International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

https://icac.lk

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A Scalable Microkernel Inspired Architecture for Transparent and Adaptive Supply Chain Management
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Vithanage H.D; Dehipola H.M.S.N; Manditha K.D.R.; Weedagamaarachchi K.S.; Jayasinghearachchi, V; Perera, J
Supply Chain Management (SCM) in industries such as coconut peat production increasingly demands systems that are not only efficient but also transparent and adaptable to rapid changes. Traditional monolithic architectures often fail to scale effectively and struggle to integrate emerging technologies such as IoT and blockchain, limiting their usefulness in dynamic environments. To address these limitations, this research proposes a microkernel-inspired architecture tailored for SCM. The architecture separates core system functions from domain-specific processes through the use of dynamically loadable plugins, allowing for modularity, fault isolation, and real-time adaptability. The impact of this study lies in its integration of multiple technologies to enhance transparency and operational flexibility. The system incorporates IoT sensors for real-time data acquisition, blockchain for immutable traceability, gRPC for high-performance communication, and K3S for lightweight container orchestration. A workflow customization tool allows non-technical users to define or modify supply chain processes without altering core logic. We employed the Architectural Trade-off Analysis Method (ATAM) and Cost-Benefit Analysis Method (CBAM) to evaluate architectural decisions and conducted runtime performance testing using simulated workloads. Results showed improved scalability, flexibility, and fault tolerance, with moderate latency introduced by inter-process communication and CGo overhead. Despite some limitations in performance variability, the architecture maintained high availability and was able to recover from plugin failures seamlessly. These findings suggest that the proposed model provides a viable foundation for next-generation SCM systems. The contributions include a modular, resilient framework that effectively integrates advanced technologies to support adaptable and transparent supply chain operations across various industries.
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A Virtual Reality Immersive Experience of Sri Lanka's Sacred Heritage: VR Meditation Experience at the Ruwanweli Maha Seya using Heart Rate and HRV Tracking
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Herath, S; Hapangama, C; Gamage, U; Soyza, N; Gamage, I; Nismi, N
Meditation is a widely recognized practice that reduces stress, improves focus, and promotes mental well-being. For various reasons, it has become difficult for religious devotees to travel to more intimate places of worship, such as their sacred sites, and to worship and meditate in harmonious environments. This system involves creating a Unity-based VR application that simulates several sacred places in Anuradhapura city, including four of the eight sacred places among the “Atamasthana"” and a short five-minute meditation session. A user evaluation was conducted with 25 participants. The results showed a decrease of 5.4 BPM in average heart rate and a 10 increase in heart rate variability after the meditation session. In this research, the sacred city of Anuradhapura has been used as a sample, but any religious sacred place can be used for this purpose. Through this harmonious environment recreated through Virtual Reality (VR), users can experience mental relaxation regardless of physical location. This research is more important because it combines cultural and religious preservation with modern technological innovations. By incorporating physiological sensations into a VR meditation experience, it is possible to enhance user engagement and monitor the results of the meditation. It can be understood that the physiological effects of this VR-based meditation are a good basis to advance both the study of digital wellness and cultural scarcity.
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Adaptive AI-Based Enhancement of Critical External Sounds in Insulated Vehicle Cabins for Improved Safety
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayaka D.B; Wickramasuriya L.H.N.Y; Walpalage J.V; Rathnayake, S
The increasing acoustic insulation in modern and electric vehicles improves passenger comfort but unintentionally suppresses critical external sounds such as ambulance sirens, car horns, and train alarms, creating potential safety risks. While existing research has explored sound detection or localization in isolation, few systems integrate both capabilities in a unified framework for real-time vehicular deployment. This research proposes an adaptive AI-based system that detects, classifies, and selectively enhances these critical sounds in real time while providing directional awareness. Using a convolutional recurrent neural network (CRNN) trained on the UrbanSound8K dataset, the system processes incoming audio from external microphones, extracts Mel-frequency cepstral coefficients (MFCCs), and distinguishes safety-relevant cues from non-essential background noise. A dual-microphone setup enables the estimation of sound direction (left or right), providing additional spatial awareness to the driver. Detected signals are isolated through spectral filtering and relayed into the cabin with sub-30 ms latency, ensuring timely driver and passenger awareness without compromising comfort. Experimental results achieved 91.2% classification accuracy and 87.4% directional accuracy,confirming the system's feasibility for enhancing safety in insulated vehicle cabins and supporting future autonomous driving environments.
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Adaptive Video Game Content Generation through Player Centered Modeling
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Hapuarachchi H.A.R.S; Herath H.M.N.R; Kaveesha B.G.S; Deheragoda D.M.L.M; Rathnayake, S; Chamara, D
This research introduces a novel, integrated AI system for Adaptive Video Game Content Generation Through Player Centered Modeling, designed to overcome the limitations of conventional static game mechanics by dynamically modifying gameplay elements (levels, quests, music, and enemy behavior) in real-time based on player biometric and behavioral data. Key contributions include the Personalized Quest Generation System, where the CatBoost model performed well in predicting player engagement, and shifting to the YOLO11X-CLS classification model substantially reduced computational lag for real-time emotional adaptation. For Level Generation, the implementation of a bootstrapping methodology during DCGAN training enabled continued refinement, resulting in lower Symmetry Error and Block Diversity Error, while the Intelligent Enemy Agent, trained using Dueling DQN, demonstrated a clear upward trend in Mean Rewards, successfully generalizing learned pursuit strategies to a real-time environment. Despite these successes, limitations include potential mild overfitting in the emotion recognition model, evidenced by a slight increase in validation loss after 20 epochs, and the CNN used for dynamic music adjustment struggled with low-resolution webcam inputs, leading to occasional minor misclassifications and slight delays in music transitions when multiple biometric parameters changed simultaneously.
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Agile User Story Builder: Transform Real-Time User Requirement Conversations into Well-Structured Agile User Stories
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Sathsarani, S; Sandun, Y; Rusith, R; Himaranga, S; Kumari, S; Supunya Swarnakantha, N.H.P. R
Agile software development methodologies rely heavily on well-structured user stories to bridge technical and business requirements. However, the traditional manual process of converting stakeholder conversations into structured user stories presents significant challenges including transcription errors, communication ambiguity, and inefficient processing. This research presents an automated Agile User Story Builder system that transforms requirement conversations into well-structured user stories. The system integrates cloud speech recognition, Natural Language Processing (NLP), machine learning, and Retrieval-Augmented Generation (RAG) for automated requirement processing within a microservices architecture. The system was developed using synthesized audio from 1,247 SRS documents (achieving 88% transcription accuracy) and validated on 75 real stakeholder conversations. Real-world validation demonstrates 78% speech-to-text accuracy (95% CI: 74-82%), 82% task classification precision, 73% reduction in manual documentation effort, and 86% format compliance in generated stories. The system achieves 20% timeline and 62% budget prediction improvement while providing measurable benefits including enhanced collaboration and streamlined Agile workflow efficiency.
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AI - Driven Smart Bin for Waste Management
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeygunawardhana, A. G. D. T.; Shalinda, R. M. M. M.; Bandara, W. H. M. D.; W. D. S. Anesta, D.; Kasthurirathna; Abeysiri, L.
With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.
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AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Shanmugarajah, S.; Tharmaseelan, J.; Sivagnanam, L.
Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude.
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AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System
(IEEE, 2019-12-05) Amarasinghe, A. M. S. N; Wijesinghe, W. A. C. H; Nirmana, D. L. A; Jayakody, A; Priyankara, A. M. S
Security of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.
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AI Based Depression and Suicide Prevention System
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Kulasinghe, S.A.S.A.; Jayasinghe, A.; Rathnayaka, R.M.A.; Karunarathne, P.B.M.M.D.; Silva, P.D.S.; Anuradha Jayakodi, J.A.D.C.
Suicide is a major issue in the world. The number one reason for suicide is untreated depression. That is why it was decided to focus on depression symptoms more and identify them in order to prevent suicidal attempts. To cure depression, the best way is to talk about their feelings with someone they trusted and release their pain inside of them. Because of that this system has a Chat-bot for the user to interact with. Chat-bot will gather information about the users feelings through text and voice analysis. Also by analyzing their Facebook statuses and recent web history, the application gather more information about their mental state so that the system take more accurate conclusions. After analyzing all the information from each component the back brain will decide on how the chat-bot should act on the user. At the end, the product was able to give more than 75% accurate results for each component.
