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Omnicomputing: Integrating Mobility, Ubiquity, Learning and Intelligence in the Fourth Industrial Revolution
Subject area: Science,Engineering and Technology · Area of research: Software Intelligent Systems
Abstract
Omnicomputing is a computing paradigm where intelligent systems are seamlessly embedded into everyday environments, offering pervasive, context-aware, and user-centric services. This paper describes how omnicomputing integrates mobility, ubiquity, learning and real-time intelligence, enabling continuous access to computational resources across devices and locations. Powered by AI and machine learning, omnicomputing adapts to user behaviours and environmental changes, facilitating smart homes, education, healthcare, and industrial automation and revolutionizing how humans engage with technology and fostering seamless, efficient, and intuitive experiences.
References
[1] Abdulwahab, I., & Boyinbode, O. K. (2023). An artificial neural network model for predicting distance learning students' performance in Nigeria. International Journal of Novel Research and Development, 148-152.
[2] Abe, O. O. S., Obe, O. O., Boyinbode, O. K., & Olagbuji, N. B. (2023). Early gestational diabetes mellitus diagnosis using classification algorithms: An ensemble approach. In Proceedings of the 2023 IEEE AFRICON (pp. 01-06). Nairobi, Kenya
[3] Abe, O. S., Obe, O. O., Boyinbode, O. K., & Olagbuji, N. B. (2021). Classifier algorithms and ensemble models for diabetes mellitus prediction: A review. Journal of Advanced Trends in Computer Science and Engineering, 10(2), 1-10.
[4] Adewale, O. S., Agbonifo, O. C., Ibam, E. O., Makinde, A. I., & Boyinbode, O. K. (2018). Affect-adaptive activities in a personalized ubiquitous learning system. International Journal of Learning, Teaching and Educational Research, 17(7), 43-58.
[5] Adewale, O. S., Agbonifo, O. C., Ibam, E. O., Makinde, A. I., Boyinbode, O. K., Ojokoh, B. A., & Olatunji, S. O. (2022). Design of a personalized adaptive ubiquitous learning system. Interactive Learning Environments, 1-21.
[6] Ally, M. (Ed.). (2009). Mobile learning: Transforming the delivery of education and training. Athabasca University Press.
[7] Aina, E., Boyinbode, O. K., Daramola, O., & Moses, K. (2024). A Bayesian linear regression model for predicting Lassa fever in Nigeria: A case study of Ondo State. International Journal of Novel Research and Development, 9(4).
[8] Arora, N., & Saini, J. R. (2013). A fuzzy probabilistic neural network for student’s academic performance prediction. International Journal of Innovative Research in Science, Engineering and Technology, 2(9), 4425-4432.
[9] Boyinbode, O. K., & Akintola, K. G. (2008a). Toward a model of m-learning for enhancing dissemination of information among Nigerian farmers. Oriental Journal of Computer Science and Technology (OJCST), 1(2), 99-116.
[10] Boyinbode, O. K., Bagula, A., & Ngambi, D. (2011). An opencast mobile learning framework for enhancing learning in higher education. International Journal of u- and e- Service, Science and Technology, 4(3), 11-18.
[11] Boyinbode, O. K., & Bagula, A. S. (2011). An adaptive and personalized ubiquitous learning middleware support for handicapped learners. In 2011 Eighth International Conference on Information Technology: New Generations (pp. 632-637). Las Vegas, Nevada, USA, April 11-13.
[12] Boyinbode, O. K., Bagula, A., & Ng’ambi, D. (2012a). An interactive mobile learning system for enhancing learning in higher education. In Proceedings of the IADIS International Mobile Learning Conference (pp. 331-334). Berlin, Germany, March 11-13.
[13] Boyinbode, O. K., Bagula, A., & Ng’ambi, D. (2012b). A mobile learning application for delivering educational resources to mobile devices. In Proceedings of the International IEEE Conference on Information Society (i-Society) (pp. 120-125). London, June 25-28.
[14] Boyinbode, O. K., Ng’ambi, D., & Bagula, A. (2012c). An interactive mobile lecturing tool for aggregating learning resources. International Journal for Infonomics (IJI), 5(3), 646-654.
[15] Boyinbode, O. K., Ng’ambi, D., & Bagula, A. (2013). An interactive mobile lecturing model: Enhancing student engagement with face-to-face sessions. International Journal of Mobile and Blended Learning, 3(2), 1-21.
[16] Boyinbode, O. K., & Ng’ambi, D. (2013). An interactive mobile lecturing tool for empowering distance learners. International Journal of Interactive Mobile Technologies, 7(4), 33-38.
[17] Boyinbode, O. K., & Ng’ambi, D. (2015). MOBILect: An interactive mobile lecturing tool for fostering deep learning. International Journal of Mobile Learning and Organization, 9(2), 182-200.
