Research Publications
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This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.
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Item Embargo Intelligent Traffic Management Using Fuzzy Logic and Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2026-06-12) Gunarathna R.P; Randima K.M.G.D; Tennakoon I.M.S.R.; Palihakkara P.I; Rajapaksha, S; Kahatapitiya, Kfic violations, and inefficient signal con-trol. Conventional traffic management systems rely on manual monitoring and fixed signal timings, making them ineffective in handling dynamic real-time traffic conditions. This research proposes an Intelligent Traffic Management System (ITMS) that integrates real-time traffic monitoring, adaptive signal control, traffic violation detection, and accident risk prediction through a unified analytical dashboard. The system is designed for an IoT-based four-way junction where sensors and cameras detect vehicle density and dynamically prioritize lanes with higher traffic volume. Using video-based vehicle detection, the system measures vehicle speed in real time and identi-fies violations such as over-speeding, red-light violations, and illegal parking. Drivers receive notifications through a mobile application where they can check violation details and pay fines calculated based on predefined traffic rules. Additionally, a dynamic accident risk scoring mechanism combines real-time vehicle speed data with historical violation records to identify high-risk driving behavior. Analytical dashboards visualize traffic density, violations, and risk levels to support data-driven decision making. The proposed system demonstrates how real-time traffic monitoring, violation detection, and dynamic signal control can improve road safety and traffic efficiency, contributing to the development of advanced smart city traffic management solutions.Publication Open Access 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.Publication Embargo Fly-Energy Ecosystem: A Game-Theoretic Hybrid SWIPT Framework for UAV-Assisted Rural Wireless Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Sooriarachchi, V.P; Jayakody, D. N.K; Muthuchidambaranathan P.The increasing use of IoT and related solutions in rural environments brings the growing need for energy-efficient and energy-aware solutions. This paper proposes a novel Stack-elberg game-theory-assisted hybrid wireless energy harvesting approach for unmanned aerial vehicle (UAV), which incorporates SimultaneousWireless Information and Power Transfer (SWIPT) systems designed specifically for remote and rural environments with conventional wireless power transfer (WPT). A multi-UAVs, multi-user scenario is considered where UAVs collect information from ground-level users while simultaneously providing WPT to the users. The proposed framework enables sustainable operation of remote monitoring systems in rural areas where conventional power infrastructure is limited or unavailable, contributing to more resilient and energy-efficient IoT deployments in challenging environments. The simulation results show that the proposed method achieves scalable performance and significant improvements in SINR and energy harvesting efficiency.Item Embargo Autonomous Water Quality Monitoring: Integrating UWB Ad-Hoc Networks, Sensor Calibration, and Kubernetes Cloud Architecture(IEEE Computer Society, 2025) Tharindu, K; Abeysinghe, M; Karunarathne, S; Dilshan, K; Primal, D; Jayakody, AWater quality monitoring plays a critical role in ensuring environmental sustainability and public health. Traditional methods, while accurate, are time-consuming and lack the ability to provide real-time insights. This study proposes a secure, scalable IoT-based solution utilizing autonomous sensor-equipped boats designed to measure pH, turbidity, and temperature in aquatic environments. The boats navigate predefined grid coordinates generated through a Python-based script and communicate data using UWB in a decentralized ad hoc network operating under the AODV routing protocol. Preprocessed sensor data is transmitted to a base station and securely forwarded to a Kubernetes-based cloud infrastructure for real-time processing and visualization. Communication between the base station and cloud services is secured using HTTPS/TLS encryption. Experimental trials confirm reliable navigation, high sensor accuracy, low latency, and robust security. The system remains cloud-agnostic and is compatible with a range of open-source Kubernetes distributions, enabling deployment flexibility across various environments. This research demonstrates an effective, autonomous approach to real-time water quality monitoring, advancing scalable and sustainable environmental managementItem Open Access Intelligent Systems for Comprehensive Dog Management(Association for Computing Machinery, 2025-06-28) Katipearachchi, M.E; Sachethana, O; Gunawardena, G. N.A; Ruwanara, D.C; Krishara, J; Kasthurirathna, DIn recent years, the integration of advanced technologies with canine welfare has gained significant attention, leading to the development of comprehensive platforms for dog management. The "Research Pooch-Paw"initiative addresses the multifaceted needs of dog owners and stray dog populations through an innovative platform that incorporates machine learning, wearable sensors, and real-time data processing. The platform facilitates early disease detection, behaviour analysis, and health monitoring using IoT-enabled devices, and provides personalized care guidance. Additionally, it includes features for stray dog identification