IT-OT Convergence
Essentials of IoT device data management SOURCE: ISTOCK
A key to IT-OT convergence is device data management solutions for effectively monitoring, processing and managing large amounts of data from IoT devices. Traditional embedded database solutions fall short in understanding and fulfilling the sophisticated data processing and management requirements of IoT devices.
Intelligent systems in the IoT age collect and analyze mass quantities of data, and findings should be accessible to edge devices and embedded systems. SMART SENSORS AND DEVICES ARE BECOMING an important part of the Internet of Things, IoT, and are continuously changing the way we automate tasks. We employ intelligent systems to improve production in factories, manage smart home energy to monitor and reduce energy costs, build industrial automation systems to replace human assignments, and develop autonomous transportation to improve driver safety. Inside these embedded systems are sensors which rapidly transmit data that must be immediately captured, processed, and acted on. But how are we able to capture, process and manage the flow of massive amount of data continuously coming to the system and empower devices to make decisions or take actions? Traditional embedded database solutions fall short in understanding and fulfilling the sophisticated data processing and management requirements of IoT devices. IoT edge database solutions designed to understand the continuous stream of data produced by sensors enable devices to make important decisions in microseconds. What are the available device data management
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solutions for IoT devices to monitor, process, and manage data? In this article, we will review some embedded IoT device data management options available for edge devices and highlight the distinct design primitives’ approach for addressing device data processing.
IoT edge device data challenges
First, what is an embedded system? An embedded system is a device that performs tasks automatically through self-learning and self-management, which often connects to other systems. These systems are starting to use the IoT to improve lives. But as a significant volume of data accumulates on each connected device, a comprehensive approach to data management is required. Across these embedded systems, the primary challenge is to monitor, capture and process the data to intelligently ensure safe behavior and fault‐free operation of the devices. More than simply streaming data and receiving commands, these devices run complex, high level software programs that operate with or without a network and cloud connection.
Meanwhile, devices embedded in these systems must handle large transactions for various tasks and need to be able to connect to each other on multiple networks. Therefore, IoT data management needs to be divided into real-time interaction with objects, or things, as well as offline mass storage and long-term trend analysis. In the real world, device manufacturers seek a scalable edge data management solution to deploy hundreds of IoT devices, so each can collect, analyze, and manage the flood of data that IoT sensors produce, without losing performance. These devices do not need to permanently store all real time data but must capture and store critical information. Simultaneously, each IoT node must make independent decisions that trigger appropriate reactions. Database queries enable device applications to gain the intelligence to make informed decisions in real time: efficiently and without delay. Succeeding on the IoT requires both the right data management software and the capability to quickly collect and connect device data at the right throughput rate to achieve low latency.
in d u s t r ial et h er ne t b o o k
04.2021
