School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item Performance Analysis of Path Loss Models for Wireless Communications at 3.5 GHz and 23 GHz in a Regular Urban Environment(IEEEE, 2023) Farouq E. Shaibu; Elizabeth N. Onwuka; Nathaniel Salawu; Oyewobi S. StephenItem A Smart Optimization-Enabled Omnidirectional Dipole Antenna Design for Campus and Conventional FM Radio: A Case Study of 92.3 MHz Campus Radio FUT Minna,(ATBU Journal of Science, Technology and Education, 2023) O. Akamike; C. Alenoghena; Oyewobi S. StephenAn antenna performs the most important function in any communication system, this underscores the reason most researchers in the field of antenna design pay close attention to specifics in designing smart antennas. Radio on the other hand is a medium or channel through which information can be disseminated to the audience, to achieve this, an antenna is required. This paper presents a novel optimization based technique to design a smart antenna to meet the demands of both educational institutions and the broadcasting industry. There has been a paradigm shift from conventional antenna designs to smart antennas designs to meet the ever changing world of communication where end users demand high quality of service, and high connectivity, especially with the roll out of 5G wireless network and other evolving technologies. Optimization techniques can be leveraged when the available resources cannot be replaced. A case study of Search FM 92.3MHz FUT Minna has been carried out to address the poor signal coverage within Minna and environs.Item Deployment of an Artificial Intelligent Robot for Weed Management in Legumes Farmland(ABUAD Journal of Engineering Research and Development (AJERD), 2023-08-14) A. A. Adeniji; K. E. Jack; M. K. Idris; Oyewobi S. Stephen; H. Musa; A. O. OyelamiThis groundbreaking research introduces an AI-based approach for revolutionizing weed management in legume farmland, addressing the limitations of traditional methods and introducing a new era of cost-effective and precise weed detection and removal. Traditional methods of removing weeds from farmland involving machinery or chemicals often resulted in high costs and imprecise outcomes. To address these challenges, an advanced image recognition algorithm was proposed, which harnessed smart machines to minimize costs and environmental risks. By utilizing computer vision technology, weeds were accurately identified and targeted for removal. A machine learning model was trained using relevant datasets to enable precise weed management. The AI-powered robot, equipped with advanced image recognition algorithms, demonstrated exceptional accuracy and speed, performing weed removal and decomposition 1.2 times faster than traditional manual labour. This breakthrough in weed management technology offers farmers a means to optimize crop yields, enhance food production, and minimize the environmental impact associated with chemical herbicides. A prototype of the robot was fabricated and evaluated in real-world farming conditions. Field tests were conducted on a bean farm and it’s demonstrated the robot's exceptional accuracy, with only a 2% deviation from the actual weed quantity. This research showcased the potential of AI-based weed management systems in legume farming, offering cost-effective and precise weed detection and removal. This research sets a precedent for the integration of AI in modern agriculture, driving the industry toward a more environmentally conscious and economically viable future. The AI-based weed management system empowers farmers, ensuring bountiful harvests, increased profitability, and a greener, more sustainable tomorrow while attention should be given to manufacturing this model for industrial and or commercial applications.Item Enhancing Industrial Workplace Safety With Internet of Things Using Arduino Micro-Controller(SIPET & SEET, Nigeria Journal of Engineering and Applied Sciences (NJEAS), 2023) Oyewobi S. Stephen; Usman A. U,; Innocent C; Abdulbaki O. A; Jack K. EIoT (Internet of Things) technologies are widely used in many industrial sectors as a result of their rising technological performance and reduced buying costs. For these reasons, IoT has been considered for use in many emerging fields of application, such as workplace safety. The industry workplace which comprises human and nonhuman factors is becoming more saturated on a daily. These rising activities expose and make the industrial environment prone to uncertainties, e.g. accidents, injuries, explosions, fire outbreaks, and other industrial hazards that undermine man’s industrial environment safety. However, over the past few years, IoT technologies have proffered industrial solutions for a variety of industrial projects, particularly to dynamically manage