Learn what Sparkplug is in plain English. Together, we'll build an IoT company, discover problems along the way and show how Sparkplug solves those problems. Learn about the key components of Sparkplug: sensors, devices and edge of network nodes. We'll continue on to talk about business benefits and the future of Sparkplug.
By developing a fully portable and hardware-agnostic edge architecture, Cummins reduced Edge IoT software development costs and time-to-market by over 50%. This new architecture is launching on our edge telematics hardware in Q4-2023. We are collaborating with Eclipse SDV and numerous commercial vehicle partners to adapt this archtecture to third-party telematics hardware so that it can be adopted across the commercial vehicle industry.
Edge Computing is about distributing computational power and data storage wherever needed. From a developer's perspective, the whole point is to bring cloud-native tools and techniques outside the data center. Naturally, this includes Kubernetes. But is Kubernetes needed on the Edge? Or rather, do *you* need Kubernetes for your project? Many of the leading Edge platforms available right now can orchestrate applications and services independently. Others, however, integrate with Kubernetes or are directly based on it. Which one is the right approach for you?
In a world that is constantly changing, we believe that it is essential to drive forward a technology concept that specifically and deeply meets the consumers, devices makers and developers needs.
Panel including Member representatives of the Oniro project. Using the the information provided by the IoT & Edge Commercial Adoption report as starting point, the participants in the panel will describe some of the challenges they predict they wil be facing in the coming 2-3 years as well as how they envision open collaboration in an organization like Eclipse Foundation as a key factor..
Through the conversation, atendees will get an idea of the direction the project will go in the coming couple of years.
AI software and hardware advancements are resulting in a flood of smart interactive devices that can recognize and react to sights, sounds, and other environmental stimuli while executing much of the processing at the edge and so not requiring a constant internet connection. We will report on the activities of the Oniro project to develop a modern AI-supporting operating system that allows for the quick and easy integration of ready-to-use AI components, as well as its ability to interact with other available frameworks such as OpenHarmony and other opensource distributions.
The Eclipse Hara project aims to provide solid open-source reference implementations of a hawkBit DDI client. In this talk, we’ll focus on the new hara-ddiclient, a Kotlin library designed to facilitate and speed up the development of DDI API clients for any device capable of running on the JVM. Furthermore, the hara-ddiclient library is designed to be agnostic with respect to the type of update to install (apps, OS OTA).
The smart city ecosystem is huge and varied - trying to keep pace with new actors, devices, standards and protocols can be an unending task. Adding new data sources to your system, or even just updating existing sources, sometimes requires huge updates and breaking compatibility. The Eclipse sensiNact project provides a simple lightweight smart city platform hosting a digital twin of your city infrastructure, suitable for deployment at the edge or in the cloud. Eclipse sensiNact makes accessing data or triggering actions for any of your city’s smart capabilities consistent, simple and flexible, regardless of what native protocol or data format is used underneath. In addition, sensiNact provides push notifications, integrates with AI to automatically respond to data changes, and can deploy business logic to the edge for fast “reactions” even if the wider internet is unreachable.
Dataflow programming is a fundamental software paradigm widely adopted in various applications, ranging from Signal Processing to Artificial Intelligence (AI), Autonomous Vehicles (AV), Data Processing and Robotics. The underlying concept is rather simple: a computing task is decomposed into a graph of operators through which flows the data to be processed.
IoT has been spreading like wildfire: millions of small devices have been gathering an increasing amount of data to be computed to centralized data centers. Until now. That amount of data has become so massive to push networks to the limit.
Edge computing aims to solve this issue by processing the data close to where it is produced. This distributes data centers, moving them to the edge of the network and creating clusters of embedded devices. However, distributed computation opens up to a number of novel design, deployment, and communication challenges.
