
ijsrd.com
Design and implementation of variable range energy aware dynamic source routi...
Design and implementation of variable range energy aware dynamic source routi...
IAEME Publication
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ijasuc
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ijasuc
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
cscpconf
Implementation and analysis of multiple criteria decision routing algorithm f...
Implementation and analysis of multiple criteria decision routing algorithm f...
prjpublications
Más contenido relacionado
La actualidad más candente
Effect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routing
iaemedu
04 15029 active node ijeecs 1570310145(edit)
04 15029 active node ijeecs 1570310145(edit)
nooriasukmaningtyas
A survey on weighted clustering techniques in manets
A survey on weighted clustering techniques in manets
IAEME Publication
CDMA PARAMETER
CDMA PARAMETER
Tempus Telcosys
ENERGY EFFICIENCY IN AD HOC NETWORKS
ENERGY EFFICIENCY IN AD HOC NETWORKS
ijasuc
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
IJECEIAES
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
IRJET Journal
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
IOSR Journals
Effective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using grid
iaemedu
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
IJCSEIT Journal
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
chokrio
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
IJERA Editor
Og2423252330
Og2423252330
IJERA Editor
Mobile ad hoc networks and its clustering scheme
Mobile ad hoc networks and its clustering scheme
International Journal of Science and Research (IJSR)
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Alexander Decker
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
TELKOMNIKA JOURNAL
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
ijsrd.com
Design and implementation of variable range energy aware dynamic source routi...
Design and implementation of variable range energy aware dynamic source routi...
IAEME Publication
La actualidad más candente
(18)
Effect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routing
04 15029 active node ijeecs 1570310145(edit)
04 15029 active node ijeecs 1570310145(edit)
A survey on weighted clustering techniques in manets
A survey on weighted clustering techniques in manets
CDMA PARAMETER
CDMA PARAMETER
ENERGY EFFICIENCY IN AD HOC NETWORKS
ENERGY EFFICIENCY IN AD HOC NETWORKS
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with T...
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
IRJET- Analysis of Energy Efficiency and Network Lifetime of Various Clus...
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
Application of Gravitational Search Algorithm and Fuzzy For Loss Reduction of...
Effective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using grid
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
DISTRIBUTED COVERAGE AND CONNECTIVITY PRESERVING ALGORITHM WITH SUPPORT OF DI...
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
Transforming an Existing Distribution Network Into Autonomous MICRO-GRID usin...
Og2423252330
Og2423252330
Mobile ad hoc networks and its clustering scheme
Mobile ad hoc networks and its clustering scheme
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Dg source allocation by fuzzy and sa in distribution system for loss reductio...
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
Device Discovery Schemes for Energy-Efficient Cluster Head Rotation in D2D
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
Connected Dominating Set Construction Algorithm for Wireless Sensor Networks ...
Design and implementation of variable range energy aware dynamic source routi...
Design and implementation of variable range energy aware dynamic source routi...
Similar a Assignment of cells to switches using firefly algorithm
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ijasuc
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ijasuc
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
cscpconf
Implementation and analysis of multiple criteria decision routing algorithm f...
Implementation and analysis of multiple criteria decision routing algorithm f...
prjpublications
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wirel...
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wirel...
IJECEIAES
Computational Investigation of Asymmetric Coplanar Waveguides Using Neural Ne...
Computational Investigation of Asymmetric Coplanar Waveguides Using Neural Ne...
Konstantinos Karamichalis
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
ijmnct
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
ijmnct_journal
The hybrid evolutionary algorithm for optimal planning of hybrid woban
The hybrid evolutionary algorithm for optimal planning of hybrid woban
IAEME Publication
The hybrid evolutionary algorithm for optimal planning of hybrid woban (1)
The hybrid evolutionary algorithm for optimal planning of hybrid woban (1)
iaemedu
Ijetcas14 357
Ijetcas14 357
Iasir Journals
Statistical performance analysis of wireless communication in public transports
Statistical performance analysis of wireless communication in public transports
IAEME Publication
Distributed Spatial Modulation based Cooperative Diversity Scheme
Distributed Spatial Modulation based Cooperative Diversity Scheme
ijwmn
Analysis of data transmission in wireless lan for 802.11
Analysis of data transmission in wireless lan for 802.11
eSAT Publishing House
Analysis of data transmission in wireless lan for 802.11 e2 et
Analysis of data transmission in wireless lan for 802.11 e2 et
eSAT Journals
3 3 energy efficient topology
3 3 energy efficient topology
IAEME Publication
3 3 energy efficient topology
3 3 energy efficient topology
prjpublications
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...
