Journal Articles

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5G RedCap enhancement towards Improved Cellular LPWAN/5G-IoT for Smart Cities and Industrial IoT using genetic algorithm-based neural network
(2024-06) Ogbodo, EU; Abu-Mahfouz, Adnan MI; Kurien, AA
Abstract: Background: The low power wide area networks (LPWANs) technologies significantly impact numerous IoT deployment use cases, especially in the smart cities' scenario. LPWAN is used to support low data rate use cases. Unfortunately, medium data rate (up to 50 Mbps and more) IoT applications are not operational by LPWAN. Hence, a 5G reduced capability (RedCap) new ra dio (NR) device was provided to address this limitation. However, the 5G RedCap suffers a cover age loss due to the reduction of the physical layer complexity of the 5G legacy user equipment (UE). Therefore, 5G RedCap enhancements require coverage loss compensation. Objective: This paper aims to improve the performance of 5G RedCap in terms of coverage, energy efficiency, and throughput for Smart Cities and Industrial IoT (IIoT) using a genetic algorithm based neural network (GA-NN) model. Method: The method involves using a GA-NN model for a two-fold enhancement of the 5G Red Cap. This enhancement includes a specialized-enhancement RedCap (se-RedCap) for low data rates and an enhanced RedCap (eRedCap) for high data rates (up to 300 Mbps) support. The GA NN model has been implemented and assessed in MATLAB Global Optimization and 5G Toolbox. Furthermore, an introduced and modified parametric rectified linear unit (ePReLU) activation func tion fA evaluates the final summation data parameters trained with a specific threshold for the best performance. Results: The numerical results confirm that the specialized-enhancement RedCap (se-RedCap) and enhanced RedCap (eRedCap) outperform legacy cellular LPWANs and conventional RedCap when considering coverage, energy efficiency, and throughput. Conclusion: This paper successfully covers two types of usage scenarios: the very low data rate typically seen in LPWAN and the high data rate of up to 300 Mbps, which is not yet compatible with the existing RedCap system. As a result, the GA-NN model creates se-RedCap and eRedCap, providing support for these two scenarios, respectively.
A comparative analysis between isotropic and optimization binary diagram influenced by nickel-doping on the LiMn2O4 spinel type
(2024-12) Malatji, K; Maphanga, Rapela R; Ngoepe, P
The study explores the Ni-rich transition metal as a dopant on the Mn (16d) site of the LiMn1-xNixO4 cathode material. However, lithium manganese oxide is hindered by a limited cycle life, caused by the dissolution of manganese into the electrolyte during electrochemical cycling. Doping lithium-ion battery materials with TM generally enhances their ability to maintain electrochemical capacity over many cycles without compromising the initial reversible capacity at room temperature. This study utilised the genetic algorithm approach and first-principles calculations to investigate the LiMn2O4 spinel structure. This method identified the most stable phases by simplifying atomic interactions in the LiMn2O4-LiNi2O4 system using a series of clusters, which facilitated the corresponding thermodynamic analysis. The comparison and exploration between the full optimized and volume optimized binary calculation was to observe the % difference of the two binary diagrams yielding 13 meV and 1.1 meV, respectively. The two binary ground state diagrams depict the miscibility constituent’s behaviour, producing new phases (62 and 77) with different coordinates. The study revealed the five most stable phases at the ground state line, one of which is the opposite (LiMn0.5Ni1.5O4) at X = 0.75 of the high-potential cathode material LiMn0.5Ni1.5O4.
