Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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Has files: YesSubject: search.filters.subject.Internet of Things (IoT)

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    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, A
    Water 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 management
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    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, D
    In 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.
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    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.
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    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.