Browsing by Author "Ramotsoela, Daniel"

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    A study into scalable transport networks for IoT deployment
    (2021) Sizamo, Yandisa; Ramotsoela, Daniel
    The growth of the internet towards the Internet of Things (IoT) has impacted the way we live. Intelligent (smart) devices which can act autonomously has resulted in new applications for example industrial automation, smart healthcare systems, autonomous transportation to name just a few. These applications have dramatically improved the way we live as citizens. While the internet is continuing to grow at an unprecedented rate, this has also been coupled with the growing demands for new services e.g. machine-to machine (M2M) communications, smart metering etc. Transmission Control Protocol/Internet Protocol (TCP/IP) architecture was developed decades ago and was not prepared nor designed to meet these exponential demands. This has led to the complexity of the internet coupled with its inflexible and a rigid state. The challenges of reliability, scalability, interoperability, inflexibility and vendor lock-in amongst the many challenges still remain a concern over the existing (traditional) networks. In this study, an evolutionary approach into implementing a "Scalable IoT Data Transmission Network" (S-IoT-N) is proposed while leveraging on existing transport networks. Most Importantly, the proposed evolutionary approach attempts to address the above challenges by using open (existing) standards and by leveraging on the (traditional/existing) transport networks. The Proof-of-Concept (PoC) of the proposed S-IoT-N is attempted on a physical network testbed and is demonstrated along with basic network connectivity services over it. Finally, the results are validated by an experimental performance evaluation of the PoC physical network testbed along with the recommendations for improvement and future work.
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    Hydraulic Data Preprocessing for Anomaly Based Intrusion Detection on SCADA Level of Water Treatment Systems
    (2024) Mboweni, Ignitious; Ramotsoela, Daniel
    The confidentiality, integrity and availability of critical infrastructure is crucial for any economy to operate efficiently. Critical water systems infrastructure is a target of many attackers who aim to penetrate the system for malicious reasons. The use of cyber-physical systems (CPSs) in Water Treatment Systems (WTSs) unveils many vulnerabilities that attackers can use. Although preventative security mechanisms are put into place they too can be defeated, and in this case, a second layer of security is essential. Intrusion detection mechanisms are important reactive security mechanisms to limit the damage done by a successful attack in the system. The ability to uncover data patterns and gather knowledge from data is a significant benefit of machine learning (ML), however factors such as noise, missing values, excessive features, and inconsistent and redundant data negatively affects the performance of the model, hence a need for data preprocessing which makes it possible to achieve speed and accuracy on a ML process by unveiling veracity in the data ergo making it valuable. Although many ML techniques for intrusion detection have been studied, comprehensive data preprocessing is scarcely documented. This begets a need for an adoptable data preprocessing workflow specifically for critical water systems infrastructure sensor and actuator data that researchers who intend on working on advancing cyber security in CPSs can utilise. The work provided in this dissertation explores data preprocessing techniques on secure water treatment (SWaT) testbed data and provides ideal critical water systems infrastructure specific data preprocessing techniques for a resultant informative dataset to yield high results when applied on machine learning (ML) classification models. The SWaT dataset was chosen as it was designed for cyber security research with a WTS use case. The techniques in this study can be applied to a similar kind of dataset collected from a similar environment and not limited to water treatment. Experiments were set up to evaluate the effect of preprocessing measures and the results showed good improvement on the model's performance which is a good indication of the impact that the data preprocessing has. The best performance was achieved when the preprocessed dataset was randomly split into training and testing, yielding a significant improvement in accuracy, F1 score and time to detection for both algorithms used in the study, namely Fine Tree and Boosted Trees Ensemble.
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    Mobility Management in 5G Heterogeneous Networks: A Handover Scheme for Reducing Handover Failures
    (2023) Monaheng, Reitumetse; Ramotsoela, Daniel; Lysko Albert
    Mobile/cellular communications have become very popular and advanced significantly in recent decades. Communications will inevitably evolve into the next generation of wireless communications, in which users will be connected via heterogeneous networks. Small cell (SC)-based ultra-dense heterogeneous networks (HetNets), which are underlaid on the coverage of a macro cell, are among the most promising alternatives for increasing capacity and coverage in 5G cellular networks. An ultra-dense network (UDN) refers to a setup in which the density of Radio Access Technologies (RATs) in a geographical area is increased. As a result, the areas covered by individual RATs begin to overlap. UDNs are regarded as a critical technology for 5G due to their capability to enhance connection quality and expand system capacity. There, small base stations (SBS) are located close to each other in a UDN. As a result, signals from two or more SBS can be received by a single user equipment (UE). This could lead to severe inter-cell interference. This usually happens when a handover has been delayed. If that happens, then, the handover command message (HCM) will not be received by the UE from its serving BS and, a handover failure (HOF) will be declared. Inter-cell interference is so severe in dense small cells that it occurs frequently, thus degrading signal quality and hence resulting in poor services to users. This dissertation focuses on reducing handover failures due to the unavailability of resources at the target cell when a user equipment moves out of small cells, which have the highest rate of handover failure. By utilising a semi-Markov mobility prediction algorithm for handover management, we have implemented a handover scheme that reduces the number of handover calls dropping. This paves the way for resources in the target base station to be reserved beforehand and thus reducing the number of handover failures significantly. The results of the proposed scheme were validated with a simulation in MATLAB with an environment consisting of small cells with different radio access technologies. From the simulation results, the prediction of the next location of the user equipment yielded lower handover call dropping as compared to new calls blocking as they were not predicted and hence resources not reserved for them.
