Service Provisioning and Data Management in IIoT using Improved Bi-LSTM: A Comprehensive Review

With the advancement of technology, the industries are developing the internet of things with numerous applications to work with heterogeneous groups of devices and creating the Industrial Internet of Things (IIoT) communication environment. Almost every productive service in IIoT is firmly related to the operations of production control and requires better Distributed Communication networks (DCN) needs support for information exchange with minimum delay. Currently deep learning is occupying an important role in the paradigm of machine learning which enables the collaborative learning of the models in the distributed communication environment. This paper will study and provide various possibilities in order to fulfill the requirements among IIoT environment such as uninterrupted communication among the equipment and employees with low delay and reduced communication cost. The main service prediction in the IIoT can be processed with a trending method which is popular Improved Bidirectional Long Short-term memory (IBiLSTM) included framework which makes ease of providing the services with the high-quality data nodes in the existing IIoT environment also there is a need for Data management in the environment that could be achieved by the deep learning methods.