Concurrent Engineering: Research and Applications (CERA)– An international journal: Special issue on “Data Analytics in Industrial Internet of Things (IIoT)”

Abstract

The network of interconnected and synchronized machines, instruments, and other such devices in the industrial sphere is known as the Industrial Internet of Things. Smart sensors and actuators are integrated into industrial machines to enhance industrial activities and business-related applications with little to no human input. The analysis of the real-time data that is obtained from this vast internetwork of machinery allows for greater streamlining in the industrial processes and thereby provides an even greater benefit to businesses which adopt the IIoT framework. This special edition focuses on analyzing the interdependence and unavoidable overlap of big data analytics and IIoT. Businesses and industrial pursuits are often shaped by dynamic demands, changing environments, and even socio-political flux. In the rapidly evolving world of today, these catalysts of change may make it difficult for businesses to keep pace. As a solution to this problem, IIoT effectively facilitates intelligent industrial and customer-level operations by using advanced data analytics to positively transform business outcomes. With the accelerated advancements in IIoT, we can soon expect billions of interconnected machines to stream unprecedented volumes of sensor data at remarkable speeds. According to a report by the International Data Corporation (IDC), the big data and analytics market, which reached $60 billion worldwide in 2018, is expected to grow at a 5-year compound annual growth rate of 12.5%. An incline of this magnitude can be attributed at large to the growing importance of automation in industrial enterprises. This explosive growth in the number devices in IIoT networks and the consequential rise in the amount of data produced and consumed is an apt reflection of how the growth of big data and IIoT are mutually beneficial to one another. Businesses are benefitted by IIoT in terms of increased revenue, reduced costs, and increased efficiency. However, merely generating a large amount of data is not the end goal. The data streamed from IIoT sensors only become useful if the data is appropriately analyzed. Considering the sheer volume of the influx of data, storing, processing, and analyzing this data is prone to become problematic due to limitations in computational power, inadequate networking capacities, and insufficient storage. Security concerns also pose a large threat to the convergence of IIoT and data analytics. Securely handling data, maintaining it, and extracting the necessary insights from it require a robust security framework to prevent mismanagement and fraudulent use. Implementing such a framework successfully has been a challenge as data analytics in the IIoT context is still at its infancy. IIoT has taken a stronghold in the industrial paradigm with the intention to simplify, streamline, and automate industrial activities to achieve maximum output. Overcoming issues regarding efficient data storage, optimized data processing and analysis, and effective data security is paramount for IIoT to be fully functional. However, with the application of the appropriate techniques and algorithms, data analytics and IIoT would function hand in hand to solve challenges of automation in the industrial environment. Topics that are relevant to the overall theme of this edition include, but are not limited to: