Dr Abayomi Otebolaku

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Dr Abayomi Otebolaku BSc, MSc, PhD, FHEA

Senior Lecturer


Summary

Dr Otebolaku is a Senior Lecturer in Computing in the School of Computing and Digital Technologies. His research focuses on the intersection of mobile and distributed computing, specialising in context sensing, mobile data management, computational trust, and IoT-driven service personalisation.

Leveraging machine learning and AI, Dr. Otebolaku is interested in how data from our devices can help deliver personalized digital services for quality and improved engagement by exploring the future of technology through Internet of Intelligent Things (IoIT), Edge Computing, Ambient Intelligence (AmI) and Computational Trust. His work has contributed to local and international projects, including contributions to European H2020 projects.

About

Dr Otebolaku received his PhD in Electrical and Computer Engineering (Telecommunication Engineering) from the Electrical and Computer Engineering department, Faculty of Engineering, University of Porto, Portugal. Prior to his PhD, he received BSc and MSc (by research) degrees in Computer Engineering and Computer Science respectively. He holds a Postgraduate Certificate in Teaching and Learning in Higher Education. He is a fellow of the Higher Education Academy (FHEA) and a member of the Institute of Electrical and Electronics Engineers (IEEE).

Prior to studying for postgraduate degrees, he worked in the ICT industry as an IT Engineer specialising in networking. After his PhD, he was a Postdoctoral Research Associate in the Department of Computer Science, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK.  He was also a Postdoctoral Research Associate in the Department of Electronics, Telecommunications and Informatics, University of Aveiro, and the Institute of Telecommunications, Aveiro, Portugal. He was a Research and Development Engineer at the Centre for Telecommunications and Multimedia, INESC TEC (formerly INESC Porto) also in Portugal. He received the INESC TEC grants for doctoral research and the Portuguese’s government prestigious Fundacao para Ciencia e a Tecnologia (Foundation for Science and Technology, FCT) full doctoral research grants (Bolsa de Deutoramento).

Dr Otebolaku is an active researcher with participation in several research projects, including European H2020 Projects where his research outputs have been applied. He has published widely in leading thematic peer-reviewed international journals and conferences. In addition, he serves as a technical committee member for several reputable international conferences and workshops, and as guest editor for reputable thematic journals. He is also a peer reviewer for several journals and conference proceedings.

His research focuses broadly on mobile and pervasive computing, and specifically on context-aware systems, context sensing, mobile data management, computational trust, and IoT-driven personalized services.  He is passionate about the application of Artificial Intelligence (AI) and Machine Learning (ML) within his research focus to address emerging challenges.  

In the past, his research has contributed inputs in the domain of context-aware mobile multimedia systems, providing personalized mobile multimedia delivery and consumptions using intelligence obtained from mobile sensory data. Besides, he has also contributed inputs in area of ambient context sensing and recognition, and its applications in the design and implementation of context-aware personalised user preferences. These involve the design, development, and deployment of intelligent applications on smart devices. His current work focuses on the application of AI/ML, Edge Computing and Software Engineering to address real-world problems in healthcare, energy, and environments.

His main teaching interests are in Computer Science and Software Engineering including Distributed Programming, programming concepts and practice, applied machine learning/artificial intelligence, and Data Science.

 

Teaching

School of Computing and Digital Technologies

College of Business, Technology and Engineering

Computer Science and Software Engineering

Courses taught:

  • BSc Computer Science
  • BEng Software Engineering
  • MEng Software Engineering
  • MSc Big Data Analytics

Modules taught:

  • Programming Concepts and Practice
  • Networked Software Development
  • Machine Learning Algorithms and Heuristics
  • Distributed Programming and Technologies
  • Software Project
  • Android Mobile App development

Research

Research projects

OHL
The Overhead Line (OHL) network is comprised of conductors, infrastructure and electrical assets associated with the overhead network (132 kV to 230 V). In most cases, the OHL conductors are not insulated, therefore if either an object/person comes into contact, or in close proximity to it, it can cause serious injury or death. These injuries can have both physical and mental impacts on the affected person’s quality of life and their families.

Disruption to power, caused by these incidents, and the impact it can have on customers is also an issue for the DNOs.
Health and Safety (HSE) guidance note GS6, is a guidance note for people who may be planning to work near overhead lines where there is a risk of contact with the wires, and describes steps that should be taken to prevent contact with them. Despite this guidance document, cable strikes still occur.

This project aims to develop innovative solutions to assist in preventing OHL strikes.

