DEPARTMENT OF COMPUTER ENGINEERING

This project proposes the design and implementation of a smart meter management system. The system will focus on electricity metering and leverage smart meter technology to collect realtime consumption data. Improvements in metering infrastructure are required due to the growing need for better customer service and energy efficiency. The “Design and Implementation of Smart Meter Management System” project aims to create an efficient and intelligent system for managing energy consumption using smart meters. Smart meters, equipped with advanced sensors and communication modules, enable real-time data collection and bidirectional communication between utility providers and consumers. This smart meter management system has the potential to revolutionize energy management by providing valuable insights into consumption patterns, enabling optimized billing practices, and empowering consumers to make informed choices about their energy use. The system will gather, analyze, and manage smart meter data to improve energy usage monitoring, billing accuracy, and resource optimization.

The efficiency and reliability of photovoltaic (PV) systems are largely determined by their
ability to extract maximum power under varying environmental conditions. This project
presents the implementation of an IoT-based investigative system for Maximum Power Point
Tracking (MPPT) in photovoltaic arrays, focusing on the comparative performance of the
MPPT and Pulse Width Modulation (PWM) charge controllers. The system integrates voltage
and current sensors with an ESP32 microcontroller to measure and record PV parameters in
real time. Through IoT connectivity, the collected data is transmitted to a cloud-based platform
for remote monitoring, analysis, and visualization, enabling real-time tracking of PV
performance.
Experimental tests were conducted under different irradiance and temperature levels to
evaluate the charging efficiency, dynamic response, and adaptability of both controllers. The
MPPT controller dynamically adjusted the operating point of the PV module to maximize
energy extraction, while the PWM controller maintained a simpler, fixed switching
mechanism. Additionally, the system allowed for a detailed analysis of the relationship
between light intensity, temperature, and PV output performance, with the readings interpreted
from real-time graphical charts. These insights revealed how environmental variations affect
energy generation and charge controller efficiency.
This project develops a real-time, IoT-enabled system capable of monitoring and comparing
the operational efficiency of MPPT and PWM charge controllers in photovoltaic applications.
The results demonstrate that the MPPT controller achieves superior power utilization and
battery charging efficiency compared to the PWM controller. Overall, the system provides a
reliable, data-driven investigative platform for analyzing solar charge control strategies and
supports further optimization of PV energy systems through intelligent IoT integration

The traditional African rotational savings system, popularly known as Esusu, has long been a trusted model for communal financial support. However, the manual methods still commonly used, such as notebooks or physical record-keeping, were inefficient, prone to error, and lacked the transparency needed to prevent disputes and mismanagement. This project successfully developed a trust-preserved digital application using the MERN stack specifically designed for Esusu groups. The aim was to promote accountability, simplify group savings management, and maintain the cultural integrity of rotational contributions by replacing manual processes with a secure and intuitive digital solution. To achieve this, the following key features were successfully implemented: a digital platform solely for rotational group savings, enabling users to create or join existing groups with clearly defined schedules and Payout Numbers. The application incorporated the Dual Balance Display (Group Total and Individual Total Paid) and automated system functionalities, including in-app alerts for critical activities. All members within a group gained real-time access to the contribution history. A logic-based Integrity Alerts System was also implemented to monitor skipped contributions, automatically block suspicious payout claims, and transparently log all admin actions. The core finance mechanism was secured by the Automated Rotational Payout System integrated with the Paystack API. It is confirmed that a functional, user-friendly, and transparent digital platform for managing Esusu-style group savings was achieved. This justified the need for modernizing traditional savings systems in a way that preserved communal trust while drastically reducing the burden of manual tracking and strengthening administrator accountability through auditable logs. Ultimately, the implemented platform offered a secure, transparent, and culturally familiar solution that enhances the group savings culture in the digital age.

