Architecture Framework for Deep Learning Systems and IoT
Sahil Mehta, Jimmy Mehta, Yaman Hooda, Haobam Derit Singh · 2024
In the rapidly evolving landscape of technology, deep learning (DL) stands out as a transformative subfield of machine learning with the potential to revolutionize knowledge extraction. DL techniques empower us to unearth intricate data representations and hidden insights, promising a future driven by unparalleled performance and accuracy. Concurrently, the Internet of Things (IoT) has emerged as a pivotal force, ushering in an era of interconnected devices that transcend mere buzzwords. From wearable technology to smart cities and industrial ecosystems, the IoT has permeated our lives, promising innovation through data analysis, exploitation, and secure management. IoT architectures, tailored to diverse industries, share foundational attributes of cost-effectiveness, scalability, and functionality. These two technological juggernauts, DL and the IoT, hold the potential to reshape industries and drive innovation, underscoring their transformative power as we navigate the digital frontier. Therefore, this chapter embarks on a comprehensive survey of DL and IoT, delving into their core principles, contemporary methodologies, advantages, drawbacks, architectural paradigms, and applications.