Institute of Networked and Embedded Systems, University of Klagenfurt, 9020 Klagenfurt, Austria.
Ubiquitous Sensing Systems Lab, University of Klagenfurt-Silicon Austria Labs, 9020 Klagenfurt, Austria.
Sensors (Basel). 2022 Jun 23;22(13):4730. doi: 10.3390/s22134730.
Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.
能源和安全是无线传感器网络中的主要挑战，它们的作用是相反的。随着安全复杂性的增加，电池消耗也会增加。由于无线传感器网络的功率有限，由于传感器之间的通信性质和不断变化的网络拓扑结构，依靠普通协议中包含的加密和密钥管理的安全性的选项是徒劳的。因此，机器学习算法是通过包括监控和决策智能为这种类型的网络提供安全服务的一种解决方案。机器学习算法在训练和训练所需数据量方面带来了额外的障碍。本文为无线传感器网络基础设施及其面临的安全挑战提供了一个方便的参考。它还讨论了通过在几个领域降低无线传感器网络的安全成本来利用机器学习算法的可能性；除了通过使用机器学习算法提高传感器识别威胁、攻击、风险和恶意节点的能力来提出的挑战和解决方案外。此外，本文还讨论了与适应机器学习算法以适应这种类型网络中传感器的能力相关的开放问题。
