Deep Learning Architectures for IoT Data Analytics
Snowber Mushtaq, Omkar Singh · 2021
Internet of Things (IoT) is internet over a network covering different devices or things and facilitating communication among them, thus generating a massive amount of fast, real-time data. Applications have become intelligent with the rise of the IoT and the connection of different types of equipment has explored all aspects of modern needs. The IoT has gained massive popularity and used in a large variety of applications, due to active developments in hardware- and software-based connected devices with communication between them. IoT has created a new dimension in the internet world, by new forms of communication such as between humans and things, and between two things. A large volume of data is generated from different types of equipment. Deep Learning (DL) techniques are applied to boost the intellect and the capabilities of an application. DL, a branch of Machine Learning (ML), is a novel technology and has attracted researchers due to its magical results. DL can handle a tremendous amount of complex, multidimensional, unstructured data, and can better recognize and extract features. Thus, it helps to deploy the IoT in a complex environment. Applying it to the IoT discovers valuable information worthy of decision-making and quality control of crucial devices. DL technology has recently been deployed in the IoT to facilitate real-time data from the complex environment and to build real-time applications with accuracy and time like real-time health monitoring, recognition of activities of students in a class, etc. They require a high level of quality and accuracy to monitor critical conditions of the environment and evaluate performance with less response time and accuracy. This chapter provides you with a brief study of multiple DL architectures and their applications in IoT.