Adopting the Industrial Internet of Things (IIoT) to a facility or operation still faces challenges.
One continuing challenge is the need for two different organizational groups within an enterprise to work together. To realize the potential benefits of IIoT applications, the information technology (IT) and operations technology (OT) teams need to leverage and apply each other’s technology and skillsets. This situation continues to be a challenge on both the technical and cultural fronts.
IoT platforms and middleware are the software that must exist between physical devices (sensors, actuators, relays, and so on) or data endpoints, and higher-level software applications like artificial intelligence, predictive analytics, and cognitive computing.
IIoT platforms and middleware move data between the physical and digital realms and provide software resources powerful enough to cope with the big data generated from the billions of IIoT devices the industry is planning to ship.
While middleware is not a new technology, the IIoT has given rise to a need for a new type of middleware specifically designed for IIoT applications. IIoT applications require a middleware platform that addresses new concerns, including:
- Scalability
- Edge computing
- Efficient communication architectures
- Protocol support
- Cognitive computing
Scalability
IIoT platforms and middleware need to be optimized to support tens of thousands of devices, all trying to exchange not just information with a central hub server or application but with each of the other devices in the application.
Addressing, configuring, and managing thousands—even tens of thousands—of devices must be accounted for.
Edge computing
Centralized intelligence and control topologies are being reevaluated in favor of distributed architectures, with intelligence pushed into each edge device or data endpoint.
IIoT applications involve thousands of devices intercommunicating, often with the requirement for near real- time communication and control between devices.
Edge computing uses intelligence at the edge of the network to decrease network latency, deliver real-time control and monitoring, and offer report by exception to reduce data volume.
Efficient communication architectures
Many IIoT applications will be deployed in areas with unreliable and low-bandwidth networks. As a result, a more efficient network and communication architecture will be required.
Protocols like MQTT (Message Queue Telemetry Transport) that use a publish/subscribe architecture and low overhead packets can reduce network latency and improve real-time communication speed between devices and endpoints.
Protocol support
Combining OT and IT technologies requires wide support of different OT and IT protocols.
At least in the short run, both OT and IT protocols need to be translated through middleware, so that OT devices can communicate with IT devices and software.
In the long run, it is likely that OT devices will adopt IT protocols and communication standards, as they’ve already adopted Ethernet and TCP/IP as the main bus and data transmission protocols.
Cognitive computing
The key value-add of IIoT applications is predictive analytics.
Knowing when a part is going to fail before it actually fails can bring almost immeasurable value to IIoT applications through reduced truck rolls, safety improvements, and optimizing overall equipment efficiency (OEE).
The basis of predictive analytics is cognitive computing— essentially, computers that mimic the way the human brain works. For IIoT platforms to perform predictive analytics, they’ll need support for cognitive computing.
Today and for some years to come, IIoT applications will require connecting legacy systems and devices to cutting-edge IT systems. And a massive gap exists in technology, communication protocols, and standards between equipment designed several decades ago and the equipment shipping today. That’s the gap IoT middleware is trying to fill.
Until that day arrives, here are the major IIoT platforms in the market today, their key strengths, and their potential weaknesses.
GE Predix
Predix uses a platform as a service (PaaS) model and is a cloud-based operating system designed for IIoT applications. According to GE, Predix is built on Cloud Foundry, an open-source platform, and is optimized for secure connectivity and analytics at scale, both in the cloud and on the edge.
Key Strengths: Predix targets system-wide optimization. Rather than making one piece of equipment better, the software aims to create a detailed model that spans the entire system. The view created by this model allows both improved optimization of each part of the system and optimization of the entire system.
Potential Weakness: Predix in its current form is fairly new to the market, having been released in February 2016. Reports have surfaced claiming that core parts of GE’s Predix software rely on partnerships with other companies, including PTC.
Cisco IoT Cloud
Cisco’s offering is designed around six pillars of technology: network connectivity, fog computing, data analytics, security (cyber and physical), management and automation, and application enablement. The Cisco IoT Cloud addresses challenges across a variety of industries, including manufacturing, utilities, oil and gas, transportation, mining, and the public sector.
Key Strengths: Cisco has a strong background and support for IIoT applications at layers 1 through 4 and potentially 5 of the OSI model of interconnectivity. This is a wide product offering for general networking and Internet connectivity.
Potential Weakness: It lacks direct support for legacy endpoint devices including sensors, instrumentation, and other OT-specific assets. The core function of the Cisco IoT Cloud appears to be network connectivity, with OT integration needs being a lower priority.
IBM Watson IoT
Another platform as a service based on open standards (Cloud Foundry, Docker, OpenStack), Watson IoT Platform should simplify cognitive IoT development. The platform connects sensors to cloud applications using IBM Bluemix, which includes the Node-RED development environment (an open-source tool for wiring together hardware devices, APIs, and online services).
Key Strengths: IBM Watson IoT leverages both open technologies, such as RESTful API architecture, and inhouse- built advanced cognitive computing and artificial intelligence capabilities.
Potential Weakness: Constant internal IBM development cycles can slow down users during application development; current documentation can be missing or hard to find.
PTC ThingWorx
PTC Thingworx has three pillars of technology: core application enablement, connection services with device and cloud adopters, and edge connectivity using the Edge MicroServer and Edge “Always On” SDK.
Key Strengths: The platform architecture takes a holistic approach to connectivity, from end data points and devices all the way to the cloud. Thingworx integrates with cloud providers such as AWS IoT Service, Microsoft Azure IoT Hub, Salesforce IoT Cloud, and others and has vast OT protocol support through recent acquisition of Kepware Technologies.
Potential Weakness: Core features include software tools and products acquired through company acquisitions; internal technology integration pitfalls are a potential concern.
Material for this article was excerpted from “2017: State of the IIoT, Key Trends and Predictions for the Industrial Internet of Things, by Opto 22.
Opto 22
www.opto22.com
Filed Under: IoT • IIoT • Internet of things • Industry 4.0