Energy efficient clustering in industrial Iot using a quantum informed artificial hummingbird optimization algorithm.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-10-10 DOI:10.1038/s41598-025-19358-z

S Rajkumar, R Gopalakrishnan

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引用次数: 0

Abstract

In Industrial Internet of Things (IIoT), Clustering facilitates the proves of organizing similar types of devices or data points into different clusters for the objective of enhancing resource utilization, network management and data processing. This clustering in IIoT helps in addressing the challenges that are associated with the process of handling network complexity, satisfying requirements of real-time processing and dealing with massive data volumes. In specific, swarm intelligent optimization algorithms are used for selecting optimal CHs and determining reliable route through the network such that the parameters of data aggregation, delay and energy consumptions are handled with maximized performance. IIoT networks when blended with optimization algorithms-based clustering aids in improving scalability and energy efficiency which results in more cost-effective and reliable industrial applications. In this paper, Energy Efficient Quantum-Informed Artificial Hummingbird Optimization Algorithm (EEQIAHBOA) is proposed for maximizing the performance of IoT networks and addressing the energy preservation problem such that the information is gathered and sent to the base station for reactive decision making. This EEQIAHBOA approach is proposed as a reliable routing algorithm which is implementation with the determination of information heuristic factors and efficient encoding scheme. It is proposed as a significant clustering algorithm for the objective of achieving network lifetime such that the factors of residual energy, and distance between the cluster member IoT nodes and energy consumptions during the selection of Cluster Heads (CHs). The simulation experiments of EEQIAHBOA approach conducted with different network scenarios confirmed 32.12% improvement in energy efficiency and 35.62% enhancement in network lifetime under different live nodes compared to the baseline approaches used for investigation.

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基于量子信息人工蜂鸟优化算法的工业物联网节能聚类。

在工业物联网（IIoT）中，聚类有助于将类似类型的设备或数据点组织到不同的集群中，以提高资源利用率、网络管理和数据处理。工业物联网中的这种集群有助于解决与处理网络复杂性、满足实时处理需求和处理大量数据量相关的挑战。其中，采用群智能优化算法，通过网络选择最优CHs，确定可靠路由，使数据聚合、时延、能耗等参数得到最大性能的处理。工业物联网网络与基于优化算法的集群相结合，有助于提高可扩展性和能源效率，从而实现更具成本效益和可靠的工业应用。本文提出了一种能量高效量子信息人工蜂鸟优化算法（Energy Efficient Quantum-Informed Artificial Hummingbird Optimization Algorithm， EEQIAHBOA），以最大限度地提高物联网网络的性能，并解决节能问题，将信息收集并发送到基站进行被动决策。EEQIAHBOA方法是一种可靠的路由算法，它通过信息启发式因子的确定和高效的编码方案来实现。它是一种重要的聚类算法，其目标是实现网络生命周期，使剩余能量、集群成员物联网节点之间的距离和簇头（CHs）选择过程中的能量消耗因素。EEQIAHBOA方法在不同网络场景下的仿真实验证实，与基线方法相比，不同活节点下的能效提高32.12%，网络寿命提高35.62%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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