IoT is scaling up across industries. But what is the best way to extract actionable insights from increasingly digitized enterprises? In this Ericsson Research post, we take a deep dive into the world of Industrial IoT and intelligence data pipelines. Read more below.
IoT data pipelines- Establishing upon the connectivity layer provided by Internet of Things (IoT) platforms, today’s industries are moving towards management and computation solutions which enable Artificial Intelligence (AI) services for data intensive applications.
To extract actionable insights from these applications, we are providing what we call Intelligence Data Pipelines, as well as an architecture in which to deploy them. This, in short, is the ability to extract, understand, and use knowledge (intelligence), from factual information (data), with a set of elements connected where the output of one element is the input of another one (data pipeline).
The framework, Ericsson Research AI Actors (ERAIA), is an actor-based framework which provides a novel basis to build intelligence and data pipelines. In doing so, it addresses two main challenges of Industrial IoT (IIoT) applications:
- the creation of processing pipelines for data employed by the AI algorithms
- the distribution and orchestration of data and AI computation resources supporting these pipelines
As IoT adoption scales up , the explosion of data generated by devices is unavoidable. Hardware development (such as tensor processing units and graphic processing units) significantly increase systems’ capabilities for processing big data and conducting AI computations. Nevertheless, frameworks with clear IoT requirements to enhance the computation capabilities of distributed systems are still rare.
This leads to extra demands on IoT platforms to enhance the intelligence computations while:
- fulfilling latency requirements on AI computations and data processing for critical use-cases
- offering computation capabilities to provide quick insights through distributed data resources
- supporting live configuration updates in dynamic environments
- providing safety functionality that can run in the device edge
- providing resilient systems that recover easily from disturbances
All the while, the IoT platform should ensure optimized operational and infrastructure costs. These demands illustrate the need to move away from traditional centralized (and data center) IoT platforms to highly distributed platforms.
ERAIA is a novel solution for the computation related demands in data-intensive IoT applications. It’s a reactive system built using an actor model and intends to provide a responsive, resilient and elastic system required by the scalable and distributed IoT deployments (from edge devices, to gateways, network infrastructure and data-centers). By making use of all the end-to-end nodes for conducting divided computation tasks, AI computation and data processing are spread between components in the IoT landscape. Heavy raw data does not always need to travel from edge devices to the cloud, and by providing edge-centric data processing, latency can be reduced; communication resources are reduced; bandwidth is optimized through filtered and processed data, and nodes’ computation utilization is thus increased.