https://dr.ur.ac.rw/handle/123456789/820 Book Chapters from the College of Science and Technology Mon, 13 Apr 2026 19:55:58 GMT 2026-04-13T19:55:58Z https://dr.ur.ac.rw/handle/123456789/2547 IoT based smart monitoring system for community water wells UWAMAHIRWE, Yvonne One of the biggest issue facing the world today is water pollution, which affects not only the health of people but also the entire ecology. In order to ensure the potable of water, its quality needs to be monitored. With the IoT, this research project provides a novel solution of a low-cost and realtime monitoring system developed to continuously monitor community water wells. The system employs turbidity and pH sensors to detect water quality and MCU to process the collected data. The system is also equipped with various actuators, including LCD, LED, buzzer, and SMS notifications to alert water quality to diverse community members based on their abilities. The system with GSM technology integration allows the processed data to be accessed online, which enables water supply authorities to monitor water quality remotely. Master's Dissertation Tue, 16 Jan 2024 00:00:00 GMT https://dr.ur.ac.rw/handle/123456789/2547 2024-01-16T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2543 IoT based system to monitor and prevent intruders at genocide memorial centers: Case study of Kibagabaga memorial center MUGIRANEZA, Claude Rwanda's Genocide Memorial Centres are powerful symbols of the country's tragic past, carrying substantial historical and cultural weight. Nevertheless, these locations are susceptible to encroachment and vandalism, both of which have the potential to harm the healing process and tarnish the memory of the victims. In response to this difficulty, An AI and IoT based security system has been proposed. Strategically positioned throughout the memorial centre are motion detectors, cameras, light-level detectors (LLDR), and buzzers that comprise the system. In response to motion sensor detection of unauthorised movements, nearby cameras initiate the process of capturing images of potential intruders. Real-time analysis of these images by Convolutional Neural Network (CNN) algorithms detects suspicious activity. By differentiating between day and night conditions, the LLDR sensors guarantee precise threat detection. The system activates its camera to transmit real-time video to a centralised control room in response to detected potential threats. In addition, sophisticated image processing and CNN algorithms scrutinise the footage to detect atypical activities. Additionally, a buzzer is promptly triggered by the system, notifying on-site security personnel. By means of our web interface, authorised personnel are granted access to real-time video feeds and sensor data via remote monitoring functionalities. This significantly mitigates the requirement for a perpetual on-site security presence and facilitates prompt off-site responses. The preservation of the integrity of these memorial sites serves to guard the remembrance of the victims and facilitates the process of national reconciliation. The integration of CNN algorithms, motion sensors, cameras, LLDR sensors, and buzzers serves as a prime illustration of the efficacy of IoT solutions in safeguarding and conserving historic sites of significance. The potential of technology-driven strategies to bolster security measures for historical and cultural assets is highlighted in this case study. Master's Dissertation Fri, 22 Mar 2024 00:00:00 GMT https://dr.ur.ac.rw/handle/123456789/2543 2024-03-22T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2522 Interoperability of information systems: Case of higher learning institutions in Rwanda-challenges and the way forward HABINSHUTI, Jean de Dieu Interoperability of information systems is challenging as it is influenced by different factors contributing to the success or failure of organization data sharing, quality service delivery, communication delays, and any undesirable effects. Literature analysis shows that in developing countries, the interoperability of information systems is constrained by the following factors: ICT Infrastructure, top management support, human resources, data, and information integration, security and privacy, business process issues, IT standards, technical expertise challenges, government regulation, collaboration and coordination matters, trust issues between agencies, slow implementation of policies, lack of compliance in adopting the Minimum Interoperability Standards (MIOS), and financial constraints. In higher education sectors, the interoperability of information systems has different constraints related to linked data strategy to connect existing multiple information, siloed data, data security, limited communications across institutions, heterogeneity of platforms with different programming languages individual applications, and IT architecture. However, there is limited knowledge about issues challenging the interoperability of Information systems from higher education institutions in Rwanda. This research takes a case of high-learning institutions in Rwanda and investigates challenges behind information systems interoperability in those institutions. A qualitative data method is adopted whereby document analysis and interviews will be used. The findings show that the lack of a common architecture, existing of information systems designed in silos in higher learning institutions, the availability of diverse platforms for data exchange in different higher education sectors, ICT infrastructure issues, and less investment in high-quality information systems at challenges of having technical interoperability in higher learning institution. Based on challenges and suggested solutions from respondents, RHEIS (Rwanda Higher Education Information Systems) architecture was proposed to have information systems communicating with each other. This research implies that solutions will be drawn to address issue of information systems interoperability in high learning institutions in Rwanda. This will contribute to HEC's (High Education Cancel) mission to improve the organization and functioning of high learning institutions. It will also advise the Government of Rwanda in all matters related to high education policy and strategies; to monitor the adherence to norms and standards in high learning institutions vi | P a g e and to follow up all activities concerning learning, teaching, and performance evaluation on high learning institutions. Master's Dissertation Fri, 14 Jun 2024 00:00:00 GMT https://dr.ur.ac.rw/handle/123456789/2522 2024-06-14T00:00:00Z https://dr.ur.ac.rw/handle/123456789/2521 IoT based air quality monitoring in Neonatal intensive care unit (NICU) using fuzzy inference system UMUGWANEZA, Angelique Environment indoor air quality is a very important aspect of the health and comfort of human beings. To ensure the well-being of people, we should consider air quality in every aspect of life. Air quality becomes more challenging when it comes to indoor places overcrowded with a lot of people. Most health care centers have enough infrastructure however; they still find it hard to ensure the good air quality for the patients. Air quality in Neonatal Intensive Care Unit (NICU) need to be monitored and maintained for the sake of the baby‟s health. Poor monitoring of air quality in NICU leads to the exposition of babies to ambient air pollutants which can affect their health status. This research proposes an IoT-based air quality monitoring and prediction in the NICU and other indoor places using fuzzy inference system that will monitor air pollutants and thermal comfort and generate a report in real-time. The system will assist in controlling the air quality parameters in the room for ensuring good conditions for the health of babies and NICU staff. A cloud server will hold all the information collected by sensors, which include the DHT11 temperature and humidity sensor, Mq7 sensor (carbon monoxide sensor), particulate matters(PM), and Mq135 (air quality sensor) to show the NICU's status for the protection of the baby‟s life. Proposed solution will be integrated with fuzzy inference system to identify, classify and predict the level of indoor air quality as well as provide a warning about poor air quality in the room. Master's Dissertation Sat, 20 May 2023 00:00:00 GMT https://dr.ur.ac.rw/handle/123456789/2521 2023-05-20T00:00:00Z