Research Publications/Outputs

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An architecture to negotiate and monitor energy exchanges in the smart microgrid
(2022-08) Smith, Andrew C
Traditional electrical power sources and their long distribution networks can no longer cope with the ever-increasing need for energy. Distributed energy generation in close geographical proximity to the consumption point is an alternative approach to energy provisioning. The complexities introduced by this approach require an advanced management system. The smart microgrid addresses this need. A second need identified in developing regions is for a management system that is both affordable and non-proprietary. This paper presents a smart microgrid architecture, based on open-source platforms, that addresses these needs. The architecture is explained by means of a use case. A database design is given with tables to reflect the contracts and associated energy exchanged between producers, consumers, and energy store devices.
AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset
(2022) Onumanyi, Adeiza J; Molokomme, Daisy N; Isaac, Sherrin J; Abu-Mahfouz, Adnan MI
The elbow technique is a well-known method for estimating the number of clusters required as a starting parameter in the K-means algorithm and certain other unsupervised machine-learning algorithms. However, due to the graphical output nature of the method, human assessment is necessary to determine the location of the elbow and, consequently, the number of data clusters. This article presents a simple method for estimating the elbow point, thus, enabling the K-means algorithm to be readily automated. First, the elbow-based graph is normalized using the graph’s minimum and maximum values along the ordinate and abscissa coordinates. Then, the distance between each point on the graph to the minimum (i.e., the origin) and maximum reference points, and the “heel” of the graph are calculated. The estimated elbow location is, thus, the point that maximizes the ratio of these distances, which corresponds to an approximate number of clusters in the dataset. We demonstrate that the strategy is effective, stable, and adaptable over different types of datasets characterized by small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. We provide the clustering community with a description of the method and present comparative results against other well-known methods in the prior state of the art.
A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations
(2023-10) Bello-Salau, H; Onumanyi, Adeiza J; Adebiyi, RF; Adekale, AD; Bello, RS; Ajayi, O
Road infrastructure is essential to national security and growth. Potholes on the road surface causes accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. Analysis shows that deep learning computer vision-based algorithms are most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. Meanwhile, the sensor-based solutions are cost-effective and can be utilized in developing nations for potholes detection.
A critical appraisal of various implementation approaches for realtime pothole anomaly detection: Towards safer roads in developing nations
(2023-10) Bello-Salau, H; Onumanyi, Adeiza J; Adebiyi, RF; Adekale, AD; Bello, RS; Ajayi, O
Road infrastructure is essential to national security and growth. Potholes on the road sur- face causes accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. Analysis shows that deep learning computer vision-based algorithms are most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. While, the sensor-based solutions are cost-effective and can be utilized in developing nations for potholes detection.
A delegated proof of proximity scheme for industrial internet of things consensus
(2020-10) Ledwaba, Lehlogonolo PI; Hancke, GP; Mitrokotsa, A; Isaac, Sherrin J
Recently, work with Distributed Ledger Technologies (DLTs) has focussed on leveraging the decentralised, immutable ledger for use outside of cryptocurrency. One industry poised to benefit from DLTs is the Industrial Internet of Things (IIoT); as the inherent cryptographic mechanisms and alternative trust model make DLTs an attractive solution for distributed networks. Existing DLTs are unsuitable for the IIoT, owing to the large computational and energy requirements for consensus operations and the slow throughput of validated blocks. With limited processing, energy and storage resources and a deadline sensitive operational environment, DLTs in their current state could serve to introduce intolerable latency into IIoT processes and deplete constrained, device resources. Designed for the IIoT context, and based off Delegated Proof of Stake, this work serves to introduce a new consensus mechanism called Delegated Proof of Proximity (DPoP). Using existing location discovery processes, nodes in close proximity to a sensor event are elected as delegates; whose role is to handle consensus and block generation. In using information already known to IIoT devices, DPoP aims to reduce wasted effort, improve throughput by limiting the number of nodes required for consensus operations and improve scalability and flexibility of DLT solutions as the IIoT network continues to grow.
Edge intelligence in Smart Grids: A survey on architectures, offloading models, cyber security measures, and challenges
(2022-08) Molokomme, DN; Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI
The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.
