基于量子计算技术的物联网网络传感器最优间隔

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS
G. Krishna, A. Saha
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引用次数: 1

摘要

近年来,量子计算由于其兼容性和在各个领域的应用而获得了优势。物联网环境是巨大的,几乎整个世界都依赖于它来获取高效的数据传输。将量子计算与物联网相结合,可以极大地提高系统的性能。提出的技术重点是将量子计算引入物联网,在不降低传感器感知能力的情况下优化传感器空间。为了优化传感器空间,引入了基于量子计算的骑乘优化(QCRO),该优化具有受骑乘优化算法(ROA)启发的最优全局搜索行为。在优化传感器空间后,使用基于连续量子行走(GTCW)的粘接树来最小化网络中的能量损失。连续量子行走被引入到粘接树(GT)中,以找到可以提供最小错误率的最佳路径。实验结果表明,该方法在数据精度、数据时间效率和数据成本方面优于其他现有技术。图形抽象
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal sensor spacing in IoT network based on quantum computing technology
ABSTRACT Quantum computing has gained an advantage in recent years due to its compatibility and usage in various fields. The IoT environment is huge, and almost the whole world relies on it to acquire efficient data transmissions. Combining quantum computing with the IoT improves the system’s performance to a drastic level. The proposed technique focuses on introducing quantum computing into the IoT to optimise the sensor space without degrading the sensing ability of the sensors. To optimise the sensor space, the Quantum Computing based Rider Optimisation (QCRO) is introduced with optimal global search behaviour inspired by the Rider Optimisation Algorithm (ROA). After optimising sensor space, the Glued Tree based on Continuous quantum Walks (GTCW) is used to minimise the energy loss in the network. The continuous quantum walks are introduced into the Glued Trees (GT) to find the optimal path that can provide a minimised error rate. The experimental results demonstrated the effectiveness of the proposed approach against the other existing techniques in terms of data accuracy, data temporal efficiency and data cost. GRAPHICAL ABSTRACT
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
27
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