A DFT study of the ternary metal chalcogenides (XAlS2) materials for photovoltaic and high-temperature applications
(2024) Maphanga, Rapela R; Santosh, MS; Rugute, E; Dima, Ratshilumela S; Mondal, P; Maleka, Prettier M; Tshwane, David M; Maluta, E; Rtimif, S
This work employs density functional theory (DFT) to investigate the structural, electronic, and optical properties of XAlS2 (X =Li, Na, K, Rb, and Cs) nanomaterials for potential use in photovoltaic applications. A comprehensive first-principles analysis has been conducted using GGA-PBE, GGA-PBEsol, and LDA functionals to examine LiAlS2, NaAlS2, KAlS2, RbAlS2, and CsAlS2. The findings reveal distinctive band gaps within this set of materials, with LiAlS2 and NaAlS2 exhibiting indirect band gaps and KAlS2, RbAlS2, and CsAlS2 possessing direct band gaps. Analyzing the partial density of states indicates that the valence band predominantly arises from S-3p and Al-3p orbitals, showcasing covalent bonding through hybridization. Furthermore, the examination of the optical properties of XAlS2 materials suggests their notable light absorption in the ultraviolet range, positioning them as promising candidates for photovoltaic applications. Additionally, the lattice thermal conductivity of two dynamically stable systems has been investigated and their thermoelectric properties have been calculated. Notably, a dimensionless figure of merit of 2.78 for LiAlS2 has been identified, marking it as a strong contender for high-temperature thermoelectric applications.
A proposed bitcoin blockchain investigation methodology: Based on a case study approach
(2025-01) Botha, Johannes G; Singh, Kreaan D; Leenen, L
Criminal investigations involving cryptocurrencies are still premature with no standard investigative process to follow. This paper proposes a high-level methodology using open-source and analysed data to perform such investigations. It focuses on situations where Bitcoin is involved, but where other similar blockchains are concerned, the technical investigator should apply this methodology only after careful consideration. A case study approach is used to illustrate a cryptocurrency scamming platform, a giveaway scam, and divorce fraud. In all the cases, one needs to follow or trace the funds on the blockchain, referred to as on-chain analysis. The end goal of on-chain analysis is to find a destination address linked to identifiable information obtained from open-source data platforms—such as websites, social media, or a cryptocurrency exchange. Law enforcement can then be engaged to instruct the exchange to reveal all personal and transactional information linked to the address through a subpoena. A successful investigation will result in criminal prosecution and a potential recovery of funds. To maintain familiar investigation processes, the researchers looked at traditional (or non-technical) as well as technical investigation techniques.
A review of machine learning techniques for optical wireless communication in intelligent transport systems
(2024-11) Sefako, T; Yang, F; Song, J; Balmahoon, Reevana; Cheng, L
Intelligent Transport Systems (ITS) are crucial for safety, efficiency, and reduced congestion in transportation. They require efficient, secure, high-speed communication. Radio Frequency (RF) technologies like Fifth Generation (5G), Beyond 5G (B5G), and Sixth Generation (6G) are promising, but spectrum scarcity mandates coexistence with Optical Wireless Communication (OWC) networks, which offer high data rates and security, forming a strong foundation for hybrid RF/OWC applications in ITS. In this paper, we delve into the application of Machine Learning (ML) to enhance data communications within OWC systems in ITS. We commence by conducting an in-depth examination of the data communication prerequisites and the associated challenges within the ITS domain. Subsequently, we elucidate the compelling rationale behind the convergence of heterogeneous RF technologies with OWC for data communications in ITS scenarios. Our investigation then pivots towards elucidating the indispensable role played by ML in optimizing data communications via OWC within ITS. To provide a comprehensive perspective, we systematically evaluate and compare a spectrum of ML methodologies employed in OWC ITS data communications. As a culmination of our study, we proffer a set of valuable recommendations and illuminate promising avenues for future research endeavors that warrant further exploration within this critical intersection of ML, OWC, and ITS data communications.
A survey on NB-IoT random access: Approaches for uplink radio access network congestion management
(2024-06) Liyambo, L; Hancke, G; Abu-Mahfouz, Adnan MI
Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for enabling reliable communication among low-power, and low cost devices present in massive machine-type communications (mMTC). In NB-IoT, random access (RA) is implemented in the medium access control (MAC) layer to resolve access contention among massive IoT devices. Efficient network access techniques are required to effectively solve the massive access issues in NB-IoT, guaranteeing increased throughput and high spectrum utilization. In this paper, we present a comprehensive overview of NB-IoT towards supporting mMTC, with focus on the NB-IoT coexistence with 5G, as well the design challenges and requirements of RA in NB-IoT. Moreover, available literature is reviewed to highlight the RA congestion control schemes proposed during the past few years to alleviate RA collisions. While existing RA approaches mainly focus on conventional contention-based techniques for performing RA, intelligent learning based and grant-free Non-Orthogonal Multiple Access (NOMA) have been identified as a potential candidates to increase the transmission efficiency of mMTC applications.
