Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

Interface Selection in 5G vehicular networks

ITA Negli ultimi anni, la quantità di dati condivisa nel mondo è aumentata esponenzialmente grazie alle applicazioni innovative che riguardano la sicurezza (e.g. domotica, smart cities, controllo del traffico stradale, veicoli autonomi) e i servizi di intrattenimento (e.g. audio e video streaming, ricerche web, videogiochi online di massa). Per supportare questo trend, le principali compagnie nell’industria delle telecomunicazioni stanno sviluppando nuovi standard che saranno disponibili agli utenti finali nei prossimi anni e che saranno presentati come la Quinta Generazione di Reti Cellulari (5G). Questi standard prevedono miglioramenti ai precedenti standard 4G (e.g. LTE, WiMax, DSRC) e tecnologie completamente nuove (e.g. onde millimetriche, comunicazione con luce visibile) per permettere la diffusione di nuovi servizi che richiedono un throughput estremamente alto e una latency bassa. Nella maggior parte dei casi, queste tecnologie dovranno cooperare per assicurare una rete affidabile e accessibile in ogni situazione. Una delle applicazioni più promettenti di questa nuova generazione di tecnologie sono le reti veicolari, un insieme di servizi che includono la comunicazione con le infrastrutture, come il download di un film da Internet o la ricezione di informazioni riguardanti l’ambiente circostante (e.g. un semaforo manda un messaggio a un veicolo in avvicinamento per farlo fermare), o la comunicazione direttamente tra veicoli, in questo caso il datarate è tipicamente più basso dato che l’uso più tipico sarà, per esempio, mandare informazioni riguardanti le macchine più vicine per fare in modo di diminuore il numero di incidenti stradali o gestire il traffico. Questa tesi è focalizzata sulle applicazioni per reti veicolari, l’obiettivo è di analizzare le prestazioni del protocollo IEEE 802.11p a diversi datarate in un tipico scenario V2V, e di confrontare LTE e mmWaves usando una comunicazione V2I in diverse circostanze, per mostrare come ogni tecnologia offra vantaggi per determinate applicazioni mentre non è adatta per altre. ENG In the last years, the amount of data shared among the world is increased exponentially thanks to the novel applications for security (e.g. home automation, smart cities, traffic control, autonomous vehicles) and infotainment (e.g. audio and video streaming, web browsing, massive online videogames). To support this trend, the major companies in the telecommunication industry are developing new standards that will be available to the final users in the next years and that will be presented as the Fifth Generation of Cellular Networks (5G). These standards provide improvements to the 4G standards (e.g. LTE, WiMax, DSRC) and brand new technologies (e.g. mmWaves, Visible Light Communication) to enable new services that demand extremely high throughput and low latency. In most cases these technologies will cooperate to ensure a reliable and accessible network in every situation. One of the most promising applications of these new generation technologies is vehicular networks, a set of services that includes the communication with infrastructures, such as the download of a film from the Internet or the reception of information about the surrounding environment (e.g. a traffic light sends a message to an incoming vehicle to make it stop), or the communication between vehicles, in this case the datarate is tipically lower since the typical use will be, for example, to send information about the closest cars in order to decrease the number of accidents or to manage the traffic. This thesis is focalized on the vehicular networks applications, it aims to analyze the performance of IEEE 802.11p protocol at different datarates in a typical V2V scenario, and to compare LTE and mmWaves using a V2I communication in different circumstances to show how each technology offers advantages for some applications while is not suitable for others

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

Optimisation of Traffic Steering for Heterogeneous Mobile Networks

Mobile networks have changed from circuit switched to IP-based mobile wireless packet switched networks. This paradigm shift led to new possibilities and challenges. The development of new capabilities based on IP-based networks is ongoing and raises new problems that have to be tackled, for example, the heterogeneity of current radio access networks and the wide range of data rates, coupled with user requirements and behaviour. A typical example of this shift is the nature of traffic, which is currently mostly data-based; further, forecasts based on market and usage trends indicate a data traffic increase of nearly 11 times between 2013 and 2018. The majority of this data traffic is predicted to be multimedia traffic, such as video streaming and live video streaming combined with voice traffic, all prone to delay, jitter, and packet loss and demanding high data rates and a high Quality of Service (QoS) to enable the provision of valuable service to the end-user. While the demands on the network are increasing, the end-user devices become more mobile and end-user demand for the capability of being always on, anytime and anywhere. The combination of end-user devices mobility, the required services, and the significant traffic loads generated by all the end-users leads to a pressing demand for adequate measures to enable the fulfilment of these requirements. The aim of this research is to propose an architecture which provides smart, intelligent and per end-user device individualised traffic steering for heterogeneous mobile networks to cope with the traffic volume and to fulfil the new requirements on QoS, mobility, and real-time capabilities. The proposed architecture provides traffic steering mechanisms based on individual context data per end-user device enabling the generation of individual commands and recommendations. In order to provide valuable services for the end-user, the commands and recommendations are distributed to the end-user devices in real-time. The proposed architecture does not require any proprietary protocols to facilitate its integration into the existing network infrastructure of a mobile network operator. The proposed architecture has been evaluated through a number of use cases. A proof-of-concept of the proposed architecture, including its core functionality, was implemented using the ns-3 network simulator. The simulation results have shown that the proposed architecture achieves improvements for traffic steering including traffic offload and handover. Further use cases have demonstrated that it is possible to achieve benefits in multiple other areas, such as for example improving the energy efficiency, improving frequency interference management, and providing additional or more accurate data to 3rd party to improve their services

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates