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

Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Gabis, Asma Benmessaoud; Mirjalili, Seyedali

doi:10.1007/978-981-19-2948-9_29

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…In paper [8] conducted by Chandra set of placement methods provided to address network capacity limits, wireless interference, fault tolerance, and uctuating tra c needs. The asymptotic capacity of wireless multi-hop networks was investigated by Gupta and Kumar [11]. As described in references [21] and [12], several models were employed to evaluate the capacity of wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…The range and coverage of a network may be increased by the transmission of data packets from one node to another [15,16]. The problem of gateway selection in wireless mesh networks (WMNs) has been studied extensively in the past [8][9][10][11][12][13][14][15]. Wireless transceivers are often installed in each node in a wireless mesh network so that they may exchange data with one another.…”

Section: Related Workmentioning

confidence: 99%

“…This discrepancy arises when certain gateway nodes are underutilized while others are clogged with excessive tra c. If there is a shorter connection between the source node and the gateway node, but it has a signi cant probability of interference and packet loss, then the hop count metric may not be the best option. In [11], the authors described a proactive algorithm for centralized gateway selection in multi-hop wireless networks using a single radio and a single channel (SRSC). In this investigation, the hop count of the wireless hosts was similar to that of the gateways.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

Amer,

Soliman,

Hassan

et al. 2024

Neural Comput & Applic

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Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

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

Cited by 2 publications

References 17 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

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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