Browsing by Author "Ochiel, Michael"

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An Internet of Things Based system for Road Surface Condition Assessment Using Machine Learning
(IEEE, 2023-09) Kaijage, Shubi; Leo, Judith; Abashe, Japheth; Riwa, Janeth; Ochiel, Michael
, ,Road surface condition assessment is crucial for maintaining road safety and preserving road infrastructure. However, techniques for assessing road conditions employed in Tanzania are manual and labor-intensive, leading to slow and ineffective repair practices. To address these challenges, this project developed an Internet of Things-based system for Road Surface Condition Assessment using Machine Learning. The system collects real-time data on road surface conditions using camera and gyroscope sensors; processes the data using machine learning algorithms to detect defects on the road surface. The data is used for decision support by the maintenance authorities to ease their process by automation of road condition surveys. The effectiveness of the developed system was evaluated through a qualitative study that collected data on road condition assessment practices in Tanzania. The results indicate that the proposed system provides a more efficient, cost-effective, and comprehensive method for assessing road conditions in Tanzania. The study found that the proposed system facilitated efficient road maintenance. This project highlights the potential of IoT and machine learning in addressing road safety and labor requirement challenges faced by developing countries in the field of infrastructure management, it also offers a valuable contribution to the field of transportation and engineering.
A predictive analytics-driven battery management system for sustainable e-mobility in East Africa
(NM-AIST, 2024-08) Ochiel, Michael
In pursuit of the United Nations Sustainable Development Goals (UN SDGs) seven and thirteen, East African countries are swiftly transitioning to electric mobility solutions for clean transportation and climate action. However, this transition presents a challenge in repurposing and maintenance of used electric vehicle (EV) batteries due to limited specialized knowledge and equipment in the region. Despite the growing popularity of electric vehicles, a significant gap exists in understanding viable battery components for second life applications in East Africa. This study addresses this gap by designing a predictive analytics-driven battery management system tailored to the region's needs. The developed system integrates hardware and software, employing a data-driven approach to analyze sensor data for decision support and enable remote monitoring of repurposed batteries. Compared to existing works, this research emphasizes the use of predictive algorithms to monitor battery health in second life applications and provision for remote monitoring. This innovative approach significantly advances the understanding and implementation of battery repurposing in East Africa. By offering a sustainable solution for e-mobility, this study promotes a cleaner and greener future while reducing energy costs for organizations and domestic users.