ResearchSpace :: Browsing by Author "Onumanyi, Adeiza J"

Browsing by Author "Onumanyi, Adeiza J"

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Adaptive threshold techniques for cognitive radio-based low power wide area network
(Wiley Online Library, 2020-02) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
Some low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR-LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)-based applications. However, in order to be effective under dynamic sensing conditions, CR-LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR-LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR-LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR-LPWAN systems.
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.
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.
Cognitive radio in low power wide area network for IoT applications: Recent approaches, benefits and challenges
(IEEE, 2019-11) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, the present paper takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoTbased applications are also presented, and the paper closes with a few research challenges and future research directions in this regard. This paper aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications.
A comparative analysis of local and global adaptive threshold estimation techniques for energy detection in cognitive radio
(Elsevier, 2018-08) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
In this paper, we compare local and global adaptive threshold estimation techniques for energy detection in Cognitive Radio (CR). By this comparison we provide a sum-up synopsis on the effective performance range and the operating conditions under which both classes best apply in CR. Representative methods from both classes were implemented and trained using synthesized signals to fine tune each algorithm’s parameter values. Further tests were conducted using real-life signals acquired via a spectrum survey exercise and results were analysed using the probability of detection and the probability of false alarm computed for each algorithm. It is observed that while local based methods may be adept at maintaining a low constant probability of false alarm, they however suffer a grossly low probability of detection over a wide variety of CR spectra. Consequently, we concluded that global adaptive threshold estimation techniques are more suitable for signal detection in CR than their local adaptive thresholding counterparts.
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 cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio
(Springer, 2019-11) Abdullahi, A; Onumanyi, Adeiza J; Zubair, S; Abu-Mahfouz, Adnan MI; Hancke, GP
The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.
A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy
(Elsevier, 2019-08) Oyewobi, SS; Hancke, GP; Abu-Mahfouz, Adnan MI; Onumanyi, Adeiza J
Cognitive radio has emerged as an enabling technology in the realization of a spectrum-efficient and delay-sensitive industrial wireless communication where nodes are capable of responding in real-time. However, particularly for time-critical industrial applications, because of the link-varying channel capacity, the random arrival of a primary user (PU), and the significant delay caused by spectrum handoff (SH), it is challenging to realize a seamless real-time response which results in a quality of service (QoS) degradation. Therefore, the objectives of this paper is to increase spectrum utilization efficiency by allocating channel based on the priority of a user QoS requirements, to reduce SH delay, to minimize latency by preventing avoidable SHs, and to provide real-time response. To achieve an effective spectrum utilization, we proposed an integrated preemptive/non-preemptive priority scheme to allocate channels according to the priority of user QoS requirements. On the other hand, to avoid significant SH delays and substantial latency resulting from random PU arrival, a unified spectrum sensing technique was developed by integrating proactive sensing and the likelihood estimation technique to differentiate between a hidden and a co-existence PU, and to estimate the mean value of the busy and the idle periods of a channel respectively. Similarly, to prevent poor quality channel selection, a channel selection technique that jointly combines a reward system that uses metrics, e.g. interference range, and availability of a common channel to ranks a set of potential target channels, and a cost function that optimizes the probability of selecting the channel with the best characteristics as candidate channels for opportunistic transmission and for handoffs was developed. The simulation results show a significant performance gain of the delay-PritSHS in terms of number of SHs, Latency, as well as throughput for time-critical industrial applications in comparison to other schemes.
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.
An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial Internet of Things
(MDPI, 2019-03) Oyewobi, SS; Hancke, GP; Abu-Mahfouz, Adnan MI; Onumanyi, Adeiza J
The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.
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.
Exploratory analysis of modified deep learning model for potholes data augmentation
(2025-04) Adebiyi, RF; Bello-Salau, H; Onumanyi, Adeiza J; Adebiyi, BH; Adekale, AD; Bello-Salahuddeen, R
