Oduware Okosun

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

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.

This study presents the design and implementation of a smart, lightweight, federated deep learning system that integrates solar-powered cameras for automated attendance, unauthorized entry prevention and real-time cyber threat detection in academic environments. Using TensorFlow Lite, Python, and a Flask-based web interface, the model achieved high accuracy in facial recognition while maintaining low computational and energy costs. A structured SQLite3 database supported efficient local data handling, while solar energy integration enabled autonomous and sustainable operation. This project validates the potential of combining renewable energy, artificial intelligence, and federated learning to enhance classroom management and IoT security in low-resource settings.