Real-Time Data Analytics for Industrial IoT Systems: Edge and Cloud Computing integration

Abstract

Following the rapid evolution of Industrial Internet of Things (IIoT) 
technologies, we enter a new era where industrial data is used to make 
data driven decisions and enhance operational efficiency across multiple 
industrial sectors. Now that the volume and velocity of data driven by 
interconnect devices keep increasing, it becomes necessary to have robust 
real time analytics. In this article, I describe what happens if we blend in 
edge and cloud computing paradigms into our industrial IoT systems with the 
purpose of performing sophisticated real time data analytics. It finds that 
the convergence of edge and cloud computing offers a powerful solution 
for those challenges inherent in traditional cloud-centric architectures: 
latency, bandwidth constraints, and data privacy. The alignment of the 
strengths of the two paradigms enables organizations to leverage IIoT 
deployments to create new innovative solutions, optimizing processes, and 
to gain unmatched insights in order to increase their overall operational 
performance. In this comprehensive exploration we will explore fundamental 
concepts, architectural considerations, the benefits, challenges and emerging 
trends influencing the running of real time data analytics in Industrial IoT 
environments. This article attempts to give a complete take on the subject 
by going from the study of the role of the edging computing in the reducing 
of the latency and the data processing capacity as well as the study of the 
advanced analytic techniques and the security consideration.