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All Journal Journal of Information Systems and Informatics Innovation in Research of Informatics (INNOVATICS)
Inyang, Saviour Joshua
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Towards Sustainable Smart Living: Cloud-Based IoT Solutions for Home Automation Etuk, Ubong E; Omenaru, Gabriel; Inyang, Saviour Joshua; Umoren, Imeh
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.621

Abstract

In recent times, the realm of home automation systems has garnered significant attention, thanks to the ever-evolving landscape of communication technology. The concept of a smart home, essentially an application of the Internet of Things (IoT), leverages the power of the internet to oversee and employ household appliances through a sophisticated automation infrastructure. Nevertheless, challenges persist within the existing home automation systems, such as constrained wireless transmission reach, a deficiency in backup power management, and the substantial financial outlay involved. Addressing these limitations, our study introduces an economical and resilient solution that combines cloud based IoT with an uninterrupted power management system, making a cutting-edge home automation prototype. This system relies on a microcontroller unit, specifically the ESP-32, which functions as a Wi-Fi-enabled gateway for connecting a variety of sensors and transmitting their data to the Blynk IoT cloud server. The data assembled from a multitude of sensors, including vibration sensors and voltage detectors, becomes readily accessible on users' devices, be it smartphones or laptops, irrespective of their geographical location. The system is further strengthened by a set of relays that link the ESP-32 with household appliances, allowing for centralized control. Structurally, the design uses a control box that can be seamlessly integrated into a real home environment, offering the means to both monitor and govern an array of household devices. This IoT-based home automation solution not only efficiently manages internet-connected appliances but also provides an effective emergency power management system, enabling remote initiation and deactivation of backup generators. It represents a innovative leap in the evolution of home automation systems, steering in convenience, efficiency, and cost-effectiveness.
A Clustering-Based Artificial Intelligence Approach for Minimizing of Ionizing Radiation Exposure in Uyo Metropolis Nigeria Umoren, Imeh; Inyang, Saviour Joshua; Etuk, Ubong E.; Essien, Daniel
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13024

Abstract

Electromagnetic Field (EMF) radio frequency exposure is a growing concern due to its impacts on public health and the environment. This study aims to develop a data-driven framework for clustering and analyzing long-term far-field EMF exposure in Uyo Metropolis, Nigeria, with a focus on identifying exposure patterns and assessing their implications. Data were measured at multiple locations using smart meter strategically deployed across three major roads in uyo metropolis to capture variations in exposure levels. The preprocessing steps involved data cleaning and normalization to enhance data quality and reliability for meaningful analysis.  Four clustering algorithms, namely, K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Model (GMM), were employed to analyze the distribution of radiation levels. The Silhouette score was used to evaluate the different clustering methods with respect to cohesion within clusters and separation from other clusters. The best results were obtained by Hierarchical Clustering and GMM, each achieving a mean Silhouette score of 0.81, indicating well-defined and highly contrasting clusters. K-Means performed moderately well, with an average Silhouette score of 0.73, while DBSCAN, due to its sensitivity to noise and parameter settings, achieved a lower score of 0.62. These findings highlight significant spatial variability in EMF exposure across different urban zones, emphasizing the need for targeted regulatory measures. The study underscores effectiveness of machine learning and offers a scalable approach for characterizing EMF exposure. Results reported offer scalable and data-driven framework for characterizing exposure patterns, with important implications for public health policies, urban planning strategies, and regulatory interventions.
Co-Authors Essien, Daniel Etuk, Ubong E Etuk, Ubong E. Omenaru, Gabriel Umoren, Imeh