Journal Articles

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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.
B-CAVE: A robust online time series change point detection algorithm based on the between-class average and variance evaluation approach
(2024) Gupta, A; Onumanyi, Adeiza J; Ahlawat, S; Prasad, Y; Singh, V
Change point detection (CPD) is a valuable technique in time series (TS) analysis, which allows for the automatic detection of abrupt variations within the TS. It is often useful in applications such as fault, anomaly, and intrusion detection systems. However, the inherent unpredictability and fluctuations in many real-time data sources pose a challenge for existing contemporary CPD techniques, leading to inconsistent performance across diverse real-time TS with varying characteristics. To address this challenge, we have developed a novel and robust online CPD algorithm constructed from the principle of discriminant analysis and based upon a newly proposed between-class average and variance evaluation approach, termed B-CAVE. Our B-CAVE algorithm features a unique change point measure, which has only one tunable parameter (i.e. the window size) in its computational process. We have also proposed a new evaluation metric that integrates time delay and the false alarm error towards effectively comparing the performance of different CPD methods in the literature. To validate the effectiveness of our method, we conducted experiments using both synthetic and real datasets, demonstrating the superior performance of the B-CAVE algorithm over other prominent existing techniques.
Blockchain for securing electronic voting systems: a survey of architectures, trends, solutions, and challenges
(2024-09) Ohize, HO; Onumanyi, Adeiza J; Umar, BU; Ajao, LA; Isah,RO; Dogo, EM; Nuhu, BK; Olaniyi, OM; Ambafi, JG; Sheidu, VB; Ibrahim, MM
Electronic voting (e-voting) systems are gaining increasing attention as a means to modernize electoral processes, enhance transparency, and boost voters’ participation. In recent years, significant developments have occurred in the study of e-voting and blockchain technology systems, hence reshaping many electoral systems globally. For example, real-world implementations of blockchain-based e-voting have been explored in various countries, such as Estonia and Switzerland, which demonstrates the potential of blockchain to enhance the security and transparency of elections. Thus, in this paper, we present a survey of the latest trends in the development of e-voting systems, focusing on the integration of blockchain technology as a promising solution to address various concerns in e-voting, including security, transparency, auditability, and voting integrity. This survey is important because existing survey articles do not cover the latest advancements in blockchain technology for e-voting, particularly as it relates to architecture, global trends, and current concerns in the developmental process. Thus, we address this gap by providing an encompassing overview of architectures, developments, concerns, and solutions in e-voting systems based on the use of blockchain technology. Specifically, a concise summary of the information necessary for implementing blockchain-based e-voting solutions is provided. Furthermore, we discuss recent advances in blockchain systems, which aim to enhance scalability and performance in large-scale voting scenarios. We also highlight the fact that the implementation of blockchain-based e-voting systems faces challenges, including cybersecurity risks, resource intensity, and the need for robust infrastructure, which must be addressed to ensure the scalability and reliability of these systems. This survey also points to the ongoing development in the field, highlighting future research directions such as improving the efficiency of blockchain algorithms and integrating advanced cryptographic techniques to further enhance security and trust in e-voting systems. Hence, by analyzing the current state of e-voting systems and blockchain technology, insights have been provided into the opportunities and challenges in the field with opportunities for future research and development efforts aimed at creating more secure, transparent, and inclusive electoral processes.
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.
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.
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).
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.
Performance analysis of machine learning algorithms for energy demand–supply prediction in smart grids
(2022-02) Cebekhulu, Eric; Onumanyi, Adeiza J; Isaac, Sherrin J
The use of machine learning (ML) algorithms for power demand and supply prediction is becoming increasingly popular in smart grid systems. Due to the fact that there exist many simple ML algorithms/models in the literature, the question arises as to whether there is any significant advantage(s) among these different ML algorithms, particularly as it pertains to power demand/supply prediction use cases. Toward answering this question, we examined six well-known ML algorithms for power prediction in smart grid systems, including the artificial neural network, Gaussian regression (GR), k-nearest neighbor, linear regression, random forest, and support vector machine (SVM). First, fairness was ensured by undertaking a thorough hyperparameter tuning exercise of the models under consideration. As a second step, power demand and supply statistics from the Eskom database were selected for day-ahead forecasting purposes. These datasets were based on system hourly demand as well as renewable generation sources. Hence, when their hyperparameters were properly tuned, the results obtained within the boundaries of the datasets utilized showed that there was little/no significant difference in the quantitative and qualitative performance of the different ML algorithms. As compared to photovoltaic (PV) power generation, we observed that these algorithms performed poorly in predicting wind power output. This could be related to the unpredictable wind-generated power obtained within the time range of the datasets employed. Furthermore, while the SVM algorithm achieved the slightly quickest empirical processing time, statistical tests revealed that there was no significant difference in the timing performance of the various algorithms, except for the GR algorithm. As a result, our preliminary findings suggest that using a variety of existing ML algorithms for power demand/supply prediction may not always yield statistically significant comparative prediction results, particularly for sources with regular patterns, such as solar PV or daily consumption rates, provided that the hyperparameters of such algorithms are properly fine tuned.
