Uthayakumar Chindiyababy, Jayaraman Ramkumar, Raja Hadi A, Daniel Kamran
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia.
Sensors (Basel). 2025 Sep 19;25(18):5872. doi: 10.3390/s25185872.
Advancements in communication technologies and the proliferation of smart devices have significantly increased the demand for wireless sensor networks (WSNs). These networks play an important role in the IoT environment. The wireless sensor network has many sensor nodes that are used to monitor the surrounding environment. Energy consumption is the main issue in WSN due to the difficulty in recharging or replacing batteries in the sensor nodes. Cluster head selection is one of the most effective approaches to reduce overall network energy consumption. In recent years, quantum technology has become a growing research area. Various quantum-based algorithms have been developed by researchers for clustering. This article introduces a novel, energy-efficient clustering scheme called the quantum-inspired clustering scheme (QICS), which is based on the Quantum Grover algorithm. It is mainly used to improve the performance of cluster head selection in a wireless sensor network. The research conducted simulations that compared the proposed cluster selection method against established algorithms, LEACH, GSACP, and EDS-KHO. The simulation environment used 100 nodes connected via specific energy and communication settings. QICS stands out as the superior clustering method since it extends the lifetime of the network by 30.5%, decreases energy usage by 22.4%, and increases the packet delivery ratios by 19.8%. The quantum method achieved an increase in speed with its clustering procedure. This study proves how quantum-inspired techniques have become an emerging approach to handling WSN energy restrictions, thus indicating future potential for IoT systems with energy awareness and scalability.
通信技术的进步和智能设备的普及显著增加了对无线传感器网络（WSN）的需求。这些网络在物联网环境中发挥着重要作用。无线传感器网络有许多用于监测周围环境的传感器节点。由于传感器节点中的电池充电或更换困难，能量消耗是无线传感器网络中的主要问题。簇头选择是降低网络整体能量消耗的最有效方法之一。近年来，量子技术已成为一个不断发展的研究领域。研究人员开发了各种基于量子的聚类算法。本文介绍了一种新颖的、节能的聚类方案，称为量子启发聚类方案（QICS），它基于量子格罗弗算法。它主要用于提高无线传感器网络中簇头选择的性能。该研究进行了模拟，将提出的簇选择方法与已有的算法LEACH、GSACP和EDS-KHO进行了比较。模拟环境使用了通过特定能量和通信设置连接的100个节点。QICS作为一种优越的聚类方法脱颖而出，因为它将网络寿命延长了30.5%，减少了22.4%的能量使用，并将数据包传输率提高了19.8%。量子方法在其聚类过程中实现了速度的提升。这项研究证明了量子启发技术如何成为处理无线传感器网络能量限制的一种新兴方法，从而表明了具有能量感知和可扩展性的物联网系统的未来潜力。