A two-tailed pricing scheme for optimal EV charging scheduling using multiobjective reinforcement learning
(2024-03) Adetunji, KE; Hofsajer, IW; Abu-Mahfouz, Adnan MI; Cheng, L
Electric vehicles (EVs) are crucial to the reduction of carbon emissions. However, their charging poses a threat to power system networks. Hence, EV charging control strategies are developed to curb this challenge, using charging prices to incentivize EV drivers to choose EV charging stations (EVCS) favourable to the grid's stability. The challenge of this strategy is the likelihood of EV drivers accepting EVCS suggestions. To increase the probability of accepting EVCS suggestions, we introduce a two-tailed incentive pricing (TTIP) scheme in an EV charging coordination model, where incentives are offered as charging prices and parking time. We formalized the EV charging problem as a multiobjective Markov decision process and proposed a deep deterministic policy gradient (DDPG) to solve it. To tackle the challenge of continuous action space that leads to the dimensionality curse, the proposed DDPG models the action space using a metaheuristic-based technique. The proposed scheme implements a multiple reward system to generate Pareto optimal solutions and a decision-making technique to choose the compromise reward. Using real-world electricity prices and the IEEE 33-bus distribution network, numerical simulations show that our proposed TTIP scheme yields an average of 18% improvement in grid stability than the sustainable policy following, random, and price-greedy algorithms. It also improves the EV charging profit margins by an average of 28%.
Accessibility, affordability, and equity in long-term spatial planning: Perspectives from a developing country
(2022-05) Van Heerden, Quintin; Karsten, Carike; Holloway, Jennifer P; Petzer, Engela; Burger, Paul AD; Mans, Gerbrand G
City planners attempt to create more equitable spaces by providing and improving access to benefits of living in cities, especially for previously disadvantaged urban communities. To this extent, evidence-based decision making is required to adequately plan for and improve accessibility to several types of facilities. Accessibility studies in literature focus mostly on one element, which is sufficient when presenting methodological advancements, but it is limiting when providing decision support to city planners. This paper argues that these measurements should be expanded and there is a need for a nuanced view on accessibility for improved urban planning practices. Such a view is presented by simultaneously considering various categories of supply (employment, housing, transportation, health, education, police), multiple modes of transport (walking, private vehicle, numerous transit modes), two cost thresholds (distance-based and monetary cost), level of access (percentage of facilities that can be reached), while distinguishing between the socio-economic profiles of regions in the city on the demand side. This improves the understanding of affordability and equity in the study of accessibility. Furthermore, this paper expands two categories (education and housing) to explain the influence of capacity on accessibility and equity. Lastly, it couples a land-use model to some of the accessibility measures to show the usefulness of using such a combination in long-term spatial planning and what the effects will be without government intervention, again contributing to the understanding of, and planning for, more inclusive and equitable cities.