IJECEIAES
50120140501004
50120140501004
IAEME Publication
Efficient power allocation method for non orthogonal multiple access 5G systems
Efficient power allocation method for non orthogonal multiple access 5G systems
IJECEIAES
Similar a Assignment of cells to switches using firefly algorithm
(20)
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
EDD CLUSTERING ALGORITHM FOR WIRELESS SENSOR NETWORKS
Implementation and analysis of multiple criteria decision routing algorithm f...
Implementation and analysis of multiple criteria decision routing algorithm f...
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wirel...
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wirel...
Computational Investigation of Asymmetric Coplanar Waveguides Using Neural Ne...
Computational Investigation of Asymmetric Coplanar Waveguides Using Neural Ne...
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICES
The hybrid evolutionary algorithm for optimal planning of hybrid woban
The hybrid evolutionary algorithm for optimal planning of hybrid woban
The hybrid evolutionary algorithm for optimal planning of hybrid woban (1)
The hybrid evolutionary algorithm for optimal planning of hybrid woban (1)
Ijetcas14 357
Ijetcas14 357
Statistical performance analysis of wireless communication in public transports
Statistical performance analysis of wireless communication in public transports
Distributed Spatial Modulation based Cooperative Diversity Scheme
Distributed Spatial Modulation based Cooperative Diversity Scheme
Analysis of data transmission in wireless lan for 802.11
Analysis of data transmission in wireless lan for 802.11
Analysis of data transmission in wireless lan for 802.11 e2 et
Analysis of data transmission in wireless lan for 802.11 e2 et
3 3 energy efficient topology
3 3 energy efficient topology
3 3 energy efficient topology
3 3 energy efficient topology
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...
50120140501004
50120140501004
Efficient power allocation method for non orthogonal multiple access 5G systems
Efficient power allocation method for non orthogonal multiple access 5G systems
Más de iaemedu
Tech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech in
iaemedu
Integration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniques
iaemedu
Adaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow application
iaemedu
Survey on transaction reordering
Survey on transaction reordering
iaemedu
Semantic web services and its challenges
Semantic web services and its challenges
iaemedu
Website based patent information searching mechanism
Website based patent information searching mechanism
iaemedu
Revisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modification
iaemedu
Prediction of customer behavior using cma
Prediction of customer behavior using cma
iaemedu
Performance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presence
iaemedu
Performance measurement of different requirements engineering
Performance measurement of different requirements engineering
iaemedu
Mobile safety systems for automobiles
Mobile safety systems for automobiles
iaemedu
Efficient text compression using special character replacement
Efficient text compression using special character replacement
iaemedu
Agile programming a new approach
Agile programming a new approach
iaemedu
Adaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environment
iaemedu
A survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow application
iaemedu
A survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networks
iaemedu
A novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classify
iaemedu
A self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imagery
iaemedu
A comprehensive study of non blocking joining technique
A comprehensive study of non blocking joining technique
iaemedu
A comparative study on multicast routing using dijkstra’s
A comparative study on multicast routing using dijkstra’s
iaemedu
Más de iaemedu
(20)
Tech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech in
Integration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniques
Adaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow application
Survey on transaction reordering
Survey on transaction reordering
Semantic web services and its challenges
Semantic web services and its challenges
Website based patent information searching mechanism
Website based patent information searching mechanism
Revisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modification
Prediction of customer behavior using cma
Prediction of customer behavior using cma
Performance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presence
Performance measurement of different requirements engineering
Performance measurement of different requirements engineering
Mobile safety systems for automobiles
Mobile safety systems for automobiles
Efficient text compression using special character replacement
Efficient text compression using special character replacement
Agile programming a new approach
Agile programming a new approach
Adaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environment
A survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow application
A survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networks
A novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classify
A self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imagery
A comprehensive study of non blocking joining technique
A comprehensive study of non blocking joining technique
A comparative study on multicast routing using dijkstra’s
A comparative study on multicast routing using dijkstra’s
Assignment of cells to switches using firefly algorithm
1.
