Articles under Review

1. Victor Osuagwu, Augustine Ikpehai, Mike Ojo, Kelvin Anoh, Abayomi Otebolaku, Alex Shenfield, and Bamidele Adebisi, Empirical Evaluation of Low-Cost SDN-enabled QoS for Industrial IoT Network(2025)

2. Jude Ameh, Abayomi Otebolaku, Augustine Ikpehai, Alex Shenfield, Dauda Sule, Machine Learning Approaches in Software Vulnerability Detection: A Systematic Review and Analysis of Contemporary Methods, under review(Artificial Intelligence Review, 2025)

Peer-Reviewed Journal Articles

3. Performance Analysis of Compressed Transformer Models for Healthcare Privacy Threat Detection ( 30th European Symposium on Research in Computer Security (ESORICS) 2025,  France, accepted)

4. Ameh, J. E., Otebolaku, A., Shenfield, A., & Ikpehai, A. (2025). C3-VULMAP: A Dataset for Privacy-Aware Vulnerability Detection in Healthcare Systems. Electronics, 14(13), 2703. https://doi.org/10.3390/electronics14132703

5. Ibude, F.; Otebolaku, A.; Ameh, J.E.; Ikpehai, A. Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks. J. Low Power Electron. Appl. 2024, 14, 54. https://doi.org/10.3390/jlpea14040054

6. Alsaeedi, A., Al-Sharqi, M., Alkafagi, S., Nuiaa, R., D. Alfoudi, A., Manickam, S., Mahdi, A., & Otebolaku, A. (2023). Hybrid Extend Particle Swarm Optimisation (EPSO) model for Enhancing the performance of MANET Routing Protocols. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(1), Comp Page 127-136.

7. Abayomi Otebolaku & Debola Omolaja. Context-Aware Complex Human Activity Recognition using Hybrid Deep Learning Models, 2022 (MDPI Applied Sciences, IF: 2.7)

8. Qusay Mosa, Ali Alfoudi, Ahmed Brisam, Abayomi Otebolaku, Gyu Myoung Lee (2022).  Driving Active Contours to Concave Regions

9. Mohammed Abed Abed, Ali H. Alsaeedi, Ali D. Alfouldi, Abayomi Otebolaku, Yasmeen Sajid Razooqi, Palm Vein Identification based on hybrid features selection  model(2021)

10.  T. Enamamu, A.M. Otebolaku, J Marchang, J Dany;  Continuous m-Health Monitoring and Patient Verification Using Bioelectrical Signals MDPI Sensors(2020, IF:3.9)

11. A.M Otebolaku, T.  Enamamu, A. Alfoudi, A. Ikpehai, J. Marchang,  Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks. (2020, IF: 3.9)

12.  A. S. D. Alfoudi, S. H. S. Newaz, A. Otebolaku, G. M. Lee and R. Pereira, An Efficient Resource Management Mechanism for Network Slicing in a LTE Network,in IEEE Access,  vol.  7, pp. 89441-89457, 2019, doi: 10.1109/ACCESS.2019.2926446. (2019, IF:3.9) 

13. 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

14. A.M. Otebolaku and G.M. Lee, A Framework for Exploiting Internet of Things for Context-Aware Personalized Services  Invited Contribution Journal of Mobile Information Systems (2018, 1.802)

15.  A.M. Otebolaku and M.T. Andrade  Context-Aware Personalization Using Neighborhood-based Context Similarity. Published in Springer Personal Wireless Communications, Vol.94 (3), pp 1595–1618 (June 2017, IF:2.2).

16. A.M. Otebolaku and M. T. Andrade. User Context Recognition Using Smartphone Sensors and Classification Models in Elsevier Journal of Networks and Computer Applications, Vol 66(c), pp.33-51(May 2016, IF:8.7). 

17. A.M. Otebolaku and M. T. Andrade. Context-Aware Media Recommendations for Smart Devices. In Springer Journal of Ambient Intelligence and Humanized Computing Vol. 6 Issue 1, pp.13-36( February 2015, IF: 7.104). 

 Book Contributions

18. A.M. Otebolaku and M.T. Andrade. Context-Aware Personalization for Mobile Services. Invited book chapter accepted in IGI-Global 4th Edition of the Encyclopedia of Information Science and Technology, Pennsylvania, Chapter 59, USA 4TH Edition, 2019. 

19. A.M. Otebolaku and M.T. Andrade. Context-Aware Personalization for Mobile Services. Invited book chapter accepted in IGI-Global 4th Edition of the Encyclopedia of Information Science and Technology, Pennsylvania, USA 4TH Edition, 2018. 

20. A.M. Otebolaku and M.T. Andrade.  Context-Aware Multimedia Content Recommendations for Smartphone Users.  Book Chapter published in the IGI-Global 3rd Edition of the Encyclopedia of Information Science and Technology, pp 5658-5666, 2015, USA. 

21.  A.M. Otebolaku and M.T. Andrade. A Context-Aware Media Recommendation Framework on Smartphones.  In L.De Stricker (Ed.), ECUMICT March 2014, Lecture Notes in Electrical Engineering 302, pp 87-108, Springer International Publishing Switzerland.

International Peer Reviewed Conference Papers

22. A. Alfoudi, A. Otebolaku, R Pereira, G.M. Lee. Mobility Management Architecture in Difference RATs Based Network Slicing, Accepted 32nd IEEE International Conference on Advanced Information Networking and Applications, 2018.