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AI Based Monitoring System for Social Engineering
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, K.; Udara, S.W.I.; Wijayawardane, U.P.B.; Kularatne, K.N.P.; Navaratne, N.M.P.P.; Dharmaphriya, W.G.V.U
Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.
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AI-Based Child Care Parental Control System
(IEEE, 2022-12-09) Jayasekara, U; Maniyangama, H; Vithana, K; Weerasinghe, T; Wijekoon, J; Panchendrarajan, R
Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.
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AI-Driven Autonomous Bee Health and Ecosystem Management System
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Wanniarachchi P.W.A.S.V; Ghanarathna K.M.P.M.; Vihansith W.G.P; Wannigama S.V; Chathumali, C; Siriwardana, S. E.R.
Global honeybee population decline continues to threaten agricultural productivity and ecological stability, with annual colony losses exceeding 35%. Traditional hive inspections are labor-intensive, disruptive, and inadequate for early detection of diseases and environmental stress. This study presents an AI-driven autonomous bee health and ecosystem management system that combines IoT-based sensing, machine learning, and edge computing to enable real-time hive monitoring and intelligent automation. The system integrates four functional modules: environmental monitoring with time-series forecasting, threat detection and autonomous control, AI-optimized hive site selection using geospatial analytics, and multimodal health assessment via visual and acoustic data. Field evaluations conducted across multiple apiaries in Sri Lanka achieved 92.6% accuracy in bee health assessment and 88.7% recall in threat detection, while improving honey yield by 23% compared with traditional methods. The proposed solution demonstrates how multimodal AI and IoT integration can advance sustainable apiculture through proactive, data-driven decision-making.
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AI-Driven Behavioral Assessment and Intervention for ADHD
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dharmasena U.D.S.V; Manamperi R.S; Dilshani H.T.D.P; Halliyadda H.U.M.S.; Kasthuriarachchi, S; Samaraweera, M
Attention Deficit Hyperactivity Disorder (ADHD) affects millions of children worldwide, causing cognitive, behavioral,and academic challenges. Traditional diagnostic methods rely on subjective assessments, leading to inconsistent evaluations and delayed interventions. This research addresses these limitations by developing an AI-driven gamified behavioral assessment and intervention platform that integrates machine learning, real-time emotion recognition, and adaptive learning techniques. The system features interactive games that dynamically adjust difficulty based on behavioral and emotional responses captured through facial expression analysis. Data collection includes gameplay metrics, parent-reported questionnaires, and expert evaluations to train AI models for ADHD classification and personalized intervention. The emotion recognition module achieved 89% accuracy using Convolutional Neural Networks, while reinforcement learning algorithms enabled real-time game adaptation. Classification models, including Random Forest (91.3% accuracy), demonstrated strong predictive capabilities. The platform provides continuous monitoring dashboards for caregivers and educators, enabling data-driven decision-making. Results indicate that AI-based behavioral assessments offer improved accuracy and flexibility compared to traditional methods, with emotion-adaptive gaming enhancing engagement. This research demonstrates the potential of AI-powered solutions to transform ADHD diagnosis and intervention through improved efficiency, personalization, and accessibility.
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Air Visio: Air Quality Monitoring and Analysis Based Predictive System
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Dissanayaka, A.D.; Taniya, W.A.D.; De Silva, B.P.A.N.; Senarathne, A.N.; Wijesiri, M.P.M.; Kahandawaarachchi, K.A.D.C.P.
Sri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.
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Air Visio: Air Quality Monitoring and Analysis Based Predictive System
(IEEE, 2019-12-05) Dissanayaka, A. D; Taniya, W. A. D; De Silva, B. P. A. N; Senarathne, A. N; Wijesiri, M. P. M; Kahandawaarachchi, K. A. D. C. P
Sri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.
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Algorithms for Automatic Identification and Analysis of Sri Lankan Anopheles Mosquito Species
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Palanisamy, V.; Thiruchenthooran, V.; Noble Surendran, S.; Ratnarajah, N.
