Research Interest

Dr Otebolaku's research focuses on :

Seeking collaborations with companies  :

1. He seeks to work with energy companies that are working to improve the quality of customer experience to expand their customer base, and who require people with state-of-the-art AI/Machine learning technologies and software engineering skills to understand their customers’ consumption patterns for better, trustworthy, and personalised service provisioning, and are open to collaborating with a university partner.

2. He seeks to work with healthcare service providers who are working to provide trust-based and personalised management of chronic health and mental health services, and require people with expertise in the state-of-the-art AI/Machine learning technologies, ambient intelligence, and software engineering skills, and are open to collaborating with a university partner.

3. He seeks to work with stakeholders/service providers in the transport sector or intelligent urban mobility systems who are working on transport network optimisation and smart decision-making, using real-time data for efficient and sustainable use of transport services, and require state-of-the-art AI/Machine learning technologies and software engineering skills, and are open to collaborating with a university partner.

Past and current Research Projects

CAMR: Context-Aware Personalization for Mobile Multimedia(2015)

Goal: CAMR aims to explore advanced methods for context-aware personalization by leveraging the built-in sensors of mobile devices to gather contextual data. This includes monitoring user behavior and consumption patterns to implicitly construct dynamic user profiles. By applying data mining and semantic-based techniques to this collected data, CAMR can derive deeper insights and deliver personalized recommendations that adapt to the user’s current context—such as their activity, device characteristics, time of day, location, network status, and environmental conditions. Furthermore, CAMR introduces a mechanism to assess whether a user-selected resource from the recommended set requires adaptation to meet network or device constraints.

Wise-IoT(2018)

Goal: Wise-IoT is a collaborative project between Europe and Korea. From the EU side, it is funded under the H2020 framework program for research of the European Commission. It aims at deepening the interoperability and interworking of IoT existing systems. Use case driven, the project uses the experiences available in the consortium to build a comprehensive mediation framework that can be used between various IoT systems.

Swarms-EU(2016)

Goal: The oceans are considered to host a substantial part of human and industrial resources, namely oil and gas, whose industry will move to ever deeper waters, and where renewable energy continue to be harvested from the seas in offshore wind farms, but also increasingly through tidal, currents and wave energy converters. Furthermore, minerals such as cobalt, nickel, and copper, rare earths, silver and gold will be mined from the seafloor (deep sea mining). To this end, new offshore and port infrastructure will need to be built, monitored and maintained or repaired. The goal is to expand the use of AUV/ROVs and facilitate the creation, planning and execution of maritime and offshore operations to reduce operational cost and improve safety of tasks assigned to divers.


The Overhead Lines(OHL)Collision Avoidance(to start in 2023)

His participation among others will include the use of mobile phones’ in-built sensory data to design mobile app for tracking variability of magnetic field, which will be used to determine the proximity to the OHL with increasing loud alerts to warn users. 

AESSEAL Project(on hold)

1) Improving the rotating shaft running speed estimation of the machinery. Currently, the expert system uses a range of analysis techniques in conjunction with c. 150 rules. The accuracy of results from some of those analysis techniques are severely hampered by inaccurate shaft speeds and would benefit from more accurate determination / estimation of this running speed. 

 

2) Using machine learning (ML) / artificial intelligence (AI) to perform zero auxiliary knowledge fault detection (i.e. just using the raw time series vibration waveform). Ideally this would produce a score as to how close the bearing is to failure (e.g., 1 (bearing is like new) - 5 (bearing is at end of life)).  

 

3) Trend analysis of bearing state deterioration. Using multivariate statistical modelling techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), more accurate prediction models for the speed of bearing deterioration may be achievable (e.g. based on historical observations, how long does a given bearing have before complete failure?). 

Prospective Research Projects

AI As Microservices for emerging IoT applications

As IoT applications increasingly rely on data-driven decision-making, especially across the Edge-Cloud continuum, AI algorithms such as deep learning and reinforcement learning are often deployed as centralised, monolithic services. However, this model is inefficient for latency-critical use cases such as health monitoring, industrial automation, and emergency response, where real-time processing is essential.

This project introduces a practical architecture that enables AI to function as microservices within IoT ecosystems. By integrating Roof, Fog, and Cloud computing with microservice principles, the architecture supports the deployment of lightweight, autonomous AI modules directly at the edge. These modules can process data locally, reducing latency and improving responsiveness.

Practical applications include:

The proposed system offers a scalable, robust, and intelligent platform for deploying cross-platform IoT applications that demand ultra-low latency, real-time analytics, and adaptive intelligence—ultimately enhancing user experience and operational efficiency.

Leveraging Context-awareness to Support Intelligent Decisions in the IoT-Edge-Cloud Computing Continuum

The Internet of Things (IoT) is transforming distributed computing by connecting diverse sensing devices not only to each other but also to cloud infrastructures. This decentralisation is enabling a new generation of intelligent applications and distributed data centers. However, latency-sensitive applications—such as those found in Smart Grids—require faster, more localised processing due to the computational demands and security concerns involved.

To address this, Edge and Fog computing have emerged as viable solutions, allowing data processing to occur closer to the source. In such environments, orchestrating AI-driven analytics and intelligent services requires automated, context-aware systems that can dynamically determine where computation should take place—whether at the edge, in the cloud, or on specific edge devices. Context-awareness also plays a key role in deciding optimal data storage locations for efficient processing.

This research focuses on engineering seamless, intelligent services that span from Edge to Cloud and vice versa, using context-awareness to minimise latency. Practical applications include:

By enabling intelligent decision-making closer to where data is generated, the proposed system enhances responsiveness, reliability, and efficiency in Smart Grid operations—ultimately improving service delivery and user experience.

Trust-based Intelligent Service recommendations in the edge.

Research Collaboration

He's actively seeking research collaborators.

Prospective PhD students are welcome to contact him. 

If you are interested in working with him as a postgrad student(Master's or PhD), drop him an email. a.otebolaku{at}shu.ac.uk