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

Bình, Lê Hữu; Truong, Tung Khac

doi:10.3390/s22155494

Cited by 15 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, graph theory was used to describe the WMN. We then define some metrics to use for the objective function of the RNP problem, similar to [ 11 ]. Finally, the RNP problem was formulated as a nonlinear programming problem.…”

Section: Rnp Problemmentioning

confidence: 99%

“…For accurate results, each script must be executed multiple times, and then the average of all executions is obtained. For example, the authors of [ 8 , 11 ] executed each simulation scenario 50 times. Furthermore, with heavy node density and wide-area WMNs, solving the RNP problem with approximation algorithms often presents many difficulties, necessitating a more effective solution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Binh,

Duong

2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Rnp Problemmentioning

confidence: 99%

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although a number of earlier research, such as [16] and [13] investigated the placement of wireless routers in consistent network zones, this investigation prevented the spread of mesh routers. On the other hand, some other studies took into consideration a continuous deployment region, which allows for a larger level of pliability in the positioning of mesh routers and ultimately leads to an improvement in network design.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

et al. 2023

View full text Add to dashboard Cite

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .

show abstract

“…In contrast to previous studies, 8,9,11,15,20 we used

m

for normalization rather than

m + n

since we must calculate the number of covered mesh clients without considering mesh routers, as specified by the mesh client coverage definition and mesh networks standard 2…”

Section: The Proposed Objective Functionsmentioning

confidence: 99%

“…Binh et al 8 applied the Multi‐Verse Optimizer (MVO) algorithm to solve the MRP‐WMN problem. They found the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average compared to other algorithms.…”

Section: Introductionmentioning

confidence: 99%

An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

Nouri

Aliouat

Naouri

et al. 2023

Int J Communication

View full text Add to dashboard Cite

One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a biobjective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings.The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.clients coverage, mesh routers placement, Moth-flame optimization algorithms, network connectivity, wireless mesh networks Recently, the pervasive use of WiFi devices has increased dramatically. The penetration rates of this emerging technology are massive in many deployment areas, thanks to the decreasing prices of wireless devices. A wireless mesh network (WMN) could be formed by hundreds of hotspots covering multiple regions or a single hotspot covering a small area. As illustrated in Figure 1, a generic wireless mesh network consists of mesh routers (MRs) and mesh clients (MCs). In this architecture, mesh routers are static nodes, forming the wireless backbone. Mesh clients are mobile users who can access the network through these routers.

show abstract

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

Cited by 15 publications

References 26 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

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm

Contact Info

Product

Resources

About