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All Journal Journal of Multidisciplinary Science: MIKAILALSYS ALSYSTECH Journal of Education Technology International Journal of Education, Management, and Technology
David, Fakokunde Babatunde
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Physics Social Sciences Other

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Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology Olope, Olumide Innocent; Akintayo, Taiwo Abdulahi; David, Fakokunde Babatunde; Chiamaka, Kalu Henrietta
Journal of Multidisciplinary Science: MIKAILALSYS Vol 2 No 3 (2024): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v2i3.3841

Abstract

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.
Design of Microcontroller Based Power Supply Unit with Multiple Input and Output David, Fakokunde Babatunde
ALSYSTECH Journal of Education Technology Vol 2 No 3 (2024): ALSYSTECH Journal of Education Technology
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/alsystech.v2i3.3782

Abstract

This innovative project showcases a versatile power supply module, capable of delivering both AC and DC outputs, regardless of the input power source. This user-friendly device offers multiple output options from a single unit, catering to diverse power requirements. At its core, a microcontroller (8051) expertly manages the switch and relay configuration, ensuring seamless operation. Through rigorous simulation (Matlab/Simulink) and practical testing (prototype device), our results demonstrate the module's effectiveness in providing a reliable, multi-output power supply solution, enhancing convenience and efficiency.
Transforming Data Analytics with AI for Informed Decision-Making Akintayo, Taiwo Abdulahi; Paul, Chadi; Queenet, Madumere Chiamaka; Nnadiekwe, Oluchi Anthonia; Victoria, Shittu Sarah; David, Fakokunde Babatunde; Joel, Ogundigba Omotunde; Agada, Olowu Innocent; Ngozi, Egenuka Rhoda; Arinze, Ugochukwu Ukeje; Ojemerenvhie, Grace Alele; Oluwadamilola, Adebesin Adedayo; Nnamani, Chinenye Cordelia; Olayinka, Usman Wasiu
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.3812

Abstract

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.
Co-Authors Agada, Olowu Innocent Akintayo, Taiwo Abdulahi Arinze, Ugochukwu Ukeje Chiamaka, Kalu Henrietta Joel, Ogundigba Omotunde Ngozi, Egenuka Rhoda Nnadiekwe, Oluchi Anthonia Nnamani, Chinenye Cordelia Ojemerenvhie, Grace Alele Olayinka, Usman Wasiu Olope, Olumide Innocent Oluwadamilola, Adebesin Adedayo Paul, Chadi Queenet, Madumere Chiamaka Victoria, Shittu Sarah