With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.

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Implementation of a New Replacement Method in WMN-PSO Simulation System and Its Performance Evaluation

With the fast development of wireless technologies, wireless mesh networks WMNs are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation PSO in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method CM, random inertia weight method RIWM, linearly decreasing Vmax method LDVM and linearly decreasing inertia weight method LDIWM. Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component SGC and the number of covered mesh clients NCMC. The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.

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Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

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, called WMN-PSO, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. We evaluate WMN-PSODGA by computer simulations. The simulation results show that the WMN-PSODGA has good performance when the number of GA islands is 64.

Design and Implementation of a Hybrid Intelligent System Based on Particle Swarm Optimization and Distributed Genetic Algorithm

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) 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 four different replacement methods. The simulation results show that the rational decrement of Vmax method and linearly decreasing inertia weight method have better performance compared with constriction method and random inertia weight method.

Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods

The Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because they have many advantages such as low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. We analyze the performance of WMN-PSODGA by computer simulations considering different client distributions. Simulation results show that the WMN-PSODGA has good performance when the client distribution is Normal compared with the case of Exponential distribution.

Performance Analysis of Simulation System Based on Particle Swarm Optimization and Distributed Genetic Algorithm for WMNs Considering Different Distributions of Mesh Clients

Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. Multiple gateways are needed, which take time and cost budget to set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. This paper concentrates on the challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

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Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

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This paper illustrates a humanlike self-balancing robot's control and sensor fusion with an inherently imbalanced dynamical model. Using a state space model, a two-wheeled self-balancing robot (TWSBR) is modelled as an inverted pendulum. A comparative study of the system performance is conducted, with different control techniques, namely, PD, PID, and Linear Quadratic Regulator (LQR). The system status is monitored and fed back to the controller using an internal measurement unit (IMU) with data filtering through sensor fusion. The controlled system is simulated using MATLAB/ Simulink and Simscape. The quantitative performance of different controllers is calculated using seven different indices: maximum overshooting, settling time, rise time, root-mean-square error (RMSE), steady-state error, controller effort, and max control action. The results proved that the LQR gives the best performance with the lowest RMSE and reasonable rise, settling time, energy consumption, and saturation level; therefore, it is the most suited for our system requirements.

Ahmed M. Rabie

Papers

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Modelling and Control of a Self-Balancing Robot Using Sensor Fusion and LQR Controller