Performance Evaluation of WMNs for Normal and Uniform Distribution of Mesh Clients Using WMN-PSOSA Simulation System

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 Normal and Uniform distributions of mesh clients. Simulation results show that WMN-PSOSA performs better for Normal distribution compared with the case of Uniform distribution.

[1]  Leonard Barolli,et al.  Performance Evaluation of WMN-PSOHC and WMN-PSO Simulation Systems for Node Placement in Wireless Mesh Networks: A Comparison Study , 2017, EIDWT.

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

[3]  Leonard Barolli,et al.  Performance Evaluation of Intelligent Hybrid Systems for Node Placement in Wireless Mesh Networks: A Comparison Study of WMN-PSOHC and WMN-PSOSA , 2017, IMIS.

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

[5]  Leonard Barolli,et al.  Implementation of Intelligent Hybrid Systems for Node Placement Problem in WMNs Considering Particle Swarm Optimization, Hill Climbing and Simulated Annealing , 2017, Mobile Networks and Applications.

[6]  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..

[7]  Leonard Barolli,et al.  Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Random Inertia Weight Method and Linearly Decreasing Vmax Method , 2017, BWCCA.

[8]  Leonard Barolli,et al.  Performance analysis of WMNs by WMN-GA simulation system for two WMN architectures and different TCP congestion-avoidance algorithms and client distributions , 2018, Int. J. Commun. Networks Distributed Syst..

[9]  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.

[10]  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.

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

[12]  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.

[13]  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.

[14]  Leonard Barolli,et al.  Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods , 2019, Soft Comput..

[15]  Fatos Xhafa,et al.  A simulation system for WMN based on SA: performance evaluation for different instances and starting temperature values , 2014, Int. J. Space Based Situated Comput..

[16]  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.

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

[18]  Leonard Barolli,et al.  Performance Evaluation of WMN-PSOSA Considering Four Different Replacement Methods , 2018, EIDWT.

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

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

[21]  Leonard Barolli,et al.  Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Constriction and Linearly Decreasing Inertia Weight Methods , 2017, NBiS.

[22]  Leonard Barolli,et al.  Performance analysis of two Wireless Mesh Network architectures by WMN-SA and WMN-TS simulation systems , 2017, J. High Speed Networks.

[23]  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..

[24]  Leonard Barolli,et al.  Performance Analysis of Simulation System Based on Particle Swarm Optimization and Distributed Genetic Algorithm for WMNs Considering Different Distributions of Mesh Clients , 2018, IMIS.

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

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

[27]  Leonard Barolli,et al.  Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Constriction and Linearly Decreasing Vmax Methods , 2017, 3PGCIC.

[28]  Leonard Barolli,et al.  Performance evaluation of a QoS-aware fuzzy-based CAC for LAN access , 2016, Int. J. Space Based Situated Comput..

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

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

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

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

[33]  Xu An Wang,et al.  Advances in Internetworking, Data & Web Technologies, The 5th International Conference on Emerging Internetworking, Data & Web Technologies, EIDWT-2017, Wuhan, China, June 10-11, 2017 , 2018, EIDWT.

[34]  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).

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

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

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

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

[39]  Leonard Barolli,et al.  Design and Implementation of a Hybrid Intelligent System Based on Particle Swarm Optimization and Distributed Genetic Algorithm , 2018, EIDWT.

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