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Deep Learning-Based Intelligent Sensing in IoT
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Author(s): V. A. Velvizhi (Sri Sairam Engineering College, Anna University, India), G. Senbagavalli (AMC Engineering College, Visvesvaraya Technological University, India)and S. Malini (AMC Engineering College, Visvesvaraya Technological University, India)
Copyright: 2023
Pages: 29
Source title:
Convergence of Deep Learning and Internet of Things: Computing and Technology
Source Author(s)/Editor(s): T. Kavitha (New Horizon College of Engineering (Autonomous), India & Visvesvaraya Technological University, India), G. Senbagavalli (AMC Engineering College, Visvesvaraya Technological University, India), Deepika Koundal (University of Petroleum and Energy Studies, Dehradun, India), Yanhui Guo (University of Illinois, USA)and Deepak Jain (Chongqing University of Posts and Telecommunications, China)
DOI: 10.4018/978-1-6684-6275-1.ch003
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Abstract
The heart of Industry 4.0 is established by a new technology called the internet of things (IoT). Through the internet, the IoT makes it possible for machines and gadgets to share signals. Using artificial intelligence (AI) approaches to manage and regulate the communications between various equipment based on intelligent decisions is made possible by the internet of things (IoT) technology. Data collection devices can be fundamentally altered to “lock in” to the best sensing data with regard to a user-defined cost function or design constraint by utilizing inverse design and machine learning techniques. By allowing low-cost and small sensor implementations developed through iterative analysis of data-driven sensing outcomes, a new generation of intelligence sensing systems reduces the data load while significantly enhancing sensing capabilities. Machine learning-enabled computational sensors can encourage the development of widely distributed applications that leverage the internet of things to build robust sensing networks that have an influence across a variety of industries.
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