Placement optimization of wireless mesh routers using firefly optimization algorithm

Sayad, Lamri; Aı̈ssani, Djamil; Bouallouche-Medjkoune, Louiza

doi:10.1109/saconet.2018.8585626

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…In this section, we study the performance of the proposed COA in solving the mesh router nodes placement problem in WMNs. Thus, COA is compared with ten algorithms namely FA [20], GA [49], PSO [28], WOA [50], BA [27], AVOA [51], AO [52], BES [53], CHIO [54], and SSA [55]. Its performance is assessed considering three metrics: i) user coverage; ii) network connectivity; iii) objective function value.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In the same context, Sayad et al proposed three new algorithms based on Chemical Reaction Optimization (CRO) algorithm [19], Firefly optimization (FA) algorithm [20], and Electromagnetism-like Mechanism (EM) meta-heuristic [21]. These algorithms were validated using many generated instances with various number of mesh clients and mesh routers, taking into account the coverage and connectivity metrics.…”

Section: Introductionmentioning

confidence: 99%

“…D. CONVERGENCE ANALYSISThe convergence analysis of COA, FA, GA, BA, WOA, PSO, AVOA, AO, BES, CHIO, and SSA algorithms is shown in Figure[17][18][19][20] and Table11. We considered four network instances of different sizes (i.e.…”

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confidence: 99%

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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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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…In this paper, the underlying optimization algorithm remains the same, but the network has an additional gateway router to be considered while deploying communication protocols. The same objective was achieved in [26] using the firefly optimization algorithm, instead of simulated annealing. Other coverage constraints are beyond the scope of this work, as it is focused on ensuring the connectivity of fixed EDs to the BS.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Router Placement of Indoor Wireless Sensor Networks in Smart Buildings for IoT Applications

Alanezi

Bouchekara

Javaid

2020

Sensors

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Internet of Things (IoT) is characterized by a system of interconnected devices capable of communicating with each other to carry out specific useful tasks. The connection between these devices is ensured by routers distributed in a network. Optimizing the placement of these routers in a distributed wireless sensor network (WSN) in a smart building is a tedious task. Computer-Aided Design (CAD) programs and software can simplify this task since they provide a robust and efficient tool. At the same time, experienced engineers from different backgrounds must play a prominent role in the abovementioned task. Therefore, specialized companies rely on both; a useful CAD tool along with the experience and the flair of a sound expert/engineer to optimally place routers in a WSN. This paper aims to develop a new approach based on the interaction between an efficient CAD tool and an experienced engineer for the optimal placement of routers in smart buildings for IoT applications. The approach follows a step-by-step procedure to weave an optimal network infrastructure, having both automatic and designer-intervention modes. Several case studies have been investigated, and the obtained results show that the developed approach produces a synthesized network with full coverage and a reduced number of routers.

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A Hybrid Firefly Algorithm and Particle Swarm Optimization Algorithm for Mesh Routers Placement Problem in Wireless Mesh Networks

Taleb

Meraihi

Gabis

et al. 2022

Lecture Notes on Data Engineering and Communications Technologies

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Placement optimization of wireless mesh routers using firefly optimization algorithm

Cited by 8 publications

References 10 publications

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

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

Optimizing Router Placement of Indoor Wireless Sensor Networks in Smart Buildings for IoT Applications

A Hybrid Firefly Algorithm and Particle Swarm Optimization Algorithm for Mesh Routers Placement Problem in Wireless Mesh Networks

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