Two possible architectures of IoT systems that can use data analytics
The IoT system consists of various functional blocks to facilitate various utilities to the system such as identification and sensing. The main functional blocks include the device, communication, services, management, security, and application (Ray, 2018). There are various domains in IoT platforms such as research, application development, device management, visualization, and analytics. The IoT architectures are domain-specific based on broad areas such as RFID, sensor network, and big data.
The possible architecture of IoT system that can use data analytics can have layers such as web portal, dashboard, APIs, event processing and analytics with data storage, aggression/bus layer with ES and message broker, relevant transports such as HTTP and XMPP, device manager, and identity and access management (Fremantle, 2016). The device layer can be different types of devices such as smartphones where they are connected to the internet. Similarly, devices need an identity such as a unique identifier (UUID). Likewise, the communication layer supports the connectivity of the devices, and the aggregation/bus layer aggregates and brokers communications to support an HTTP server and other protocols. The event processing and analytics layer can support big data tools and technologies such as Apache Hadoop and provide scalable MapReduce analytics on the data coming from the devices (Fremantle, 2016).
The other IoT architecture can consist of IoT devices, cloud gateway, device provisioning, stream processing, machine learning, data transformation, user management, and security monitoring components (Microsoft, 2020). The devices can connect to the cloud to communicate using a cloud gateway for security. Similarly, for registering and connecting devices, a device provisioning service is recommended. The stream processing analyzes large streams of data records and evaluates using tools such as Azure Stream Analytics. The machine learning component allows predictive algorithms to execute using historical data. The data transformation manipulates the stream data and business process integration performs actions based on insights from the device data. The user management restricts users to perform certain actions that can be dangerous. Finally, security monitoring helps with end to end security solution for IoT system (Microsoft, 2020).
References
Fremantle, P., 2016. A Reference Architecture For The Internet of Things. [Online] Available at: https://wso2.com/whitepapers/a-reference-architecture-for-the-internet-of-things/ [Accessed 28 November 2020].
Microsoft, 2020. Azure IoT reference architecture. [Online] Available at: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/iot [Accessed November 28 2020].
Ray, P. P., 2018. A survey on Internet of Things architectures. Journal of King Saud University — Computer and Information Sciences, Volume 30, pp. 291–319.
