ABSTRACT
Mobile broadband (MBB) penetration has deepened globally over the last twenty years. This is largely due to the adoption of smart devices, improved mobile communications network coverage, and the perpetual drive to develop ever faster mobile and wireless communication technologies. However, information on the quality of service (QoS) delivered by MBB operators to the end users remains an issue of concern. This has driven independent researchers and mobile communication industry regulators to develop methodologies for independent and unbiased evaluation of the QoS offered by MBB networks. This paper provides a detailed review of MBB adoption and penetration across several regions of the world. It also includes the existing methodologies for evaluating the performance of MBB systems as experienced by the end user. Specifically, methodologies such as the drive and walk tests, crowd-sourced mobile device-based methods and the software applications they employ, and the dedicated measurement testbeds are reviewed. Based on this, the challenges of adopting each of the methods are discussed in order to make a case for the development of more robust, partially autonomous and scalable MBB measurement platforms for the future.
ABSTRACT
There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.