Mesh Router Nodes Placement for Wireless Mesh Networks Based on an Enhanced Moth–Flame Optimization Algorithm

Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Mirjalili, Seyedali; Acheli, Dalila; Ramdane-Chérif, Amar; Gabis, Asma Benmessaoud

doi:10.1007/s11036-022-02059-6

Cited by 11 publications

(5 citation statements)

References 60 publications

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“…Where, the uniformly distributed randomized number is denoted as 𝑈 𝑟𝑛 . After completion of every iteration, the mongoose tends to vary its sleeping mound which is mathematically expressed in equation (7).…”

Section: Alpha Phasementioning

confidence: 99%

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Prema,

Ushadevi,

Smitha

2024

IJEER

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Wireless Mesh Networks (WMNs) are part of wireless technologies that are known for their flexibility and extended coverage. Wireless applications have reached their peak in applications related to various fields such as healthcare, image processing, and so on. However, delay and energy efficiency are considered the two aspects that diminish the performance of WMNs. To overcome the aforementioned issues, this research introduces an effective routing method using Multi-Objective Dwarf Mongoose Optimization Algorithm (MO-DMOA). The MO-DMOA performs routing by considering the multiple paths using an enriched population resource. The nomadic behaviour of MO-DMOA helps in detecting the optimal routing path with minimized over-exploitation. The proposed MO-DMOA is evaluated with different routing schemes such as Load Balance and Interference Avoid-Partially Overlapped Channels Assignment (LBIA-POCA) framework, and Multi-Objective Dyna Q-based Routing (MODQR). The outcomes obtained through the experimental analysis show that the proposed approach acquires a better throughput of 13.5×105kbps for 22 flows, whereas the existing LBIA-POCA achieves a throughput 60× 103 kbps

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Section: Alpha Phasementioning

confidence: 99%

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Prema,

Ushadevi,

Smitha

2024

IJEER

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“…The QoS restrictions for this study are the delay, relay load, and Internet gateway capacity. In [35], the authors suggested an improved version of the Moth Flame Optimization (MFO) algorithm, namely, Enhanced Chaotic Le ´vy Opposition-based MFO (ECLO-MFO), for solving the RNP problem. To improve the optimization performance of MFO, the proposed method integrates three strategies: the chaotic map concept, Le ´vy flying strategy, and Opposition-Based Learning (OBL) technique.…”

Section: Plos Onementioning

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

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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.

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“…The transmission routes are initialized as individuals using ML-AROA which is upgraded based on two strategies such as detour foraging (exploration phase) and random hiding (exploitation phase) which is mentioned in Eqs. ( 3) and (6). After these two enhancements, the position of the i th node is renewed which is presented in Eq.…”

Section: Iterative Process In Discovering the Optimal Route Using Ml-...mentioning

confidence: 99%

“…Moreover, the router is responsible to handle the Inbound and outbound packets. Every individual packet of the router is stored in the input buffer and queued for the output buffer through queueing process [6,7]. WMNs are employed with more routing protocols where each protocol is designated with its routing strategy and the protocols are classified into proactive, reactive and hybrid techniques [8].…”

Section: Introductionmentioning

confidence: 99%

Effective Routing Using Multi-Objective Levy flight- Artificial Rabbit Optimization Algorithm for Wireless Mesh Networks

2023

IJIES

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Wireless mesh networks (WMNs) are familiar due to their unique characteristics such as adaptability, flexibility and less time for transmitting data packets. The routing technique acts a significant role in transmitting data among the nodes. Moreover, the routing technique is mainly responsible to improve the performance of WMNs with various applications. With the rapid development of various applications, energy, and distance are fundamental aspects which aid in next-level communication. But, recent research has not provided a clear idea of improving the transmission quality by offering an optimal route. These challenges are overwhelmed using the proposed multiobjective levy flight artificial rabbit optimization algorithm (ML-AROA) for energy and distance aware (EDA) route discovery. The proposed ML-AROA is an improvisation of the ARO algorithm where the Levy flight technique is implemented to create a random number in a regular manner which is characterized by the generation of arbitrary numbers and helps to jump out the local solutions. Moreover, the convergence accuracy gets enhanced by offering flexibility to detect an optimal route for the transmission of data packets. The results obtained from the experiment shows that the suggested ML-AROA attained better network throughput of 5 12 10  Kbps which is comparatively higher than load balance and interference avoid-partially overlapped channels assignment (LBIA-POCA) framework and multi-objective dyna Q based routing (MODQR) technique.

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Mesh Router Nodes Placement for Wireless Mesh Networks Based on an Enhanced Moth–Flame Optimization Algorithm

Cited by 11 publications

References 60 publications

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

Energy Efficient Routing in Wireless Mesh Networks using Multi-Objective Dwarf Mongoose Optimization Algorithm

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

Effective Routing Using Multi-Objective Levy flight- Artificial Rabbit Optimization Algorithm for Wireless Mesh Networks

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