Publication: Machine Learning for Wireless Sensor Ne...
Master data
Title: | Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues |
Subtitle: | |
Abstract: | 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. |
Keywords: | wireless sensor networks, machine learning, WSNs security, 6LoWPAN, ZigBee |
Publication type: | Article in journal (Authorship) |
Publication date: | 23.06.2022 (Online) |
Published by: |
Sensors
Sensors
(
MDPI Publishing;
)
to publication |
Title of the series: | - |
Volume number: | - |
Issue: | - |
First publication: | Yes |
Version: | - |
Page: | - |
Versionen
Keine Version vorhanden |
Publication date: | 23.06.2022 |
ISBN (e-book): | - |
eISSN: | 1424-8220 |
DOI: | http://dx.doi.org/10.3390/s22134730 |
Homepage: | https://www.mdpi.com/1424-8220/22/13/4730 |
Open access |
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Assignment
Organisation | Address | ||||
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Fakultät für Technische Wissenschaften
Institut für Vernetzte und Eingebettete Systeme
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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