WiseIoT
The EU H2020 WiseIoT project. WiseIoT is a collaborative project between Europe and South Korea. It aimed at deepening the interoperability and interworking of IoT existing systems. WiseIoT also aimed to develop federated and interoperable platforms ensuring end-to-end security and trust management for reliable business environments. Building synergies with national and international initiatives in both Europe and South Korea, the project also focuses on standardisation to foster IoT application development and interoperability.

SWARMs
The  SWARMs project. The goal of the EU H2020 SWARMs project was to expand the use of autonomous underwater and surface vehicles (AUVs, ROVs, USVs) to facilitate conception, planning and execution of maritime and offshore operations and missions.

Publications

Key Publications

Ibude, F., Otebolaku, A., Ameh, J., & Ikpehai, A. (2024). Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. Journal of Low Power Electronics and Applications, 14 (4). http://doi.org/10.3390/jlpea14040054

Omolaja, A., Otebolaku, A., & Alfoudi, A. (2022). Context-Aware complex human activity recognition using hybrid deep learning models. Applied Sciences, 12 (18). http://doi.org/10.3390/app12189305

Otebolaku, A., Enamamu, T., Alfoudi, A., Ikpehai, A., Marchang, J., & Lee, G.M. (2020). Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks. Sensors, 20 (13), 3803. http://doi.org/10.3390/s20133803

Alfoudi, A., Newaz, S., Otebolaku, A., Lee, G.M., & Pereira, R. (2019). An efficient resource management mechanism for network slicing in LTE network. IEEE Access, 7, 89441-89457. http://doi.org/10.1109/ACCESS.2019.2926446

Jayasinghe, U., Otebolaku, A., Um, T.-.W., & Lee, G.M. (2018). Data centric trust evaluation and prediction framework for IOT. Proceedings of the 2017 ITU Kaleidoscope Academic Conference: Challenges for a Data-Driven Society, ITU K 2017, 2018-J (2018-J), 1-7. http://doi.org/10.23919/ITU-WT.2017.8246999

Otebolaku, A., & Lee, G.M. (2017). Towards context classification and reasoning in IoT. Proceedings of the 14th International Conference on Telecommunications, ConTEL 2017, 147-154. http://doi.org/10.23919/ConTEL.2017.8000051

Otebolaku, A., & Andrade, M.T. (2016). Context-aware personalization using neighborhood-based context similarity. Wireless Personal Communications, 94 (3), 1595-1618. http://doi.org/10.1007/s11277-016-3701-2

Otebolaku, A., & Andrade, M.T. (2016). User context recognition using smartphone sensors and classification models. Journal of Network and Computer Applications, 66, 33-51. http://doi.org/10.1016/j.jnca.2016.03.013

Otebolaku, A.M., & Andrade, M.T. (2014). A Context-Aware Framework for Media Recommendation on Smartphones. In Lecture Notes in Electrical Engineering, (pp. 87-108). Springer International Publishing: http://doi.org/10.1007/978-3-319-05440-7_8

Otebolaku, A., & Andrade, M.T. (2014). Context-aware media recommendations for smart devices. Journal of Ambient Intelligence and Humanized Computing, 6 (1), 13-36. http://doi.org/10.1007/s12652-014-0234-y

Journal articles

Mosa, Q.O., Alfoudi, A.S., Brisam, A.A., Otebolaku, A., & Lee, G.M. (2022). Driving Active Contours to Concave Regions. Webology, 19 (1), 5131-5140. http://doi.org/10.14704/web/v19i1/web19345

Alfoudi, A., Alsaeedi, A., Abed, M., Otebolaku, A., & Razooqi, Y. (2021). Palm Vein Identification Based on Hybrid Feature Selection Model. International Journal of Intelligent Engineering and Systems, 14 (5), 469-478. http://doi.org/10.22266/ijies2021.1031.41

Dighriri, M., Otebolaku, A., Alfoudi, A., & Lee, G.M. (2020). Slice Allocation Management Model in 5G Networks for IoT Services with Reliable Low Latency. . http://doi.org/10.20944/preprints202007.0536.v1

Enamamu, T., Otebolaku, A.M., Marchang, J., & Joy, D. (2020). Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction. Sensors, 20, 5690. http://doi.org/10.3390/s20195690

Otebolaku, A., Enamamu, T., Alfouldi, A., Ikpehai, A., & Marchang, J. (2020). Deep Sensing: Inertia and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks. . http://doi.org/10.20944/preprints202005.0430.v1

Enamamu, T., Otebolaku, A., Marchang, J., & Dany, J. (2020). Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction. Sensors, 20 (19). http://doi.org/10.3390/s20195690

