Deep Learning Frameworks for Internet of Things

Abstract

AbstractArtificial intelligence is a common platform in which the concept of machine learning (ML) and deep learning (DL) appears. The DL is becoming a hot research topic in recent years as it enables various smarter applications and services, including the Internet of Things (IoTs). DL discovers characteristics and responsibilities straightforwardly from data including pixels, images, shapes, dimensions, text, and sound. DL is also considered as an end-to-end learning approach because the tasks are associated with learning from data. Several hidden layers consist of a neural network, and therefore, it is also known as a deep neural network (DNN). The convolution neural network (CNN) is commonly used in DNN which contains a significant number of hidden layers. This chapter aims to explore DL frameworks for IoT. The chapter begins with a discussion on the development and architecture of the DL framework. We then discuss various DL models associated with deep reinforcement learning approaches for IoT. The potential applications, including smart grid management, road traffic management, industrial sector, estimation of crop production, and detection of various plant diseases, are discussed. Various design issues and challenges in implementing DL are also discussed. The findings reported in this chapter provide some insights into DL frameworks for IoT that can help network researchers and engineers to contribute further toward the development of next-generation IoT.KeywordsArtificial intelligenceDeep learningDeep neural networkFrameworkIoTMachine learning