Название: AIoT Technologies and Applications for Smart Environments
Автор: Mamoun Alazab, Meenu Gupta, Shakeel Ahmed
Издательство: The Institution of Engineering and Technology
Год: 2023
Страниц: 333
Язык: английский
Формат: pdf (true)
Размер: 19.2 MB

Although some IoT systems are built for simple event control where a sensor signal triggers a corresponding reaction, many events are far more complex, requiring applications to interpret the event using analytical techniques to initiate proper actions. Artificial Intelligence of Things (AIoT) applies intelligence to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best with minimum human intervention. With the power of AI, AIoT devices are not just messengers feeding information to control centers. They have evolved into intelligent machines capable of performing self-driven analytics and acting independently. A smart environment uses technologies such as wearable devices, IoT, and mobile internet to dynamically access information, connect people, materials and institutions, and then actively manages and responds to the ecosystem's needs in an intelligent manner.

In this edited book, the contributors present challenges, technologies, applications and future trends of AIoT in realizing smart and intelligent environments, including frameworks and methodologies for applying AIoT in monitoring devices and environments, tools and practices most applicable to product or service development to solve innovation problems, advanced and innovative techniques, and practical implementations to enhance future smart environment systems. Chapters cover a broad range of applications including smart cities, smart transportation and smart agriculture.

Time series modeling and forecasting is an indispensable field of supervised Machine Learning (ML) because of its esteemed influences on several research works and real-life applications involving companies, industries, science, and engineering. Consequently, significant contributions were devoted to the development of proficient extrapolative models. On the other hand, the Internet of Things (IoT) has enhanced the surveillance of various environmental sensations, such as air pollution, through a wireless sensor network (WSN). This chapter presents an inclusive time-series predictive model that uses supervised ML techniques and the data gathered from IoT devices. The aim is to develop an artificial intelligence-IoT (AIoT) time series analytical using IoT and ML techniques in an automated and intelligent air quality-control system. A comprehensive framework of the predictive system displaying internal subsystems and modules is summarized to form a roadmap for AIoT time series model designers.

This book is a valuable resource for industry and academic researchers, scientists, engineers and advanced students in the fields of ICTs and networking, IoT, AI and machine and deep learning, Data Science, sensing, robotics, automation and smart technologies and smart environments.