and emergency response using deep learning algorithms and image processing techniques. The research underscores the potential of leveraging modern technology to enhance the quality of life for dogs and improve the effectiveness of canine welfare strategies.Publication Open Access Low Cost – Remote Passive Sensory Based Weather Prediction System with Internet of Things(SLIIT, 2022-02-11) Tennekoon, S; Chandrasekara, S; Abhayasinghe, NClimate effects many major daily aspects of the society, from the food sources and transport infrastructure to the choice of fashion and certain daily routines. Due to these reasons, the demand for means to accurately foresee climatic changes have increased. Weather forecasting, especially in Sri Lanka, has been hampered due to numerous reasons and this has resulted in erroneous predictions that has adversely affected many areas of development ranging from agriculture, irrigation, and the tourism industry to certain branches of engineering. Many researchers have analyzed and proposed solutions to these problems. However, the need for accurate predictions prevails due to the hardship of accurate data acquisition, processing, and transmission. To address these problems, in this paper, a system that adheres to the rules and regulations set forth by the World Meteorological Organization (WMO) to carry out well informed and reliably accurate weather predictions based on the data attained from a wireless passive remote sensory medium has been implemented. This task was carried out by means of feeding the relevant climatic parameter readings measured via multiple wireless passive remote sensory nodes placed within the proximity of a considered area to a selected computational model, which in turn was implemented to yield considerably accurate predictions compared to the weather prediction systems currently available in the market. The paper comprises of the implementation of the category, Low-Cost Automatic Weather Station (LC-AWS) specified by the WMO and Internet of Things (IoT), one of the latest technologies, for the transmission of attained data even in the absence of Wi-Fi. The research was further conducted to perform an analytical comparison between highly accurate weather stations and the implemented low-cost weather station when compromising accuracy due to low cost. The hardware and related software implementation yielded an acceptable success rate and was concluded successfully.Publication Embargo Smart Intelligent Advisory Agent for Farming Community(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Illandara, T.S.; De Silva, H.L.H.; Madurawala, K.S.H.; Dayasena, B.R.D.; Srimath, U.; Samaratunge Arachchillage, S.; Buddhika, T.The currently available agricultural services have few limitations because of the traditional cultivation methods and the unavailability of experts. This research attempts to solve the major problems faced by farmers using an Intelligent Expert Advisory Agent (EAA) that would act as a human counterpart to provide reliable solutions in real-time to the farmers using Machine Learning (ML), Image Processing (IP), and Internet of Things (IoT) technologies. A web application is developed to provide meaningful information to the user by representing agriculture instructors. Using the web application, the farmer can obtain information about predicted weather up to two months. Once the crop is selected, suitable organic fertilizers are suggested to maximize the productivity of the cultivation. After planting, the farmer can continuously monitor the condition of the plants in real-time using the IoT system. Based on this information, the farmer can check if the conditions are optimum for the growth of the plant by interacting with the knowledge base system. If the plants get infected with diseases, the user can capture an image of the diseased plant using the implemented mobile application and send to the IP system to identify the diseases and suggests remedies to overcome the situation.Publication Embargo Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Ilukkumbure, S.P.M.K.W.; Samarasiri, V.Y.; Mohamed, M.F.; Selvaratnam, V.; Rajapaksha, U.U.S.Flooding has been a very treacherous situation in Sri Lanka. Therefore, developing a structure to forecast risky weather conditions will be a great aid for citizens who are affected from flood d isasters. I n t his s tudy, t he a uthors explore the use of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and crowdsourcing to provide insights into the development of the pre and post flood r isk management system as a solution to manage and mitigate potential flood risks. Machine learning and deep learning algorithms are used to predict upcoming flooding s ituations and r ainfall occurrences by using predicted weather information and historical data set of flood a nd r ainfall. Crowdsourcing i s u sed a s a n ovel method for identifying flood t hreatening a reas. Weather i nformation is gathered from citizens and it will help to build a procedure to notify the public and authorities of imminent flood risks. The IoT device tracks the real-time meteorological conditions and monitors continuously. The overall outcome showcases that machine learning models, deep learning algorithms, IoT and crowdsourcing information are equally contributing to predict and forecast risky weather conditions. The integration of the above components with machine learning techniques, together with the availability of historical data set, can forecast flood occurrences and disastrous weather conditions with above 0.70 accuracy in specific areas of Sri Lanka.