Microscopic digital image processing algorithms are presented here to automatically detect primary morphological features of Sri Lankan anopheline mosquitoes, as an essential step towards the development of automated identification and analysis of various species of anopheline mosquitoes. Mosquitoes that belong to genus Anopheles spread the causative pathogen of malaria. Perfect and speedy species identification is crucial in any surveillance and control strategies. Currently, morphological taxonomic keys are used to identify various species. Two or more primary morphological characteristics, such as a number of dark spots of wings and pale bands of legs, are used in each step of the hierarchical key. To achieve the automatic detection of the primary morphological features, image processing algorithms performed at three levels. At the pre-processing level, methods work with raw, possibly noisy pixel values, with noise reduction and smoothing. In the mid-level, algorithms are utilized pre-processing results for further means with background removing and spots/bands segmentation. At the final level, techniques try to extract the semantics of spots/bands and counting the spots/bands from the information provided. Thirty samples of anopheline mosquitoes' wings and legs microscopic images were analysed with satisfactory results.
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An Approach to detect Advanced Persistent Threats using Machine Learning Techniques
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bary A.A; Wijerupa W.D.O.D; Padukka P.V.G.G; Atapattu A.L.V.J.; Pandithage, D; Wijesooriya, A
Advanced Persistent Threats (APTs) pose significant risks to organizations due to their stealthy, prolonged nature and ability to evade traditional detection mechanisms. Traditional solutions often analyze separate data elements, such as network traffic or endpoint activity, limiting their effectiveness against sophisticated APT campaigns. This research proposes a holistic machine learning (ML)-driven approach to detect APTs by integrating three critical data dimensions: user behavior anomalies, endpoint activity monitoring, and network traffic analysis. The system further incorporates Tactics, Techniques, and Procedures (TTP) analysis using the MITRE ATT&CK framework to provide actionable intelligence. A real-time dashboard visualizes the threat detection results, TTP mappings, and mitigation strategies, enabling cybersecurity teams to respond proactively. The integration of multiple ML models enhances detection accuracy while bridging the gap between threat identification and contextual understanding. Experimental validation demonstrates the system's capability to detect APT indicators across diverse attack vectors and prioritize high-risk TTPs. This work contributes to advancing APT detection methodologies by offering a scalable, multi-dimensional solution tailored for modern cybersecurity operations.
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Analyzing Payment Behaviors And Introducing An Optimal Credit Limit
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Bandara, H.M.M.T.; Samarasinghe, D.P.; Manchanayake, S.M.A.M.
Identifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.
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Anomaly Detection in Microservice Systems Using Autoencoders
(IEEE, 2022-12-09) de Silva, M; Daniel, S; Kumarapeli, M; Mahadura, S; Rupasinghe, L; Liyanapathirana, C
The adaptation of microservice architecture has increased massively during the last few years with the emergence of the cloud. Containers have become a common choice for microservices architecture instead of VMs (Virtual Machines) due to their portability and optimized resource usage characteristics. Along with the containers, container-orchestration platforms are also becoming an integral part of microservice-based systems, considering the flexibility and scalability offered by the container-orchestration media. With the virtualized implementation and the dynamic attribute of modern microservice architecture, it has been a cumbersome task to implement a proper observability mechanism to detect abnormal behaviour using conventional monitoring tools, which are most suitable for static infrastructures. We present a system that will collect required data with the understanding of the dynamic attribute of the system and identify anomalies with efficient data analysis methods.
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Application of RFID and IoT technology into specimen logistic system in the healthcare sector
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Thwe Chit, M.M; Srisiri, W.; Siritantikorn, A.; Kongruttanachok, N.; Benjapolakul, W.
The invention and innovation of RFID technology changed the world and many sectors (such as logistics, railways, healthcare, and so on) are now deployed with RFID technology instead of using barcode systems. With the numerous advantages, Radio Frequency Identification (RFID) got many expectations in the healthcare sector. The main objective of this research work is to implement the RFID technology in Specimen collection in the healthcare sector and the IoT (Internet of Things) network supports the transaction while the specimen test box is being delivered. The system uses a Sparkfun RFID reader to read/write patient information to the Gen2 RFID tag, which is attached to the test tube collected from the patients. When the test box is delivered to another laboratory, we develop an IoT network to know the box’s temperature, humidity, and GPS location instantly, with the help of an NB-IoT shield. The major advantage of the combination between IoT technology and RFID is that the management of test box overall condition becomes much easier. To summarize, this method is highly competent in identifying the location of medical devices in real-time and reduces the time-consuming of data logging than the barcode system.