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Mind the Edge: Digital world's new protagonist, the Edge Computing!

Covid-19

Mind the Edge: Digital world's new protagonist, the Edge Computing!


As the adoption of Industrial IoT (IIoT) has increased to attain greater efficiency and to reduce costs through Big Data Analytics, IoT and Smart devices, this risen to handle the massive data transmissions to and from many remote sites. By doing so, the biggest challenge is to manage the different network connections with the need for Real-Time analytics and to get real insights from all the data that is collected. The answer to the challenge is "Edge Computing".

Edge Computing a new concept in the digital ecosystem. In Edge the storage and processing of data are performed closer to the Edge of the network, i.e., the data is distributed rather than carrying it to the data centre. The IoT gateway gathers data from sensors, meters or CCTVs or any devices directly to the control centre. Now, when the gateway connects to the cloud computing resources, the local storage and local processing happen at the Edge.

Data management is a central concern for companies in the future due to the interconnected mesh of IoT, and other sensors and devices which push colossal data up to the cloud, which makes the process expensive and the response time slower. As per the future prediction, 507.5 zettabytes of data will be generated from 5m devices around the globe, thus affecting bandwidth, latency and reliability. As the number of IoT implementations increases, Edge Computing is likely to become more prevalent due to its design of decentralizing data handling and reducing network congestion.

Benefits of the edge computing:

  • Low Latency: Shifting the data to the edge gateway reduces network latency, consumes less bandwidth and operates on limited connectivity, thus providing tangible benefits
  • High Reliability: Edge computing allows sensitive data to be filtered at the source rather than sent to the central data system. The data packet does not travel and stays at the source node; therefore, it pushes the data in the secure zone.
  • Speed: Fast-moving manufacturing robots, heavy mining machinery, smart grid controls, road traffic management, and self-driving cars depend on immediate responses to raw data for functioning safely and effectively. The architecture optimizes internet devices and web applications by bringing computing closer to the source of the data, which overall reduces the network congestion and improves the response time, when the Edge is present, no service interruption when the link is down!
  • Agility: Through virtualization of the network, Edge provides insights and agility while reducing the number of devices in the remote sites within the securely connected IIoT gateway.

Aximind is working with client partners to implement Edge Computing across companies like OEM partners, manufacturing, automotive and power plants. Not just through Edge Computing, a distributed channel was developed, but also a secured gateway to ensure no physical access to the units.

Some of the key areas where Aximind is helping clients on the Edge Computing are:

  • Creating machine learning algorithms like Anomaly Detection for cybersecurity.
  • Creating a Condition-Based Monitoring system to monitor the assets remotely for maintenance services, new business models for Aximind's clients for a managed service for uptime.
  • Predictive Maintenance through Edge systems for a closed call-off to pre-emptively detect when a machine will fail through advanced machine learning algorithms.
  • Through AR/VR for Maintenance, Repair and Operations (MRO) services by which the data processing and rendering the stream from an edge compute node that eliminates the latency problem which occurs otherwise in a cloud.
Edge computing is not only relevant in orchestrating and filtering the data but also critical for running the Artificial Intelligence and Machine Learning algorithms especially in a manufacturing Operation Technology setup where PLC/SCADA machines are used extensively. The Edge is helpful to process a large amount of data from multiple devices in real-time; as a result, the machine learning algorithms can run without the latency issue. Therefore, creating a precision monitoring and control system for manufacturing or industrial assets.

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