netObjex Offers ‘PiQube’ For End-To-End Automation

APA Bureau

The US-based netObjex has introduced PiQube, a smart, secure and edge programmable gateway device. “The versatile PiQube has many Analogue and Digital I/O ports for connections to devices and sensors. It also supports a variety of communication protocols such as 3G/4G/LTE, Sigfox/LoRA/NB-IOT, Zigbee/Zwave. PiQube is programmable and unlike in several other gateways, its firmware code is modifiable over the air and one can code these edge devices in C/C++, Java, Python, and PHP. All communications between the edge and the cloud are tamper-proof,” Raghu Bala, CEO, netObjex, told AutoParts Asia.

As manufacturers embrace Industry 4.0 practices, it is important they have the right architecture and equipment in place. “NetObjex with PiQube, its flagship cloud platform and Smart Secure Edge Gateway device, offers enterprises a robust turnkey system for end-to-end automation,” he added.

The fourth Industrial Revolution that is otherwise called Industry 4.0 refers to a fusion of technologies that blur the lines between the physical, digital, and biological ecosystems. These systems are known as cyber-physical systems like robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology, the Internet of Things (IoT), 3D printing and autonomous vehicles.

As Industry 4.0 takes hold, it requires an Operating Platform that links all of the moving parts in the ecosystem. The typical Industry 4.0 platform may have several elements such as Instrumentation, Big data and analytics platforms, AI/machine learning, enterprise systems, and messaging.

Instrumentation: The instrumentation of industrial processes quite often begins with the collection of data. Data is the lifeblood of Machine Learning and Blockchain, and much attention has to be paid to the manner in which data is collected and transmitted. This instrumentation often involves the use of sensors and gateway devices to transmit data to the cloud where other elements of the technical architecture reside.

Big Data And Analytics Platforms: Data collected from industrial processes need to be stored and inferences drawn using AI/Machine Learning. These repositories of data also form the basis of dashboards to provide management useful insights into operations.

AI/Machine Learning: AI systems can process data and find anomalies or opportunities for optimisations by finding statistically significant patterns.

Enterprise Systems: Most likely, as new technologies come into the mix, they would not operate in a vacuum. Rather, they would have to share data with legacy systems such as ERP, CRM, CMS etc. Hence a bridge to such systems would most likely be needed.

Messaging: This is a useful mechanism to disseminate information to human operators in the ecosystem through various messaging protocols e.g. SMS, Email and Mobile Push,

According to Bala, “if there is an ecosystem of organisations working collaboratively, then blockchain can be a good way of sharing data between them in real time.”

Technical Implementation

Quite often, in Industry 4.0 ecosystems, as in automotive manufacturing, the machinery on the factory floor has to be instrumented with sensors. These sensors communicate through wires or wirelessly to a gateway device connected to the Internet. The information transmitted is known as payload. Unlike PiQube, most gateway devices in the market today are boxes with various Input/Output ports – either digital or analogue (for older devices).

In industrial situations, the Internet connection is not always reliable. PiQube has 250GB to 1TB storage to ensure it can store and burst data in intervals when Internet connectivity is spotty; or stream data when Internet connectivity is solid. These capabilities enable one to construct an architecture that combines both cloud and edge intelligence. “Let’s consider a traffic ecosystem with 10 traffic lights, and each light supported by a PiQube. If a pedestrian presses the WALK sign at one of these lights, the decision as to when to bring traffic to a smooth halt to enable the pedestrian to cross the road would fall on that specific traffic light. It is a case of local optimisation. Now, if there is a traffic jam due to an accident, the decision on how best to adjust the lengths of red vs. green light at each traffic light would rest on the AI system in the cloud. Recommendations can be made and actuated in real time to help dissipate the jam and ensure smooth traffic flow. This would be an example of global optimisation,” Bala said.

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