The increasing complexity of modern programming environments has created a growing need for intelligent, real-time support systems capable of assisting students and developers in understanding, writing, and debugging code. This project presents the design and implementation of an AI-powered chatbot web application that leverages Large Language Model (LLM)- based Natural Language Processing (NLP) and Remote Code Execution APIs to address programming queries interactively. The system integrates Google’s Gemini API for natural language understanding and JDoodle API for live code execution within a secure, microservicebased architecture. The chatbot provides conversational assistance, real-time code validation, debugging support, and algorithmic explanations through a responsive web interface built with React, TypeScript, Tailwind CSS, and Vite. The backend is implemented using Node.js, Express.js, and MongoDB, with modular microservices for authentication, chatbot intelligence, and code execution, all orchestrated through an API Gateway. This architecture ensures scalability, maintainability, and independent service deployment, while also improving accessibility for users in resource-constrained environments. The system demonstrates how combining LLMs with execution APIs can create a more reliable and contextaware programming assistant than traditional static or rule-based systems. The outcome of this project is a robust, user-friendly, and intelligent chatbot that enhances learning efficiency, developer productivity, and accessibility to programming support, particularly for students in developing regions. It contributes to ongoing research in AI-driven education, NLP, and intelligent tutoring systems, offering a sustainable model for future AIintegrated learning platforms.

In this study a web-based Result Management System was designed and developed for the Department of Computer Engineering, University of Benin to improve the processing, storage and management of students’ academic records using React.js, Node.js, Express.js and PostgreSQL while JSON Web Token (JWT) and bcrypt authentication model were adopted to ensure access control and data security. Reviewed works revealed that many existing result processing systems still depend on manual operations, standalone applications and weak authentication structures which expose academic records to computational errors, unauthorized access, poor scalability and delayed result processing. Some systems could only operate within local networks while others lacked centralized databases, transcript automation and web accessibility.

The developed system automated course registration, result generation, GPA/CGPA computation, transcript printing and result viewing through role-based operations involving administrators, lecturers, course advisers and students. The backend grading engine automatically computed total scores and assigned grades based on departmental grading standards thereby reducing human errors and improving result consistency. Secure PDF printing with verification codes was also integrated to reduce result falsification and improve document authenticity. The output generated from the tested modules matched the expected academic grading and reporting format without computational inconsistencies even under multiple user operations.

The control of academic records through a centralized and secure web architecture increased transparency, reduced paperwork, improved accessibility and strengthened the reliability of result processing within the department. Comparing the developed system with existing manual and semi-automated methods further justifies the importance of such research in improving academic record management in Nigerian universities.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.

A remote monitoring and control system enables real-time monitoring and management of devices or processes remotely. It enhances operational efficiency through continuous data acquisition and automated action. The system uses communication technologies to enable simple interaction between users and remote assets. Its applications cross industries such as manufacturing, agriculture, energy, and smart homes. The aim of this project was to make a remote monitoring and control system that can control and measure power supply remotely from a mobile application that we will also be developing. This remote monitoring and control system was made with the ESP32 microcontroller, making use of its inbuilt Wi-Fi and Bluetooth modules, while the mobile application used to monitor, control, and configure the system was made with the react-native framework. The interface between the mobile application and the system was the Blynk.cloud library and API, which was used to handle the remote online connection, while the BLE (Bluetooth Low Energy) protocol was used to update the Wi-Fi credentials of the system through the mobile application. This project presented the results obtained with the development and testing of the system, as well as the advantages of using a Wi-Fi-enabled system in place of GSM device

Photovoltaic systems have drawn growing research interest in recent decades. PV generators show nonlinear current–voltage and power–voltage behavior, and their maximum power output changes with irradiance and temperature. Because PV arrays convert sunlight with relatively low efficiency, they require maximum power point tracking control to harvest as much energy as possible as light levels, shading, temperature, and module characteristics change. MPPT algorithms automatically adjust the power interface so the solar operating voltage stays near the maximum power point under varying atmospheric conditions. MPPT has become a key factor when evaluating PV system performance. This study reviews various MPPT techniques, summarizes background concepts, implementation topologies, grid interconnection issues, and solar microinverter requirements found in the literature, and offers comparative analysis with concise discussion. The review also covers MPPT advantages, disadvantages, and classification to serve as a reference for future research aimed at optimizing solar power generation. Conventional MPPT methods are simple to implement but suffer from oscillations around the maximum power point and slower tracking due to fixed perturbation steps. Intelligent methods perform better, producing smaller steady state oscillations and faster tracking compared with conventional approaches.