ABSTRACT
In the recent past, the Industrial Internet of Things (IIoT) plays a significant role in building a powerful industrial system to provide comfort to real life with minimal human intervention. IIoT integrates millions of smart devices to collect, process, and communicate with one another through a wireless communication channel. Since wireless sensor networks generate and collect a massive amount of sensitive data, it always introduces several challenges with existing architectures that are still unresolved. The challenges include scalability, availability, and security to improve system efficiency. In addition to these challenges, various other significant challenges, including confidentiality, authentication, and access control, are the significant concerns in IIoT. With the aim of knowledge addressing, an advanced architecture designed for gathering and analyzing a large amount of data generated by industrial sensor networks could be a key objective to review in this chapter proposal. Also, proposed an ECC-based privacy-preserving techniques through homomorphic re-encryption and asynchronous dynamic critical state-of-the-art approaches based on the deep learning model that is applied to facilitate privacy in IIoT in order to provide secure communication between devices and industrial systems. The experimentation shows that the performance of the proposed work is more than the other existing techniques.