Efficient synthesis of activated carbon (AC) from biomass for catalytic systems: A green and sustainable approach
(2021-04) Timothy, A; Akande, Amos A; Odoh, CK; Philip, M; Fidelis, TT; Amos, PI; Banjoko, OO
Tremendous efforts in developing sustainable processes for integrated production of value-added products/chemicals and fuels in biorefineries increase through delicate designs towards sustainability. This review focuses on the synthesis of activated carbon (AC) from renewable precursors and its utilisation in catalytic systems for a gentle and sustainable approach. Owing to the unique shape and porosity-controlled properties, these carbon materials could offer strong, active phase-support interactions, leading to unusual catalytic activities and selectivity in biomass upgrading. Porous carbons have been developed and used as heterogeneous solid catalysts in fine chemical and biofuels synthesis as a sustainable and economical alternative over homogeneous catalytic systems. This review revealed the AC's significance and potential as solid catalysts/supports in renewable feedstocks' valorisation. The literature showed that bio-derived activated carbon could be a promising and sustainable solid catalyst or support for producing biofuels/ value-added products with appreciable BET surface area (750 m2/g) and total pore volume (0.37 cm3/g). However, the surface area and pore volume vary with the treatment/nature of cellulose used as the precursor for AC production. Finally, the utilisation of these renewable feedstocks/waste streams presents us with the avenues to realise sustainable synthesis through green process and design for a sustainable future.
eHealth: A survey of architectures, developments in mHealth, security concerns and solutions
(2022-10) Alenoghena, CO; Onumanyi, Adeiza J; Ohize, HO; Adejo, AO; Oligbi, M; Ali, SI; Okoh, SA
The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.
Experimental investigation into deploying a Wi-Fi6 mesh system for underground gold and platinum mine stopes
(2024-08) Chetty, Brenton L; Walingo, TM; Kruger, Carel P; Isaac, Sherrin J
Stopes suffer from unreliable wireless communication due to their harsh environment. There is a lack of confidence within industry regarding the effectiveness of existing solutions in providing reliable high-bandwidth performance in hard rock stopes. This work proposes that Wi-Fi6 is a good candidate for reliable high-bandwidth communications in underground hard rock stopes. Experiments in a tunnel and mine stope were conducted to evaluate the performance of Wi-Fi6 in terms of latency, jitter, and throughput. Different criteria, such as multi-hop systems, varying multipath, mesh routing protocols, and frequencies at different bandwidths, were used to evaluate performance. The results show that Wi-Fi6 performance is greater in stopes compared to tunnels. Signal quality evaluations were conducted using the Asus RT-AX53U running OpenWrt, and an additional experiment was conducted on the nrf7002dk running Zephyr OS to evaluate the power consumption of Wi-Fi6 against the industry standard for low-powered wireless communications, IEEE 802.15.4. Wi-Fi6 was found to be more power-efficient than IEEE 802.15.4 for Mbps communications. These experiments highlight the signal robustness of Wi-Fi6 in stope environments and also highlights its low-powered nature. This work also highlights the performance of the two most widely used open-source mesh routing protocols for Wi-Fi.
An experimental investigation into high bandwidth wireless communication standards for the underground mine stope
(2023-09) Chetty, Brenton L; Isaac, Sherrin J; Walingo, T
There are many challenges associated with developing a feasible wireless communication system for the harsh underground mining environment. Radio signals suffer from strong attenuations due to the excessive reflections, diffractions, scatterings, and multipath caused by the irregular surface characteristics of the stope. This necessitates the need for a robust underground mine wireless communication network that can withstand these phenomena. The network will still need to provide enough bandwidth for productivity enhancement applications as well as maintain low latency alongside robustness for critical mine safety applications. Coded OFDM (COFDM) was identified as the wireless standard for dealing with the harsh mining environment. However, due to the standard not being mainstream, the implementation of such a system will be extremely expensive. This research identifies the possibility of utilising Wi-Fi6 (802.11ax) which utilizes Orthogonal Frequency Division Multiple Access (OFDMA) as a more cost-effective alternative as compared to COFDM. Experimental investigations for comparing COFDM and Wi-Fi6 have been conducted in terms of latency, jitter and throughput measurements in several different topology and configurations. The uncoded Wi-Fi6 was found to be quite capable for high bandwidth productivity enhancement applications for the stope at an economical price point as compared to the more robust COFDM. Hence the more expensive COFDM should only be deployed for the critical mine safety applications.
Familiar household items as program objects
(2021-07) Smith, Andrew C
The English language is but one of many languages globally in use, yet it seems to dominate the world of automation in its application to computer programming. Our research considers a programming environment in which the written word is not relevant; instead of text, we consider the use of physical objects to represent simple computer programs. This paper presents a reality in which artefacts, based on culturally significant objects, are chosen and arranged by the layperson in order to control the behaviour of light bulbs in a domestic environment. This work incorporates aspects of Gestalt theory and visual perception theory. We give an overview of prior work and conclude with an example.