InternationalINTERNATIONAL Communication Engineering
& Technology (IJECET), ISSN 0976 – Journal of Electronics and JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), pp. 211-218 IJECET © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEME www.jifactor.com ASSIGNMENT OF CELLS TO SWITCHES USING FIREFLY ALGORITHM Deepak Sharma1, Rajesh Kumar2 and Shrikant3, 1 (Department of Electronics and communication, Panipat Institute of Engg. and Technology, Samalkha, Panipat, India, tcmdeepak@gmail.com) 2 (Department of Electronics and communication, Panipat Institute of Engg. and Technology, Samalkha, Panipat, India,kr_rajesh88@rediffmail.com) 3 (Department of Electronics and communication, Panipat Institute of Engg. and Technology, Samalkha, Panipat, India, writetoshrikantjoshi@gmail.com) ABSTRACT In this paper we consider the problem of assignment of cells to switches in an optimum manner. It is a combinatorial optimization problem that is known to be NP-hard. We consider three types of costs. One is the cost of handoff between cells. The other is the cost of cabling or trunking between a cell and its associated switch. The third cost is switching cost which includes the cost for transferring a call to other switch. The problem is constrained by the capacity of switch and assignment of a cell to a unique switch. Firefly algorithm is implemented in this paper to solve the problem of assignment of cells to switches and results are compared with that of Particle swarm optimization. Keywords: Firefly algorithm, Optimization, Particle swarm optimization. I. INTRODUCTION During the last 30 years, there has been a tremendous growth in the deployment of mobile communication systems. Now a days the mobile networks are migrating towards broadband services based on high speed wireless access technologies [1]. Even though significant improvement to communication infrastructure has been aimed in the mobile industry, the issues concerning the assignment of cells to switches in order to minimize the cabling cost, handoff cost and switching cost in a reasonable time still remain challenging. The same problem is addressed in this paper. Our motive is to minimize three costs namely: cabling cost, handoff cost and switching cost. Cabling cost involves the consumption of resources while maintaining communication link 211
2.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME between two users [2]. A handoff occurs when the mobile network transfer the call from one base station to another. Merchant [3] gave a comprehensive description about handoffs. Intuitively, the cells among which the handoff frequency is high should be assigned to the same switch as far as possible to reduce the cost of handoffs. The consumption of resources when a call is switched from one Mobile Switching Center (MSC) to another comes under switching cost. Switching cost occurs when two user which are communicating belong to different MSCs. However, since the call handling capacity of each switch is limited, this should be taken as a constraint. Incorporating the cabling cost, handoff cost and switching cost that occurs when a call is connected between a cell and a switch, we have an optimization problem, called the cell to switch assignment problem. In the field of deployment of mobile communication system Arif Marchant and Bhaskar Sengupta [5] added there precious idea of assignment of cells to the switches in 1995. After then a number of ideas comes in the same series. In 1999 Partha Sarathi Bhattacharjee, Debashis Saha, Amitava Mukherjee [9] presented a paper for comparative study of various heuristics proposed to solve the problem of optimally assigning cells to switches in a Personal Communication Services (PCS) Network. In 2000 some work has been done by Partha Sarathi Bhattacharjee, Debashis Saha, Amitava Mukherjee [12] with the problem of balancing traffic (load) amongst MSCs. SwarupMandal, DebashisSaha, AmbujMahanti [13] in 2002 presented a paper in which the assignment of cells to switches was done using the novel Block Depth First Search (BDFS) [13] algorithm using an admissible heuristic so as to minimize the paging, updating and physical infrastructure costs. Samuel Pierre and Fabien Houeto [14] solved the same problem using Taboo Search. In 2002 Alejandro Quintero and Samuel Pierre solved the same problem of using Memetic Algorithm [15]. In 2004 SwarupMandal, DebashisSaha, AmbujMahanti proposed a technique to assign the cells to the switches to minimize a total cost of operation (TCO) [16]. Syam Menon and Rakesh Gupta [17] in 2004 presented the idea of price mechanism using