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 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

Internet of Things edge intelligence requires convolutional neural network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent commercial edge devices. It comprises of single-ISA heterogeneous cores grouped into multiple homogeneous clusters that enable power and performance tradeoffs. All cores are expected to be simultaneously employed in inference to attain maximal throughput. However, high communication overhead involved in parallelization of computations from convolution kernels across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs pipelined design to split convolutional layers across clusters while limiting parallelization of their respective kernels to the assigned cluster. We develop a performance-prediction model that utilizes only the convolutional layer descriptors to predict the execution time of each layer individually on all permitted core configurations (type and count). Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in a 39% higher throughput than the highest antecedent throughput.

High-Throughput CNN Inference on Embedded ARM Big.LITTLE Multicore Processors

Smart city strategies developed by cities around the world provide a useful resource for insights into the future of smart development. This study examines such strategies to identify plans for the explicit deployment of artificial intelligence (AI) and robotics. A total of 12 case studies emerged from an online keyword search representing cities of various sizes globally. The search was based on the keywords of “artificial intelligence” (or “AI”), and “robot,” representing robotics and associated terminology. Based on the findings, it is evident that the more concentrated deployment of AI and robotics in smart city development is currently in the Global North, although countries in the Global South are also increasingly represented. Multiple cities in Australia and Canada actively seek to develop AI and robotics, and Moscow has one of the most in-depth elaborations for this deployment. The ramifications of these plans are discussed as part of cyber–physical systems alongside consideration given to the social and ethical implications.

/pdf/artificial-intelligence-and-robotics-in-smart-city-n7e131x0ri.pdf

Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Development

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

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A Smart Classroom Based on Deep Learning and Osmotic IoT Computing