IoT has become a mainstream technology with vital potential for advancing the lifestyle of modern societies. Internet of Things (IoT) has undergone a rapid transformation with rapidly expanding internet-connected devices worldwide. This blog aims to provide a checklist for choosing the right IoT platform while developing an IoT based software or hardware model.
What are the important features expected from an IoT platform?
- Device management and integration support
- Data security and protocols for data collection
- Efficient analytics and support for visualizations
Device Management and Integration Support
Device management is an essential feature expected from any IoT platform. The platform should be able to list the connected devices and track its operation status. It should handle firmware (or any other software) updates, configuration, and provide device-level error reporting with error handling. IoT device users should be able to get individual device level statistics.
Support for integration is also an important feature expected from an IoT platform. The API should be able to provide access to the important operations and the data that needs to be exposed from the IoT platform. REST APIs are commonly used to achieve this aim.
It should be noted that data security measures for an IoT platform are much stronger than general software applications and services. With a huge number of devices getting connected to an IoT platform, it demands the platform to handle that many number of vulnerabilities Network connection between the IoT devices and the IoT software platform should have a strong encryption to avoid inflitrations.
While security is a risk, often the low-cost low-powered devices involved in current IoT platforms might not be able to support advanced data security measures. This places the data security responisbilities on the IoT software platform to handle the device-level issues.
How can an IoT platform enhance data security?
- Separation of IoT traffic into private networks
- Strong data security at the cloud application level
- Regular password updates
- Support upgradeable firmware by way of authentication
- Signed software updates
- Use efficient data collection protocols
The types of data collection protocols used for data communication between the components of an IoT platform should be light-weight. This means, it should facilitate low energy use as well as low network bandwidth functionality. Data collection can be classified under several categories – such as application, payload container, messaging, and legacy protocols.
The data received from the sensors connected in IoT platform needs to be analyzed intelligently to gather insights.The four main types of analytics that can be conducted on IoT data are,
- Real-time analytics : this conduct online (on-the-fly) analysis of the streaming data. Example operations include window-based aggregations, filtering, transformation, and so on.
- Batch analytics : it runs operations on a set of accumulated data. Batch operations run at scheduled time periods and it may last for several hours or days.
- Predictive analytics : focused on making predictions based on various statistical and machine learning techniques.
- Interactive analytics : runs multiple exploratory analysis on both streaming and batch data.
Some examples for IoT software platforms are AWS IoT platform, IBM IoT Foundation Device Cloud, Ericsson Device Connection Platform (DCP) – MDM IoT Platform and Bosch IoT Suite – MDM IoT Platform. The pain point for these platforms is the huge network bandwidth consumption between the sensor devices and the IoT server.
By using lightweight communication protocols it can be reduced, but there’s a better solution – edge analytics.Edge analytics can reduce the amount of raw data transmitted to the IoT server. This method of analytics could be implemented even in simple hardware embedded systems, such as an Arduino.
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