Simulated Annealing. In 2008 a paper is published by Siba K. Udgata, U. Anuradha, G. Pawan Kumar, Gauri K. Udgata [22] in which the problem of assignment of cells to switches is solved using an algorithm of Swarm Intelligence. This paper presented idea to solve the problem of assignment of cells to switches using Firefly Algorithm. In next section the mathematical formulation has been done for this problem. In the next section it is shown how firefly algorithm can be implemented for this problem. Then in section VI experiments and results are displayed and the last section contains conclusion and future scope. II. PROBLEM FORMULATION The problem of assignment of cells to switches was first introduced by Arif Merchant and BhaskarSengupta [3] in 1995. The assignment of cell to the switches is an NP-Hard problem, having an exponential complexity (n cells and m switches). He introduced two types of costs namely handoff cost and cabling cost. He also proposed a heuristic method to solve this problem. Now in this paper another cost called Switching cost [6] is introduced which is the cost for 212
3.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME transferring a call form one switch to another. Thus in this paper we have an optimization problem for minimizing the above costs which is stated as follows: Assign all the cells in a geographical area to the available number of switches in order to minimize the total cost which is the sum of cabling cost, handoff cost and switching cost maintaining the following two constraints. 1) Each cell must be assigned to exactly one switch and 2) Each switch has some limited capacity and assignment of cells must be done in such a way so that the total load on the switch should not exceed the capacity of the switch. Various symbols used are: • Let no. of cells be ‘n’ and no. of switches be ‘m’ • hij – handoff cost between cell i and cell j • cik – cabling cost between cell i and switch k • dij – distance between cell i and switch (MSC) j • Mk – call handling capacity of switch k • λi - No of communication in cell i • Yij – 1 if cell I and j are assigned to same switch and 0 otherwise. • Xik – 1 if cell I is assigned to switch k and 0 otherwise. For all cases, the range of i, j and k are defined as: 1 ≤ ݅ ≤ ݊, 1 ≤ ݆ ≤ ݊, 1 ≤ ݇ ≤ ݉ 2.1 Formulation of Constraints 1) Each cell must be assigned to exactly one switch ݔ = 1, 1 ≤ ݅ ≤ ݉ (1) ୀଵ 2) Each switch has some capacity ߣ ݔ ≤ ܯ , 1 ≤ ݇ ≤ ݉ (2) ୀଵ 2.2 Formulation of cost Function 2.2.1 Total Cabling Cost This is formulated as a function of distance between base station and switch and number of calls that a cell can handle per unit time [7]. ܿ (ߣ ) is the cost of cabling per kilometre which is also modelled as a function of the number of calls that a cell can i handles as: ܿ = ܣ + ܤ ߣ (3) ܿ (ߣ )݀ ݔ , ݅ = 1,2, … ݊ (4) ୀଵ 2.2.2 Total handoff cost We consider two types of handoffs, one which involves only one switch and another which involves two switches. The handoff that occurs between cells that belong to the same switch consume much less network resources than what occurs between cells that belongs to two different switches. 213
4.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME ℎ (1 − ݕ ) (5) ୀଵ ୀଵ 2.2.3 Total Switching Cost Let βi is the total no of calls MSC i can handle per unit time and Fi(βi) is the cost function of switching a call in MSC i. Thus the load at MSC i is given by: ߚ = ߣ ݔ , ݅ = 1 … ݉ (6) ୀଵ Fi(βi) involves both the cost of switching and the cost of maintaining a call at MSC. Thus Fi(βi) can be represented as: ܨ (ߚ ) = ߙ⁄(ߤ − ߚ ) , ߚ < ߤ (7) Where ߤ denote the call switching capacity of MSC i and α is a constant. The total switching cost involved is defined by: ߚ ܨ (ߚ ) (8) ୀଵ So our objective is to minimize the total cost which can be formed by the summation of all three costs. The objective function is given by: ݂ = ܿ (ߣ )݀ ݔ + ℎ (1 − ݕ ) + ߚ ܨ (ߚ ) (9) ୀଵ ୀଵ ୀଵ ୀଵ III. EXISTING METHODOLOGIES Assigning cells to switches in cellular mobile network being an NP-hard problem and enumerative search methods are not appropriate to solve large sized instances of this problem [8]. Thus, heuristic approaches, like Genetic Algorithm [9] [10], Tabu Search [8], Simulated Annealing [2], Memetic Algorithm [11], Ant Colony Optimization [12] and Particle Swarm Optimization [13] have been developed for this kind of problem. These are the heuristics and meta-heuristics proposed by various authors to solve assignment of cells to switch problem. An experimental result from various authors shows that those techniques are to their best depending upon the problem