A comparison study of Weibull, normal and Boulevard distributions for wireless mesh networks considering different router replacement methods by a hybrid intelligent simulation system

Barolli, Admir; Bylykbashi, Kevin; Qafzezi, Ermioni; Sakamoto, Shinji; Barolli, Leonard

doi:10.1007/s12652-021-03680-1

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The strategic placement of gateways is of paramount importance in the optimization of tra c routing, equitable distribution of workload, and maximization of the effective exploitation of network resources. The strategic placement of gateways is crucial in optimizing network speed, reducing latency, and improving the overall user experience [44][45][46].…”

Section: Expected Link Quality (Elq)mentioning

confidence: 99%

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

Naghdiani,

Jahanshahi,

Matin

2023

Preprint

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Wireless mesh networks facilitate the provision of Intranet and Internet connectivity across diverse environments, catering to a wide range of applications. It is anticipated that there will be a significant volume of traffic on these networks. The selection and placement of gateway nodes is a significant research concern due to their responsibility for transmitting traffic load. This issue holds importance as it has the potential to optimize network capacity utilization and mitigate congestion effects. Furthermore, the implementation of a multi-radio multi-channel architecture is regarded as a highly promising approach to enhance performance and mitigate interference. Channel assignment is the process of determining the optimal associations between channels and radios for the purpose of transmitting and receiving data concurrently across multiple channels. In order to maximize throughput in multi-radio multi-channel wireless mesh networks, this research investigates the problem of gateway selection and location. Our solution is distinct from the many others described in the literature because it explicitly models the delay overhead associated with channel switching. In addition, we factor in the latency problem while developing our processes. In our research, a Garter Snake Optimization Algorithm (GSO) is used to strategically place gateways. Based on our research, we know that the suggested scheme performs within a constant factor of the best solution as measured by the achieved throughput. The simulation results show that compared to random deployment, fixed deployment, and grid-based techniques, our suggested mechanism makes better use of available resources and delivers much higher network performance.

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Section: Expected Link Quality (Elq)mentioning

confidence: 99%

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

Naghdiani,

Jahanshahi,

Matin

2023

Preprint

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“…W IRELESS Mesh Network (WMN) is considered as an emerging communication technology that has seen an accelerated deployment since 2005 all over the world [1]- [3]. The main reasons behind this accelerated deployment are due to its easy implementation at a low cost, dynamic selforganization, self-configuration, and self-adaptive nature.…”

Section: Introductionmentioning

confidence: 99%

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

Taleb

Meraihi

Gabis³

et al. 2022

IEEE Access

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Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and metaheuristic) is essential. This motivates our attempts to present an application of the Coyote Optimization Algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Bat Algorithm (BA), African Vulture Optimization Algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm Algorithm (SSA).

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“…We consider different router replacement methods for each distribution of IoT devices in order to find good solutions for different scenarios. In our previous work [ 12 ], we considered normal, Weibull and Boulevard distributions of mesh clients and presented the results for five router replacement methods. In this paper, we consider the Stadium distribution and present a comparison study not only for different router replacement methods but also for roulette wheel and random selection methods.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices

Barolli

Sakamoto

Bylykbashi

et al. 2022

Sensors

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As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.

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A comparison study of Weibull, normal and Boulevard distributions for wireless mesh networks considering different router replacement methods by a hybrid intelligent simulation system

Cited by 7 publications

References 20 publications

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

Gateway selection and placement for Multi-radio multi-channel Wireless Mesh Networks with Garter Snake Optimization algorithm

Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices

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