Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Constriction and Linearly Decreasing Inertia Weight Methods

Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system for node placement in WMNs, called WMN-PSO. Also, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in WMNs, called WMN-SA. In this paper, we implement a hybrid simulation system based on PSO and SA, called WMN-PSOSA. We evaluate the performance of WMN-PSOSA by conducting computer simulations considering two replacement methods: Constriction Method (CM) and Linearly Decreasing Inertia Weight Method (LDIWM). Simulation results show that the LDIWM has better performance than CM.

[1]  Fatos Xhafa,et al.  Performance evaluation considering iterations per phase and SA temperature in WMN-SA system , 2014, Mob. Inf. Syst..

[2]  Fatos Xhafa,et al.  A Comparison Study of Simulated Annealing and Genetic Algorithm for Node Placement Problem in Wireless Mesh Networks , 2013, J. Mobile Multimedia.

[3]  Ian F. Akyildiz,et al.  Wireless mesh networks: a survey , 2005, Comput. Networks.

[4]  Fatos Xhafa,et al.  An Integrated Simulation System Considering WMN-PSO Simulation System and Network Simulator 3 , 2016, BWCCA.

[5]  Leonard Barolli,et al.  Experimental Results of a MANET Testbed for Different Settings of HELLO Packets of OLSR Protocol , 2013, J. Mobile Multimedia.

[6]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[7]  Leonard Barolli,et al.  A Testbed for Admission Control in WLAN: A Fuzzy Approach and Its Performance Evaluation , 2016, BWCCA.

[8]  Seyyed M. T. Fatemi Ghomi,et al.  Development of a PSO-SA hybrid metaheuristic for a new comprehensive regression model to time-series forecasting , 2010, Expert Syst. Appl..

[9]  Fatos Xhafa,et al.  Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks , 2016, Int. J. Commun. Networks Distributed Syst..

[10]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[11]  Fatos Xhafa,et al.  Implementation of a New Replacement Method in WMN-PSO Simulation System and Its Performance Evaluation , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).

[12]  C. Siva Ram Murthy,et al.  Node Placement Algorithm for Deployment of Two-Tier Wireless Mesh Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[13]  Fatos Xhafa,et al.  QoS routing in ad-hoc networks using GA and multi-objective optimization , 2011, Mob. Inf. Syst..

[14]  Fatos Xhafa,et al.  Investigation of Fitness Function Weight-Coefficients for Optimization in WMN-PSO Simulation System , 2016, 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS).

[15]  Leonard Barolli,et al.  An Integrated System for Wireless Cellular and Ad-Hoc Networks Using Fuzzy Logic , 2014, 2014 International Conference on Intelligent Networking and Collaborative Systems.

[16]  Andrew Lim,et al.  k-Center problems with minimum coverage , 2004, Theor. Comput. Sci..

[17]  C. Hwang Simulated annealing: Theory and applications , 1988, Acta Applicandae Mathematicae - An International Survey Journal on Applying Mathematics and Mathematical Applications.

[18]  Leonard Barolli,et al.  A Secure-Aware Call Admission Control Scheme for Wireless Cellular Networks Using Fuzzy Logic and Its Performance Evaluation , 2015, J. Mobile Multimedia.

[19]  Leonard Barolli,et al.  Integrating Wireless Cellular and Ad-Hoc Networks Using Fuzzy Logic Considering Node Mobility and Security , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[20]  Edoardo Amaldi,et al.  Optimization models and methods for planning wireless mesh networks , 2008, Comput. Networks.

[21]  Feng Qian,et al.  A Hybrid Algorithm Based on Particle Swarm Optimization and Simulated Annealing for Job Shop Scheduling , 2007, Third International Conference on Natural Computation (ICNC 2007).

[22]  M. Hannikainen,et al.  Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning , 2007, 2007 4th International Symposium on Wireless Communication Systems.

[23]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[24]  Yoshikazu Fukuyama,et al.  A Hybrid Particle Swarm Optimization for Distribution State Estimation , 2002, IEEE Power Engineering Review.

[25]  Catherine Rosenberg,et al.  Single Gateway Placement in Wireless Mesh Networks , 2008 .

[26]  Maolin Tang,et al.  Gateways Placement in Backbone Wireless Mesh Networks , 2009, Int. J. Commun. Netw. Syst. Sci..

[27]  Fatos Xhafa,et al.  Ad Hoc and Neighborhood Search Methods for Placement of Mesh Routers in Wireless Mesh Networks , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems Workshops.

[28]  Dharma P. Agrawal,et al.  Efficient Mesh Router Placement in Wireless Mesh Networks , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[29]  Albert A. Groenwold,et al.  A Study of Global Optimization Using Particle Swarms , 2005, J. Glob. Optim..

[30]  Fatos Xhafa,et al.  A comparison study of Hill Climbing, Simulated Annealing and Genetic Algorithm for node placement problem in WMNs , 2014, J. High Speed Networks.

[31]  Takahiro Hara,et al.  Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks , 2013, Mob. Inf. Syst..

[32]  Maria da Conceição Cunha,et al.  Water Distribution Network Design Optimization: Simulated Annealing Approach , 1999 .

[33]  Fatos Xhafa,et al.  Application of WMN-SA Simulation System for Node Placement in Wireless Mesh Networks: A Case Study for a Realistic Scenario , 2014, Int. J. Mob. Comput. Multim. Commun..

[34]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[35]  Tarek M. Mahmoud,et al.  Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods , 2014 .