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All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat PeTeKa Jurnal Ilmiah Wahana Pendidikan Education and Counseling Journal Journal of Education Technology Information Social Sciences and Health Indonesian Research Journal on Education Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Umar, Abubakar
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Journal : Vokasi UNESA Bulletin of Engineering, Technology and Applied Science

 1 
Development of a Head Gesture-Controlled Robot Using an Accelerometer Sensor Ore-Ofe, Ajayi; Umar, Abubakar; Ibrahim, Ibrahim; Abiola, Ajikanle Abdulbasit; Olugbenga, Lawal Abdulwahab
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 2 (2024)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i2.35114

Abstract

In this research, a head gesture-controlled robot was designed and developed to assist individuals with disabilities in performing tasks by translating head movements into robot commands. Using an accelerometer sensor embedded in a headgear device, the system interprets specific gestures—such as forward nods for forward movement, backward nods for reversing, and lateral tilts for turning left or right—into corresponding robotic actions. The design involved constructing a mechanical framework for the robot, assembling the headgear, and integrating both with Arduino-based programming to ensure accurate and responsive movements. Testing was conducted in a controlled setting, where the robot consistently followed head gestures with a high degree of accuracy, showing rapid response times to user inputs. Quantitative results demonstrated the system’s reliability, with over 95% accuracy in gesture recognition and minimal latency. This innovative system underscores the potential of head gesture-controlled robotics in assistive technology, offering an affordable, user-friendly solution to enhance mobility and autonomy for individuals with limited physical capabilities
A review on Energy Consumption Model on Hierarchical clustering techniques for IoT- based multilevel heterogeneous WSNs using Energy Aware Node Selection. Iyobhebhe, Matthew; Tekanyi, Abdooulie Momodou. S.; Abubilal, K. A; Usman, Aliyu. D; Isiaku, Yau; Agbon, E. E; obi, Elvis; Chollom, Botson Ishaya; Ezugwu, Chukwudi; Eleshin, Ridwan. O.; Abdulkareem, H. A.; Ashafa, Fatima; Abubakar, Saba; Umar, Abubakar; Ajayi Ore-Ofe; Thomas Muge, Paul
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.34882

Abstract

This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. The review article deliberates on the challenges posed by dynamic wireless sensor network conditions, node heterogeneity like energy-based, and scalability challenges that affect energy management. This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. We scrutinize different factors affecting efficient node selection, comprising residual energy, transmission distance, and sensor node reliability while juxtaposing these techniques with traditional node selection schemes. Furthermore, the importance of developed modeling techniques was highlighted. Finally, future research directions were outlined, by accentuating the incorporation of energy harvesting and collective models to improve the stability and operation of Wireless Sensor Networks. This holistic overview aims to offer appreciated insights for authors and practitioners in WSNs.
Mathematical Modelling of Truck Platoon Formation Based on a Dynamic String Stability Ajayi, Ore-Ofe; Umar, Abubakar; Ibrahim, Ibrahim; Olugbenga, Lawal Abdulwahab; Abiola , Ajikanle Abdulbasit
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.34941

Abstract

In this research, the development of a fuzzy logic-based cooperative adaptive cruise control scheme for truck platooning string stability was developed. String stability, which is critical to the operation of truck platooning in the area of enhancing traffic flow and reducing fuel consumption can be affected by unknown uncertainties such as truck incapacitation, delay of platoons and inability to maintain a constant inter-vehicular gap. A commonly reported approach in addressing truck platooning string stability is the Cooperative Adaptive Cruise Control (CACC) scheme. The CACC scheme consists of Adaptive Cruise Control (ACC) and vehicle-to-vehicle (V2V) communication. However, the CACC lacks the requisite flexibility in dealing with unexpected disturbances that can result in the inability to maintain a constant speed and inter-vehicular gap.
Early Heart Disease Prediction Using Data Mining Techniques Sylvester Aondonenge, Dugguh; Ore-Ofe, Ajayi; Hassan Taiwo , Kamorudeen; Umar, Abubakar; Abdulrazaq Imam , Isa; Daniel Emmanuel , Dako; Ibrahim , Ibrahim
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36735

