Recent Submissions
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.
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.
Faster Than the Teacher, Smarter Than the Student: Classifying with Wisdom via Knowledge Distillation in LLMs
(Faculty of Engineering, 2025-09-09) Gobihanath B.; Abishethvarman V.; Prasanth S; Banujan K.; B.T.G.S Kumara
Large language models (LLMs) have achieved remarkable success across various natural language processing (NLP) tasks, driven by their ability to capture complex language patterns through large-scale
pretraining. However, their substantial computational demands limit their deployment in resourceconstrained environments. To address this, this research introduced Knowledge distillation-based
framework for text classification using a multiclass approach across three domains: entertainment, sports, and politics. We utilize both hard labels (ground-truth categories) and soft labels (logits from a teacher model) to train a student and a distilled model. The teacher model is accurate but computationally expensive. The student model is lightweight and fast, yet less accurate. Through distillation, we derive a task-specific distilled model that balances speed and accuracy. We also compare the performance of these models against traditional classifiers such as LSTM, SVM, and Naive Bayes. Traditional models excel comparing to the LLMs. Considering only task agnostic language models, evaluation shows that the distilled model performs significantly better than the student and competitively against the teacher, offering
a practical trade-off. Our study demonstrates the value of soft label transfer and semantic alignment for improving classification performance in resource-constrained environments. The text classification code
can be found at: https://github.com/Abishethvarman/KD-Text-Classification
Enhancing Patient Safety with the MedAlert SYstem (MASY): A Low- Cost Timer for Medication Administration Alerts
(Faculty of Engineering, 2025-09-09) Rodrego, P; Fernando, N; Wijekoon, N; Wanigasekara, D; Herath, J
When it comes to patient safety, the timely administration of medication tends to be a critical factor in almost all the healthcare related environments, especially in hospital wards of any scale and clinics where
multiple patients simultaneously require any form of medication. In many small to medium scale hospital wards and rural healthcare environments, where commercially available systems which are developed for patient management tend to be extremely costly to deploy due to many factors such as the scale of the facility and lack of staff with the necessary knowledge to run and maintain such a sophisticated system, staff always seem to rely on logbooks for the purpose of tracking the issued doses and the times at which these specific medications are issued. This process is prone to human error, inefficiency, data tampering, and lack of accountability. This paper therefore presents an economical solution addressing the issues mentioned above: the MedAlert System (MASY), which is a low-cost, standalone timer unit specifically designed and developed for healthcare settings. This system, based on the popular Arduino open-source platform, enables medical staff members to manage multiple medication timers simultaneously with no reliance on any logbooks or other external references. This system also offers both visual and auditory alerts to ensure that medication is always administered at the correct time. Unlike commercial patient monitoring systems implemented in large-scale hospitals, MASY can operate independently, with no reliance on servers or internet connections. Due to its simplicity, this system can be easily modified and implemented according to local workflows. This paper discusses the system’s software, design, human machine interaction, and the future scope of the system’s development. It is believed that implementing such a simple device has the potential to reduce missed or delayed medication doses and, in turn, improve patient safety in almost any setting.
Smart Train-Elephant Collision Management System
(Faculty of Engineering, 2025-09-09) Perera G.P.K.N; Nimsara P.P.; Perera P.R.D.N.; Weerasinghe T.G.J.N; Kularathna P.H.G.U.; Morapitiya S.S
This paper presents a novel solution for the Train-elephant collision issue. It is a national-level issue, and a higher number of elephants die yearly. Approximately 47 elephants died from 2021 to 2024 due to a trainelephant collision. Therefore, we introduce a novel technical management system to avoid train-elephant collisions. The primary objective of this work is to explore the details of the issue and implement the
system to repel the elephant using a real-time warning system. Simulation and hardware implementation were both carried out for the final outputs. In addition, introduce a communication system to make the
train driver and the two nearby stations aware. The study demonstrates the potential real-time implementation system for the train-elephant national-level issue.
The SLIIT Research Document Archive (RDA) is the institutional repository of SLIIT, managed by the SLIIT Library. The primary purpose of SLIIT RDA is to manage, store, and disseminate SLIIT research output with its community and beyond, reaching the wider public. This plays a pivotal role in preserving the academic legacy of the institute.
The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.