As the name implies, edge computing is deployed on-premise or “edge” of cloud computing where data is generated in its native format. This novel approach allows data to be collected, analyzed and synchronized with one or more edge computing devices. It can then make a local decision of process and storage before sending only relevant data up to the cloud for further complex computation such as A.i and other mathematical modeling.
In essence, edge computing improves reliability and performance locally without delay associated with round trip communication delay from the internet. This is of critical importance when the process and data sampling time are measured down to milliseconds instead of seconds.
Edge Computing Layers
The three fundamental layers involved in an edge computing setup consist of sensors or edge devices, edge gateway, and the cloud or central server. As the data travels through these layers, each layer also enables decision-making. Let’s find out how.
Smart sensors or edge devices have an embedded microprocessor that collects vital measurements from a variety of sensors it is connected with.
These devices can collect data measurements such as time-stamp, hours of operation, connectivity, calibration conformance and a host of other micro-operations. It can even operate autonomously as long as it has power with the ability to sync up data if connectivity was lost, providing continuous data assurance. The Smart Sensors can even provide local control outputs in various forms for alarms to actuation.
An edge gateway sits between the edge devices and the cloud. It is the central repository for the edge devices data as well as synchronizing with another edge gateway. The edge gateway is also the gatekeeper for all the edge devices connected to it granting them secure authentication and provisioning. Only higher-order data processing is transmitted to the cloud for modeling and analytics.
Edge gateways are set up to run independently of the cloud while providing many of the benefits of the cloud. More than one edge gateway can be deployed in a large factory setting, each working on specific data metrics that can ultimately be synchronized and unified at the cloud. Then the heavy data crunching can be computed without impacting any local edge devices and gateways.
Cloud in an interconnected network of virtual servers and web services hosted on the internet. It is where the higher-order data from the edge gateways get stored, processed and analyzed.
Benefits of Using Edge Computing for IIoT
Moving computing and time-sensitive decision making to the edge of the network brings many benefits, especially in an IIoT environment. Below are some potential ones.
Speed and Latency
Edge computing reduces the time needed to exchange messages instead of solely relying on the internet. This reduction in time is critical when dealing with time-sensitive processes and measurements.
For example, when monitoring equipment performance, failures, or accidents, the data generated must be analyzed instantly. There is simply insufficient time for the data to travel back and forth between the cloud. By cutting down the network latency, edge computing drastically improves the response time for real-time IIoT applications.
Security and Reliability
In a traditional cloud environment, data generated throughout the organization moves through a centralized architecture. This type of set up is more vulnerable to attacks such as DDoS (Distributed Denial-of-Service) and other cyber threats. For an industrial business that relies on data generated by operating processes, such vulnerabilities can disrupt entire operations.
With edge computing, there is a distributed security risk split between the edge devices and the cloud. This does not mean that cybersecurity threat is eliminated, rather it is mitigated in the sense that both edge and cloud infrastructure must be compromised together in a single attack to impose a threat to the IIoT infrastructure.
The distributed architecture of edge computing can also have a positive impact on reliability. By bringing computation and storage on-premise; its reliability and availability are improved with less dependency on internet connectivity. The smart edge and gateway devices will continue to operate even if they have lost communication with the cloud in case of temporary disruptions in the internet connection.
Edge Computing offers some cost savings when computation and data storage; the two most expensive cost of cloud computing is distributed on-premise. Cost of network data transmission, frequency of data upload/download and time-series data manipulation can reduce the cost associated with cloud subscription services.
The computing needs of an organization will likely grow as their digital factory operations begin to scale up. The distributed nature of edge and cloud computing allow how much (or even how little) an edge or cloud computing can be cost-effectively scaled up, redistributed (and even scale down) without imposing costly shutdown of critical operating resources, for example shutting down entire or partial factory operations.
Omega Smart Sensors for Edge Computing
Omega’s Smart Probe (SP series) sensors are an excellent example of industrial edge computing devices. These sensors have the differentiated feature of onboard local control & closed-loop control at the point of sense, eliminating the need for multiple single-purpose devices that need separate configuration.
Some of the SP series smart sensors also feature real-time autonomous control through the alarm and control.
For example, the SP-001-2 Series IR Sensor allows local control of outputs based on measured values and operating parameters. The sensor can be set to transmit data only when there is a desired significant change in the value. This feature extends the battery life of the device.
Besides, the smart sensor is also capable of triggering Digital I/O pins that can be connected to an alarm or relay information even when the network is down.
Edge Computing is the Future
An increasing number of industrial organizations are deploying IIoTdevices to bring more efficiency into their operations. This number will increase over the coming years.While the data generated through these IIoTdevices offer businesses new opportunities, it brings a new challenge to store, manage, and process the enormous amounts of data. Utilizing the traditional cloud infrastructure in such a case only burdens the data center with traffic load and consumes more computing resources.
Edge computing removes this bottleneck by distributing data processing on-premise and edge devices that are close to the source. The immediate benefit organizations can get is a real-time response from their IIoT applications, as data gets stored and processed right at the information generating source.
This, in turn, leads to faster decision-making, which is necessary for an industrial operating environment where there is a need to analyze the data instantly. Edge computing also addresses the problems of connectivity efficiently and reduces the cost of transferring the data to a centralized server or the cloud.
While cloud computing was seen as a promising technology, the growth in the number of IIoT applications points to a future where edge computing will play a pivotal role.