To handle data deluge and realize value instantaneously, manufacturing organizations are looking for mechanisms that enable data acquisition and data processing right near the source of data. With challenges such as network latency, security and cost of network bandwidth and data storage inherent in a purely centralized/cloud-based set-up, a new paradigm of data acquisition and processing is required to reduce the rate of information decay and optimize the time to value. Edge processing, as an approach, addresses these challenges in conjunction with Cloud-based IoT Platform and enables increased business agility, higher service levels, and improved safety.
Data Acquisition and Processing
Data on the factory floor can be acquired from sensors that are mounted on devices, controllers that are connected to devices and sensors, data historians and any local data sources.
One of the approaches for processing the acquired data is by ingesting the data to a cloud-based centralized IoT platform that can process data in real-time. The cloud-based IoT platform aggregates data from disparate data sources, applies business rules on the live feed of data, and triggers actions based on the outcome. Actions include notification to user downstream, command back to the device upstream, etc.
Challenges With the Approach
Cloud-based data processing leverages a centralized networked storage unit and computing capability of systems to deliver the necessary outcome. A critical success factor for this approach is the ubiquitous availability of network bandwidth and low latency. However, manufacturing plants and enterprises face challenges like limited network connectivity, high latency, rising storage and processing costs, and potential security breach.
Here are some scenarios depicting the challenges arising in a centralized data processing set-up.
Example 1 - Protecting Equipment From Damage by Overheating
When a Thermocouple determines that the temperature on a pump/motor has exceeded the defined threshold, the pump should be shut down in milliseconds without any decision latency. The time value of the temperature information decays rapidly as delayed response can result in damage.
Example 2 - Reducing Safety Risks
According to an estimate, an offshore oil platform generates between 1 TB and 2 TB of time-sensitive data related to production and drilling safety per day. With satellite communication data speeds ranging from 64 kbps to 2 Mbps, it might take 12 days to transmit one day’s worth of data back to a central site for processing and could have significant operational and safety implications.
Time-Value Graph
Edge Processing – A New Paradigm for Data Processing
A framework for measuring and monitoring productivity, reducing cascading failures, and responding to events in real-time calls for a decentralized model with distributed storage, processing, analysis, decision making, and control. In this new paradigm, data is processed right where it is produced and sent to the cloud selectively.
Edge processing at the Gateway
- The data from the control system is sent to an OPC server, which converts the data into a uniform protocol such as OPC/OPC-UA
- Data from the OPC server is further sent to an IoT gateway, which filters, analyses, processes, and stores the data for transmission to the cloud. Alternately, the control system could directly communicate with the IoT gateway.
Is Edge Processing the Panacea for All Industrial Scenarios?
Complex statistical analyses, references to historical data, contextualization with process and operations, and advanced visualization require large storage and processing capacity and are better off done on a centralized, scalable cloud-based IoT platform.
Driving Business Value by Combining Capabilities of Edge and Cloud
An integrated approach for data processing leverages the capabilities of Edge for handling time-critical decisions and the Cloud for long-term storage, statistical performance modeling, and data visualizations. Executing this approach requires a set of integrated, standards-based software capabilities in the form of a cloud-based IoT Platform which should:
- Be a set of loosely coupled services with storage and computing capabilities extended from the cloud to devices and the edge
- Delegate to Edge the aspects of interoperability, responding to events in real-time, supporting offline interactions, facilitating machine-to-machine communication, securing the data transfer from the factory floor to the cloud
- Maintain a digital twin for each of the devices and gateways in the cloud to enable device management, remote monitoring, and control of operations
- Include the aspects of device management, data management, enterprise integrations, and advanced analytics in cloud-based processing
Representative Architecture for Distributed Data Processing
Following is a representative architecture with processing distributed across the Edge and the Cloud
Edge Processing accelerates response to events by eliminating a round trip to the cloud for analysis. It avoids the need for costly bandwidth additions by offloading gigabytes of network traffic from the core network. It also protects sensitive IoT data by analyzing it within company walls. The IIoT platform, along with the IoT Edge illuminates operational visibility, enhances data availability and access, thereby facilitating data-driven decision making. This drives manufacturing and industrial industries to become digital businesses. Sasken has been providing enterprise clients with seamless data management enabling them to tap into new avenues of value creation. We continue to optimize faster time to value and reduce the rate of information decay increasing business safety and higher service levels.
