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The upcoming 5th Generation (5G) mobile networks will be different from the previous mobile network generations in the fact that it will enable the mobile networks industry, besides offering superior broadband services, to enhance Internet of Things (IoT) industries such as vehicular communication system, factory automation, smart healthcare system and many more. Many of these use cases have challenging and quite often contradicting requirements in terms of data rate, latency, throughput and so on. This suggests that 5G mobile networks need to adopt flexible models that can adapt to different IoT device and traffic requirements. Consequently, a fresh look into how mobile networks are currently designed and deployed is needed. Historically, mobile networks have relied on the axiomatic role of cells as the cornerstone of the Radio Access Networks (RAN). Mobile network systems have witnessed several recent trends such as the increased heterogeneity in heterogeneous types of IoT services infrastructure and spectrum as well as the rise of different traffic types with different Quality of Services (QoS) requirements. In this direction, this thesis focuses on improving the performance of cell-edge users or IoT devices in 5G mobile networks by initially implementing the network slicing management approach, particularly as, with the fast growth of IoT, billions of devices will join the internet in the next few years. Hence, the latest 5G mobile technologies expected to offer massive connectivity and management ability of high volume of data traffic at the presence of immense interferences from a mobile network of IoT devices. Further, it will face challenges due to congestion and overload of data traffic due to a humongous number of IoT devices. Besides, these devices likely to demand high throughput, low latency and high level of reliability especially for critical real-time smart systems in density and small zone, such as in Vehicular Communication System (VCS), these vehicles mainly rely on connectivity aspects. Furthermore, IoT devices transmit small and large-sized packets with different radio resource requirements. For example, Smart Healthcare System (SHS) devices transmit small-sized of a data with utilizing a small portion of Physical Resource Block (PRB) as the smallest radio resource unit, which is allocated to a single device for data transmission in 5G mobile networks. In the IoT services with transmitting a small-sized data, the capacity of the PRB is not fully utilized, which causes wastage and unfairness of using PRB among these IoT devices or services. The novelties made in this thesis significantly advance a Slice Allocation Management (SAM) model based on critical services such as (VCS) to satisfy low latency demand. The proposed model aims at providing dedicated slices based on service requirements such as expected low latency for (VCS). To ensure such performance to data traffic of IoT devices in Uplink (UL)of Relay Node (RN) cells in the 5G mobile networks by slicing the RAN, along with assigning the nearest Mobile Edge Computing (MEC) with isolating slices depend on technical and QoS requirements for each IoT nodes. Also, this thesis proposes a Data Traffic Aggregation (DTA) model for efficient utilization of the smallest untie of PRB by aggregating the data traffics of several IoT devices, which can support IoT node throughput such as SHS. Also, this thesis presents a comprehensive comparison of the packet scheduling mechanisms include Priority Queuing (PQ), First-In-First-Out (FIFO) and Weighted Fair Queuing (WFQ) applied based on data traffic slicing model through RN cells. These thesis models are validated through the OPNET simulator to measure the performance of the SAM and DTA Models along with the assessment of packet scheduling mechanism. The simulation considers IoT devices in various smart systems such as VCS, SHS and smartphones also, different protocols include Simple Mail Transfer Protocol (SMTP), File Transfer Protocol (FTP), and Voice over Internet Protocol (VoIP) and Real-time Transport Protocol (RTP). Simulation results show a significant improvement in IoT nodes packets transmission via RNs and Donor eNodeB (DeNB) cells, in My SAM Model scenario comparing with other scenarios. The model has improved such as End-to-End (E2E) delay in FTP node by reaching 1ms, loading in VoIP node by 80% and throughput of all nodes in the uplink side of networks by 66%. In addition, the results display significant impact of IoT data traffic with different priority, networks E2E performance is improved by aggregating data traffic of several IoT devices with DTA model, which is determined by simulating several scenarios, considerable performance improvement is achieved in terms of averages cell throughput, upload response time, packet E2E delay and radio resource utilization. Finally, the result found PQ packet scheduling mechanism as the appropriate scheduling mechanism in case of supporting several of priorities queuing for data traffic.