A major factor contributing factor resulting to a large proportion of vehicular-related traffic accidents in developing nations is the poor condition of road networks, characterized by potholes, bumps, and other anomalies. Despite efforts by authorities to address these issues, they persist. A new approach involves equipping vehicles with sensors to detect road anomalies, enabling drivers to make informed decisions. Various models using road surface images to detect and classify these anomalies have been proposed, with recent methods leveraging deep learning. The effectiveness of these models depends on the presence of abundant and well-labelled training datasets. To address this need, a modified Deep Denoising Diffusion Probabilistic Model (mDDPM) was proposed, enhancing the U-Net backbone architecture to improve the original DDPM's performance in augmenting pothole images. The mDDPM generates more diverse augmented images, evaluated through subjective and objective assessments, including the Fréchet Inception Distance (FID) score. Experimental results showed that 98% of participants could not distinguish between real and synthetic images, classifying the augmented images as real. Additionally, an FID score of 0.52 indicated that the augmented images closely resemble real pothole images. This demonstrates the model's effectiveness in generating training data for deep learning models aimed at road anomaly detection and classification, contributing to the development of robust models for detecting and classifying potholes and other road anomalies.
Generalized self-tuning system for adaptive threshold estimators in cognitive radio systems using swarm and evolutionary-based approaches
(2021-01) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
Parameter-based adaptive threshold estimators (ATEs) are widely used for signal detection in cognitive radio (CR) systems. However, their performance deteriorates under dynamic spectra conditions owing to a lack of valid methods to accurately self-tune the different parameters of such ATEs. In this article, we address this limitation by proposing a generalized system for self-tuning the parameters of any ATE based only on the input signal measured per time. We adopt swarm and evolutionary-based metaheuristic optimization techniques to effectively search for the optimal parameter values of any ATE. Our system controls the search process by applying the between-class variance function adapted from Otsu's algorithm as the objective function. We tested the system using five different metaheuristic optimization algorithms (MOAs) to self-tune two different ATEs, namely the recursive one-sided hypothesis testing (ROHT) technique and the histogram partitioning algorithm under Rayleigh and Rician fading channels, as well as under different modulation schemes, including the 4-quadrature amplitude modulation and 4-phase shift keying schemes. Our findings suggest that our proposed system yields generally a small error rate irrespective of the MOA used. In addition, the ROHT-cuckoo search optimization configuration yielded a reasonably high and low probability of detection and probability of false alarm, respectively, as a function of the signal-to-noise-ratio of the input signal at a fast average processing time of 0.0699 seconds. We concluded that our system presents an effective mechanism that can be used to automatically tune the parameters of any ATE for useful signal detection in CR.
Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio–based industrial wireless sensor networks
(Wiley Online Library, 2019-07) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
Modern energy detectors typically use adaptive threshold estimation algorithms to improve signal detection in cognitive radio–based industrial wireless sensor networks (CR‐IWSNs). However, a number of adaptive threshold estimation algorithms often perform poorly under noise uncertainty conditions since they are typically unable to auto‐adapt their parameter values per changing spectra conditions. Consequently, in this paper, we have developed two new algorithms to accurately and autonomously estimate threshold values in CR‐IWSNs under dynamic spectra conditions. The first algorithm is a parametric‐based technique termed the histogram partitioning algorithm, whereas the second algorithm is a fully autonomous variant termed the mean‐based histogram partitioning algorithm. We have evaluated and compared both algorithms with some well‐known methods under different CR sensing conditions. Our findings indicate that both algorithms maintained over 90% probability of detection in both narrow and wideband sensing conditions and less than 10% probability of false alarm under noise‐only conditions. Both algorithms are quick and highly scalable with a time complexity of O(V), where V is the total number of input samples. The simplicity, effectiveness, and viability of both algorithms make them typically suited for use in CR‐IWSN applications.
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.
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).
Low power wide area network, cognitive radio and the Internet of Things: Potentials for integration
(MDPI, 2020-11) Onumanyi, Adeiza J; Abu-Mahfouz, Adnan MI; Hancke, GP
The Internet of Things (IoT) is an emerging paradigm that enables many beneficial and prospective application areas, such as smart metering, smart homes, smart industries, and smart city architectures, to name but a few. These application areas typically comprise end nodes and gateways that are often interconnected by low power wide area network (LPWAN) technologies, which provide low power consumption rates to elongate the battery lifetimes of end nodes, low IoT device development/purchasing costs, long transmission range, and increased scalability, albeit at low data rates. However, most LPWAN technologies are often confronted with a number of physical (PHY) layer challenges, including increased interference, spectral inefficiency, and/or low data rates for which cognitive radio (CR), being a predominantly PHY layer solution, suffices as a potential solution. Consequently, in this article, we survey the potentials of integrating CR in LPWAN for IoT-based applications. First, we present and discuss a detailed list of different state-of-the-art LPWAN technologies; we summarize the most recent LPWAN standardization bodies, alliances, and consortia while emphasizing their disposition towards the integration of CR in LPWAN. We then highlight the concept of CR in LPWAN via a PHY-layer front-end model and discuss the benefits of CR-LPWAN for IoT applications. A number of research challenges and future directions are also presented. This article aims to provide a unique and holistic overview of CR in LPWAN with the intention of emphasizing its potential benefits.

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