Articles

Challenges of IIoT in Manufacturing

by Ashley Jan IoT | DevOps | Product Marketer
The implementation of Industry 4.0 empowers industrial users to leverage information and analytics safely for predictive analysis, decreased downtime of machines, centralized storage and remote monitoring of assets. However, IIoT—the latest technological wave comes with its own set of challenges that manufacturers and enterprises must address in order to reap the benefits of connected manufacturing.

Let us understand some common challenges of Industry 4.0 and how to solve them.

Interoperability: As per IoT Nexus survey, 77% of IoT professionals saw interoperability as the biggest challenge in the Industrial Internet. The manufacturing environment is flooded with machines and protocols that are yet to be interconnected and most often not interoperable. So, connecting the legacy industrial systems and ensuring interoperability between them is a challenge.

Security: As manufacturing processes are becoming smarter (with the use of SCADA Systems), the production processes are becoming more technology-driven, in terms of wireless M2M technologies. Most of the connected machines share information directly to the cloud and hence get exposed to security threats and attacks. In other words, any ‘thing’ or “device” or “asset” that is controlled by the network, or the internet is vulnerable to attacks and hacks.


Solutions to deal with IIoT challenges
IIoT Gateway: An IIoT gateway or industrial IoT gateway can help the existing device infrastructure (even the legacy systems) to securely connect to any industrial infrastructure For e.g., IoT gateway can connect the industrial SCADA or Distributed Control Systems (DCS) directly with the Cloud using industrial protocols such as MODBUS, OPC, ISA100 Wireless Technology, PROFIBUS for edge to gateway connectivity and CoAP/MQTT for gateway to cloud connectivity. This solves the problem of interoperability and machine-to-machine communication.


Edge Computing: Instead of sending a bunch of data on the cloud, Edge computing allows only relevant data to transfer further for analytics. In edge computing, a number of gateways having different functions are connected with each other to form a cluster of gateways and this clustering leads to distributed edge computing. Here distributed edge nodes allow processing of data near the edge and near the source before transmitting it to the cloud, which results in reduced latency. Then the filtered data can be directly sent to fog node or cloud for further processing. Further, individual clusters form fog node and a combination of fog nodes allow distributed fog computing. This helps in fast data transfer and real-time data analysis, enabling faster fault response time.

TPM, TTM, and TNM: Industrial units can implement TPM (Trusted Perception Module), TTM (Trusted Terminal Module) and TNM (Trusted Network Module) to overcome security issues. Moreover, there are several data-centric security solutions which ensure safety of data encryption while in transit or in rest, which includes Web Application Firewall, Application Delivery Controller, and Secure Web Gateway, etc.

IoT Gateway Clustering: IoT Gateway clustering ensures the integration of IT systems, such as ERP systems and CRM applications with OT systems such as MES and SCADA systems. It also helps ensure the continuity of cloud communication and storage of data, which solves the problem of IT and OT convergence.



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About Ashley Jan Innovator   IoT | DevOps | Product Marketer

24 connections, 0 recommendations, 75 honor points.
Joined APSense since, July 26th, 2019, From Texas, United States.

Created on Sep 24th 2019 04:43. Viewed 414 times.

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