This article proposes an approach to enhance users' experience of video streaming in the context of smart cities. The proposed approach relies on the concept of MEC as a key factor in enhancing QoS. It sustains QoS by ensuring that applications/services follow the mobility of users, realizing the "Follow Me Edge" concept. The proposed scheme enforces an autonomic creation of MEC services to allow anywhere anytime data access with optimum QoE and reduced latency. Considering its application in smart city scenarios, the proposed scheme represents an important solution for reducing core network traffic and ensuring ultra-short latency through a smart MEC architecture capable of achieving the 1 ms latency dream for the upcoming 5G mobile systems.

Latency minimization is a pivotal aspect in provision of real time services while adhering to Quality of Experience (QoE) parameters for assuring spectral efficiency. Edge Cloud Computing, being a potential research dimension in the realm of 5G networks, targets to enhance the network efficiency by harnessing effectiveness of both cloud computing and mobile devices in user’s proximity. Keeping in view the far ranging impact of Edge Cloud Computing in future mobile generations, a comprehensive review of the prevalent Edge Cloud Computing frameworks and approaches is presented with a detailed comparison of its classifications through various QoS metrics (pertinent to network performance and overheads associated with deployment/migration). Considering the knowledge accumulated, procedures analysed and theories discussed, the paper provides a comprehensive overview on sate-of-the-art and future research directions for multi-access mobile edge computing.

In recent years, advanced magnetic resonance imaging (MRI) methods including functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) have indicated an increase in the prevalence of neuropsychiatric disorders such as autism spectrum disorder (ASD), effects one out of six children worldwide. Data driven techniques along with medical image analysis techniques, such as computer-assisted diagnosis (CAD), benefiting from deep learning. With the use of artificial intelligence (AI) and IoT-based intelligent approaches, it would be convenient to support autistic children to adopt the new atmospheres. In this paper, we classify and represent learning tasks of the most powerful deep learning network such as convolution neural network (CNN) and transfer learning algorithm on a combination of data from autism brain imaging data exchange (ABIDE I and ABIDE II) datasets. Due to their four-dimensional nature (three spatial dimensions and one temporal dimension), the resting state-fMRI (rs-fMRI) data can be used to develop diagnostic biomarkers for brain dysfunction. ABIDE is a collaboration of global scientists, where ABIDE-I and ABIDE-II consists of 1112 rs-fMRI datasets from 573 typical control (TC) and 539 autism individuals, and 1114 rs-fMRI from 521 autism and 593 typical control individuals respectively, which were collected from 17 different sites. Our proposed optimized version of CNN achieved 81.56% accuracy. This outperforms prior conventional approaches presented only on the ABIDE I datasets.

The emerging techniques, such as the fifth-generation communications (5G), Internet of Things (IoT), blockchain, artificial intelligence, etc., are operating in unison will drive the transformation of global business forward. The 5G technology is expected to unleash a massive IoT ecosystems by providing massive connectivity for huge number of IoT devices with faster data rate, ultra-low latency, and low-cost access. The 5G networks will be designed to bring the level of performance needed for massive IoT and will enable a perceived fully ubiquitous connected world. Meanwhile, the blockchain being promoted as the fundamental for new business model in Future IoT (FIoT). This paper attempts to provide a set of new directions and ideas for research in 5G/6G enabled IoT and new technique trends in IoT. The current IoT are facing a number of challenges, such as massive IoT devices access, network performances, security, standardization, and critical applications. This paper investigates new technologies, such as 5G, zero-trust, and blockchain will catalyse the innovation in IoT. Specifically, a zero-trust security architecture for FIoT is proposed and a blockchain-based device authentication in IoT environment (BasIoT) is proposed that can provide massive secure device access in FIoT.

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spent millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.