A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things

Hosseinzadeh, Mehdi; Feleagă, Liliana; Ionescu, Bogdan Ștefan; Sadrishojaei, Mahyar; Kazemian, Faeze; Rahmani, Amir Masoud; Khan, Faheem

doi:10.3390/math10224331

Cited by 18 publications

(2 citation statements)

References 41 publications

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“…To achieve a more uniform distribution of cluster heads, Hosseinzadeh et al [ 29 ] presented a clustering method utilizing swarm intelligence. The HAOFA technique is presented here for IoT clustering and routing operations by the combination of AO and FA.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…Each cluster is headed by a Cluster Head (CH) whose responsibility is to gather the data of its members. Clustering can provide scalability, conserve bandwidth, and reduce the routing problem among all sensors [19]. The CHs are responsible for relaying the data to the sink node, thus reducing the total number of hops needed [20].…”

Section: Introductionmentioning

confidence: 99%

Clustering Based on Gray Wolf Optimization Algorithm for Internet of Things over Wireless Nodes

Hu¹,

Zhou²,

Lv³

2023

IJACSA

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The Internet of Things (IoT) creates an environment where things are permitted to act, hear, listen, and talk. IoT devices encompass a wide range of objects, from basic sensors to intelligent devices, capable of exchanging information with or without human intervention. However, the integration of wireless nodes in IoT systems brings about both advantages and challenges. While wireless connectivity enhances system functionality, it also introduces constraints on resources, including power consumption, memory, and CPU processing capacity. Among these limitations, energy consumption emerges as a critical challenge. To address these challenges, metaheuristic algorithms have been widely employed to optimize routing patterns in IoT networks. This paper proposes a novel clustering strategy based on the Gray Wolf Optimization (GWO) algorithm. The GWO-based clustering approach aims to achieve energy efficiency and improve overall network performance. Experimental results demonstrate significant improvements in key performance metrics. Specifically, the proposed strategy achieves up to a 14% reduction in energy consumption, a 34% decrease in end-to-end delay, and a 10% increase in packet delivery rate compared to existing approaches. The findings of this research contribute to the advancement of energy-efficient and high-performance IoT networks. The utilization of the GWO algorithm for clustering enhances the network's ability to conserve energy, reduce latency, and improve the delivery of data packets. These outcomes highlight the effectiveness and potential of the proposed approach in addressing resource limitations and optimizing performance in IoT environments.

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