Abstract

This study develops a predictive model for early heart disease detection using data mining techniques to enhance timely and accurate diagnosis. Heart disease prediction is complex due to the need to analyze various risk factors, such as age, cholesterol, and blood pressure. The model integrates multiple machines learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach, aiming to achieve higher prediction accuracy and reliability. The methodology follows five phases which include data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The data was split into an 80:20 ratio for model training and testing to assess model performance across various classification algorithms. The hybrid model achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, surpassing the individual algorithms tested. These findings indicate that the hybrid approach effectively supports early intervention for heart disease, particularly in healthcare settings with limited diagnostic resources. The study demonstrates that advanced data mining techniques offer a viable solution for improving patient outcomes through early detection of heart disease.
A Review on Battery Life and Energy Management in HWSNs using Adaptive Energy Harvesting Techniques Iyobhebhe, Matthew; Momodou. S. Tekanyi, Abdoulie; Abubilal, K. A.; D. Usman, Aliyu.; H. A. Abdulkareem; Yau Isiaka; E. E. Agbon; Elvis Obi; Chukwudi Ezugwu; Botson Ishaya Chollom; Ajayi Ore-Ofe; Umar, Abubakar; O. Eleshin, Ridwan.; Ashafa, Fatima; Thomas Muge, Paul
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.37897

Abstract

This review article scrutinizes the limitations and improvements in battery life and energy management models within HSWNs that employ adaptive energy harvesting techniques. As Wireless Sensor Networks (WSNs) advance into various implementations, including monitoring environmental parameters and smart cities, effective energy management is vital. This review paper examines the responsibility of communication protocols, precisely routing and Medium Access Control (MAC) protocols, in optimizing energy efficiency. We scrutinize existing energy-efficient schemes and their comparative performance, thereby, concentrating on their adaptability to changing energy sources. Examining these protocols, we accentuate the best techniques and potential areas for development. Furthermore, the review highlights the necessity for advanced approaches integrating energy harvesting with resilience communication frameworks to maximize network lifespan. The discoveries are targeted to lead future research and advancement in the field of WSNs, hence, providing more reliable and efficient energy management schemes for WSNs.
Assessing the Strategic Impact of Artificial Intelligence - Robotic Process Automation on Enterprise Architecture in the Telecommunications Industry Umar, Abubakar; Abdulrazaq Imam, Isa; Ore-Ofe, Ajayi Ore-Ofe; Daniel Emmanuel, Dako; Sylvester Aondonenge, Dugguh; Abdulwahab Olugbenga , Lawal
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 3 (2024): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i3.36736

Abstract

This project explores the strategic impact of Artificial Intelligence (AI)-enhanced Robotic Process Automation (RPA) on Enterprise Architecture (EA) within the telecommunications industry. Traditionally, RPA has been applied to automate repetitive tasks without altering underlying IT infrastructure, focusing primarily on operational efficiency. However, the integration of AI introduces cognitive capabilities to RPA, enabling more dynamic interactions within complex organizational systems. This project assesses how AI-driven RPA can influence EA by enhancing system efficiency, supporting business-IT alignment and promoting digital transformation. Through case studies and analyses of various telecommunications operations, the project investigates the dual role of AI-enhanced RPA in both streamlining enterprise-wide processes and maintaining adaptability to meet industry demands. The findings indicate that, while AI-RPA integration holds significant promise for accelerating operational improvements, it also presents unique challenges related to governance, scalability and long-term sustainability. This work contributes insights into the adoption of AI-driven RPA as a transformative tool for telecommunications, offering guidance on best practices for aligning automated systems with enterprise strategic goals.
Design Of an Enterprise Network Terminal Security Solution Idris Abubakar, Muhammad; Ore-Ofe, Ajayi; Umar, Abubakar; Ibrahim, Ibrahim; Olugbenga, Lawal Abdulwahab; Abdulbasit Abiola, Ajikanle
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39105