Gas sensing materials roadmap
(2021-06) Wang, H; Ma, J; Zhang, J; Feng, Y; Vijjapu, MT; Yuvaraja, S; Surya, SG; Salama, KN; Tshabalala, ZP; Akande, Amos A
Gas sensor technology is widely utilized in various areas ranging from home security, environment and air pollution, to industrial production. It also hold great promise in non-invasive exhaled breath detection and an essential device in future internet of things. The past decade has witnessed giant advance in both fundamental research and industrial development of gas sensors, yet current efforts are being explored to achieve better selectivity, higher sensitivity and lower power consumption. The sensing layer in gas sensors have attracted dominant attention in the past research. In addition to the conventional metal oxide semiconductors, emerging nanocomposites and graphene-like two-dimensional materials also have drawn considerable research interest. This inspires us to organize this comprehensive 2020 gas sensing materials roadmap to discuss the current status, state-of-the-art progress, and present and future challenges in various materials that is potentially useful for gas sensors.
Interconnected smart transactive microgrids—A survey on trading, energy management systems, and optimisation approaches
(2024-03) Machele, IL; Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Kurien, AM
The deployment of isolated microgrids has witnessed exponential growth globally, especially in the light of prevailing challenges faced by many larger power grids. However, these isolated microgrids remain separate entities, thus limiting their potential to significantly impact and improve the stability, efficiency, and reliability of the broader electrical power system. Thus, to address this gap, the concept of interconnected smart transactive microgrids (ISTMGs) has arisen, facilitating the interconnection of these isolated microgrids, each with its unique attributes aimed at enhancing the performance of the broader power grid system. Furthermore, ISTMGs are expected to create more robust and resilient energy networks that enable innovative and efficient mechanisms for energy trading and sharing between individual microgrids and the centralized power grid. This paradigm shift has sparked a surge in research aimed at developing effective ISTMG networks and mechanisms. Thus, in this paper, we present a review of the current state-of-the-art in ISTMGs with a focus on energy trading, energy management systems (EMS), and optimization techniques for effective energy management in ISTMGs. We discuss various types of trading, architectures, platforms, and stakeholders involved in ISTMGs. We proceed to elucidate the suitable applications of EMS within such ISTMG frameworks, emphasizing its utility in various domains. This includes an examination of optimization tools and methodologies for deploying EMS in ISTMGs. Subsequently, we conduct an analysis of current techniques and their constraints, and delineate prospects for future research to advance the establishment and utilization of ISTMGs.
Investigating distance bounding for delegated proof-of-proximity consensus within IIoT
(2022-06) Ledwaba, Lehlogonolo PI; Hancke, GP; Isaac, Sherrin J
With limited processing, energy and storage along with a deadline sensitive operational environment, the combination of the Industrial IoT (IIoT) with distributed ledger technologies (DLTs) could serve to introduce intolerable latency into network processes; counteracting the potential advantages that come from combining the two technologies. In an effort to improve the compatibility of DLTs for the industrial informatics context, the authors developed a lightweight consensus for IIoT environments based off delegated proof of stake (DPoS), called Delegated Proof of Proximity (DPoP), to limit the processing and energy effort required by DLTs. DPoP will, however, require an existing, IIoT neighbour discovery process to facilitate a proof of proximity for the consensus process. Thus, this preliminary work aims to evaluate distance bounding as a possible mechanism for establishing a secure proof of proximity and neighbour discovery between nodes during the DPoP consensus process to improve the scalability and flexibility of DLT solutions, like Ethereum, for IIoT use cases.
Load-driven resource allocation for enhanced interference mitigation in cellular networks
(2021-07) Asaka, OT; Adejo, A; Onumanyi, Adeiza J; Bello-Salau, H; Oluwamotemi, FT
Cellular users are often considered to be uniformly distributed within the communication network for the purposes of simplified analysis. Based on this assumption, the inter-cell interference experienced by users has been handled using soft frequency reuse (SFR) techniques. However, in real networks, the distribution of users in the network regions are not uniform. Therefore, analysis for random deployment of users under SFR is essential for improved accuracy of analysis and better handling of interference. This research presents an SFR algorithm (Load-Driven SFR) that intelligently adjusts resource allocation parameters (base station bandwidth assignment) according to the load distribution in the network. Interference mitigation is enhanced and Load-Driven SFR outperforms several implementations of the standard SFR algorithm using fixed bandwidth allocation, especially for edge user’s SINR (up to 3.2% improvement) and edge user’s Capacity (up to 202% improvement).
A micro grid information architecture with open source components
(2021-12) Smith, Andrew C; Mkhize, Cyprian B; Mahlobo, Boitumelo T; Isaac, Sherrin J
Presented is an information system architecture based on open source platforms to support the successful management of a micro grid. The paper justifies the use of open source platforms in the micro grid context. Also given are the benefits of a micro grid as it relates to distributed energy generation and subsequently the need explained for an information system that keeps a record of all devices and their interconnections, while simultaneously providing access to instantaneous data along various points in the micro grid. Three examples of services made possible by the architecture are given.