size. The experiment conducted by the author Shxyong Jian Shyu [14] shows that ACO gives better results for cell assignment problem compared to other existing methods. Siba K. Ugata [6] uses PSO for solution of same problem and found better results then ACO. All these algorithms considered only cabling cost and handoff cost except [6]. In our approach we also added switching cost to the total cost. IV. IMPLEMENTATION OF FIREFLY ALGORITHM Firefly algorithm is developed by Xin-She Yang [7] in 2008 which is inspired by the mutual attraction of fireflies based on the absorption of light and distance between two fireflies. Algorithm considers that each firefly has fixed position in the space and it always move towards a greater light source, then is his own. 214
5.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME Firefly algorithm idealizes some of the characteristics of the firefly behavior. They follow three rules: 1) All the fireflies are unisex, 2) Each firefly is attracted only to the fireflies, that are brighter than itself; strength of the attractiveness is proportional to the firefly’s brightness, which attenuates over the distance; the brightest firefly moves randomly and, 3) Brightness of every firefly determines its quality of solution; in most of the cases, it can be proportional to the objective function. Using these three rules Firefly Algorithm may look as follows: Step 1 • Initialize the number of cells (n), switches (m) and number of fireflies (p) in the solution space. • Initialize position of cells and switches randomly in the search space. • Calculate distance between each cell and switch. Here this distance is simple Euclidean distance. Step 2 • Generate the assigned matrix (xij) for each firefly where each particle is between 0 and 1. • The row of the matrix represents switches and column represents cells. Step 3: Obtain solution matrix from the assigned matrix by making the largest value of each column to 1 and all other are set to 0. Step 4: Calculate the total cost based on this solution matrix using (9). Step 5 • On the basis of cost calculated in step 4 the brightest firefly is found which has the minimum cost for the assignment. • Now update the position of all fireflies based on the attractiveness and distance of other firefly. • Now update the position of best firefly randomly. Step 6: Repeat step 3 to 5 until stopping criterion is met. V. EXPERIMENTS AND RESULTS To test the effectiveness of Firefly Algorithm for the cell assignment problem, we design and conduct a series of experiments. All the experiments are done using a MATLAB code for various cases of cells and switches for firefly algorithm. Regarding the test problems, we assume that the cells lie on a hexagonal grid of roughly equal dimensions in 2 dimensions. The parameters used for firefly algorithm are number of cells (n), number of switches (m) and number of fireflies (p). Values of constants used are as follows: • Randomness, α=1 • absorption coefficient, γ=1 • brightness at source, β=1 The parameters used in the initialization of problem are: 215
6.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME • handoff cost between two cells= 0 to 14 per hour • constant A used in cabling cost=1 • constant B used in cabling cost=0.001 • call handling capacity of a switch=98000 • constant α used in switching cost=40 • number of communication in a cell=0 to 200 per hour Table - 1 Experimental Result Switches, Total cost Total Cost Execution Execution cells (Firefly) (PSO) Time (Firefly) Time (PSO) 2, 25 1915 1936 0.152 0.201 2, 50 7894 7954 0.174 0.228 2, 100 33414 33528 0.205 0.259 2, 150 78213 78279 0.342 0.383 2, 200 134200 134280 0.489 0.514 2, 250 216370 216410 0.542 0.668 3, 25 2417 2478 0.163 0.203 3, 50 10973 11022 0.187 0.229 3, 100 45208 45290 0.213 0.261 3, 150 102230 102310 0.254 0.315 3, 200 181870 181920 0.305 0.357 3, 250 288090 288160 0.364 0.388 5, 25 2979 3011 0.201 0.282 5, 50 13642 13735 0.225 0.311 5, 100 54395 54481 0.251 0.345 5, 150 124880 124940 0.292 0.369 5, 200 220030 220160 0.355 0.377 5, 250 344020 344180 0.403 0.409 10, 25 3711 3800 0.278 0.304 10, 50 15273 15411 0.296 0.318 10, 100 61999 62158 0.321 0.339 10, 150 138280 139020 0.367 0.371 10, 200 248710 249360 0.409 0.411 10, 250 390140 391420 0.461 0469 216
