Delwar Tahesin Samira, Aras Unal, Mukhopadhyay Sayak, Kumar Akshay, Kshirsagar Ujwala, Lee Yangwon, Singh Mangal, Ryu Jee-Youl
Department of Smart Robot Convergence and Application Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.
Sensors (Basel). 2024 Oct 1;24(19):6377. doi: 10.3390/s24196377.
This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.
本研究全面考察了无线传感器网络（WSN）与机器学习（ML）在提升安全性方面的重要交叉点。无线传感器网络在广泛的应用中发挥着关键作用，但其固有的限制带来了独特的安全挑战。为解决这些问题，众多机器学习算法已被用于提高无线传感器网络的安全性，并特别强调了它们的优缺点。显著的困难包括定位、覆盖、异常检测、拥塞控制和服务质量（QoS），这凸显了创新的必要性。本研究通过对现有实验的全面综述，深入探讨了机器学习在增强无线传感器网络安全性方面的有益潜力。本研究强调，在日益互联的环境中，要发挥机器学习的潜力，同时巧妙地解决细微差别，以维护无线传感器网络的完整性和可靠性。