[18] Boyinbode, O. K., & Akintade, F. (2015). A cloud-based mobile learning interface. In Lecture Notes in Engineering and Computer Science: Proceedings of the World Congress on Engineering and Computer Science 2015 (pp. 353-356). San Francisco, USA, October 21-23.
[19] Boyinbode, O. K., & Akinyede, R. O. (2015). An RFID-based inventory control system for Nigerian supermarkets. International Journal of Computer Applications, 116(7), 7-12.
[20] Boyinbode, O. K., & Ogundipe, T. (2017). Exploring the suitability of handheld devices for mobile learning. International Journal of Multimedia and Ubiquitous Engineering, 12(2), 143-160.
[21] Boyinbode, O. K., Agbonifo, O. C., & Ogundare, A. (2017). Supporting mobile learning with WhatsApp based on media richness. Journal of Circulation in Computer Science, 2(3), 37-46.
[22] Boyinbode, O. K. (2018a). Development of a gamification-based English vocabulary mobile learning system. International Journal of Computer Science and Mobile Computing, 7(8), 183-191.
[23] Boyinbode, O. K., & Tiamiyu, A. (2020). A mobile gamification English vocabulary learning system for motivating English learning. IOSR Journal of Mobile Computing & Application (IOSR-JMCA), 7(2), 14-29.
[24] Boyinbode, O. K., Ayankunle, O., & Obe, O. (2020a). A soft computing model for predicting students' academic performance in tertiary institutions. International Journal of Computer Applications, 176(23), 50-54.
[25] Boyinbode, O. K., & Osagiede, N. A. (2020). Design and implementation of a mobile application for disseminating information among Nigerian farmers. International Journal of Computer Sciences and Engineering, 8(5), 156-165.
[26] Boyinbode, O. K., Omopariola, A., & Obe, O. (2020b). Implementation of a RFID-based Internet of Things library information system. International Journal of Control and Automation, 13(2), 1235-1245.
[27] Boyinbode, O. K., Oyesanmi, F. G., Obe, O. O., & Boyinbode, O. F. (2020c). Internet of Things framework for structural health monitoring in Nigeria. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3308-3313.
[28] Boyinbode, O. K., Olotu, P., & Akintola, K. (2020d). Development of an ontology-based adaptive personalized e-learning system. Applied Computer Science, 16(4).
[29] Boyinbode, O. K., & Fatoke, T. (2021). Context-aware recommender system for adaptive ubiquitous learning. International Journal of Mobile Learning and Organisation, 15(4), 409-426.
[30] Boyinbode, O. K., Amodu, K. C., & Obe, O. (2021). An adaptive neuro-fuzzy inference system-based ubiquitous learning system to support learners with disabilities. International Journal of Multimedia Data Engineering and Management (IJMDEM), 12(3), 1-16.
[31] Boyinbode, O. K., Oyesanmi, F. G., Obe, O. O., & Boyinbode, O. F. (2022). Implementation of Internet of Things for structural health monitoring in Nigeria. In Proceedings of the 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, November 1-3.
[32] Boyinbode Olutayo, & Akoji Francis (2024) "Development of a Mobile Learning Support System" Iconic Research and Engineering Journals, 8(4), 151-158
[33] Boyinbode Olutayo Kehinde; Daramola Oladunni Abosede; Ijaola Joseph Boluwatife; Ashiru Taofeek Oladayo (2024). "Implementation of an Internet of Things Parking System: Case Study (Federal University of Technology Akure)" Iconic Research and Engineering Journals Volume 8 Issue 5 2024 Page 134-141.
[34] Castells, M. (1996). The space of flows. The rise of the network society, 1, 376-482.
[35] Cheng, S., Hwang, W., Wu, S., Shadiev, R., & Xie, C. (2010). A mobile device and online system with contextual familiarity and its effects on English learning on campus. Journal of Educational Technology & Society, 13(3), 93–109.
[36] Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining "gamification". Proceedings of the 15th International Academic MindTrek Conference.
[37] Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., & Burgelman, J. C. (2001). Scenarios for Ambient Intelligence in 2010. IST Advisory Group, European Commission.
[38] Giurgiu, L. (2017). Microlearning an evolving elearning trend. Scientific Bulletin, 22(1), 18-23.
[39] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660.
[40] Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? A literature review of empirical studies on gamification. Proceedings of the 47th Hawaii International Conference on System Sciences.
[41] Hasegawa T., Makoto K. & Hiromi B. (2015). An English vocabulary learning support system for the learner’s sustainable motivation. Hasegawa et al. Springer Plus In: Proceedings of the 2nd international conference on digital interactive media in entertainment and arts. NY, USA: ACM; 2007. Pp 142–146. http://gamificationintheclassroom.weebly.com/advantages–disadvantages-of-amification.html
[42] Hwang, G. J., Tsai, C. C., & Yang, S. J. (2008). Ubiquitous computing technologies in education. Educational Technology & Society, 11(2), 1-2.