Marchang, J., Wang, J., Otebolaku, A., Enamamu, T., Porter, D., & Sanders, B. (2019). Multidimensional: User with File Content and Server’s status based Authentication for Secure File Operations in Cloud. Current Trends in Computer Sciences & Applications (CTCSA), 1 (5), 108-118. http://doi.org/10.32474/CTCSA.2019.01.000121

Otebolaku, A., & Lee, G.M. (2018). A Framework for Exploiting Internet of Things for Context-Aware Trust-Based Personalized Services. Mobile Information Systems, 2018, 1-24. http://doi.org/10.1155/2018/6138418

Alsaeedi, A.H., Al-Sharqi, M.A., Alkafagi, S.S., Nuiaa, R.R., D. Alfoudi, A.S., Manickam, S., ... Otebolaku, A.M. (n.d.). Hybrid Extend Particle Swarm Optimization (EPSO) model for Enhancing the performance of MANET Routing Protocols. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15 (1). http://doi.org/10.29304/jqcm.2023.15.1.1160

Conference papers

Alfoudi, A.S.D., Dighriri, M., Otebolaku, A., Pereira, R., & Lee, G.M. (2018). Mobility management architecture in different RATs based network slicing. Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018, 2018-J (2018-J), 270-274. http://doi.org/10.1109/WAINA.2018.00097

Otebolaku, A.M., & Andrade, M.T. (2014). Context-Aware Media Recommendations. 2014 28th International Conference on Advanced Information Networking and Applications Workshops, 191-196. http://doi.org/10.1109/waina.2014.40

Otebolaku, A.M., & Andrade, M.T. (2014). Context-Aware User Profiling and Multimedia Content Classification for Smart Devices. 2014 28th International Conference on Advanced Information Networking and Applications Workshops, 560-565. http://doi.org/10.1109/waina.2014.92

Otebolaku, A.M., & Andrade, M.T. (2014). Supporting Context-Aware Cloud-Based Media Recommendations for Smartphones. 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, 109-116. http://doi.org/10.1109/mobilecloud.2014.26

Otebolaku, A.M., & Andrade, M.T. (2013). Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation. 2013 IEEE 14th International Conference on Mobile Data Management, 142-147. http://doi.org/10.1109/mdm.2013.84

Otebolaku, A., & Andrade, M.T. (2011). Context representation for context-aware mobile multimedia content recommendation. Proceedings of the 15th IASTED International Conference on Internet and Multimedia Systems and Applications, IMSA 2011, 68-75. http://doi.org/10.2316/P.2011.746-004

Otebolaku, A.M., Iyilade, J.S., & Adigun, M.O. (2008). CAAM: A Context Aware Adaptation Model for Mobile Grid Service Infrastructure. 2008 11th IEEE International Conference on Computational Science and Engineering - Workshops, 419-425. http://doi.org/10.1109/csew.2008.37

A.M., O., M.O., A., Iyilade, J.S., & O.O., E. (2007). On Modeling Adaptation in Context-Aware Mobile Grid Systems. Third International Conference on Autonomic and Autonomous Systems (ICAS'07), 52. http://doi.org/10.1109/conielecomp.2007.90

Book chapters

Otebolaku, A.M., & Andrade, M.T. (2019). Context-Aware Personalization for Mobile Services. In Advances in Computer and Electrical Engineering. (pp. 818-830). IGI Global: http://doi.org/10.4018/978-1-5225-7598-6.ch059

Otebolaku, A., & Andrade, M. (2017). Context-Aware Personalization for Mobile Services. In Khosrow-Pour, M. (Ed.) Encyclopedia of Information Science and Technology, 4th edition. (pp. 6031-6042). IGI-Global: https://www.igi-global.com/book/encyclopedia-information-science-technology-fourth/173015

Otebolaku, A.M., & Andrade, M.T. (2014). Context-Aware Multimedia Content Recommendations for Smartphone Users. In Advances in Information Quality and Management. (pp. 5658-5666). IGI Global: http://doi.org/10.4018/978-1-4666-5888-2.ch559

Other activities

Dr Otebolaku serves as technical committee member for several thematic IEEE conferences and others.

He regularly serves as reviewer and guest editor for several key thematic journals.

He is also a reviewer for Manning Publications.

Postgraduate supervision

Currently supervises MSc and PhD candidates. Prospective PhD candidates are encouraged to contact me for PhD projects in the area of  Ambient Intelligence, Mobile Data Management, Computational Trust, Internet of Intelligence Things (IoIT) and Applications.

Current PhD Candidates:

Jude Enenche Ameh  (as Director of Studies), 2023

Mohammed Al-otaibie (as co-supervisor), 2024

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