Recent ARC IIoT blogs have highlighted the numerous changes coming to the industrial network edge as a result of the advent of the industrial internet. The Industrial Network Gateways: Beacons into the Future of the Industrial Network Edge blog underlined the functional migration of infrastructure products from simple protocol conversion to sensor-to-cloud
integration, while The Battle for the Industrial Network Edge focused on the migration of enterprise IIoT platform agents to the industrial edge for the same purpose.
Sensor-to-cloud integration is an essential component of the build-out of the industrial internet architecture, one that is currently pursued by both sensor/device/asset manufacturers and cloud platform suppliers alike. Savvy suppliers and IIoT adopters, however, both recognize that these activities represent pursuit of the means rather than the ends associated with IIoT adoption.
Improvements in productivity and overall operating performance are the holy grails sought by most manufacturers considering or pursuing adoption of the industrial internet. Platforms and internet-based architectures can enable
these objectives and deliver the data-driven means of achieving these ends, but it is the applications executed throughout the architecture that are and will truly deliver the business value proposition.
Enterprise-level applications are now largely cloud-based, with the aforementioned emphasis on sensor-to-cloud integration designed to serve their data needs. Attention is now turning to what the network edge application environment should look like, given recognition of the need to filter the data deluge from the field, execute applications locally for real-time performance, and, in some cases, provide a more secure environment.
The question of appropriate application distribution, including the amount of processing that should occur in edge devices, is one of the leading subjects of debate in the IIoT community today and varies widely by application and customer. Hardware suppliers are readying their offerings in the form of microprocessor-based devices, some supporting enterprise platform software agents, in anticipation of the need to accommodate local processing requests. At the same time, the platform suppliers themselves are courting application software developers in hopes of convincing them to write apps for their platforms.
The application profile of the enterprise platform space is rather mature in this regard, with analytics packages as the latest darlings. The network edge, on the other hand, essentially represents open white space. Initially populated by enabling but not end applications, such as remote monitoring and access, visualization, and similar applications, we anticipate a near-term migration in the applications executed in edge devices.
As edge devices assume more IT-associated functionality, and greater emphasis is placed on real-time feedback at the asset source in order to maximize business value propositions, these devices will increasingly act as application execution environments. In today’s market this trend is associated with terms such as edge or fog computing. Edge analytics applications that serve the business objectives of reduced downtime, maximized performance, and optimized production are now emerging as the killer apps in this area. Availability of analytical feedback near or on the target assets will deliver speedy, ultimately even real-time feedback toward the objective of reduced machine downtime, as well as reduce operational security concerns.
Edge computing will also help offload enterprise applications from the potential tsunami of data emanating from edge devices. Edge computing can assist by identifying and flagging data anomalies that may be associated with problems in the device and/or process, plus perform filtering, offloading, and storing data not immediately required by the enterprise application. System response can also be improved as edge applications may not be able to tolerate the latency inherent in delivering data back and forth to the cloud for analysis and feedback.
One of several current and anticipated activities in this realm is the recently-announced alliance between Cisco and IBM. In the words of IBM, this Watson IoT-centric partnership is designed to enable immediate IoT analytics at the edge of the network, collect data for longer term analysis in the cloud, and provide an architectural approach to address critical challenges for distributed environments. It is further intended to allow clients to process information at the right point relevant to its source. GE is likewise pursuing application development for its Predix platform via dedicated developer conferences and hardware-software startup kits, among other activities. These initiatives and others like them will
need to go beyond delivery of simple processing power and into execution of business and production-focused apps in order to generate the desired business value proposition.
Reprinted with permission, original blog was posted here
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