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Article A Practical Distributed Lightweight Multi-Hop Time Synchronization Algorithm for Linear Wireless Sensor Networks Implemented on a Pic Based System With Realistic Experimental Analysis(Sakarya University, 2020) Erpay, A.; Al Imran, M.A.; Kara, A.Time synchronization is fundamental in the distributed networked systems, especially in Wireless Sensor Networks where a global time is essential to make sense of the events like collection of data and scheduled sleep/wake-up of nodes. There exists numerous time synchronization algorithms and techniques in the literature. Nonetheless, these proposed methods lack realistic experimentation of the synchronization process which is vital from the realization point of view. This study aims to bridge that gap by presenting a distributed lightweight time synchronization protocol implemented on an inexpensive PIC platform. Furthermore, PIC-based systems hadn’t been investigated before and gives an idea of the simplicity of the algorithm. Experimental analysis was done to see the performance of the protocol. The core motivation of the experiments was to the study the impact of the environment (e.g. indoor, outdoors, temperature variations and interference) on the synchronization. Our findings show that temperature indeed impedes the synchronization accuracy. © 2020, Sakarya University. All rights reserved.Conference Object Securing the Internet of Things: Challenges and Complementary Overview of Machine Learning-Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Isin, L.I.; Dalveren, Y.; Leka, E.; Kara, A.The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors' previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors' ongoing project. © 2024 IEEE.
