Category Archives : Internet of Things

12

Dec

Extracting insights from IoT data using the warm path data flow

This blog continues our coverage of the solution guide published by Microsoft’s Industry Experiences team. The guide includes the following components:

Ingesting data Hot path processing Cold path processing Analytics clients

We already covered the recommendation for processing data for an IoT application in the solution guide and suggested using Lambda architecture for data flow. To reiterate the data paths:

A batch layer (cold path) stores all incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view. It is a slow-processing pipeline, executing complex analysis, combining data from multiple sources over a longer period (such as hours or days), and generating new information such as reports, and machine learning models, etc. A speed layer and a serving layer (warm path) analyze data in real time. This layer is designed for low latency, at the expense of accuracy. It is a faster-processing pipeline that archives and displays incoming messages, and analyzes these records, generating short-term critical information and actions such as alarms.

This blog post covers the warm path processing components of the solution guide.

Azure Event Hubs is a big data streaming platform and event

Share

10

Dec

Extracting insights from IoT data using the cold path data flow

This blog continues our coverage of the solution guide published by Microsoft’s Industry Experiences team. The guide covers the following components:

Ingesting data Hot path processing Cold path processing Analytics clients

We already covered the recommendation for processing data for an IoT application in the solution guide and suggested using Lambda architecture for data flow. To reiterate the data paths:

A batch layer (cold path) stores all incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view. It is a slow-processing pipeline, executing complex analysis, combining data from multiple sources over a longer period (such as hours or days), and generating new information such as reports and machine learning models. A speed layer and a serving layer (warm path) analyzes data in real time. This layer is designed for low latency, at the expense of accuracy. It is a faster-processing pipeline that archives and displays incoming messages, and analyzes these records, generating short-term critical information and actions such as alarms.

This blog post covers the cold path processing components of the solution guide.

We have covered timeseries analysis with Azure Time Series Insights (TSI) in

Share

06

Dec

Johnson Controls tackles a $15b building industry problem with Azure Cosmos DB

This blog post was co-authored by Nikisha Reyes-Grange, Senior Product Marketing Manager, Azure Marketing, and Balamurugan Balakreshnan, Cloud Solution Architect, CSU-Data and AI.

Johnson Controls has been a pioneer in building management solutions and services since founder Warren Johnson invented the first electric room thermostat in 1885. Johnson Controls has introduced many innovations to the building industry over the years and are now tackling a problem that costs the building industry billions each year.

Modern buildings include multiple systems that handle everything from building management to HVAC to security. These systems are managed by protocols, proprietary systems, and applications without a common data model, which prevents interoperability, limits scalability, and costs the industry an estimated $15 billion annually.

Solution

To help building operators gather and understand data about their buildings, operations, and occupants, Johnson Controls created Digital Vault to integrate internal and external data sources and present a harmonized view of energy usage, security breaches, fire alarm status, temperature controls, and other building management systems. Digital Vault, powered by Azure Cosmos DB, simplifies object relationship management through a single Application Programming Interface (API) layer for IoT data and events while at rest and in motion. Another API layer leverages Azure

Share

06

Dec

IoT in Action: 4 innovations that are revolutionizing IoT

The Internet of Things (IoT) is reshaping every industry from manufacturing to medicine, and opportunities to transform business are nearly limitless. And while IoT is a complicated endeavor requiring multiple partners, skillsets, and technologies, new innovations are making projects easier to deploy, more secure, and more intelligent than ever.

Below I’ve called out four innovations that are revolutionizing the IoT industry. To learn more about how to take advantage of these innovations, be sure to register for our upcoming IoT in Action Virtual Bootcamp.

1. Artificial intelligence (AI) and cognitive capabilities

Cognitive services and AI used to come with a high price tag. But times have changed, and these capabilities are becoming increasingly accessible.

IoT Hub and Cognitive Services enable you to tailor IoT solutions with advanced intelligence without a team of data scientists. Not only do AI and Cognitive Services make it easier to infuse IoT solutions with capabilities such as image recognition, speech analytics, and intelligent recommendations, but they also help companies act on the data being gathered and realize the true value of IoT. Scenarios are virtually limitless. Companies like UBER are using visual identity verification to increase platform security, and Spektacom is making cricket better

Share

06

Dec

Azure IoT Java SDK provides improved Android support

Transforming mobile devices into Internet of Things (IoT) devices have been gaining traction in the IoT space. Mobile devices have a wide range of sensors and a big screen, but most importantly, they are ubiquitous and they are getting cheaper. After releasing native iOS support in April 2018, we have been improving our support for Android. We are happy to share that the Azure IoT Java SDK has the following updates:

Improved testing on the Android platform with every release including unit tests, integration tests, and end-to-end tests.  For updated information in regard to platform support, please check out our documentation, “Azure IoT SDKs Platform Support.” New samples for Device SDK and Service SDK using Gradle and Android Studio. New quickstarts to jump-start your development.