Micro nano manufacturing methods for chemical, gas and bio sensors, water purification and energy technologies
(Intechopen, 2020-12) Akande, Amos A; Adeleye, AA; Adenle, AA; Mwakikunga, Bonex W
This chapter reports on the various methods of fabricating and manufacturing micro and nano sensor, membrane and energy devices. Firstly, the characteristic often sought after by scientists and engineers for effective and efficient performance of these technologies were thoroughly discussed in details together with the characterization techniques for evaluating them. Several state-of-the-art fabricating techniques for sensor devices, water and medical based-membranes, solar cells and batteries were also discussed.
Mitigating the rock fall and rockburst risk in South African gold and platinum mines through advanced knowledge of the ore body
(2023-10) Pienaar, M; Durrheim, RJ; Manzi, MSD; Nwaila, GT; Grobler, HCI; Kgarume, Thabang E; Pretorius, Dean D; Van Schoor, Michael; Oberholster, AJ
The Mandela Mining Precinct was launched in 2018 with the goal of modernizing the South African mining industry. It comprises three major initiatives that seek to improve efficiency, health and safety in current mining operations; develop fully mechanized systems to mine narrow tabular ore bodies in hard rock; and develop non-explosive rock-breaking systems. The crosscutting Advanced Orebody Knowledge (AOK) program seeks to develop technologies to characterize the rock mass ahead of mining and identify potentially hazardous geological features. Mining methods, layouts and rock support systems will be adjusted accordingly to mitigate the risk of rock falls and bursts. Technologies include rock drilling, light detection and ranging (LiDAR), ground penetrating radar (GPR), electrical resistance tomography (ERT), and various acoustic, thermographic and seismic techniques. Machine learning methods are being implemented to improve data processing and interpretation. This paper describes the status of the research program at 31 May 2023.
One-dimensional titanate nanotube materials: Heterogeneous solid catalysts for sustainable synthesis of biofuel precursors/value-added chemicals—a review
(2021-09) Adeleye, AT; John, KI; Adeleye, PG; Akande, Amos A; Banjoko, OO
One-dimensional (1D) titanate nanotubes materials (protonated titanate nanotube (HTNT) and sodium titanate nanotube (NaTNT)) have been reported as low-cost and efficient catalytic materials in chemical syntheses for the production of biofuel precursors with interesting catalytic performance exhibited, even better than some commonly used zeolites, H-MOR, H-ß, SO42-/Al2O3, and H-ZSM-5 solid catalysts with environmental benign in focus when compared with homogeneous catalytic materials. This mini-review expressly revealed the significance and potential of using HTNT and NaTNT as sustainable and environmentally benign solid catalysts/supports in various chemical reactions. The critical assessment of biomass valorization and titanate nanostructured materials as catalysts/supports via Green Chemistry approach, #7 (use of renewable feedstocks), #9 (use of catalyst against stoichiometry) and United Nations (UN) Sustainable Development Goals (SDGs), #7 (affordable and clean energy; ensure access to inexpensive, reliable, sustainable, and new energy), is presented as integrated pathways to meet environmental benign technology toward sustainability. Hence, this work follows in the pattern of recent formulated features reported for solid catalysts—‘PYSSVR’ concept, which means P–production cost, Y–yield, S–stability, S–selectivity, V–versatility, and R–reusability.
Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters
(2024-08) Aliyu, HA; Muritala, IO; Bello-Salau, H; Mohammed, S; Onumanyi, Adeiza J; Ajayi, O-O
Diabetes mellitus poses a global health concern, prompting the development of machine learning algorithms designed to construct a model for the accurate classification of patients, enabling precise diagnoses and early-stage treatment. However, the efficacy of classifying diabetes patients through machine learning relies on datasets, often plagued by imbalance, leading to biased classification and inaccurate diagnoses. Previous research attempts, employing techniques like random sampling (under-sampling and oversampling) and the Synthetic Minority Oversampling Technique (SMOTE), have struggled to achieve optimally balanced datasets. Additionally, setting the best parameters for machine learning classifiers remains a challenging task. To address these issues, this research focuses on devising a methodological metaheuristic optimization, a machine learning algorithm tailored for diabetes data balancing, and classifier hyperparameter tuning. Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. The study compares the performance of the proposed metaheuristic data balancer and classifier architecture parameter tuner using classification metrics (F1 score, Average Precision–Recall (APR), AUC, and accuracy). The PSO balanced dataset emerges as the most effective in classifying diabetes, with an Average Percentage Improvement (API) in classification performance metrics: 20.78% accuracy, 16.79% area under the curve for receiver operating characteristics, and a significant 32.78% enhancement in APR. Moreover, the XGBOOST classifier trained with a genetic algorithm demonstrates minimal computational training time for the Centre for Disease Control and Prevention (CDC) diabetes dataset compared to the artificial neural network and random forest classifier. Notably, the imbalanced CDC diabetes dataset exhibits the least APR compared to random under-sampling and the PSO data balancing technique.

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