safety levels at complex workplaces. In this work, an enhanced IoT-based industrial safety system is developed and implemented using an Arduino microcontroller. The Arduino serves as an interface between the different sensors that sense signals from the environment and send messages to the Buzzer/GSM module/Internet. The Arduino is programmed to send a signal and activate the buzzer whenever the sensors sense a value <25oc or >35oc (for Temperature sensor) or low intensity (Light sensor) or smoke (Gas sensor) in the environment. A short message (SMS) is delivered to the safety operator/manager’s phone whenever the gas sensor, temperature sensor, or light sensor senses gas leakage, low light intensity, and temperature respectively. It will also activate a buzzer and upload data to a web server for remote access. A performance test of the system shows that the system was able to sense gas leakage, low light intensity, and temperature very accurately at a low response time and over a wide range effectively.Item IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution(IEEEE Access, 2020) Musa Ndiaye; Oyewobi S. Stephen; Adnan M. Abu-Mahfouz; Gerhard Hancke; Anish M. Kurien; Karim DjouaniThe novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.Item A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach(Wiley, 2020-09-09) Oyewobi S. Stephen; Karim Djouani; Anish Matthew KurienAbstract Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial-WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial-WSN. Accordingly, we have suggested and proffered appropriate CR-based solutions in mitigating some of the challenges where necessary.Item Visible Light Communications for Internet of Things: Prospects and Approaches, Challenges, Solutions and Future Directions(MDPI, 2022-02-05) Oyewobi S. Stephen; Karim Djouani; Anish Matthew KurienVisible light communications (VLC) is an emerging and promising concept that is capable of solving the major challenges of 5G and Internet of Things (IoT) communication systems. Moreover, due to the usage of light-emitting diodes (LEDs) in almost every aspect of our daily life VLC is providing massive connectivity for various types of massive IoT communications ranging from machine-to-machine, vehicle-to-infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device-to-device. In this paper, we undertake a comprehensive review of the prospects of implementing VLC for IoT. Moreover, we investigate existing and proposed approaches implemented in the application of VLC for IoT. Additionally, we look at the challenges faced in applying VLC for IoT and offer solutions where applicable. Then, we identify future research directions in the implementation of VLC for IoT.Item Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review(MDPI, 2023-11-07) Farooq E Shuaibu; Elizabeth N. Onwuka; Nathaniel Salawu; Oyewobi S. Stephen; Karim Djouani; Adnan M. Abu-MahfouzThe rapid development of 5G communication networks has ushered in a new era of highspeed, low-latency wireless connectivity, as well as the enabling of transformative technologies. However, a crucial aspect of ensuring reliable communication is the accurate modeling of path loss, as it directly impacts signal coverage, interference, and overall network efficiency. This review paper critically assesses the performance of path loss models in mid-band and high-band frequencies and examines their effectiveness in addressing the challenges of 5G deployment. In this paper, we first present the summary of the background, highlighting the increasing demand for high-quality wireless connectivity and the unique characteristics of mid-band (1–6 GHz) and high-band (>6 GHz) frequencies in the 5G spectrum. The methodology comprehensively reviews some of the existing path loss models, considering both empirical and machine learning approaches. We analyze the strengths and weaknesses of these models, considering factors such as urban and suburban environments and indoor scenarios. The results highlight the significant advancements in path loss modeling for mid-band and high-band 5G channels. In terms of prediction accuracy and computing effectiveness, machine learning models performed better than empirical models in both mid-band and high-band frequency spectra. As a result, they might be suggested as an alternative yet promising approach to predicting path loss in these bands. We consider the results of this review to be promising, as they provide network operators and researchers with valuable insights into the state-of-the-art path loss models for mid-band and high-band 5G channels. Future work suggests tuning an ensemble machine learning model to enhance a stable empirical model with multiple parameters to develop a hybrid path loss model for the mid-band frequency spectrum.