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In a previous article, we laid out ChainLink's Internet of Things (IoT) platforms framework and discussed hardware platforms and device management platforms. In this article, we examine local intelligence platforms and gateways. Local Intelligence in the context of IoT refers to processing that happens between sensors and devices and the wide-area Internet connection. For more background on the concept of local intelligence and real-world examples, see Distributed Intelligence in the IoT and IoT Distributed Intelligence Examples.
Local IoT intelligence ranges from simple to complex: The complexity of local intelligence for IoT (Figure 1) varies tremendously. Remote sensors with built-in cellular communications capabilities may have relatively little local processing, whereas large-scale site-wide systems such as manufacturing plants, oil platforms, smart mines and smart office buildings are architected with a complex hierarchy of devices and subsystems that form a sophisticated web of local intelligence.
Offline systems and knowledge bases: Local intelligence often uses data and instructions generated by offline systems. For example, in the automated welding of automobile chassis on a plant floor, data such as CAD drawings, welding knowledge bases and process models are generated offline and then used by the local system to direct the robots performing the welds in real time.
Custom embedded code: The majority of local IoT intelligence exists as custom code written for specific embedded processors. Increasingly these are being used to support IoT implementations and many are being marketed as IoT platforms.
Local intelligence 'appliances:' In addition, a growing number of standalone local computing appliances are designed to provide intelligence within IoT systems. We are using the word appliance in a broad sense here to include:
- IoT gateways -- We are seeing more and more IoT gateways, designed specifically to sit between the Internet and the 'Thing' in IoT such as those from Intel, Advantech, Freescale, NEXCOM, TI. These provide connectivity for non-IP devices, filtering, processing, transformation, aggregation, and other functions, as well controlling the IoT devices.
- Edge processors -- Network devices to fulfill the need for more server-like processing combined with router capabilities (e.g., for providing deep packet inspection). Some companies (e.g., Cisco with their Fog Computing Platform for IoT) realize this architecture can be useful for IoT platforms.
- Smart readers -- Providers of RFID readers are building increasing amounts of processing power and intelligence into some of their readers (e.g., Impinj's xArray).
- PLC, DCS, SCADA processors -- Programmable logic controllers (PLCs), distributed control systems (DCSs), and supervisory control and data acquisition (SCADA) processors provide local intelligence in various industrial automation settings, such as factory automation, building automation (e.g., elevators), material handling systems and so forth.
- Appliances -- Computing appliances, such as those from IBM and others, are being repurposed or specifically designed to address IoT requirements.
Thus, a broad range of local computing appliances provide the infrastructure for local intelligence well beyond the processing power embedded in the things themselves.
Distributed workflow and analytics systems: Several companies are providing distributed workflow or analytics platforms aimed specifically at IoT. In some cases, these companies set out to build an IoT application and decided they needed a way to distribute the intelligence across the IoT network. One example is Entrigral, whose TraxWare includes workflow 'Bots,' which are local agents that provide workflow logic that can be distributed across cloud, desktop, mobile and edge devices. GlobeRanger's iMotion Platform provides a componentized workflow architecture that makes it easy to build and distribute IoT applications. Some, such as Reylabs and ParStream, provide distributed IoT analytics platforms.
Domain-specific local algorithms: Some companies are focusing on solving domain-specific problems using local intelligence (algorithms executing partially or exclusively in the local IoT environment). One example is Impinj's Item Intelligence, which takes raw RFID reader data and turns it into higher-level intelligence about an item within the environment -- i.e., meaningful events such as an item was moved from the back of the store to the shelf on the sales floor, or it was tried on by the customer and then put back on the shelf, or it was moved to the wrong location. This requires building a rich library of algorithms over time to interpret the low-level reader data.
Autonomous vehicles represent another example of domain-specific algorithms. Product providers are building libraries of algorithms to recognize the enormous variety of environmental circumstances they may encounter on public roads and be able respond appropriately and safely. Domain-specific algorithms are often not run exclusively local, because part of the intelligence may reside in the cloud. However, often a major portion of the accumulated intelligence resides locally because of various requirements, such as the need for very low-latency real-time responsiveness, the ability to continue operation in a disconnected mode when no network is available, and network bandwidth constraints that mandate processing enormous quantities of local data and converting it into much smaller packets of higher-level intelligence.
Local intelligence IoT platforms take many forms: We have only scratched the surface of the types of platforms that help enable local intelligence in the IoT. It should be apparent that they are quite varied, including hardware appliances, distributed modular intelligence systems and a huge variety of domain-specific local processing systems. This is an area of IoT that doesn't get as much attention as it deserves, but is a hugely important component of many IoT systems.
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