Performance evaluation of WMN-GA for different mutation and crossover rates considering number of covered users parameter

Node placement problems have been long investigated in the optimization field due to numerous applications in location science and classification. Facility location problems are showing their usefulness to communication networks, and more especially from Wireless Mesh Networks (WMNs) field. Recently, such problems are showing their usefulness to communication networks, where facilities could be servers or routers offering connectivity services to clients. In this paper, we deal with the effect of mutation and crossover operators in GA for node placement problem. We evaluate the performance of the proposed system using different selection operators and different distributions of router nodes considering number of covered users parameter. The simulation results show that for Linear and Exponential ranking methods, the system has a good performance for all rates of crossover and mutation.Peer ReviewedPostprint (published version

Effects of population size for location-aware node placement in WMNs: evaluation by a genetic algorithm--based approach

Wireless mesh networks (WMNs) are cost-efficient networks that have the potential to serve as an infrastructure for advanced location-based services. Location service is a desired feature for WMNs to support location-oriented applications. WMNs are also interesting infrastructures for supporting ubiquitous multimedia Internet access for mobile or fixed mesh clients. In order to efficiently support such services and offering QoS, the optimized placement of mesh router nodes is very important. Indeed, such optimized mesh placement can support location service managed in the mesh and keep the rate of location updates low...Peer ReviewedPostprint (author's final draft

A GA-based simulation system for WMNs: comparison analysis for different number of flows, client distributions, DCF and EDCA functions

In this paper, we compare the performance of Distributed Coordination Function (DCF) and Enhanced Distributed Channel Access (EDCA) for normal and uniform distributions of mesh clients considering two Wireless Mesh Network (WMN) architectures. As evaluation metrics, we consider throughput, delay, jitter and fairness index metrics. For simulations, we used WMN-GA simulation system, ns-3 and Optimized Link State Routing. The simulation results show that for normal distribution, the throughput of I/B WMN is higher than Hybrid WMN architecture. For uniform distribution, in case of I/B WMN, the throughput of EDCA is a little bit higher than Hybrid WMN. However, for Hybrid WMN, the throughput of DCF is higher than EDCA. For normal distribution, the delay and jitter of Hybrid WMN are lower compared with I/B WMN. For uniform distribution, the delay and jitter of both architectures are almost the same. However, in the case of DCF for 20 flows, the delay and jitter of I/B WMN are lower compared with Hybrid WMN. For I/B architecture, in case of normal distribution the fairness index of DCF is higher than EDCA. However, for Hybrid WMN, the fairness index of EDCA is higher than DCF. For uniform distribution, the fairness index of few flows is higher than others for both WMN architectures.Peer ReviewedPostprint (author's final draft

Genetic algorithms for efficient placement of router nodes in wireless mesh networks

In Wireless Mesh Networks (WMNs) the meshing architecture, consisting of a grid of mesh routers, provides connectivity services to different mesh client nodes. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Thus, finding optimal or near-optimal mesh router nodes placement is crucial to such networks. In this work we propose and evaluate Genetic Algorithms (GAs) for near-optimally solving the problem. In our approach we seek a two-fold optimization, namely, the maximization of the size of the giant component in the network and that of user coverage. The size of the giant component is considered here as a criteria for measuring network connectivity. GAs explore the solution space by means of a population of individuals, which are evaluated, selected, crossed and mutated to reproduce new individuals of better quality. The fitness of individuals is measured with respect to network connectivity and user coverage being the former a primary objective and the later a secondary one. Several genetic operators have been considered in implementing GAs in order to find the configuration that works best for the problem. We have experimentally evaluated the proposed GAs using a benchmark of generated instances varying from small to large size. In order to evaluate the quality of achieved solutions for different possible client distributions, instances have been generated using different distributions of mesh clients (Uniform, Normal, Exponential and Weibull). The experimental results showed the efficiency of the GAs for computing high quality solutions of mesh router nodes placement in WMNs.Peer ReviewedPostprint (published version

Performance analysis of WMN-GA simulation system for different WMN architectures considering OLSR

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.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 two different distributions of mesh clients for two WMN architectures considering throughput, delay and energy metrics. For simulations, we used ns-3 and Optimized Link State Routing (OLSR). 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 for both distributions, the throughput of Hybrid WMN is higher than I/B WMN architecture. The delay of Hybrid WMN is a lower compared with I/B WMN. The delay for Hybrid WMN is almost the same for both distributions. However for I/B WMN, the delay is lower for Uniform distribution. For Normal distribution, the energy decreases sharply, because of the high density of nodes. For Uniform distribution, the remaining energy is higher compared with Normal distribution.Peer ReviewedPostprint (author's final draft

Performance analysis of different architectures and TCP congestion-avoidance algorithms using WMN-GA simulation system

In this paper, we evaluate the performance of two Wireless Mesh Networks (WMNs) architectures considering throughput, delay, jitter and fairness index metrics. For simulations, we used ns-3, Distributed Coordination Function (DCF) and Optimized Link State Routing (OLSR). We compare the performance of WMN for different Transmission Control Protocol (TCP): Tahoe, Reno and NewReno considering normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that for normal and uniform distributions and both WMN architectures, the PDR values are almost the same. For Hybrid WMN, the throughput of TCP NewReno is good, but for I/B WMN, the throughput of TCP Tahoe is higher than other algorithms. For normal distribution, the delay and jitter of I/B WMN are lower compared with Hybrid WMN, while for uniform distribution, the delay and jitter of TCP NewReno are a little bit lower compared with other algorithms. The fairness index of normal distribution is higher than uniform distribution.Peer ReviewedPostprint (author's final draft

Interface and results visualization of WMN-GA simulation system: evaluation for exponential and Weibull distributions considering different transmission rates

This is a copy of the author 's final draft version of an article published in the journal Computer standards & interfaces. The final publication is available at Springer via http://dx.doi.org/10.1016/j.csi.2015.04.003In this paper, we present the interface and data visualization of a simulation system for Wireless Mesh Networks (WMNs), which is based on Genetic Algorithms (GAs). We call this system WMN-GA. As evaluation parameters, we consider Packet Delivery Ratio (PDR), throughput and delay metrics. For simulations, we used ns-3 simulator and Hybrid Wireless Mesh Protocol (HWMP). From simulation results, we found that PDR for Weibull distribution is higher than Exponential distribution. But, the throughput of Exponential distribution is higher than Weibull distribution. The delay of Exponential distribution is smaller than Weibull distribution.Peer ReviewedPostprint (author's final draft