Adaptability of assistive mobility devices and the role of the internet of medical things: Comprehensive review
(2021-11) Oladele, DA; Markus, ED; Abu-Mahfouz, Adnan MI
Background: With the projected upsurge in the percentage of people with some form of disability, there has been a significant increase in the need for assistive mobility devices. However, for mobility aids to be effective, such devices should be adapted to the user's needs. This can be achieved by improving the confidence of the acquired information (interaction between the user, the environment, and the device) following design specifications. Therefore, there is a need for literature review on the adaptability of assistive mobility devices. Objective: In this study, we aim to review the adaptability of assistive mobility devices and the role of the internet of medical things in terms of the acquired information for assistive mobility devices. We review internet-enabled assistive mobility technologies and non-internet of things (IoT) assistive mobility devices. These technologies will provide awareness of the status of adaptive mobility technology and serve as a source and reference regarding information to health care professionals and researchers. Methods: We performed a literature review search on the following databases of academic references and journals: Google Scholar, ScienceDirect, Institute of Electrical and Electronics Engineers, Springer, and websites of assistive mobility and foundations presenting studies on assistive mobility found through a generic Google search (including the World Health Organization website). The following keywords were used: assistive mobility OR assistive robots, assistive mobility devices, internet-enabled assistive mobility technologies, IoT Framework OR IoT Architecture AND for Healthcare, assisted navigation OR autonomous navigation, mobility AND aids OR devices, adaptability of assistive technology, adaptive mobility devices, pattern recognition, autonomous navigational systems, human-robot interfaces, motor rehabilitation devices, perception, and ambient assisted living. Results: We identified 13,286 results (excluding titles that were not relevant to this study). Then, through a narrative review, we selected 189 potential studies (189/13,286, 1.42%) from the existing literature on the adaptability of assistive mobility devices and IoT frameworks for assistive mobility and conducted a critical analysis. Of the 189 potential studies, 82 (43.4%) were selected for analysis after meeting the inclusion criteria. On the basis of the type of technologies presented in the reviewed articles, we proposed a categorization of the adaptability of smart assistive mobility devices in terms of their interaction with the user (user system interface), perception techniques, and communication and sensing frameworks. Conclusions: We discussed notable limitations of the reviewed literature studies. The findings revealed that an improvement in the adaptation of assistive mobility systems would require a reduction in training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical for achieving real-world testing requirements. Finally, the trade-off between cost and performance should be considered in the commercialization of these devices.
Adaptive resource allocation and mode switching for D2D networks with imperfect CSI in AGV-based factory automation
(2024-12) Gbadamosi, SA; Hancke, GP; Abu-Mahfouz, Adnan MI
In industrial factory automation and control system, reliable communication for automated guided vehicles (AGVs) in dynamic, interference laden factory settings are essential particularly for real-time operations. Device-to-device (D2D) technology can enhance industrial network performance by offloading traffic and improving resource utilization. However, deploying D2D-enabled networks presents challenges such as interference control and imperfect channel state information (ICSI). In this paper, we investigate an adaptive resource allocation and mode switching strategy (ARAMS) in D2D-enabled industrial small cell (SC) networks with ICSI to maximize the system throughput and address reuse interference for AGVs. The ARAMS scheme integrates mode switching (MS), channel-quality factor (CQF), and power control (PC) within a bi-phasic resource-sharing (RS) algorithm to lower the computational complexity. In the initial phase, the operational mode for each D2D user (DU) per cell is adaptively selected based on the channel gain ratio (CGR). Subsequently, it computes the CQF for each cell with a reuse DU to identify an optimal reuse partner. The final phase employs the Lagrangian dual decomposition method to decide the DU's and industrial cellular users (CUs) optimum distributed power to maximize the system throughput under the interference constraints. The numerical results show that as channel estimation error variance (CEEV) increases, the ARAMS scheme consistently outperforms other approaches in maximizing system throughput, except for the AIMS scheme.
Adsorption of NH3 and NO2 molecules on sn-doped and undoped ZnO (101) surfaces using density functional theory
(2022) Dima, Ratshilumela S; Tshwane, David M; Shingange, Katekani; Modiba, Rosinah; Maluta, NE; Maphanga, Rapela R
The adsorption and interaction mechanisms of gaseous molecules on ZnO surfaces have received considerable attention because of their technological applications in gas sensing. The adsorption behavior of NH3 and NO2 molecules on undoped and Sn-doped ZnO (101) surfaces was investigated using density functional theory. The current findings revealed that both molecules adsorb via chemisorption rather than physisorption, with all the adsorption energy values found to be negative. The calculated adsorption energy revealed that the adsorption of the NH3 molecule on the bare ZnO surface is more energetically favorable than the adsorption of the NO2 molecule. However, a stable adsorption configuration was discovered for the NO2 molecule on the surface of the Sn-doped ZnO surface. Furthermore, the adsorption on the undoped surface increased the work function, while the adsorption on the doped surface decreased. The charge density redistribution showed charge accumulation and depletion on both adsorbent and adsorbate. In addition, the density of states and band structures were studied to investigate the electronic behavior of NH3 and NO2 molecules adsorbed on undoped and Sn-doped ZnO (101) surfaces.
