Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

/pdf/a-survey-of-multi-access-edge-computing-in-5g-and-beyond-25j4pqqhet.pdf

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.

Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems.

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Recently, the population density in cities has increased at a higher pace. According to the United Nations Population Fund, cities accommodated 3.3 billion people (54%) of the global population in 2014. By 2050, around 5 billion people (68%) will be residing in cities. In order to make lifestyles in cities more comfortable and cost-effective, the city must be smart and intelligent. It is mainly accomplished through an intelligent decision-making process using computational intelligence-based technologies. This paper explored how artificial intelligence (AI) is being used in the smart city concept. From 2014 to 2021, we examined 133 articles (97% of Scopus and 73% of WoS) in healthcare, education, environment and waste management, agriculture, mobility and smart transportation, risk management, and security. Moreover, we observed that the healthcare (23% impact), mobility (19% impact), privacy and security (11% impact), and energy sectors (10% impact) have a more significant influence on AI adoption in smart cities. Since the epidemic hit cities in 2019, the healthcare industry has intensified its AI-based advances by 60%. According to the analysis, AI algorithms such as ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM have a higher impact on the various smart city domains. 

Adoption of artificial intelligence in smart cities: A comprehensive review

Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.

/pdf/past-present-and-future-of-smart-learning-a-topic-based-9s0xgqoaep.pdf

Past, present, and future of smart learning: a topic‑based bibliometric analysis

The biggest growth rate of network traffic in the coming years will be for smartphones and Internet-connected devices, which relentless tend to perform increasingly demanding tasks on continuously increasing amounts of data. Machine Learning and Edge Computing are emerging as effective paradigms for processing huge amounts of data supplied by the Internet of Things and Smart Cities. An osmotic computing architecture for an IoT smart classroom is used for testing a deep learning model for person recognition. A comparative performance study and analysis was made by means of selecting a single deep learning model, that it was tried to be adapted to run over the cloud, a fog microserver and a mobile edge computing device. The results obtained shown some promising results and also limitations for the edge and fog computing side that will need to be addressed in order to minimize latencies and achieve real-time responses for the present IoT application.

A Smart Classroom Based on Deep Learning and Osmotic IoT Computing

Machine Learning over edge computing devices is levering up embedded and IoT intelligence and is expected to grow even more. Today, Machine Learning applications are mainly driven by Cloud Computing, but a recent trend towards on-device ML execution is taken over. We can expect that in coming years there will be smart homes and buildings populated by much more smart devices able to assist human activities more naturally. We present a work in progress, a mobile edge computing prototype built to explore and validate how ready are smartphones for on-device execution of deep neural networks (DNNs) using as a test bed a light control of a smart classroom via object recognition using three pre-trained non-optimized DNN models embedded in one mobile app. The prototype was successfully accomplished for recognition and control tasks using only smartphone's CPU/GPU processing units to run DNN models under 670ms. Finally, on-device DNN inference performance is discussed and some future work issues are presented.

Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices

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A Smart Classroom Based on Deep Learning and Osmotic IoT Computing

Smart Classrooms Aided by Deep Neural Networks Inference on Mobile Devices