SLIIT International Conference On Engineering and Technology,Industry Connect Papers Vol. 04 [SICET] 2025

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    Impact of IoT on Personal Area Networks (PANs)
    (Faculty of Engineering, 2025-09-09) Thilakarathna D.R.T.D.; Gamage N.H.; Mendis B.P.U.; Kavindya P.M.S.; Sammani H.M.; Senanayake M. M. V.
    The Internet, as a revolutionary technology, continues to develop new technologies and software, making it accessible to all. Today, the most common forms of communication are either human-to-human or human-to-device communication; however, the Internet of Things (IoT) foresees a promising future for machine-to-machine (M2M) communication. Many novel wireless technologies, including ZigBee and Bluetooth, compete to provide the Internet of Things with low-power wireless communication solutions; however, in some IoT applications, the technological options are constrained by hardware resource limitations, low power consumption requirements, and overall device costs. Low power consumption is a basic prerequisite for enabling IoT expansion. Besides low power consumption, other requirements must be considered, such as technology cost, security, manageability, usability, wireless data rates, and communication ranges, among others. This paper discusses how the Internet of Things is transforming PANs, with particular focus on proximity communication protocols such as IEEE 802.15.4, Bluetooth, and ZigBee. The scope of this study extends beyond conventional device interconnection to cover new application areas like smart homes, healthcare, wellness, and wearables, succinctly presenting key trends and challenges from current literature, technology standards, and empirical evidence while analyzing critical factors in IoT-integrated PANs such as network scalability, privacy, interference control, and data security, which introduce new complexities to design and administration. This research offers details of IoT-PAN integration by conducting an analysis of wireless integrated personal area networks to identify research gaps and propose future directions. It defines the most important communication protocols and examines adequate levels of security and privacy, analyzing relevant literature to develop a robust framework that enables researchers and practitioners to address gaps in the literature regarding IoT-PAN integration. Therefore, this paper highlights the importance of PAN solutions that are secure, adaptable, and interoperable to enable next-generation IoT ecosystems by providing future insights.
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    ItemOpen Access
    Integrating Large Language Models into Personalized Diabetes Care: A Systematic Review of Clinical Applications, Model Adaptation Strategies, and Ethical Implications
    (Faculty of Engineering, 2025-09-09) Jayakody,J.A.U.S.; Jayawardhana, S.M.M.S.; Thilakarathne, P.R.H.N.G.
    This systematic review examines the integration of Large Language Models (LLMs) into personalized diabetes care, focusing on clinical applications, adaptation strategies, and ethical considerations. As diabetes management demands increasingly personalized approaches, LLMs including GPT-3 and GPT-4 show promise for patient education, clinical decision support, and diagnostic assistance. This review synthesizes findings from studies published between 2018 and 2025 to evaluate LLM clinical applications and assess adaptation techniques. Key applications include conversational agents for patient education, personalized decision-making systems, and predictive modeling for diabetes-related complications. Model adaptation through domain-specific training and multimodal integration demonstrates enhanced performance in clinical settings. However, significant challenges persist, including data privacy concerns, model fairness issues, and limited real-world validation. Ethical considerations encompass training bias and data security, highlighting the need for privacy-preserving approaches. The review identifies critical gaps in current research and proposes future directions emphasizing explainable AI models to build trust among healthcare professionals and patients. While LLMs offer transformative potential for personalized diabetes care, their responsible integration requires addressing technical, ethical, and regulatory challenges. This synthesis provides a foundation for advancing LLM applications in diabetes management while ensuring patient safety and equitable care delivery.