Bamidele Adebisi

Manchester Metropolitan University | MMU · Department of Engineering

This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this...

This chapter offers an introduction to the emerging domain of quantum programming. Quantum computing demands a paradigm shift from classical approaches. Traditional programming constructs are insufficient for exploiting the complete capabilities of quantum algorithms. Instead, quantum programming languages such as Qiskit, Quipper, and Q# provide fr...

This study proposes a biodynamic model for managing waterborne diseases using an Internet of Things (IoT) network, leveraging the scalability of LoRa IoT technology to accommodate a growing human population. The model, based on fractional-order derivatives (FOD), facilitates smart prediction and control of waterborne pathogens using IoT infrastruct...

Advancement in technology has resulted in an increase in the use of blockchain technology in distributed systems including in energy management. While blockchain technology is conceived to be secure, recent research has evidenced that the underlying blockchain processes like smart contracts are vulnerable to cyberattacks. Thus, this chapter aims to...

Infectious diseases like COVID-19 have remained a primary public and global health concern. Internet of Things (IoT) of networked robots and physiological intervention can be combined to identify and control the spread of the different variants of COVID-19 disease. With this approach, governments and healthcare institutions can plan for such diseas...

Ensuring network security, effective malware detection is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we pr...

Malware traffic classification (MTC) is one of the important techniques to ensure the security of cyberspace, which aims to detect anomalies and classify different types of network traffic. Recently, MTC methods based on deep learning (DL) have shown their excellent performance. However, these DL-based methods rely on data sets with manually labele...

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission...

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmission...

The post-COVID-19 landscape has propelled the global telemedicine sector to a projected valuation of USD 91.2 billion by 2022, with a remarkable compounded annual growth rate (CAGR) of 18.6% from 2023 to 2030. This paper introduces an analytical wearable healthcare monitoring device (WHMD) designed for the timely detection and seamless transmissio...

The concept of the peer-to-peer local energy market (P2P LEM) is no longer novel to the energy community. Yet, its large-scale implementation within the current electricity network remains a complex challenge. One key reason is the lack of understanding of the supplier licensing models in different countries. For instance, in the UK, up to year 202...

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT....

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (Io...

Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the transmitted signal without prior knowledge of the modulation type. Deep learning (DL) based AMC methods have been proven to achieve excellent performances. However, these DL-based methods rely heavily on expe...

Agriculture is poised to suffer greatly from the effects of climate change. Prediction models, using deep learning, have been developed that can simulate and predict conditions in open field farming to combat the climate variability from climate change. However, deep learning used in precision agriculture, specifically greenhouse tunnels, is under-...

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative three-dimensional (3D) positioning methodology is introduced. The critical idea...

In addition to localization and mapping, current challenges faced by driverless (autonomous) car parks encompass computational complexity, resulting in elevated CPU utilisation and substantial memory consumption. This paper presents a memory-efficient Spike-Time-Dependent Plasticity (STDP) approach for future driverless car park infrastructure (DCP...

Non-autonomous vehicles, like the MasterMover electric Tugs, find extensive application in specialised short-range communication settings such as UK's Tesco warehouses. Employing a resilient MasterMover setup, electromagnetic attenuation within the Cyberphysical vehicular infrastructure can be fine-tuned, thereby enhancing supply chain logistics fo...

As passive energy consumers increasingly become active prosumers, quantifying the benefits of engaging in the energy market has been crucial. An example is by estimating the level of satisfaction a prosumer derives from selling its excess energy generation units. This economic theory is usually referred to as utility and could be an incentive to mo...

Based on the characteristics of the 5G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5G (or the so-called 6G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5G and Beyond 5G (or 6G) w...

p>In this paper, we propose an optimized lightweight Federated Deep Learning (FDL) method for botnet attack detection in smart critical infrastructure. First, an optimization method is developed to determine the most appropriate combination of model hyperparameters for local Deep Learning (DL) at the edge nodes. Then, an oversampling algorithm is c...

p>In this paper, we propose an optimized lightweight Federated Deep Learning (FDL) method for botnet attack detection in smart critical infrastructure. First, an optimization method is developed to determine the most appropriate combination of model hyperparameters for local Deep Learning (DL) at the edge nodes. Then, an oversampling algorithm is c...

Data transmission over power line communication (PLC) infrastructure will proliferate lightweight Internet of Things (IoT) nodes in 5G and 6G networks. Consequently, a corresponding lightweight multi-hop routing protocol (LMRP) with reduced path loss and computational complexities will be required at the edges of PLC networks to connect the cloud s...

Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for...

Malware traffic classification (MTC) plays an important role in cyber security and network resource management for the secure internet of things (IoT). Many deep learning (DL) based MTC methods have been proposed due to their robustness and effectiveness with self-designed model architecture. However, to completely adjust complex parameters in the...

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilizat...

