Comparison of IoT features and platforms

Internet of Things Concept

The development of the information technology market has led to the emergence of the Internet of Things (IoT) concept. The main idea of IoT lies in the interaction of familiar household items through high-speed networks. In a broad sense, the Internet of Things is not only a multitude of different devices and sensors connected to each other through wired and wireless communication channels and linked to the Internet, but also a closer integration of the real and virtual worlds, where communication between people and various devices plays an important role.

Levels of the Internet of Things

According to Rob van Kranenburg, IoT can be conditionally divided into four levels:

1. The first level is associated with the identification of each object.
2. The second level provides services to meet consumer needs (can be viewed as a "things" network, a private example being a "smart home").
3. The third level is linked to the urbanization of city life. This is the concept of a "smart city," where all information regarding residents is stored and analyzed for a specific neighborhood, your home, and neighboring houses.
4. The fourth level is the concept of a sensory planet.

Comparison of Internet of Things Platforms

Platform	Microsoft	IBM	Amazon	Open Source	Predix
Device SDK (Developer Tools)	Azure IoT Device SDK, ConnectTheDots.io	IBM Watson IoT Platform Client Library, Watson IoT Platform Device recipes, Paho Library	Device SDK for AWS IoT	Paho Library, Cyclonjs, and many other features	Predix Machine
Protocol Support	HTTP, AMQP, MQTT	MQTT	MQTT, HTTP	MQTT, AMQP, HTTP, etc.	MQTT, WebSocket, HTTPs
Monitoring and Management Functions	IoT Hub, Event Hubs	IBM Watson IoT Platform	AWS IoT	Protocol Bridge, Apache Kafka	RabbitMQ
Data Management Tools	Amazon DynamoDB, Amazon Redshift	Cassandra (or alternatives like MongoDB)	Asset Data, Time Series, Redis, PostgreSQL, Blobstore
Monitoring and Analytics Tools	Microsoft Stream Analytics	IoT Real-Time Insights, IBM Streaming Analytics	Amazon Kinesis	Apache Spark Streaming	Analytics Runtime
Analytics, Machine Learning	Azure ML	Predictive Analytics service (on Bluemix) + SPP Modeler (offline)	Amazon Machine Learning	Apache Spark MLlib	Custom Analytics Support (Python, Java, MATLAB)
Monitoring and Management Functions	Notification Hubs, Power BI	Embeddable Reporting, IBM Push Notifications	AWS Lambda, Amazon QuickSight, Amazon Simple Notification Service	Custom, Zeppelin (Dashboards), etc.	Mobile SDK, Dashboard Seed