ArticleScientific reports2025
Energy efficient clustering in industrial Iot using a quantum informed artificial hummingbird optimization algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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