Deployment optimization of wireless mesh networks in wind turbine condition monitoring system

Yang, Yanjun; Liu, Aimin; Xin, Hongwei; Yu, Xin; Zhang, Wen

doi:10.1007/s11276-020-02522-w

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…At regular intervals, each node sends out a short packet containing data about its Network Interface (NIF). Adjacent nodes store the data and calculate the Link Interference Factor (LIF) using their own Network Interface Factors (NIF) and the NIFs of their nearest neighbors [34,35]. The LIF is calculated by taking the larger of the two interference values into account.…”

Section: Interference Factor (If)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: Interference Factor (If)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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“…Most recent work [29] on WMN topologies tends to focus on approximate optimisations and has delivered improvements using heuristics, meta-heuristics, and hybrids. Heuristic systems tend to be more historic, and although recent work was carried out on clustering algorithms for MRs [35] and in tree-based approaches [36], it otherwise tends to focus on gateway placement. Meta-heuristics-based techniques have proven the most popular, and recent studies [37,38] have focused on using hill-climbing and simulated annealing (SA), respectively, for MR router placement through various environments.…”

Section: Topology Optimisationmentioning

confidence: 99%

Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment

2023

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Fifth-generation technologies have reached a stage where it is now feasible to consider deployments that extend beyond traditional public networks. Central to this process is the application of Fixed Wireless Access (FWA) in 5G Non-public Networks (NPNs) that can utilise a novel combination of radio technologies to deploy an infrastructure on top of 5G NR or entirely from scratch. However, the use of FWA backhaul faces many challenges in relation to the trade-offs for reduced costs and a relatively simple deployment. Specifically, the use of meshed deployments is critical as it provides resilience against a temporary loss of connectivity due to link errors. In this paper, we examine the use of meshing in a FWA backhaul to determine if an optimal trade-off exists between the deployment of more nodes/links to provide multiple paths to the nearest Point of Presence (POP) and the performance of the network. Using a real 5G NPN deployment as a basis, we have conducted a simulated analysis of increasing network densities to determine the optimal configuration. Our results show a clear advantage for meshing in general, but there is also a performance trade-off to consider between overall network throughput and stability.

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“…Typically, every parameter that exists in the network may undergo optimization. However, the situation might be more complicated, where an optimization process attained some performance improvements locally while globally, leading to worsening network performance 4,5 …”

Section: Introductionmentioning

confidence: 99%

“…However, the situation might be more complicated, where an optimization process attained some performance improvements locally while globally, leading to worsening network performance. 4,5 Network deployment is an essential mission that influences the network life cycle. Accordingly, during the planning phase of the wireless mesh networks, router placement is estimated to ensure that the client coverage and network connectivity requirements are met.…”

mentioning

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

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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.

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Deployment optimization of wireless mesh networks in wind turbine condition monitoring system

Cited by 6 publications

References 46 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

Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment

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

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