23.  U. Jayasinghe, A.M. Otebolaku, T. Um, G.M. Lee, Data Centric Trust Evaluation and Prediction Framework for IoT Accepted Conference Paper, IEEE/ITU Kaleidoscope 2017: Challenges for a data-driven society. Nanjing, China.

24.  A.M. Otebolaku and G.M. Lee. Towards Context Classification and Reasoning in IoT. In the proceedings of the 14th International Conference on Telecommunications, Zagreb, Croatia 28-30 June 2017.

25.  A.M. Otebolaku, M. T. Andrade. Context-Aware Media Recommendations. In the proceedings of the 28th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA-2014), pp.191-196, May 2014, Victoria, Canada. 

26. A.M. Otebolaku, M. T. Andrade. Context-Aware User Profiling and Multimedia Content Classification Smart Devices. In the proceedings of the 28th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA-2014), pp560-565, May 2014, Victoria, Canada. 

27. A.M. Otebolaku and M. T. Andrade.  Supporting Context-Aware Cloud-Based Media Recommendation for Smartphones.  Published in the 2nd IEEE International Conference on Mobile Cloud Computing, Services and Engineering, pp.109-116, April 2014, Oxford, UK. 

28.  A.M. Otebolaku and M.T. Andrade. Recognizing High-Level Contexts from Smartphone Built-in Sensor for Mobile Media Content Recommendation. In HumoComp 2013 workshop of 14th IEEE International Conference on Mobile Data Management, pp142-147, Milan Italy (June 3-6, 2013). 

29.  A.M. Otebolaku and M.T. Andrade. Context Representation for Context-Aware Mobile Multimedia Recommendation.  In Proceedings of the 15th IASTED International Conference on Internet and Multimedia Systems and Applications, Washington, USA, pp68-75(May 16-18, 2011). 

30. A.M. Otebolaku, J. S. Iyilade and M. O. Adigun. CAAM: A Context-Aware Adaptation Model for Mobile Grid Service.  In Proceedings, 11th IEEE International Conference on Computational Science and Engineering (CSE’08), pp. 419-425, Sao Paulo, Brazil (16-18 July 2008). 

Selected Oral Presentations and Prototype Demos

• • Towards Context Classification and Reasoning in IoT, 14th International Conference on Telecommunications, 28-30 June 2017, Zagreb, Croatia.

  • Context-Aware Media Recommendations at the 28th IEEE International Conference on Advanced Information Networking and Applications, Victoria, BC, Canada (2014) 

  • Context-Aware User Profiling and Multimedia Content Classification for Smart Devices at the 28th IEEE International Conference on Advanced Information Networking and Applications, Victoria, BC, Canada (2014) 

  •  Recognizing High-Level Contexts from Smartphone Built-in Sensors for Mobile Media Content Recommendations at the 14th IEEE International conference on Mobile Data Management, University of Milan, Italy (2013) 

  • A Smartphone based Context-Aware Framework for Mobile Multimedia Personalization- A Demo Presented as part of Media, Arts and Technology (MAT) Research Projects, at the Center for Telecommunications and Multimedia, INESC TEC, Portugal (2015). 

  • Context Representation for Context-aware Mobile Multimedia Recommendation, Washington, USA, 2011.

  • European Conference on the use on the use of Modern Information and Communication Technologies, 2014, Gent, Belgium.

  • Presentations and practical project Demos at the Center for Telecommunication and multimedia open day, INESC TEC, 2015. 

  • Several Institutional annual presentations and Prototype Demos etc. 

  • Research presentation at the MAP-TELE workshop, Institute of Telecommunications, University of Aveiro, Portugal (2011) 

  • Recognizing High-Level Contexts from Smartphone Built-in Sensor for Mobile Media Content Recommendation, Faculty of Engineering, University of Porto (2013). 

  • A.M. Otebolaku and M.T. Andrade.  Context-Aware Multimedia Adaptation.  MAP-TELE workshop, Faculty of Engineering, University of Porto, 2009.   

Journal Refereeing

• •  IEEE Access

  •  IEEE Transactions on Network and Service Management

•  IEEE Transactions on Big Data

•  IEEE Transactions on Computational Social Systems

• Journal of Networks and Computer Application

• Taylor & Francis Connection science, Journal of networks and computer applications.

• IEEE Transactions on Industrial Informatics

• ACM Transactions on Internet Technologies Internet of Things

• Wireless Personal Communications (Springer)

• MDPI Electronics, Sensors, Information, Symmetry, Sustainability, Computers, Technologies, Applied Science,   Sensors and Actuator, Computers

• Elsevier Journal of Ad Hoc Networks                                                                                                                              

• Journal of Reliable Intelligent Environments (Springer)

• European Alliance for Innovation-EAI Journal of Context-Aware Systems and Applications.                       

Ph.D. Thesis

In his Ph.D. research, titled Context-Aware Personalization for Mobile Multimedia and defended in May 2015, he developed a system that leverages the built-in sensors of mobile devices—such as accelerometers, gyroscopes, GPS, light, sound, WiFi, and magnetometers—to gather contextual data about users. This data is used to build dynamic user profiles based on consumption patterns and environmental conditions.

The system, named Context-Aware Personalized Multimedia Recommendations for Mobile Users (CAMR), delivers personalized content—such as movie suggestions—adapted to the user's current context, including device capabilities, location, time of day, and network status. It operates either seamlessly or on-demand, ensuring relevance and responsiveness.

CAMR was implemented as both an Android mobile app and a web-based application, serving as a proof-of-concept tested under real-world conditions. The project demonstrated how context-aware personalization can enhance user experience by intelligently adapting multimedia content to individual usage scenarios.