403 Google Scholar citations, h-index 9, i10-index 8 (September 2026). Full list: Google Scholar profile.

Open-Source Engineering Output

climatehealth — R package, CRAN

  • Author and maintainer. Statistical Tools for Modelling Climate-Health Impacts.
  • Implements six climate-health indicators endorsed by the United Nations Statistical Commission (57th session, New York, March 2026) and now included in the Global Set of Environment and Climate Change Statistics: temperature-related mortality, wildfire smoke exposure, air pollution, heat-related suicides, malaria and diarrhoeal disease.
  • Methods: distributed lag non-linear models (DLNM), quasi-Poisson time-series regression, case-crossover analysis, Bayesian spatio-temporal models (INLA), and multivariate meta-analysis for sub-national estimates.
  • Past 2,000 downloads since April 2026.
  • CRAN · Source · Climate-Health Platform

Preprints

  1. K. Omeke, M. Mollel, L. Zhang, Q. H. Abbasi, M. A. Imran, “Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation,” arXiv:2603.24648, March 2026. arXiv

  2. K. Omeke, A. Abubakar, M. Mollel, L. Zhang, Q. H. Abbasi, M. A. Imran, “Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation,” arXiv:2603.07413, March 2026. arXiv

Selected Peer-Reviewed Publications

Journal Papers

  1. K. G. Omeke et al., “Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT and Reinforcement Learning,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 7640–7651, 2024.

  2. K. G. Omeke et al., “DEKCS: A Dynamic Clustering Protocol to Prolong Underwater Sensor Networks,” IEEE Sensors Journal, vol. 21, no. 7, pp. 9457–9464, 2021. (109+ citations; most-downloaded article in its IEEE Xplore category, Q1 2022.)

  3. K. G. Omeke et al., “How Reinforcement Learning is Helping to Solve Internet-of-Underwater-Things Problems,” IEEE Internet of Things Magazine, vol. 5, no. 4, pp. 24–29, 2022.

  4. A. I. Abubakar, K. G. Omeke et al., “A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches,” Drones (MDPI), vol. 7, no. 3, art. 214, 2023.

  5. A. I. Abubakar, K. G. Omeke et al., “The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges and Future Outlook,” Frontiers in Communications and Networks, vol. 1, art. 575065, 2020.

  6. E. Tshukudu, S. Olaosebikan, K. G. Omeke et al., “Broadening Participation in Computing: Experiences of an Online Programming Workshop for African Students,” ACM ITiCSE, pp. 393–399, 2022.

Conference Papers

  1. K. G. Omeke et al., “Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks,” CICN, Al-Khobar, Saudi Arabia, pp. 322–328, 2022.

  2. K. G. Omeke et al., “Energy Optimisation through Path Selection for Underwater Wireless Sensor Networks,” IEEE UCET, 2020.

  3. K. G. Omeke et al., “A Q-Learning-based Route Selection Scheme for the Internet-of-Underwater Things,” IEEE WCNC.

  4. K. G. Omeke et al., “Characterization of RF Signals in Different Types of Water,” IET Antennas and Propagation Conference, Birmingham, 2019.

Co-author on three papers presented at the NSE Glasgow 2026 conference: memory-aware edge AI for low-power IoT systems, machine learning for electricity demand forecasting in Nigeria’s power grid, and bio-inspired soft robotic systems on low-cost embedded control architectures.

Doctoral Thesis

  1. K. G. Omeke, “Cognitive Networking for the Internet of Underwater Things,” PhD Thesis, University of Glasgow, 2023. DOI: 10.5525/gla.thesis.83522

Under Review

Natural language processing for low-resource languages

  1. N. Etori, M. Mollel, K. Omeke, J. Wang, I. Charles, “PawaLID: A Compact Byte-Level Multi-Task Language Identification Model for African and Low-Resource Languages.” A compact model that beats the state of the art on language identification and machine translation across dozens of East African languages. (ACL-track submission, in review.)

Communications, sensing and IoT engineering

  1. “Machine Learning Across the IoUT Protocol Stack: A Survey and Tutorial on Techniques, Deployments, and Open Challenges,” IEEE Communications Surveys and Tutorials (first author; revised and resubmitted, July 2026).

  2. “Coverage-Aware Hierarchical Federated Anomaly Detection for the Internet of Underwater Things,” IEEE Internet of Things Journal (first author).

  3. “From Pixels to Semantic Telemetry: Joint Semantic Communication and Visual-Anchor Navigation for IoUT AUV Missions,” IEEE Journal of Oceanic Engineering (first author).

  4. “Multi-Sensor IoT Fusion for Mould Risk Forecasting in Social Housing,” IEEE Internet of Things Journal (first author).

  5. “Multi-Signal IoT Anomaly Detection for Non-Intrusive Welfare Monitoring of Lone Elderly Residents,” IEEE Journal of Biomedical and Health Informatics (first author).

In Preparation

  • M. Mgonzo, M. Mollel, K. Omeke et al., “Addressing Cross-Lingual Performance Disparity in Handwritten Text Recognition: A Fine-Tuning Approach for Swahili Academic Documents.” Frontier OCR and vision-language models were benchmarked on handwritten Swahili academic documents and failed almost completely; this work builds and fine-tunes models that do not. Collaboration with PAWA AI, Brown University, and universities and schools in Tanzania.

Peer Review & Editorial Service

  • Reviewer — IEEE Internet of Things Journal (2022–present), IEEE Sensors Journal (2021–present), IEEE Transactions on Intelligent Vehicles, IEEE GLOBECOM (2021–present), IEEE WCNC (2021–present).
  • Editor, Engineering & Physical Sciences — Enago (2018–2020) and Cactus Communications (2017–2018); several hundred manuscripts edited for international submission.
  • Technical programme lead — NSE Glasgow 2026 conference: ran the abstract review process and produced the Book of Abstracts and the three-day technical programme.