Performance Evaluation of WMNs Using an Hybrid Intelligent System Based on Particle Swarm Optimization and Hill Climbing Considering Different Number of Iterations

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 Hill Climbing (HC), called WMN-HC. Then, we implemented a hybrid simulation system based on PSO and HC, called WMN-PSOHC. In this paper, we evaluate the performance of WMNs by using WMN-PSOHC considering different number of iterations. Simulation results show that, the simulation time increases with increasing the number of iterations. When the number of iterations increase twice, the simulation time increases more than twice. Thus, we conclude that the calculation time and quality of solution is a trade-off relation. In this considered scenario, 400 iterations are enough.

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

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

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

[4]  Leonard Barolli,et al.  Performance Evaluation of WMN-PSODGA System for Node Placement Problem in WMNs Considering Four Different Crossover Methods , 2018, 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA).

[5]  Leonard Barolli,et al.  Performance Analysis of WMNs by WMN-PSODGA Simulation System Considering Load Balancing and Client Uniform Distribution , 2019, IMIS.

[6]  Leonard Barolli,et al.  A fuzzy-based approach for cluster management in VANETs: Performance evaluation for two fuzzy-based systems , 2018, Internet Things.

[7]  Leonard Barolli,et al.  Performance Analysis of WMNs by WMN-PSOHC-DGA Simulation System Considering Random Inertia Weight and Linearly Decreasing Vmax Router Replacement Methods , 2019, CISIS.

[8]  Euripides G. M. Petrakis,et al.  Internet of Things as a Service (iTaaS): Challenges and solutions for management of sensor data on the cloud and the fog , 2018, Internet Things.

[9]  Leonard Barolli,et al.  A Comparison Study of Constriction and Linearly Decreasing Vmax Replacement Methods for Wireless Mesh Networks by WMN-PSOHC-DGA Simulation System , 2019, 3PGCIC.

[10]  Leonard Barolli,et al.  Implementation of Intelligent Hybrid Systems for Node Placement Problem in WMNs Considering Particle Swarm Optimization, Hill Climbing and Simulated Annealing , 2018, Mob. Networks Appl..

[11]  Leonard Barolli,et al.  A Hybrid Simulation System Based on Particle Swarm Optimization and Distributed Genetic Algorithm for WMNs: Performance Evaluation Considering Normal and Uniform Distribution of Mesh Clients , 2018, NBiS.

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

[13]  Zahoor Ali Khan,et al.  Energy harvesting techniques for routing issues in wireless sensor networks , 2019 .

[14]  Leonard Barolli,et al.  Performance Evaluation of WMNs by WMN-PSOHC System Considering Random Inertia Weight and Linearly Decreasing Inertia Weight Replacement Methods , 2019, IMIS.

[15]  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.

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

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

[18]  Leonard Barolli,et al.  A WLAN triage testbed based on fuzzy logic and its performance evaluation for different number of clients and throughput parameter , 2019 .

[19]  Wen-Chung Kao,et al.  A dynamic access-point transmission power minimization method using PI feedback control in elastic WLAN system for IoT applications , 2019, Internet Things.

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

[21]  Eslam Nazemi,et al.  An autonomic mechanism based on ant colony pattern for detecting the source of incidents in complex enterprise systems , 2019 .

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

[23]  K. Narendra Swaroop,et al.  Sub-1GHz miniature wireless sensor node for IoT applications , 2018, Internet Things.

[24]  Igor Machado Coelho,et al.  A network coding protocol for wireless sensor fog computing , 2019 .

[25]  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..

[26]  Leonard Barolli,et al.  Performance Analysis of WMNs by WMN-PSOHC-DGA Simulation System Considering Linearly Decreasing Inertia Weight and Linearly Decreasing Vmax Replacement Methods , 2019, INCoS.

[27]  Leonard Barolli,et al.  WMN-PSOSA: an intelligent hybrid simulation system for WMNs and its performance evaluations , 2019 .

[28]  Leonard Barolli,et al.  Implementation of a Web interface for hybrid intelligent systems , 2019, Int. J. Web Inf. Syst..

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