How do mobile devices fit into the IoT story? Our customers shared different scenarios, including using a mobile device as the gateway between other Bluetooth sensors and Azure IoT Hub, and using mobile devices as the end IoT device to send telemetry. Some of our customers are also using mobile devices as the controller for IoT operation, but please beware of the security risk in the case of a lost or stolen device! To reduce

Share

04

Dec

Accelerating IoT solution development and testing with Azure IoT Device Simulation

IoT solutions can be a hefty investment that you want to make sure to get right. Testing throughout the development lifecycle enables you to evaluate how well your IoT solution processes, manages, and presents device data ensuring project success. Azure IoT Device Simulation is a tool that you can rely on to help accelerate the testing and development of these solutions. Since its creation a year ago, Azure IoT Device Simulation has quickly become an essential tool in the developer’s toolbox.

We are thrilled to announce the latest update to Azure IoT Device Simulation. This latest release highlights our continued investment in the vital IoT simulation space and is available now.

Device Simulation is an open source project that you can deploy directly to your Azure subscription. The solution includes a web-based interface and a rich API allowing you to create powerful IoT simulations pushing realistic telemetry to your IoT Hub.

Device simulation helps you build simulated devices that look and behave like the real thing. With Device Simulation you can gather, process, analyze, and act on data from simulated devices allowing you to test your solution’s functionality and reliability.

Validate the solution works as expected from device

Share

04

Dec

Azure IoT Remote Monitoring extends operator capabilities to the edge

In May, we announced a major update to our Azure IoT Remote Monitoring solution accelerator. This included key functionality such as enhanced operator scenarios through the configuration of rules, easier device connectivity, and richer integration options with tools such as Power BI and Azure Data Lake. Over the past few months we have continued to improve the scenarios by adding features like integration of Time Series Insights to allow for root cause analysis, or roles-based access control through AAD.

Today, we are excited to announce another wave of functionality that will continue accelerating companies towards achieving their scenarios faster.

Manage and deploy Azure IoT Edge components from Azure IoT Remote Monitoring

Real-time data analytics and insights drive business value in IoT solutions, but not all customer scenarios can rely on cloud processing. Azure IoT Edge solves these challenges by delivering cloud intelligence locally on IoT devices. Azure IoT Edge is now seamlessly integrated with the Azure IoT Remote Monitoring solution accelerator. Customers can leverage this feature to reduce bandwidth costs by processing data locally or achieving near real-time actions by using AI at the edge. For operators, the user interface in Azure IoT Remote Monitoring allows customers to add edge

Share

04

Dec

Azure Stream Analytics on IoT Edge now generally available

Today, we are announcing the general availability of Azure Stream Analytics (ASA) on IoT Edge, empowering developers to deploy near-real-time analytical intelligence closer to IoT devices, unlocking the full value of device-generated data. With this release, Azure Stream Analytics enables developers to build truly hybrid architectures for stream processing, where device-specific or site-specific analytics can run on containers on IoT Edge and complement large scale cross-devices analytics running in the cloud.

Why run stream analytics on the Edge?

Azure Stream Analytics on IoT Edge complements our cloud offering by unlocking the power and ease-of-use of Azure Stream Analytics (ASA) for new scenarios, such as:

Low-latency command and control: For example, manufacturing safety systems need to be able to respond to operational data with ultra-low latency. With ASA on IoT Edge, you can analyze sensor data in near real time and issue commands to stop a machine or trigger alerts when you detect anomalies. Limited connectivity to the cloud: Mission critical systems, such as remote mining equipment, connected vessels, or offshore drilling, need to analyze and react to data even when cloud connectivity is intermittent. With ASA on IoT Edge, your streaming logic runs independently of the network connectivity and you

Share

04

Dec

https://azure.microsoft.com/blog/location-intelligence-for-the-enterprise-new-pricing-tier-and-sdk-updates/This blog post was co-authored by Ricky Brundritt, Senior Program Manager, Azure IoT. Today we are excited to announce multiple new features available designed to support Azure Maps customers. Azure Maps represents a new vision of map services aiding Azure READ MORE

Share

29

Nov

Using AI and IoT for disaster management

In countries around the world, natural disasters have been much in the news. If you had a hunch such calamities were increasing, you’re right. In 2017, hurricanes, earthquakes, and wildfires cost $306 billion worldwide, nearly double 2016’s losses of $188 billion.

Natural disasters caused by climate change, extreme weather, and aging and poorly designed infrastructure, among other risks, represent a significant risk to human life and communities. Globally, $94 trillion in new investment is needed to keep pace with population growth, with a large portion of that going toward repair of the built environment. These projects have long cycles due to government authorization processes, huge financial investments, and multi-year building efforts. We need to think creatively about how to accelerate these processes now.

National, state, and local governments and organizations are also grappling with how to update disaster management practices to keep up. The Internet of Things (IoT), artificial intelligence (AI), and machine learning can help. These technologies can improve readiness and lessen the human and infrastructure costs of major events when they do occur. Disaster modeling is an important start and can help shape comprehensive programs to reduce disasters and respond to them effectively.

Anticipating disasters with better data

Share