IoT Analytics Platform for Real-Time Data Ingestion, Streaming Analytics

What is IoT Analytics?

IoT analytics is an application that helps to understand the huge volume of data generated connected IoT devices. In other words, the ‘Internet of Things’ is coined by Kevin Ashton co-founder of the Auto-ID Center at the Massachusetts Institute of Technology (MIT). In other words, the Internet of Things is just an ecosystem, the physical things which are connected to the internet and we see results through the IoT analytics platform.

Layers in IoT architecture

• Hardware/Sensing Layer – This is the very first layer of the IoT architecture. Also, it consists of the hardware device which is connected to the Network layer. Wireless Sensor Networks (WSN) and radio-frequency identification (RFID) are considered as the two main building blocks of the IoT. The Arduino Microcontrollers are directly connected with the sensors. The Arduino microcontrollers are also connected to the Raspberry Pi which is also connected to the internet using Ethernet or WiFi. It transmits the data collected from the sensor in real-time to the server.
• Network/Gateway Layer – This layer acts as the bridge between Hardware /Sensing Layer and Management Layer. This layer receives the digitized data and routes it over wired LANs, Wi-Fi, or the Internet for further processing. There is a different protocol that can be used to communicate between IoT gateway and servers like MQTT, AMQP, COAP, and HTTP. We have discussed the different protocols used in the next section.
• Management Layer – This is the layer that is responsible for data modeling and security control. This is the layer where all the data handling or data processing operation will be done. At this layer, the necessary data is extracted from the data transferred by the sensor.
• Application Layer – This is the final layer of the IoT analytics architecture. This layer uses the data processed by the management layer.

Real-Time Stream Processing for IoT

Real-Time Stream processing refers to the data processing with the data stream collected from the IoT device in Real-Time. Learn more about Open Source tools for real-time Analytics Platform. These are the tasks which can be included in this processing –

• Transformation – It includes the conversion of the data which is collected from the IoT device. After this conversion, the resulting data is transferred for further analytics.
• Data Enrichment – Data enrichment process is the operation in which the sensor collected raw data is combined with the other dataset to get the results.
• Storing Data – This task includes storing the data at the required storage location.

IoT Analytics span many intervals

Protocols Used for IoT platform

• MQTT – MQTT protocol uses a publish/subscribe architecture. The central communication point of this protocol is the MQTT broker. Every client includes a topic name while publishing data to the broker. Topics are responsible for routing information for the broker. Each client that wants to receive messages subscribes to a particular topic and the broker delivers all messages with the matching topic to the client.
• COAP – COAP Protocol (Constrained Application Protocol) is a web-based protocol that has been designed to connect with lightweight devices to the Internet of things(IoT). Like HTTP protocols, COAP is also used Request-Response model. It also allows us to make API calls GET, PUT, POST, DELETE data via URL.
• AMQP – AMQP (Advanced Messaging Queuing Protocol) is an open standard for passing messages between applications and organizations. It connects the system, provides business processes with the information they need.
• HTTP – This is the standard protocol for web services and still is used in IoT analytics solutions. The most popular architectural style called RESTFul is widely used on mobile and web applications and must be considered on IoT Solutions.
• DDS – DDS stands for Data Distribution Service which is a standard for real-time IoT analytics, scalable, and high-performance machine-to-machine communication. DDS can be deployed in both low footprint devices and on the cloud as well.

Microsoft Azure IoT Architecture

Microsoft Azure Events Hub connects to MQTT via Cloud Gateway and consumes the data published by Raspberry Pi on the MQTT broker.

Azure provides Stream Analytics to data processing. Above all, we can use the data stream from Events Hub and process the real-time data stream.

After processing, the data is sent to Azure CosmoDB and can be further visualized on Power BI by using the CosmoDB connector

Google IoT Architecture

In Google, IoT analytics architecture Cloud Pub/Sub is used to listen to the data from the MQTT broker. Also, Cloud Pub/Sub is a messaging system that can handle a real-time data stream.

Cloud Pub/Sub also natively connects to other Cloud Platform services, gluing together data import, data pipelines, and storage systems.

After ingesting the data from the IoT device Cloud Pub/Sub further sends the data to the Cloud Dataflow for the data processing. Cloud Dataflow is used to create the data pipeline to perform some data transformations.

Cloud Dataflow sends the processed data to the BigQuery. BigQuery provides a fully managed data warehouse with a familiar, SQL-like interface. And further Cloud DataLab is used for the data representation.

AWS IoT Architecture

In AWS IoT architecture, Kinesis Stream is used for the data ingestion. For this, we need to define the Kinesis action which will collect data from MQTT and send it to Kinesis Analytics for further processing. After the stream-processing, the processed data is sent to Amazon Redshift and Amazon S3.

In this, we can use Amazon QuickSight for the data representation. In Amazon QuickSight, we can build our visualization dashboards perform ad hoc analysis, and quickly get business insights from your data.

Top Industrial IoT Applications

The Healthcare sector is one of the fastest sectors which is adopting the Internet of Things. There are a lot of sensors coming into the scenario and the healthcare sector is adopting it very fast. These devices are allowing the doctor to track their patients whether they are stuck to the treatment or not.

Industries IoT enabled machinery will enable operation managers to manage the factory units remotely. Hence, the use of IoT sensors in manufacturing equipment enables condition-based alerts that are designed to function within specific temperature and vibration range.

Smart Transportations Keep vehicles on the road by predicting maintenance need and Streamlining logistics using real-time data and alerts to optimize delivery routes, monitor performance, and quickly respond to delays or issues as they happen. Therefore, we can ease traffic congestion with the help of using real-time analytics.

Smart Retail Predictive equipment maintenance is used for managing energy, predicting equipment failure or detecting other issues. Hence, IoT analytics in retail helps to enable precise inventory management, and most importantly enhancing the consumer’s shopping experience.

Smart Buildings No doubt, IoT helps to Connect, reduce management and utility costs by learning from data you collected. For example, IoT analytics for smart buildings helps to personalize and automate your building’s heating and cooling, room utilization, which further helps create a more comfortable and productive work environment.

Smart Agriculture Smart Agriculture is growing rapidly in the field of agriculture. Therefore, farmers can use meaningful insights from the IoT driven data for sensing soil moisture and nutrients, controlling water usage for plant growth to get better outcomes.