Abstract

This paper develops a secured enterprise network terminal security solution that seeks to safeguard the confidentiality, integrity, and availability of critical data and network resources, the paper presents a logical approach to designing an enterprise network security solution with a primary focus on optimizing and enhancing the performance of the network terminals (and datacenter critical end devices) security solution. The traditional network infrastructure has predominantly centered the security measured on core network components such as Firewalls, Intrusion Detection Systems/Intrusion Prevention Systems (IDS/SPS) but there are encountered security incidences, this is due to the exponential growth of the Internet of Things (IoT) devices, Bring Your Device (BYOD), and remote workforce trends, the network terminals have become the key points through which users access and utilize network resources for malicious attack and in most cases critical end devices such as servers/storage are the end target. This paper presents a comprehensive framework that places considerable emphasis on improving the terminal security performance by utilizing the existing encryption techniques (VPN) to provide double-step tunnels (VPN). However, in the event of an inevitable attack, the paper also presents a framework of how data center core end components, such as server and storage can be protected from the attack. The paper starts by studying the terminal ecosystem, the current terminal security solution, and the latest terminal security solution and designing the solution deemed fit to secure the terminal network.  
Review on Energy-Efficient Model Hybrid Clustering Technique in WSNs Iyobhebhe, Matthew; Tekanyi, Abdoulie Momodou . S; Abubilal, K. A; Isiaku, Yau; Agbon, E. E; Obi, Elvis; Umar, Abubakar; Ore-ofe, Ajayi; Kwembe, Benjamin Amough; Botson, Botson Ishaya; Eleshin, Ridwan. O; Ashafa, Fatima; Muge, Paul Thomas
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.42564

Abstract

This review article explores the advancements in energy-efficient hybrid clustering techniques for Wireless Sensor Networks (WSNs), highlighting their significance in optimizing energy consumption and enhancing network longevity. As WSNs become integral to various applications, efficient energy management is crucial for prolonging node lifespan and ensuring reliable data transmission. We analyze existing models, comparing their effectiveness in minimizing energy usage while maximizing data delivery efficiency. The review article formulate model equation for determines Energy-Efficient hybrid clustering techniques, Energy consumption, Network Life time, and Data Transmission Delivery which enhances network stability, also discusses the challenges faced in the implementation of these techniques, including scalability and network dynamics. By synthesizing current research, this review aims to provide insights into emerging trends and future directions in energy-efficient clustering strategies, offering valuable guidance for researchers and practitioners seeking to improve the sustainability and performance of WSNs.
Co-Authors Abdulbasit Abiola, Ajikanle Abdulkareem, H. A. Abdulrazaq Imam , Isa Abdulrazaq Imam, Isa Abdulwahab Olugbenga , Lawal Abiola , Ajikanle Abdulbasit Abiola, Ajikanle Abdulbasit Abubakar, Saba Abubilal, K. A Abubilal, K. A. Adah, Miftahussa Agbon, E. E Agnestina, Tamia Agustini, Putri Alfiani Ajayi Ore-Ofe Ajayi, Ore-Ofe Ashafa, Fatima Astuti Darmiyanti Bambang Ismaya Botson Ishaya Chollom Botson, Botson Ishaya Chollom, Botson Ishaya Chukwudi Ezugwu D. Usman, Aliyu. Daniel Emmanuel , Dako Daniel Emmanuel, Dako E. E. Agbon Eleshin, Ridwan. O Eleshin, Ridwan. O. Elvis Obi Ezugwu, Chukwudi H. A. Abdulkareem Hassan Taiwo , Kamorudeen Ibrahim , Ibrahim Ibrahim Ibrahim Idris Abubakar, Muhammad Isiaku, Yau Iyobhebhe, Matthew Kwembe, Benjamin Amough Mashuriyah, Mashuriyah Momodou. S. Tekanyi, Abdoulie Muge, Paul Thomas Nugroho, Iqbal Adi O. Eleshin, Ridwan. obi, Elvis Olugbenga, Lawal Abdulwahab Ore-Ofe, Ajayi Ore-Ofe, Ajayi Ore-Ofe Palupi, Tiara Dwi Razak, Farhan Imam Rohman, Roja Syahrul Sylvester Aondonenge, Dugguh Tekanyi, Abdooulie Momodou. S. Tekanyi, Abdoulie Momodou . S Thomas Muge, Paul Usman, Aliyu. D Waluyo, Kasja Eki Yadi Fahmi Arifudin Yau Isiaka