How to Solve Common Challenges of IIoT in Manufacturing

In this era of high-speed computing and communication, the manufacturing industry is witnessing a transformation through the implementation of concepts like smart factories or smart manufacturing, which is widely known as IIoT or Industry 4.0. Leading manufacturing companies across various geographical locations, including USA, Europe, and Asia Pacific are continuously expanding their manufacturing operations and deploying smart manufacturing technologies. Considering this, it is expected that the Industrial IoT market will reach USD 195.47 Billion by 2022.

The adoption of Industry 4.0 is empowering the industrial users to securely leverage the data and analytics for predictive analysis, reduced machine downtime, centralized storage and remote asset monitoring. 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.

Challenges of adopting Industrial IoT

• 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.

• Data Analysis and Transfer: In a computing environment, edge devices or sensors generate and collect an enormous amount of data, but they do not have the computing power and storage resources to perform advanced analytics and machine-learning tasks. Moreover, it is difficult for large data chunks to process the data as it takes more time to respond. So, to transfer sensitive data over the internet for performing important analysis often becomes a challenge.
• IT and OT Convergence: In industry 4.0, the integration of IT (Information Technology) with the OT (Operational technology) is difficult to achieve since there is a huge technology gap between them. IT is quite mature with well-defined policies, but OT is an upcoming trend and has not traditionally been a networked technology. So, quite often it gets difficult to align manufacturing processes with IT systems.

Here, as shown in the figure, there are manufacturing assets like motors, drives, sensors, valves, and pumps that are connected with the Distributed Control Systems (DCS) or SCADA systems having microcontroller units like PLC/RTU. HMI Panels have Industry 4.0 capability and are designed to enable Industrial Internet of Things (IIoT) technologies and applications in discrete and process control industries.

To solve the challenges of IIoT, there are several solutions that enterprises and manufacturers can adopt. Let us take a look at a few of them.

Solutions to deal with IIoT challenges

1. 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.

2. 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.
3. 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.
4. 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.

Adopting IIoT or Industry 4.0 is an initiative to take the manufacturing industry forward through a complete digitalization of the manufacturing process. By implementing IoT gateway clustering with edge & fog computing capabilities, you can not only cope with the challenges of IIoT, but can rapidly scale your industrial operations, improving your bottom lines.

eInfochips provides Snapbricks intelligent IoT gateway framework, which is a secure, interoperable, and multi OS-stack IoT gateway based on a micro-services based architecture with Edge and Fog computing capabilities.