Affordable connectivity and digital entrepreneurial ecosystem for rural Africa
(2021-09) Mekuria, Fisseha; Mzyece, M; Mfupe, Luzango P; Odusola, A
Innovation on sustainable ICT technologies to realize affordable broadband connectivity for rural and underserved communities is a crucial component of the effort to achieve the aim of "leaving no one behind by 2030" as championed by the United Nations. Digital connectivity and the creation of a digital entrepreneurial rural ecosystem (DERE) are two interconnected interventions necessary to achieve digital inclusion with rural communities as the main target. This paper defines the ecosystem components for the DERE, which include affordable broadband, sustainable business models and co-creation of relevant ICT services involving beneficiary rural communities. This framework presents a proof of concept on rural SMEs-driven digital inclusion being implemented at four sites in South Africa.
Amplitude quantization method for autonomous threshold estimation in self-reconfigurable cognitive radio systems
(2021-02) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, G
Self-adaptive threshold adjustment algorithms (SATAs) are required to reconfigure their parameters autonomously (i.e. to achieve self-parameter adjustment) at runtime and during online use for effective signal detection in cognitive radio (CR) applications. In this regard, a CR system embedded with the functionality of a SATA is termed a self-reconfigurable CR system. However, SATAs are challenging to develop owing to a lack of methods for self-parameter adjustment. Thus, a plausible approach towards realizing a functional SATA may involve developing effective non-parametric methods, which are often pliable to achieve self-parameter adjustment since they are distribution-free methods. In this article, we introduce such a method termed the non-parametric amplitude quantization method (NPAQM) designed to improve primary user signal detection in CR without requiring its parameters to be manually fine-tuned. The NPAQM works by quantizing the amplitude of an input signal and then evaluating each quantized value based on the principle of discriminant analysis. Then, the algorithm searches for an effective threshold value that maximally separates noise from signal elements in the input signal sample. Further, we propose a new heuristic, which is an algorithm designed based on a new corollary derived from the Otsu’s algorithm towards improving the NPAQM’s performance under noise-only regimes. We applied our method to the case of the energy detector and compared the NPAQM with other autonomous methods. We show that the NPAQM provides improved performance as against known methods, particularly in terms of maintaining a low probability of false alarm under different test conditions.
Analysis of IoT-based vehicle anti-theft security
(2021-11) Thamoethata, K; Isong, B; Dladlu, N; Abu-Mahfouz, Adnan MI
A vehicle anti-theft system is a security system that prevents authorized use of a vehicle and its theft. Vehicle theft and hijacking are skyrocketing on daily basis despite the swift technological advancements the world is witnessing. Several vehicle anti-theft systems have been proposed, developed, and deployed using several technologies such as Internet of Things based, biometric-based or hybrid. Therefore, this paper performed the analysis of some of the existing systems to identify the solutions offered, technologies utilized, limitations and provide research directions for improvements. We considered 11 papers and the findings obtained revealed the existence of several vehicle anti-theft approaches employing common technologies and approaches to protect vehicles from theft and hijacking. However, the techniques employed are prone to manipulation and destruction thereby not preventing or reducing vehicle theft since the systems are embedded in the vehicle. Consequently, there is the need to design or develop a vehicle anti-theft and monitoring system that is not a component of the vehicle. This could go a long way to reduce the rate of vehicle theft in society.
Analysis of uniformity in steering capability of an ESPAR antenna
(2022-11) Ebrahim, Rozeena; Mthethwa, Nosipho B; Lysko, Albert A
This paper looks at design considerations for an Electronically Steerable Parasitic Array Radiator (ESPAR) antenna suitable for inexpensive large-scale wireless network technologies. This can help address the demand for a cheaper, simpler, and low power alternative to fully adaptive antennas. We use simulations to consider the effect of the number of elements on the beam-forming and steering capabilities of an ESPAR antenna with a single ring of parasitic elements. Our results validated well against published results and provide the best possible gain over the parasitic element loads considered. It was also found that at least three parasitic elements are required to achieve a reasonably high maximum gain and deep nulls. Based on our results, a 10- element ESPAR offers the most continuous and uniform steering. In addition to smart low power applications, this advantage may be translated into improving the accuracy of triangulation for positioning, localisation, and navigation applications with ESPAR antennas.
Aquaculture and fisheries decision support tool
(2023-05) Smith, Marié E; Vhengani, Lufuno M
This presentation speaks to the challenges with environmental threats such as Harmful Algal Blooms (HABs) and ocean heatwaves that have the potential to negatively affect natural and farmed marine resources. Also, monitoring these events across the large spatial scales of South African coastal waters is problematic.
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.
Artificial intelligence-driven intrusion detection in software-defined wireless sensor networks: Towards secure IoT-enabled healthcare systems
(2022-04) Umba, SMW; Abu-Mahfouz, Adnan MI; Ramotsoela, D
Wireless Sensor Networks (WSNs) are increasingly deployed in Internet of Things (IoT) systems for applications such as smart transportation, telemedicine, smart health monitoring and fall detection systems for the elderly people. Given that huge amount of data, vital and critical information can be exchanged between the different parts of a WSN, good management and protection schemes are needed to ensure an efficient and secure operation of the WSN. To ensure an efficient management of WSNs, the Software-Defined Wireless Sensor Network (SDWSN) paradigm has been recently introduced in the literature. In the same vein, Intrusion Detection Systems, have been used in the literature to safeguard the security of SDWSN-based IoTs. In this paper, three popular Artificial Intelligence techniques (Decision Tree, Naïve Bayes, and Deep Artificial Neural Network) are trained to be deployed as anomaly detectors in IDSs. It is shown that an IDS using the Decision Tree-based anomaly detector yields the best performances metrics both in the binary classification and in the multinomial classification. Additionally, it was found that an IDS using the Naïve Bayes-based anomaly detector was only adapted for binary classification of intrusions in low memory capacity SDWSN-based IoT (e.g., wearable fitness tracker). Moreover, new state-of-the-art accuracy (binary classification) and F-scores (multinomial classification) were achieved by introducing an end-to-end feature engineering scheme aimed at obtaining 118 features from the 41 features of the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset. The state-of-the-art accuracy was pushed to 0.999777 using the Decision Tree-based anomaly detector. Finally, it was found that the Deep Artificial Neural Network should be expected to become the next default anomaly detector in the light of its current performance metrics and the increasing abundance of training data.
Assessing information security behaviour: A self-determination theory perspective
(2021-10) Gangire, Y; Da Veiga, A; Herselman, Martha E
This paper outlines the development of a validated questionnaire for assessing information security behaviour. The purpose of this paper is to present data from the questionnaire validation process and the quantitative study results.
Augmented and mixed reality based decision support tool for the integrated resource plan
(2021-10) Govender, Devashen; Moodley, Jayandren; Balmahoon, Reevana
In today’s era, enormous amounts of data are generated and the means to visualize this data is becoming a challenge. Data visualization is an important support tool that allows one to make informed decisions. This paper attempts to provide a possible solution to this major challenge. The solution presented is a data analytic tool capable of visualizing complex data by leveraging the fourth industrial revolution technologies, augmented and mixed reality. Using the disruptive nature of these technologies, it could provide an application that is more intuitive and immersive to the end-user. Moreover, we focus the solution in the energy domain by exploring methods in enhancing energy data visualization to support decision-making of South Africa’s Integrated Resource Plan (IRP).

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