Author, Institution: Algirdas Dobrovolskis, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Research supervisor: Prof. Dr. Egidijus Kazanavičius (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Dalius Mažeika (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Hab. Artūras Kaklauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Vytautas Markevičius (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001)
Assoc. Prof. Dr. Anton Rassolkin (Tallinn University of Technology, Estonia,
Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Algimantas Venčkauskas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation defence meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73–402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino g. 50, Kaunas) and on the internet: A. Dobrovolskis el. dissertation.pdf
© A. Dobrovolskis, 2026 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: The Internet of Things is one of the fastest growing branches of computer science, which integrates more and more devices into the global network. Device configuration in Internet of Things systems (IoT) is becoming increasingly difficult to manage, especially as the number of devices in the systems increases. Traditional IoT management methods often require specialized knowledge, and configuration can be error-prone, especially when it is necessary to adapt smart home systems to individual scenarios. The dissertation discusses the increasing importance of explainable artificial intelligence (XAI) in smart home systems, which can explain system behaviour in a comprehensible way and increase trust for the user. A hybrid method combining expert knowledge and XAI is proposed for configuring IoT systems. The method was validated and compared with manual device programming, confirming an increase in time efficiency from 50 to 67% for deployers of smart home system. The system’s performance was tested under real-world conditions at the Kaunas University of Technology Real-Time Computer Systems Centre, in the Santaka Valley laboratories for lighting, heating, and ventilation control. A hardware pseudo-random number generator was also developed to encrypt IoT communication, featuring a 99.4% number distribution and a 4–8% increase in energy efficiency compared to software solutions.