A Smart Classroom Based on Deep Learning and Osmotic IoT Computing

Pacheco, Alberto; Cano, Pablo H. Zapata; Flores, Ever; Trujillo, Edgar Espinosa; Márquez, Pedro

doi:10.1109/coniiti.2018.8587095

Cited by 26 publications

(15 citation statements)

References 27 publications

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“…This fog-based IoT system can address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis in home/building environment. The MEC-enabled IoT frameworks in [170], [171] focus on behaviour features by monitoring the student's location and activities in school environment for safety aspect. In particular, [170] designed a platform to identify any student activities that occur at the classroom level in which the raw indoors environment data is processed at an edge computing server (Raspberry Pi) for detecting the presence of individuals in a classroom while [171] exploited the DL algorithms in an MEC-enabled IoT smart classroom for person recognition.…”

Section: ) Smart Home and Smart Citymentioning

confidence: 99%

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

et al. 2020

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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.

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Section: ) Smart Home and Smart Citymentioning

confidence: 99%

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

et al. 2020

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“…And also, the author [64] based on the SCADA infrastructure model, tries creating an IoT architecture based classroom model for the implementation of an intelligent classroom. In [65], the author proposed emotionally aware AI implemented classrooms. Whereas in [66], deep learning and osmotic computing had been experimented to provide a smart classroom.…”

Section: Existing Systems -Intelligent Classroom 41 Related Workmentioning

confidence: 99%

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

M¹

2021

TURCOMAT

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Smart Classrooms are becoming very popular nowadays. The boom of recent technologies such as the Internet of Things, thanks to those technologies that are tremendously equipping every corner of a diverse set of fields. Every educational institution has set some benchmark on adopting these technologies in their daily lives. But due to some constraints and setbacks, these IoT technological embodiments in the educational sector is still in the premature stage. The major success of any technological evolution is based on its full-fledged implementation to fit the society in the broader concern. The breakthrough in recent years by Deep Learning principles as it outperforms traditional machine learning models to solve any tasks especially, Computer Vision and Natural language processing problems. A fusion of Computer Vision and Natural Language Processing as a new astonishing field that have shown its existence in the recent years. Using such mixtures with the IoT platforms is a challenging task and and has not reached the eyes of many researchers across the globe. Many researchers of the past have shown interest in designing an intelligent classroom on a different context. Hence to fill this gap, we have proposed an approach or a conceptual model through which Deep Learning architectures fused in the IoT systems results in an Intelligent Classroom via such hybrid systems. Apart from this, we have also discussed the major challenges, limitations as well as opportunities that can arise with Deep Learning-based IoT Solutions. In this paper, we have summarized the available applications of these technologies to suit our solution. Thus, this paper can be taken as a kickstart for our research to have a glimpse of the available papers for the success of our proposed approach.

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“…While "smart" technologies embody a computational logic whereby computers are programmed to perform tasks, the RAS technologies employ AI and machine learning to make decisions and adapt processes to circumstances without direct human agency (Table 1) [5]. Although AI is currently designed to mimic human brain structures, in deep learning as part of machine learning [6] or to develop the "city brain" as brain-like tissues interacting with the internet [7] and functioning, the ultimate intent as with emotional intelligence simulations [8] is to outdo human performance (cf. [9]) and efficiency: "With AI, computers can [analyze] and learn from information at higher accuracy and speed than humans can" [1].…”

Section: Artificial Intelligence Robotics and Their Application In mentioning

confidence: 99%

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

Golubchikov

Thornbush

2020

Smart Cities

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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.

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A Smart Classroom Based on Deep Learning and Osmotic IoT Computing

Cited by 26 publications

References 27 publications

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

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

Enabling Intelligence through Deep Learning using IoT in a Classroom Environment based on a multimodal approach

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

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