[43] Ibam, E., Boyinbode, O. K., & Aladesiun, H. (2023). IoT-based farmland intrusion detection system. BIT-CS, 4(2), 39-53.
[44] Kim, S., Song, K., Lockee, B., & Burton, J. (2018). Gamification in learning and education. Springer International Publishing.
[45] Kinga, T. M., Boyinbode, O. K., & Adebowale, A. I. (2024). Implementation of a mobile skin infection diagnosis system using deep learning. International Journal of Novel Research and development, 9(4).
[46] Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & information systems engineering, 6, 239-242.
[47] Li, S., Xu, L. D., & Zhao, S. (2015). The internet of things: a survey. Information systems frontiers, 17, 243-259.
[48] Ogata, H., & Yano, Y. (2004). Knowledge awareness for a computer-supported ubiquitous learning environment. Proceedings of the IEEE International Workshop on Wireless and Mobile Technologies in Education (WMTE).
[49] Olanipekun, A. A., & Boyinbode, O. K. (2015). An RFID-based automatic attendance in educational institutions of Nigeria. International Journal of Smart Home, 9(12), 65-74.
[50] Oluwadare, S. A., Adegun, I. P., Orogun, O. A., & Boyinbode, O. K. (2024a). The design of ensemble deep learning model for the prediction of Lassa fever outbreak using multiple data sources. International Journal of Research in Engineering and Science (IJRES), 12(3), 365-373.
[51] Oluwadare S.A., Adegun I.P, Boyinbode O.K., Oyekanmi E.O. and Adeyemi A.R. (2024b). Investigating the Effect of Seasonality and Weather on Lassa Fever Outbreak in Some Selected States in Nigeria. FUOYE Journal of Pure and Applied Sciences. FJPAS Vol 9(3) ISSN: 2616-1419. pp. 197-216.
[52] Oluyege A.T., (2019) A Neuro-Fuzzy Model for Predicting Students’ Academic Performance in Nigerian Tertiary Institutions” Master of Technology Computer Science: Thesis Department of Computer Science, School of Computing, The Federal University of Technology, Akure, Nigeria.
[53] Oyelade, I. M., Boyinbode, O. K., & Adewale, O. (2023). A review of existing farmland intrusion detection systems. International Journal of Computer Applications, 185(22), 41-46.
[54] Oyelade, I. M., Boyinbode, O. K., Adewale, O. S., & Ibam, E. O. (2024). Farmland intrusion detection using Internet of Things and computer vision techniques. International Journal of Information Technology and Computer Science (IJITCS), 16(2), 32-44. https://doi.org/10.5815/ijitcs.2024.02.03
[55] Oyewo, O. A., & Boyinbode, O. K. (2020). Prediction of prostate cancer using an ensemble of machine learning techniques. International Journal of Advanced Computer Science and Applications (IJACSA), 11(3), 149-154.
[56] Patel, S. M. Patel and P. G. Scholar (2016). “Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges,” in International Journal of Engineering Science and Computing, vol. 6, no. 5, pp 1 – 10, 2016.10.4010/2016.1482.
[57] Russell, S., & Norvig, P. (2020). Artificial intelligence: a modern approach. Pearson series In Artificial intelligence
[58] Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum.
[59] Sharples, M., Taylor, J., & Vavoula, G. (2005). Towards a theory of mobile learning. Proceedings of mLearn 2005.
[60] Su, C. H., & Cheng, C. H. (2015). A mobile gamification learning system for improving the learning motivation and achievements. International Journal of Mobile Learning and Organisation, 9(4), 256–273.
[61] Traxler, J., & Kukulska-Hulme, A. (2005). Evaluating mobile learning: Reflections on current practice.
[62] Traxler, M. J. (2007). Working memory contributions to relative clause attachment processing: A hierarchical linear modeling analysis. Memory & Cognition, 35(5), 1107-1121.
[63] Warburton, K. (2003). Deep learning and education for sustainability. International Journal of Sustainability in Higher Education, 4(1), 44-56.
[64] Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94–104. https://doi.org/10.1038/scientificamerican0991-94.
How to cite this paper
@article{1707498,
author = {Olutayo Kehinde Boyinbode},
title = {Omnicomputing: Integrating Mobility, Ubiquity, Learning and Intelligence in the Fourth Industrial Revolution},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {608-623},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707498.pdf},
abstract = {Omnicomputing is a computing paradigm where intelligent systems are seamlessly embedded into everyday environments, offering pervasive, context-aware, and user-centric services. This paper describes how omnicomputing integrates mobility, ubiquity, learning and real-time intelligence, enabling continuous access to computational resources across devices and locations. Powered by AI and machine learning, omnicomputing adapts to user behaviours and environmental changes, facilitating smart homes, education, healthcare, and industrial automation and revolutionizing how humans engage with technology and fostering seamless, efficient, and intuitive experiences.},
month = {March},
}