Название: System Design Using the Internet of Things with Deep Learning Applications
Автор: Arpan Deyasi, Angsuman Sarkar, Soumen Santra
Издательство: Apple Academic Press, CRC Press
Год: 2024
Страниц: 282
Язык: английский
Формат: pdf (true)
Размер: 24.9 MB

This new volume aims to find real-world solutions to present-day problems by using IoT and related technologies. The volume explores the diverse applications of the Internet of Things in diverse areas―in healthcare, the construction industry, in wildlife monitoring, in home security systems, in agriculture, in cryptology, in hospitality employment, in data security, and more.

The chapters illustrate the aspects of defining the architecture, product design, modules, interfaces, and data for a system to satisfy specified requirements of the IoT applications discussed. The chapters show the novel results that can safely be applied in their respective domains. In this context, the editors expect that the present solutions can meet the ever-increasing demand of industries.

With chapters from both academics and industry professionals involved in IoT/AI solutions, System Design Using Internet of Things with Deep Learning Applications will be a valuable resource for students in this area as well as for those working in novel architecture design and system implementation.

The Internet of Things (IoT) was created to meet the ever-increasing demand for connectivity and easy integration with other devices as well as virtual association with big data analytic (BDA) platforms. However, many hurdles exist in reaching this goal, including the ever-growing need for customer satisfaction. This involves data collection, design issues, application connectivity, implementation hurdles, storage, and analysis in a cloud-based environment.

Big Data analytics is a technique for analyzing a large amount of data at once. Big Data and the Internet of Things (IoT) were created on independent technologies; nevertheless, with time, they have been intertwined to improve their functionality. Since there is a scarcity of Big Data analysts, the data generated by the apps is not efficiently exploited and examined. For real-time IoT applications, higher synchronization between hardware, code, and interfacing is required.

The objective of this book is to produce comprehensive cutting-edge findings (either in prototype or model format) based on Internet-of-Things embedded with Deep Learning technology, which will be beneficial for people from all walks of life. The major focus is to create a space for an open AI/ML and IoT community across India and worldwide of doctors, engineers, researchers, and industry practitioners to develop products and create frameworks for addressing open challenges and gaps towards AGI for mankind. With this, we hope that this earnest endeavour will become a success and prove useful to readers.






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