Admir Barolli

A genetic algorithm-based system for wireless mesh networks: analysis of system data considering different routing protocols and architectures

Wireless mesh networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we evaluated WMN-GA system which is based on genetic algorithms (GAs) to find an optimal location assignment for mesh routers. In this paper, we evaluate the performance of four different distributions of mesh clients for two WMN architectures considering throughput, delay and energy metrics. For simulations, we used ns-3, optimized link state routing (OLSR) and hybrid wireless mesh protocols (HWMP). We compare the performance for Normal, Uniform, Exponential and Weibull distributions of mesh clients by sending multiple constant bit rate flows in the network. The simulation results show that for HWM protocol the throughput of Uniform distribution is higher than other distributions. However, for OLSR protocol, the throughput of Exponential distribution is better than other distributions. For both protocols, the delay and remaining energy are better for Weibull distribution.

A GA-Based Simulation System for WMNs: Performance Analysis for Different WMN Architectures Considering TCP

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we implemented WMN-GA system which is based on Genetic Algorithms (GAs) and used it for node placement problem in WMNs. In this paper, we evaluate the performance of two different distributions of mesh clients for two WMN architectures considering PDR, throughput, cwnd, delay and energy metrics. For simulations, we used ns-3, Hybrid Wireless Mesh Protocol (HWMP) and TCP. We compare the performance for normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that the PDR is almost the same for both distributions and architectures. However, the throughput and cwnd are better for normal distribution and I/B WMN architecture. But, the delay and energy is better for uniform distribution.

Implementation and Experimental Results of a WMN Testbed in Indoor Environment Considering LoS Scenario

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers, because of their potential use in several fields such as collaborative computing and communications. Considering mobility of the terminals, routing is a key process for operation of WMNs. In this paper, we present the implementation of a testbed for WMNs. We analyze the performance of Optimized Link State Routing (OLSR) protocol in an indoor environment considering NLoS scenario. For evaluation we considered throughput, Packet Delivery Ratio (PDR), hop count, delay and jitter metrics. The experimental results show that the nodes in the testbed were communicating smoothly and we got hight values of throughput and PDR and low values of delay and jitter.

Solving mesh router nodes placement problem in Wireless Mesh Networks by Tabu Search algorithm

We solve the mesh router placement nodes problem in Wireless Mesh Networks by Tabu Search.The problem is a bi-objective optimization maximizing network connectivity and user coverage.The experimental evaluation showed the efficiency of TS in solving a benchmark of instances. Wireless Mesh Networks (WMNs) are an important networking paradigm that offer cost effective Internet connectivity. The performance and operability of WMNs depend, among other factors, on the placement of network nodes in the area. Among the most important objectives in designing a WMN is the formation of a mesh backbone to achieve high user coverage. Given a number of router nodes to deploy, a deployment area and positions of client nodes in the area, an optimization problem can be formulated aiming to find the placement of router nodes so as to maximize network connectivity and user coverage. This optimization problem belongs to facility location problems, which are computationally hard to solve to optimality. In this paper we present the implementation and evaluation of Tabu Search (TS) for the problem of mesh router node placement in WMNs. The experimental evaluation showed the efficiency of TS in solving a benchmark of instances.

Effect of different grid shapes in wireless mesh network-genetic algorithm system

Grid computing have emerged as scientific and technical work, where geographically distributed computers, are used to create virtual supercomputers of vast amount of computing capacity able to solve complex problems in less time than before. Thus, within the last years we have witnessed how grid computing has helped to achieve breakthroughs in meteorology, physics and other computing-intensive fields. Wireless Mesh Networks (WMNs) are particularly suited for providing the communications required for a robust, self-healing grid architecture. The good performance and operability of WMNs depend on placement of mesh routers nodes in the geographical deployment area. In this paper, we consider the grid topology and evaluate the performance of WMN-GA system for node placement problem in WMNs. We consider different distributions of mesh clients and different grid shapes. The simulation results show the effect of grid shapes in the performance of WMN-GA. The system performs better for normal distribution.

Node placement for wireless mesh networks

Abstract Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we evaluated WMN-GA system which is based on Genetic Algorithms (GAs) to find an optimal location assignment for mesh routers. In this paper, we evaluate the performance of four different distributions of mesh clients (Normal, Uniform, Exponential and Weibull) considering Packet Delivery Ratio (PDR), throughput and delay metrics. For simulations, we used ns-3 and Hybrid Wireless Mesh Protocol (HWMP) and sent multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that the implemented system can be used successfully for router placement in WMNs.

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

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.

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, 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.

Performance Evaluation of WMN-PSOSA Considering Four Different Replacement 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 four different replacement methods. The simulation results show that LDIWM have better performance than CM, RIWM and LDVM replacement methods.

Performance Evaluation of WMNs by WMN-PSOSA Simulation System Considering Random Inertia Weight Method and Linearly Decreasing Vmax Method

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 Random Inertia Weight Method (RIWM) and Linearly Decreasing Vmax Method (LDVM). The simulation results show that the LDVM has better performance than RIWM for this scenario.