This paper focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to mitigate the impact of these attacks on the network, we propose a novel AP-DLMS algorithm with adaptive threshold attack detection and...

Consumer-centric Internet of Things (CIoT) will play a pivotal role in the fifth industrial revolution (Industry 5.0) but it exhibits vulnerabilities that can render it susceptible to various cyberattacks. Recent studies have explored the potential of Federated Learning (FL) for privacy-preserving intrusion detection in IoT. However, the developmen...

Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic modulation classification (AMC). Recently, many works attempt to use different ways to realize lightweight AMC methods for EDs. However, the lightweight seems to be...

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each s...

Abstract: Due to high power consumption and other problems, it is unlikely that orthogonal frequency-division multiplexing (OFDM) would be included in the uplink of the future 6G standard. High power consumption in OFDM systems is motivated by the high peak-to-average power ratio (PAPR) introduced by the inverse Fourier transform (IFFT) processing...

Automatic modulation recognition (AMR) technique plays an important role in the identification of modulation types of unknown signal of integrated sensing and communication (ISAC) systems. Deep neural network (DNN) based AMR is considered as a promising method. Considering the complexity of a typical ISAC system, devising the DNN manually with limi...

In the past decade, visible light communication (VLC) technology has received increasing attention for numerous applications, including for indoor visible light positioning (VLP). The transmission medium for indoor VLP systems in industrial environments could include smoke particles, oil vapors, water mist, and industrial fumes. This work investiga...

Academic and research institutions need to be at the forefront of research and development efforts on sustainable energy transition towards achieving the 2030 Sustainable Development Goal 7. Thus, the most economically feasible hybrid renewable energy system (HRES) option for meeting the energy demands of Covenant University was investigated in thi...

The ripple effects of the pandemic have resulted in an unprecedented shift in sectoral energy consumption as the workforce predominantly stays and works from home. Quantifying the impact of these restrictions on energy consumption offers a new direction towards intelligent energy services in a post coronavirus (post-COVID-19) world, especially for...

Deep Learning (DL) models can be trained to automatically learn the underlying features of the traffic patterns in IoT networks to detect complex botnet attacks. However, the performance of a neural network model largely depends on the set of hyperparameters that is used for the model development. In this paper, an algorithm is proposed to determin...

Deep Learning (DL) models can be trained to automatically learn the underlying features of the traffic patterns in IoT networks to detect complex botnet attacks. However, the performance of a neural network model largely depends on the set of hyperparameters that is used for the model development. In this paper, an algorithm is proposed to determin...

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, s...

In this paper, we propose an adaptive vehicle clustering algorithm based on a fuzzy C-means algorithm, which aims at minimizing the power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized...

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: (1) model parameters transmitted in each round may be used to recover private data, which leads to...

The zero-trust principle explicitly verifies that the authentication and authorization of all actions are performed regardless of the requesting user’s credentials or permissions. This chapter presents the details of a mechanism that is capable of mitigating physical data exfiltration attacks, with a focus on physical vulnerabilities that can be ex...

Radio frequency fingerprint (RFF) identification is a popular topic in the field of physical layer security. However, machine learning based RFF identification methods require complicated feature extraction manually while deep learning based methods are hard to achieve robust identification performance. To solve these problems, we propose a novel R...

In recent years, interest in web-based automation has been rapidly increasing. A lot of inverter-design requirements such as overload protection, short circuit and over-charge protection circuits, among others, have been proposed for performance optimization. However, all these circuits do not solve the problem of power management of the inverter s...

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to thei...

In response to energy transition fueled by the increasing energy generation mix and dynamic environment, this paper presents an energy trading strategy utilising real microgrid data. Specifically, we adapted the deep Q-network (DQN) with prioritised experience replay (PER) to develop a DQN-PER-based energy market algorithm to optimise the utility d...

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state information (CSI) directly from its reciprocal uplink CSI. Hence, the estimated downlink CSI needs to be continuously fed back to the base station (BS) from the user...

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder...

An average U.K. electricity bill is made up of at least 60% service charge, with approximately 22% related to network characteristics including distance charge. This makes distance and network constraints important factors in matching prosumers on any peer-to-peer energy trading platform as assessed in this article. To realize that, a platform— $Vi...

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different direction...

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge node...

Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. Many current solutions suffer from data fragmentation, lack of reliable provenance, and diverse protocol regulations across multiple distributions and processes. Amongst other solutions, Blockchain has emerged as a leading te...

With the ubiquitous deployment and applications of internet of things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target’s internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a se...

Deep Learning (DL) has been widely proposed for botnet attack detection in Internet of Things (IoT) networks. However, the traditional Centralized DL (CDL) method cannot be used to detect previously unknown (zero-day) botnet attack without breaching the data privacy rights of the users. In this paper, we propose Federated Deep Learning (FDL) method...