Search

This paper introduces handover (HO) skipping topology analysis that adjust the HO Skipping of 5G and beyond applications to improve the overall network performance and diminish negative effects. We propose a novel Poisson point process (PPP) based context-aware HO skipping approach to focus on the impact of HO metrics such as, passenger’s trajectory, different velocities, and a mean-time of a passenger within a BS to maintain a good quality of service (QoS). Our proposed scheme, context-aware HO skipping enables a dynamic HO skipping where the skipping decision is taken based on the load of the BSs along the passenger’s trajectory. The parameters have been analysed and implemented in a dynamic simulator and have been investigated for different parameter sets in a high-speed railway simulation scenario. Our simulation results in the robustness of the framework show comparable coverage probability on various high train velocities and mean-times.

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility.

With the advent of Coronavirus Disease 2019 (COVID-19), the world encountered an unprecedented health crisis due to the severe acute respiratory syndrome (SARS) pathogen. This impacted all of the sectors but more critically the transportation sector which required a strategy in the light of mobility trends using transportation modes and regions. We analyse a mobility prediction model for smart transportation by considering key indicators including data selection, processing and, integration of transportation modes, and data point normalisation in regional mobility. A Machine Learning (ML) driven classification has been performed to predict transportation modes efficiency and variations using driving, walking and transit. Additionally, regional mobility by considering Asia, Europe, Africa, Australasia, Middle-East, and America has also been analysed. In this regard, six ML algorithms have been applied for the precise assessment of transportation modes and regions. The initial experimental results demonstrate that the majority of the world's travelling dynamics have been contrastively shaped with the accuracy of 91.21% and 84.5% using Support Vector Machine (SVM) and Random Forest (RT) for different transportation modes and regions. This study will pave a new direction for the assessment of transportation modes affected by the pandemic to optimize economic benefits for smart transportation.

The integration of Private Mobile Networks (PMN) with edge intelligence is expected to play an instrumental role in realizing the next generation of industry applications. This combination collectively termed as Intelligent Private Networks (IPN) deployed within the scope of specific industries such as transport systems can unlock several use-cases and critical applications that in turn can address rising business demands. This article presents a conceptual IPN that hosts intelligence at the network edge employing emerging technologies that satisfy a number of Next Generation Railway System (NGRS) applications. NGRS use-cases along with their applications and respective beyond 5G (B5G) enabling technologies have been discussed along with possible future research and development directions that will allow these promising technologies to be used and implemented widely.