7.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME 5.1 Analysis of Result Motion of fireflies is best for the above values of α, β and γ. For lesser values of these parameters the change in the assignment is very low. As the number of cells and switches is increased the final cost value also increased and with these large problem instances it is taking more number of iterations and more time. We have conducted experiments to find the minimum total cost by repeating the experiments for 5, 10, 15 times for each set of parameters. These experiments reveals that the optimum cost obtained in each execution is always nearer to the average cost. The CPU time taken in each execution is also noted for comparison purpose. Table 1 shows the minimized cost for different cases of cells and switches. It also compare the minimized cost by the two algorithms: Firefly and PSO and from this list we can see that comparatively lesser cost is found using Firefly and so it is better in terms of finding minimum cost. Form experiments it is noted that the total minimized cost can be reduced with larger number of iterations and also with larger number of fireflies. From experiments it is noted that in most cases the convergence is achieved in lesser number of iterations when we have large number of fireflies but this doesn’t happen in every case. In table 1 a comparison of two algorithms in terms of CPU execution time is also shown and Firefly algorithm is better then PSO in terms of execution time also. This time difference is not so much. The CPU time will be high as the number of iterations is increased. But for same number of iterations we can see that this execution time is less in FA as compared to PSO. V. CONCLUSION AND FUTURE SCOPE From the experiments performed we can conclude that firefly algorithm can be implemented successfully for the assignment of cells to the switches. As the number of fireflies is increased the probability of finding the minimum cost and in less number of iterations is increased. The CPU time requirement is less in case of firefly algorithm as compared to PSO. We can also improve the performance of this algorithm by selecting the initial position of firefly not randomly but with any of the heuristics. It will converge to the minimum cost in comparatively lesser number of iterations. REFRENCES [1] P. Bhattacharjee, D. Saha, A. Mukherjee, Heuristics for assignment of cells to switches in a pcsn: a comparative study:, Intl. Conf. on Personal Wireless Communications, Jaipur, India, 1999, pp. 331-334. [2] Syam Menon, Rakesh Gupta, Assigning cells to switches in cellular network by incorporating a pricing mechanism into simulated annealing, IEEE Transetions on system, men and cybernetics, Part B, Vol. 34, No. 1, pp. 558-565, Feb 2004. [3] Arif Merchant and BhaskarSengupta, Assignment of cells to switches in PCS network, IEEE Transections on Networking, Vol 3 No 5, pp 521-526, Oct 1995. 217
8.
International Journal of
Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME [4] Ron Wehrens and Lutgarde M.C. Buydens, Classical and non-classical optimization methods, Encyclopedia of Analytical Chemistry, R.A. Meyers (Ed.), Ó John Wiley & Sons Ltd, Chichester, pp. 9678–9689, 2000. [5] T. Schlick, optimization methods in computational chemistry, in eds. K.B. Lipkowitz, D.B. Boyd, Reviews in Computational Chemistry, VCH, New York, Chapter 1, Vol. 3, 1992. [6] Siba K. Udgata, U. Anuradha, G. Pawan Kumar, Gauri K. Udgata, Assignment of Cells to Switches in a Cellular Mobile Environment using Swarm Intelligence, IEEE International Conference on Information Technology, 2008, pp 189-194. [7] Xin-She Yang, Firefly Algorithm For Multimodal Optimization, Luniver Press, 2008. [8] P. Bhattacharjee, D. Saha, A. Mukherjee, A simple heuristic for assignment of cells to switches in a pcs network, Wireless Personal Communication, Vol 12, pp. 209-224, 2000. [9] D.E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison- Wesley, Reading, 1989. [10] T. Shigeyoshi, G. Ashish, Genetic Algorithm with a Robust Solution Searching Scheme, IEEE Transections on Evolutionary Computation, pp. 201-208, 1997. [11] P. Moscato, M.G. Norman, M. Balero, E. Onate, M. Jane, J.L. Larriba, B. Suarez, Memetic Approach for the Travelling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on message-Passing Systems, Parallel Computing and Transputer Applications, IOS Press, Amsterdam, pp. 177-186, 1992. [12] Dorigo M, Maniezzo V, Colorni A, The Ant System: Optimization by a Colony of Cooperating Agents, IEEE Transactions on Systems, Man and Cybernetics-Part B, Vol 26(1), pp. 29-41, 1996. [13] James Kennedy, Russell Eberhart Particle swarm optimization, Proc. IEEE Int'l. Conf. on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, 1995, pp.1942-1948. [14] Shxyong Jian Shyua, B.M.T. Linb, Tsung Shen Hsiaoa, Ant Colony Optimization for the Cell Assignment Problem in PCS Network, March, 2005. 218
Descargar ahora