New York City council has conducted a six-month study of new and emerging technologies that can improve the lives of its citizens. Being the largest city in the US, the challenges most cities face are compounded by scale. Many of these challenges revolve around city traffic and public transportation.
At the conclusion of their study, the city council realized that the Internet of Things (IoT) is widely available and are becoming more integrated into our daily lives. NYC can capitalize on the wide availability and affordability of IoT devices. This means physical things like traffic lights and vehicles will be able to collect and share data by connecting to the Internet. Through analytics, cities can turn this data into intelligent information that will change the way the world works.
- Application developer
- Data engineer
In this workshop you will use the unique benefits of Internet of Things (IoT) to build a smart city solution to help improve traffic and public transportation in New York City. Use a combination of the power of the cloud, along with IoT Edge devices to provide anomaly detection of city buses, engine anomalies and aggressive driving detection, location broadcasting to update bus route status, and to send traffic information to help inform the timing of traffic lights. Traffic lights will also receive new IoT devices that can help detect maintenance and performance issues, such as voltage irregularities. Easily view all this information through a centralized reporting dashboard provided by Azure Time Series Insights. Use the IoT Remote Monitoring starter solution to manage and simulate IoT devices, set alerts, and view data on a map.
By the end of this workshop, you will learn to use IoT Hub to manage IoT devices, configure and run the IoT Remote Monitoring starter solution to provision, manage, and simulate telemetry for IoT devices via IoT Hub SDKs, use Azure IoT Edge to collect vehicle telemetry data, detect anomalies, and send the summarized data to Azure IoT Hub as needed. In addition, you'll route critical alerts to a Service Bus Queue, create an Azure function that extracts critical alerts from the Service Bus Queue and stores them in Cosmos DB, as well as use Azure Time Series Insights to store, visualize, and query the large amounts of time series data generated by various IoT devices and conduct root-cause analysis and anomaly detection.
This whiteboard design session is designed to help you gain a better understanding of implementing architectures that use IoT data in new and innovative ways. You will design an IoT workflow that begins with a local IoT edge device that collects and analyzes data from various sensors that are connected to it, and intelligently aggregates and sends that data to the cloud when anomalies are detected. Once the data is uploaded, it is sent to a time-series database for rapid analysis alongside other classes of IoT data to spot and act on correlated information in real-time. You will also configure alerts when certain thresholds are exceeded and configure a custom-built application that manages and sends control messages to IoT devices located within the city limits.
At the end of this whiteboard design session, you will be better able to design an end-to-end IoT solution that processes and analyzes data both in the field and in the cloud.
In this hands-on-lab, you will build an end-to-end smart city solution, beginning with IoT Edge devices deployed with modules that you create which intelligently filters vehicle telemetry data for anomalies and transmits the related data to IoT Hub. IoT Hub is responsible for managing IoT devices and facilitating two-way communication between those devices and Azure services. The telemetry data will be stored in Time Series Insights, and all critical data will also flow through an Azure Function that routes critical alerts to a Service Bus Queue for separate processing and storage. You will deploy and configure a custom web app that displays all IoT data on a map and displays alerts based on preconfigured rules for each type of IoT device. You will also use this custom web app to configure IoT devices and send control messages to them via IoT Hub.
At the end of this hands-on lab, you will be better able to build an end-to-end IoT solution that processes and analyzes data both in the field and in the cloud.
- Azure Data Factory
- Azure IoT Hub
- Azure Stream Analytics
- Azure HDInsight
- Azure Spark & Spark SQL
- Azure Storage
- Power BI
Internet of Things