Wireless Mesh Router Placement with Constraints of Gateway Positions and QoS

Lin, Chun-Cheng; Chen, Tung-Huei; Jhong, Shun-Yu

doi:10.4108/eai.19-8-2015.2260962

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The new models significantly outperformed the conventional solutions. QoS was also considered in the RNP problem in [23]. The authors described a unique particle swarm optimization method for improving network connectivity and client coverage.…”

Section: Plos Onementioning

confidence: 99%

A novel and effective method for solving the router nodes placement in wireless mesh networks using reinforcement learning

Binh,

Duong

2024

PLoS ONE

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Router nodes placement (RNP) is an important issue in the design and implementation of wireless mesh networks (WMN). This is known as an P-hard problem, which cannot be solved using conventional algorithms. Consequently, approximate optimization strategies are commonly used to solve this problem. With heavy node density and wide-area WMNs, solving the RNP problem using approximation algorithms often faces many difficulties, therefore, a more effective solution is necessary. This motivated us to conduct this work. We propose a new method for solving the RNP problem using reinforcement learning (RL). The RNP problem is modeled as an RL model with environment, agent, action, and reward are equivalent to the network system, routers, coordinate adjustment, and connectivity of the RNP problem, respectively. To the best of our knowledge, this is the first study that applies RL to solve the RNP problem. The experimental results showed that the proposed method increased the network connectivity by up to 22.73% compared to the most recent methods.

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Section: Plos Onementioning

confidence: 99%

A novel and effective method for solving the router nodes placement in wireless mesh networks using reinforcement learning

Binh,

Duong

2024

PLoS ONE

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“…Other authors have optimized the coverage and connectivity by means of the PSO algorithms. In [ 27 ], the authors proposed an approach that can solve the problem of overlapping and coverlessness. The proposed approach can escape the local minima, thus improving the quality of solutions and convergence speed.…”

Section: Related Workmentioning

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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“…The new models are shown to outperform existing solutions significantly. In [ 23 ], the QoT was also considered in the MPR-WMN problem. The authors have presented a novel particle swarm optimization approach to maximize both network connectivity and client coverage.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm

Bình

Truong

2022

Sensors

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Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the Multi-Verse Optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), respectively.

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Wireless Mesh Router Placement with Constraints of Gateway Positions and QoS

Cited by 7 publications

References 13 publications

A novel and effective method for solving the router nodes placement in wireless mesh networks using reinforcement learning

A novel and effective method for solving the router nodes placement in wireless mesh networks using reinforcement learning

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

An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm

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