Smart microgrid energy market: Evaluating distributed ledger technologies for remote and constrained microgrid deployments
(2021-03) Ledwaba, Lehlogonolo PI; Hancke, GP; Isaac, Sherrin J; Venter, HS
The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts towards achieving universal access to electrical energy in South Africa. To try and relieve the burden on the National Grid and still progress electrification activities, the smart microgrid model and secure energy trade paradigm is considered— enabled by the Industrial IoT (IIoT) and distributed ledger technologies (DLTs). Given the high availability requirements of microgrid operations, the limited resources available on IIoT devices and the high processing and energy requirements of DLT operations, this work aims to determine the effect of native DLT algorithms when implemented on IIoT edge device so to assess the suitability of DLTs as a mechanism to establish a secure, energy trading market for the Internet of Energy. Metrics such as the node transaction time, operating temperature, power consumption, processor and memory usage are considered towards determining possible interferences on the edge node operation. In addition, the cost and time required for mining operations associated with the DLT-enabled node are determined in an effort to predict the cost to end users- in terms of fees payable and mobile data costs- as well as predicting the microgrid’s growth and potential blockchain network slowdown.
Telemedicine: A survey of telecommunication technologies, developments, and challenges
(2023-03) Alenoghena, CO; Ohize, HO; Adejo, AO; Onumanyi, Adeiza J; Ohihoin, EE; Balarabe, AI; Okoh, SA; Kolo, E; Alenoghena, B
The concept of telemedicine encompasses the use of information and telecommunication technology to render medical services irrespective of geographical separation between physicians and patients [1]. Telemedicine has been in practice as far back as the 1900s. It covers any form of electronic communication between health workers and patients from a remote location [1,2]. Recently, researchers have focused more on wireless communication technologies for telemedicine to provide effective and reliable health care service delivery from remote location especially during emergencies. Various communication technologies have been proposed and implemented for providing expert medical services to patients without the need for the conventional face-to-face encounters with patients. This has greatly reduced the cost of medical diagnosis and the need to travel long distances in search of professional consultations. Available studies on telemedicine implementations suggest the need for continuous research to address several issues and challenges [3,4]. There is a need to compare relevant studies in the field in order to provide a broad overview of available communication technologies suitable for modern designs as well as to identify the most viable means of practical implementation. This is not to say that telemedicine should completely replace the conventional practice of physical diagnostic and other medical processes, as certain services require physical face-to-face contact. Nonetheless, the deployment of telemedicine could greatly reduce congestion in hospitals, and consequently limit the spread of infectious diseases.
Transactive energy: State-of-the-art in control strategies, architectures, and simulators
(2021-09) Onumanyi, Adeiza J; Isaac, Sherrin J; Kruger, Carel P; Abu-Mahfouz, Adnan MI
The concept of transactive energy (TE) in smart grid systems is gaining increased research attention for its potential to optimize distributed energy resources, improve system reliability, as well as provide a balanced ecosystem for fair economic transaction between prosumers. TE is defined by the GridWise Architecture Council as a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. With control mechanisms being a key part of TE systems, in this article, we discuss the state-of-the-art in TE control strategies, architectures, and relevant simulators for designing, evaluating, and analysing TE systems. Most importantly, existing TE control strategies are examined and discussed via a hierarchical structure comprising four different levels wherein TE control strategies/controllers can be deployed. Architecturewise, we highlight the different types of TE architectures including the centralized, decentralized, distributed, and hierarchical architecture. In terms of existing and potential simulators for designing and evaluating TE models, we discuss and compare notable software across different characteristics of interest. We conclude this article by highlighting the basic components of a typical TE controller and other future research directions spanning across security concerns, privacy issues, communication challenges, simulation and validation demands. As a main contribution, different from existing survey articles, this article presents a synthesis of existing works regarding TE control strategies, architectures, and TE-based simulators for the benefit of the budding researcher whose interest may lie in the study of TE systems.

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