Hello everyone. I'm Professor James Won-Ki Hong from the Department of Computer Science and Engineering at Postech. Today, I will introduce Google Cloud IoT, which is the IoT cloud platform from Google. This will be the last Cloud IoT platform that I will explain in this course. Here's a table of contents for this lecture. First, I'm going to introduce Google Cloud IoT as well as Google Cloud platform. Each of the major components in the Google Cloud IoT platform will be explained. I will then present several Google Cloud IoT use cases. This is a promotion video on Google Home. This is a very good example of IoT application. I'm asking you to watch this funny video to get started with Google Cloud IoT lecture. Please watch this video and try to think about how this works. So, what is Google Cloud IoT? Google Cloud IoT is a set of fully managed and integrated services that allow you to easily and securely connect, manage and ingest IoT data from globally disbursed devices at a large scale, process and analyze, visualize the data in real time, and implement operational changes and take actions as needed. Please watch the following short video on introducing Google Cloud IoT. This figure illustrates IoT architecture on Google Cloud Platform. Google Cloud IoT Core is a fully managed service that allows you to easily and securely connect, manage and ingest data from millions of globally dispersed devices. Cloud IoT Core in combination with other services on Google Cloud IoT Platform provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real time to support improved operational efficiency. In the following slides, I will explain the major components in the Google Cloud IoT. Google IoT core has two main components, a Device Manager and the Protocol Bridge. The Device Manager allows individual devices to be configured and managed securely in our course grained way. Management can be done through a console or program medically. The protocol bridge provides connection endpoints for protocols with automatic load balancing for all device connections. The protocol bridge has native support for secure connection over an industry standard protocols such as MQTT and HTTP. The protocol bridge publishes all device telemetry to Cloud pops up, which can then be consumed by downstream analytic systems. Cloud Pub/Sub is a simple, reliable, scalable foundation for a stream analytics and even event-driven computing systems. As part of Google Cloud's stream analytics solution, the service ingests event streams and delivers them to Cloud Dataflow for processing and BigQuery for analysis as a data warehousing solution. Cloud Dataflow is a fully managed service for transforming and enriching data in stream real-time, and batch historical modes with equal reliability and expressiveness. No more complex workaround or compromises needed, and with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges while paying only for what you use. Google Cloud Functions is a lightweight compute solution for developers to create single-purpose stand-alone functions that respond to cloud events without the need to manage a server or run-time environment. Google Cloud Function provides the same functionality as Amazon Web Service Lambda, Microsoft Azure Functions, and IBM Openwhisk. Cloud Bigtable is Google's NoSQL BigData database service. It's the same database that powers many core Google services including search, analytics, maps, and Gmail. Cloud BigQuery is Google's fully managed petabyte-scale, analytics data warehouse. BigQuery is no ops. There's no infrastructure to manage and you don't need a database administrator. So, you can focus on analyzing data to find meaningful insights, use familiar SQL and take advantage of pay-as-you-go model. Storing and querying massive databases can be time-consuming and expensive without the right hardware and infrastructure. Google BigQuery is an enterprise data warehouse that solves this problem by enabling superfast SQL Queries using the processing power of Google's infrastructure. Google Machine Learning engine combines the managed infrastructure of Google Cloud Platform with the power and flexibility of TensorFlow. Google Machine Learning engine mainly does two things. One, it enables you to train machine learning models at scale by running TensorFlow training applications in the cloud. Two, it hosts those trained models for you in the cloud so that you can use them to get predictions about new data. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily, so you can focus on your data science tests. Google Data Studio turns your data into informative dashboards and reports that are easy to read, easy to share and fully customizable. Dashboarding allows you to tell great data stories to support better business decisions. Google Cloud IoT Core is probably the most important component in Google Cloud IoT Platform. Please watch the video on Google Cloud IoT Core deep dive. In this video, Gus Class talks about the high-level features of the Google Cloud IoT Core. Additionally, he explains how the underlying Google Cloud products that make up the cloud solution let you add IoT capabilities at scale for both data ingress and analytics. An end-to-end demonstration of the product concludes the presentation. This section provides some notable use cases of Google Cloud IoT. Smart Parking Limited is using the Google Cloud IoT to remotely monitor and manage parking spaces worldwide by connecting parking monitoring devices to Cloud IoT Core. They have a secure and reliable way to not only ingest that data, but then also use it to gain valuable insights. They know exactly how their systems are performing and can push updates to devices to ensure the delivery of the best services. Please watch the BBC news video on Smart Parking. The second use case is from Energyworx. Energyworx provides a SaaS platform that lets utilities and business processes manage, and mine energy data for actionable intelligence to decrease costs, meet demand for efficiently and create new business models. By uncovering the value of this data, Energyworx customers have optimized the grid and reduced losses during electricity transmission and distribution. They have also automated smart business decisions and created new business concepts and models. Oden Technologies is using the Google Cloud Platform to cut the cost and complexity of its smart factory cloud platform for manufacturing analytics. Oden Technologies unites industrial hardware, wireless connectivity and BigData architecture into a single cloud-based platform. So, manufacturers can analyze and optimize their production from any device they choose. Please read the case story given below. There are many websites providing various info about Google Cloud IoT. In this lecture, I gave an introduction to Google Cloud IoT, which is a set of cloud services for IoT from Google. In the next lecture, I will explain how to use Google Cloud IoT. See you soon.
