Category: AWS IoT Greengrass

Introduction Increasingly, business leaders are adopting Internet of Things (IoT) solutions to drive revenue growth, streamline operations, and reduce costs. Managing security and safety considerations while connecting your assets to the cloud, whether they’re industrial machines or autonomous vehicles, can be challenging. In the Ten security golden rules for Industrial IoT (IIoT) Solutions, AWS recommends establishing secure […]

As part of the Manufacturing sector’s digitization process, Industry 4.0 automation drives real-time, data acquisition from industrial equipment on the plant floor. This data and immediate feedback have become a mission-critical requirement. In addition, recent supply chain disruptions have increased the lead time to procure edge hardware resulting in several months delays to modernization projects. […]

Introduction Today, we announced the general availability of extended industrial protocol support for  AWS IoT SiteWise – a managed service that makes it easy to collect, store, organize and monitor data from industrial equipment at scale to help you make data-driven decisions. AWS IoT SiteWise Edge, a feature of AWS IoT SiteWise, extends the cloud […]

Introduction Connected mobility solutions are driving changes in the automotive industry. With remote commands, sensors, cameras, artificial intelligence, and 5G mobile networks, vehicles have become increasingly smart and connected. While connected mobility solutions deliver significant customer value, they also introduce new risks to security, safety, and privacy that must be properly managed. Automakers need to […]

Introduction Sustainability has become a cornerstone in the manufacturing industry. As stakeholders increasingly prioritize sustainability, the sector is turning to technological innovations to meet these demands. Among these technological advancements, the concept of Digital Twins stands out as particularly transformative for the manufacturing industry aiming for sustainability. Though manufacturing encompasses a wide range of topics, […]

Introduction As edge computing continues to gain traction in various industries, organizations are often looking for ways to efficiently analyze, optimize, and troubleshoot edge applications for better performance and reliability. In this blog post, we will demonstrate how to use AWS IoT Greengrass and AWS Distro for OpenTelemetry to implement tracing and observability methods that […]

Introduction As our world becomes increasingly interconnected, the Internet of Things (IoT) is becoming an unavoidable part of our daily lives. IoT devices, ranging from smart thermostats in our homes to sophisticated sensors in industrial settings, are everywhere. However, the effective management of these devices throughout their lifecycle presents a significant challenge. AWS IoT services, […]

Research suggests that Internet of Things (IoT) solution implementations typically take an average of 18-24 months to reach the market and be ready for adoption. Common scenarios related to IoT solution development include device provisioning, telemetry collection, and remote command and control. Depending on your use case, prototyping a well-architected IoT solution requires considerations for good design principles and best practices for each of these scenarios. This post illustrates how you can adopt a prototyping design pattern that combines an AWS Cloud Development Kit (AWS CDK), AWS IoT Greengrass, and Docker to accelerate your IoT solution prototyping on AWS.

The Internet of Things (IoT) has become increasingly prevalent in a variety of industries. In addition, with the increasing number of connected devices and the amount of sensitive information being transmitted, IoT security has become a top concern. As the global population continues to rise, the demand for energy has surged to unprecedented levels. In […]

Introduction Customers in manufacturing, logistics, and energy sectors often have stringent requirements for needing to run machine learning (ML) models at the edge. Some of these requirements include low-latency processing, poor or no connectivity to the internet, and data security. For these customers, running ML processes at the edge offers many advantages over running them […]