A bat-inspired algorithm for router node placement with weighted clients in wireless mesh networks

Lin, Chun-Cheng; Li, Yan-Sing; Deng, Der-Jiunn

doi:10.1109/chinacom.2014.7054274

Cited by 21 publications

(10 citation statements)

References 9 publications

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“…There are several different chaotic bat algorithm based modifications as introduced by [9,10,11]. The chaotic mechanism in use differs from one another.…”

Section: Chaotic Bat Algorithm (Cba)mentioning

confidence: 99%

“…The chaotic mechanism in use differs from one another. For example [9] using Lévy flights and chaotic maps to represent a dynamic biological system, [10] make comparisons of different chaotic map functions such as Chebyshev map, Circle map, Gauss map and a few other chaotic maps. Authors in [11] also compares several different chaotic map functions to determine the best chaotic approach to be used in the Chaotic Bat algorithm and Iterative chaotic map with infinite collapses (ICMIC) to produce the best results.…”

Section: Chaotic Bat Algorithm (Cba)mentioning

confidence: 99%

“…Further, we now look at how Bat Algorithm is used to tackle different optimization problems in the wireless ad-hoc network from existing related literatures. Authors in [53] uses Bat Algorithm to solve optimisation problem to determine dynamic router node placement (dynRNP) in MANET where each node has a different priority that is based on weight value. This investigation is extended in [54] with dynamic placement of router and gateway simultaneously in the most optimum condition in term of network connectivity and client coverage based on relay load, QOS constraints of delay for every hops and capacity of gateway.…”

Section: E Networkingmentioning

confidence: 99%

“…Bat Algorithm is used to perform complex financial decision making with a numbers of constraints such as regulations, policies and government ideology to meet the diverse budget requirements within limited financial resources. Authors in [9] additionally uses Bat Algorithm as a tool to perform estimation parameters for the reconstruction of nonlinear biological system dynamics.…”

Section: F Other Applicationsmentioning

confidence: 99%

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A Review on Applications of Optimization Using Bat Algorithm

Ahmmad¹,

Muchtar²

2020

IJATCSE

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Bat algorithm is introduced by Xin-She Yang. This article describes an overview of Bat algorithm, which is inspired by the life of a bat family, as well as an overview of bat algorithm applications in various categories for solving optimization problems. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. The categories reviewed are engineering, scheduling, transportation/vehicle, and networking. The objective of this is to provide an overview and to summarize the review of applications on the bat algorithm.

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“…There are several different chaotic bat algorithm based modifications as introduced by [9,10,11]. The chaotic mechanism in use differs from one another.…”

Section: Chaotic Bat Algorithm (Cba)mentioning

confidence: 99%

Section: Chaotic Bat Algorithm (Cba)mentioning

confidence: 99%

Section: E Networkingmentioning

confidence: 99%

Section: F Other Applicationsmentioning

confidence: 99%

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A Review on Applications of Optimization Using Bat Algorithm

Ahmmad¹,

Muchtar²

2020

IJATCSE

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“…Lin et al consider in [34] the problem of dynamic Router Node Placement (dynRNP) in Wireless Mesh Networks (WMNs), in which both mesh clients and mesh routers have mobility. Mesh clients can also switch their network access on or off at different times.…”

Section: Rn Placement For K-connectivitymentioning

confidence: 99%

A Dynamical Relay node placement solution for MANETs

Magán-Carrión

Camacho

García-Teodoro

et al. 2017

Computer Communications

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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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A bat-inspired algorithm for router node placement with weighted clients in wireless mesh networks

Cited by 21 publications

References 9 publications

A Review on Applications of Optimization Using Bat Algorithm

A Review on Applications of Optimization Using Bat Algorithm

A Dynamical Relay node placement solution for MANETs

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

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