Sickle cell disease (SCD) is a common serious genetic disease, which has a severe impact due to red blood cell (RBCs) abnormality. According to the World Health Organisation, 7 million newborn babies each year suffer either from the congenital anomaly or from an inherited disease, primarily from thalassemia and sickle cell disease. In the case of SCD, recent research has shown the beneficial effects of a drug called hydroxyurea/hydroxycarbamide in modifying the disease phenotype. The clinical management of this disease-modifying therapy is difficult and time consuming for clinical staff. This includes finding an optimal classifier that can help to solve the issues with missing values, multi-class datasets, and features selection. For the classification and discriminant analysis of SCD datasets, 7 classifiers based on machine learning models are selected representing linear and non-linear methods. After running these classifiers with a single model, the results revealed that a single classifier has provided us with effective outcomes in terms of the classification performance evaluation metric. In order to produce such an optimal outcome, this research proposed and designed combined classifiers (ensemble classifiers) among the neural network’s models, the random forest classifier, and the K-nearest neighbour classifier. In this aspect, combining the levenberg-marquardt algorithm, the voted perceptron classifier, the radial basis neural classifier, and random forest classifier obtain the highest rate of performance and accuracy. This ensemble classifier receives better results during the training set and testing set process. Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personal health-care. The web-based system developed under the supervision of the haematology specialist at the Alder Hey Children’s Hospital in order to produce such an effective and useful system for both patients and clinicians. To sum up, the simulation experiment concludes that using machine learning and the web-based system platforms represents an alternative procedure that could assist healthcare professionals, particularly for the specialist nurse and junior doctor to improve the quality of care with sickle cell disorder.

The proliferation of smart mobile devices and user applications has continued to contribute to the tremendous volume of data traffic in cellular networks. Moreover, with the feature of heterogeneous connectivity interfaces of these smart devices, it becomes more complex for managing the traffic volume in the context of mobility. To surmount this challenge, service and resource providers are looking for alternative mechanisms that can successfully facilitate managing network resources and mobility in a more dynamic, predictive and distributed manner. New concepts of network architectures such as Software-Defined Network (SDN) and Network Function Virtualization (NFV) have paved the way to move from static to flexible networks. They make networks more flexible (i.e., network providers capable of on-demand provisioning), easily customizable and cost effective. In this regard, network slicing is emerging as a new technology built on the concepts of SDN and NFV. It splits a network infrastructure into isolated virtual networks and allows them to manage network resources based on their requirements and characteristics. Most of the existing solutions for network slicing are facing challenges in terms of resource and mobility management. Regarding resource management, it creates challenges in terms of provisioning network throughput, end-to-end delay, and fairness resources allocation for each slice, whereas, in the case of mobility management, due to the rapid change of user mobility the network slice operator would like to hold the mobility controlling over its clients across different access networks, rather than the network operator, to ensure better services and user experience. In this thesis, we propose two novel architectural solutions to solve the challenges identified above. The first proposed solution introduces a Network Slicing Resource Management (NSRM) mechanism that assigns the required resources for each slice, taking into consideration resource isolation between different slices. The second proposed v solution provides a Mobility Management architecture-based Network Slicing (MMNS) where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In MMNS architecture, each slice has different mobility demands (e.g,. latency, speed and interference) and these demands are governed by a network slice configuration and service characteristics. In addition, NSRM ensures isolating, customizing and fair sharing of distributed bandwidths between various network slices and users belonging to the same slice depending on different requirements of each one. Whereas, MMNS is a logical platform that unifies different Radio Access Technologies (RATs) and allows all slices to share them in order to satisfy different slice mobility demands. We considered two software simulations, namely OPNET Modeler and OMNET++, to validate the performance evaluation of the thesis contributions. The simulation results for both proposed architectures show that, in case of NSRM, the resource blocking is approximately 35% less compared to the legacy LTE network, which it allows to accommodate more users. The NSRM also successfully maintains the isolation for both the inter and intra network slices. Moreover, the results show that the NSRM is able to run different scheduling mechanisms where each network slice guarantee perform its own scheduling mechanism and simultaneously with other slices. Regarding the MMNS, the results show the advantages of the proposed architecture that are the reduction of the tunnelling overhead and the minimization of the handover latency. The MMNS results show the packets delivery cost is optimal by reducing the number of hops that the packets transit between a source node and destination. Additionally, seamless session continues of a user IP-flow between different access networks interfaces has been successfully achieved.