The cloud technologies have revolutionised the niche of Internet of Things. The cloud technologies for Internet of Things offer a one stop solution for securely connecting devices and securely communicating between the devices. It also offers the tools to process the data gathered and also analyse it and use it further. In this article we will do an overview of Google Cloud Platform for IoT.
Google Cloud platform for IoT overview
In Google Cloud Platform we categorise the data flow into three stages on an abstract level. The three stages namely are Ingestion, Pipeline Processing, Cloud Analytics.
Ingestion: It is the process of importing information from devices into Google Cloud services. Google Cloud provides different ingestion services, depending on whether the data is telemetry or operational information about the devices and the IoT infrastructure.
Pipeline Processing: This stage does the pre processing, calibration, transformation needed. In order to have efficient system the link between various services has to be proper. Therefore, this service specifically looks after data routing for further processing and storage of data.
Cloud Analytics: The data is processed in this stage and various operations are performed over the data. In this stage we explore and gain insights into our data.
For ingesting data into the Google Cloud Platform we have three services doing different parts. The three services are Cloud IoT Core, Cloud Pub/Sub, Cloud IoT Edge.
Components of Ingestion
IoT Core: It is where the user creates registries and devices. It creates a pub sub topic whenever a registry is created. It looks into the management of devices which include adding new ones, deleting, monitoring them and updating the devices. In order to connect the device the device needs to have meta data which the GCP can recognise and thus the device can be connected to the cloud. It also has a log manager that monitors logging and log outs of devices.
CLoud Pub/Sub: It provides a globally durable message ingestion service. By creating topics for streams or channels, you can enable different components of your application to subscribe to specific streams of data without needing to construct subscriber-specific channels on each device. Pub/Sub also natively connects to other Google Cloud services, helping you to connect ingestion, data pipelines, and storage systems. If a system has quite a lot of devices the communication would become chaotic. Therefore to ensure a hassle and traffic free system it is intelligent enough to manage the incoming and outgoing traffic.
Cloud IoT Edge: As the name suggests it is the edge computing service provided by the Google Cloud Platform.
Components of Cloud Analytics
This section of the Google Cloud Platform for IoT offers services like BigQuery, Datalab, Machine Learning.
BigQuery: It provides a fully managed data warehouse with a familiar SQL interface, so you can store your IoT data alongside any of your other enterprise analytics and logs. Therefore, the performance and cost of BigQuery means you might keep your valuable data longer, instead of deleting it just to save disk space.
Datalab: It is an interactive tool for large-scale data exploration, analysis, and visualization. IoT data can end up being useful for multiple use cases, depending on which other data it’s combined with. Datalab lets you interactively explore, transform, analyze, and visualize your data using a hosted online data workbench environment based on the open source Jupyter project.
ML: IoT data is often inherently multi-dimensional and noisy by nature. These attributes can make it hard to extract insight by using conventional analytics techniques. However, this nuance and complexity is often where Deep Neural Networks excel. Tensorflow is a leading open source machine learning framework, and on Google Cloud you can apply Tensorflow in a distributed and managed training service through AI Platform.