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    RF-EMF Radiation Exposure and Radio Resources Management in 5G Wireless Network
    (2022) Ajibare, Adedotun Temitope; Ramotsoela, Daniel
    The fifth generation (5G) mobile network is expected to solve the challenge of heavy network traffic demand associated with mobile communication networks. Mobile communication network is characteristically heterogeneous with several base stations and numerous users with diverse demands. With the limited radio resources, there will be an infeasibility challenge in the network as the system capacity is unable to support the users' target quality of service (QoS) requirements. To bring solution to the limited resources, heavy traffic demands and congestion problems; an efficient management of resources such as; the transmit power, time, physical resource blocks (PRBs), frequency (sub-channels) is required. Deciding on how to efficiently allocate, manage, and control the resources dynamically among several slices and users in an isolated multi-tenancy scenario without compromise on the QoS/quality of experience (QoE) of user is very important. Also important is the consideration of the network challenges such as; interference (on user equipment (UE)) and radiofrequency electromagnetic field (RFEMF) radiation exposure (a health challenge on user) emitted in both the uplink and downlink of wireless networks, especially the 5G mobile network. Therefore, this research aims to assess the impacts of these challenges, develop and evaluate efficient radio resources management schemes and algorithms. Schemes that will effectively guarantee the users' QoS in terms of the data rate and reduced the interference and radiation exposure of the users in the network. Thus, this dissertation proposes four major solutions in 5G mobile networks. Firstly, a 5G network resource allocation scheme is proposed. A QoE resource allocation problem is formulated as an optimisation problem, the proposed scheme solves the problem using Mixed Integer Non-Linear Program (MINLP). It jointly incorporates admission control and a heuristic mechanism that takes the QoS constraints, the slice's and user's priorities into consideration to enhance resource utilisation efficiency, improve the throughput of users, and consequently, the QoE of users. In addition, this research also proposed a novel slicing carrier assignment scheme (SCAS), a joint power and sub-channel allocation scheme in a 5G network to reduce the co-users interference in the network. In SCAS, an optimisation problem is formulated to minimise the downlink transmit power while guaranteeing a minimum data rate requirement for the slice and user subject to QoS constraints, interference thresholds, gNB power budget and the sub-channel orthogonality constraints. The scheme assigns sub-channels to users considering the transmit power level of the neighbouring sub-channel before allocating the sub-channel to users by comparing the transmit power threshold of the slice to which the user belongs. Thirdly, this work investigates the impact of radiofrequency (RF) electromagnetic fields (EMF) radiation exposure induced by wireless networks, most importantly 5G cellular networks for both the uplink and downlink radio emissions using exposure-index open-loop power control algorithm (EOPCA), a novel simulation method that quantifies the realistic electromagnetic exposure of the human user. The exposure index (EI) is used to characterize the EMF exposure taking into account the power density, specific absorption rate (SAR), the electric field strength as well as considering other factors such as the environment, the conductivity and the mass density of the tissue. The radiations emitted from APs and UEs were simulated, analyzed and compared with the threshold set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for the understanding of radiation impact. Lastly, this work investigates the effect of minimising the EI and SAR induced in the 5G mobile networks and its impact on the QoS of the users in the network. It proposed a power control algorithm that solves an optimisation problem formulated to minimise the EI while guaranteeing the QoS requirement of users. Given that the radiated SAR and EI are characterized by power density in the wireless network, the proposed algorithm controls the transmit and the received powers subject to interference, power and QoS constraints. The performance of the proposed schemes were evaluated and compared with standards and other algorithms in the literature. The results show that the enhanced network efficiency, improved users' QoS, reduced users' interference and reduced radiation exposure (SARs and EIs) on the users in 5G network while satisfying the required QoS in terms of the data rate. Furthermore, the results reveal that both SAR and EI are tolerable and fall within the thresholds set by the ICNIRP and other regulators.