1,469 research outputs found
Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks
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.Peer ReviewedPostprint (author's final draft
Node placement in Wireless Mesh Networks: a comparison study of WMN-SA and WMN-PSO simulation systems
(c) 2016 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.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 a simulation system based on Simulated Annealing (SA) for solving node placement problem in wireless mesh networks, called WMN-SA. Also, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO. In this paper, we compare two systems considering calculation time. From the simulation results, when the area size is 32 Ă 32 and 64 Ă 64, WMN-SA is better than WMN-PSO. When the area size is 128 Ă 128, WMN-SA performs better than WMN-PSO. However, WMN-SA needs more calculation time than WMN-PSO.Peer ReviewedPostprint (author's final draft
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
Conception des réseaux maillés sans fil à multiples-radios multiples-canaux
Généralement, les problÚmes de conception de réseaux consistent à sélectionner les arcs et
les sommets dâun graphe G de sorte que la fonction coĂ»t est optimisĂ©e et lâensemble de
contraintes impliquant les liens et les sommets dans G sont respectĂ©es. Une modification dans le critĂšre dâoptimisation et/ou dans lâensemble de contraintes mĂšne Ă une nouvelle reprĂ©sentation dâun problĂšme diffĂ©rent. Dans cette thĂšse, nous nous intĂ©ressons au problĂšme de conception dâinfrastructure de rĂ©seaux maillĂ©s sans fil (WMN- Wireless Mesh Network en Anglais) oĂč nous montrons que la conception de tels rĂ©seaux se transforme dâun
problĂšme dâoptimisation standard (la fonction coĂ»t est optimisĂ©e) Ă un problĂšme
dâoptimisation Ă plusieurs objectifs, pour tenir en compte de nombreux aspects, souvent
contradictoires, mais néanmoins incontournables dans la réalité. Cette thÚse, composée de
trois volets, propose de nouveaux modĂšles et algorithmes pour la conception de WMNs oĂč
rien nâest connu Ă lâ avance.
Le premiervolet est consacrĂ© Ă lâoptimisation simultanĂ©e de deux objectifs
équitablement importants : le coût et la performance du réseau en termes de débit. Trois
modĂšles bi-objectifs qui se diffĂ©rent principalement par lâapproche utilisĂ©e pour maximiser
la performance du réseau sont proposés, résolus et comparés.
Le deuxiĂšme volet traite le problĂšme de placement de passerelles vu son impact sur la
performance et lâextensibilitĂ© du rĂ©seau. La notion de contraintes de sauts (hop constraints)
est introduite dans la conception du réseau pour limiter le délai de transmission. Un nouvel
algorithme basé sur une approche de groupage est proposé afin de trouver les positions
stratĂ©giques des passerelles qui favorisent lâextensibilitĂ© du rĂ©seau et augmentent sa
performance sans augmenter considérablement le coût total de son installation.
Le dernier volet adresse le problÚme de fiabilité du réseau dans la présence de pannes
simples. PrĂ©voir lâinstallation des composants redondants lors de la phase de conception
peut garantir des communications fiables, mais au détriment du coût et de la performance
du rĂ©seau. Un nouvel algorithme, basĂ© sur lâapproche thĂ©orique de dĂ©composition en
oreilles afin dâinstaller le minimum nombre de routeurs additionnels pour tolĂ©rer les pannes
simples, est développé.
Afin de résoudre les modÚles proposés pour des réseaux de taille réelle, un algorithme
évolutionnaire (méta-heuristique), inspiré de la nature, est développé. Finalement, les
méthodes et modÚles proposés on été évalués par des simulations empiriques et
dâĂ©vĂ©nements discrets.Generally, network design problems consist of selecting links and vertices of a graph G so
that a cost function is optimized and all constraints involving links and the vertices in G are
met. A change in the criterion of optimization and/or the set of constraints leads to a new
representation of a different problem. In this thesis, we consider the problem of designing
infrastructure Wireless Mesh Networks (WMNs) where we show that the design of such
networks becomes an optimization problem with multiple objectives instead of a standard
optimization problem (a cost function is optimized) to take into account many aspects, often
contradictory, but nevertheless essential in the reality.
This thesis, composed of three parts, introduces new models and algorithms for
designing WMNs from scratch.
The first part is devoted to the simultaneous optimization of two equally important
objectives: cost and network performance in terms of throughput. Three bi-objective models
which differ mainly by the approach used to maximize network performance are proposed,
solved and compared.
The second part deals with the problem of gateways placement, given its impact on
network performance and scalability. The concept of hop constraints is introduced into the
network design to reduce the transmission delay. A novel algorithm based on a clustering
approach is also proposed to find the strategic positions of gateways that support network
scalability and increase its performance without significantly increasing the cost of installation.
The final section addresses the problem of reliability in the presence of single failures.
Allowing the installation of redundant components in the design phase can ensure reliable
communications, but at the expense of cost and network performance. A new algorithm is
developed based on the theoretical approach of "ear decomposition" to install the minimum
number of additional routers to tolerate single failures.
In order to solve the proposed models for real-size networks, an evolutionary algorithm
(meta-heuristics), inspired from nature, is developed. Finally, the proposed models and
methods have been evaluated through empirical and discrete events based simulations
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