Managing IoT Data Flooding in Industry & Manufacturing

The IoT ecosystem consists of many aspects, and data storage management is one of those key aspects. From a top-level standpoint, we can define the Internet of Things (IoT) as a network of physical devices like embedded sensors, driverless vehicles, wearables, smartphones/tablets, or home appliances that generate and transfer data without human interference. Today, we see that there is a strong drive toward IoT and digitization, but this concept has been breathing air for the last ten years, with interlinked devices and applications functioning in many industries and consumables.

Today’s most visible change is the augmented capacity of connected devices, quick comms networks, standardized communication protocols, and affordable IT, adding a turbocharge to the IoT process. These things are changing the operational processes and product lifecycles across various markets and applications. The precise information provided by IoT devices empowers manufacturers to use the benefits of Industry 4.0 to function in automated production/assembly lines but not as fast as you might expect.

Data Collection Process

An unavoidable result of IoT and its connected devices and applications is the massive amount of changing data. The data provided by IoT has to be processed in real-time to churn the most useful conclusions and make swift decisions to avoid bottlenecks, keep production lines functional, and skip the smallest delay that causes huge loss. This functionality is important for manufacturers dependent on artificial intelligence (AI) and machine learning (ML). Both technologies need data, bandwidth, and demand for robust storage management processes that allow large-scale parallel processing.

Data flooding in an industrial IoT network is a three-layer process as follows:

  1. Data sources: IoT collects data from ‘n’ number of connected devices or embedded sensors. The collected data is then either processed locally or, depending on the access to the appropriate infrastructure, the sensitivity of the data, type of industry, or transported through an edge gateway to the cloud for processing and handling or a colocation facility.
  2. Data storage: The data gathered by the embedded or connected technologies are then stored properly for short-term or long-term applications. Some data might need immediate processing depending on the application, whereas some might need to be safely transferred and stored for future applications.
  3. Data analytics & applications: This is the last layer. Data is analyzed to churn useful information and provide an ace to the operations and control process. A detailed understanding of production lines and product life cycles allows for slicker operations via predictive maintenance and other troubleshooting measures. These features help skip the downtimes and outages that cause inefficiency and hinder profits.

Dealing with Data Storage

The storage system is just a part of the IoT data processing ecosystem, but it has become an important element because of insufficient storage capacity that jeopardizes the operability. The storage capacity of any IoT network must ensure data integrity, safety, and reliability. They must be agile to sustain various environments and applications while providing uniform interconnectivity between cloud edge gateways and other edge devices. Compromised quality storage is the Achilles heel for many manufacturers with outdated comms rooms that do not allow them to adopt IoT data to its full potential.

According to industry research, insufficient storage capacity results in 60 percent and 73 percent of machine-generated data being unanalyzed. For the best result, IoT data that fuels 4.0 must be processed close to the source for operability and safety reasons. Organizations reliant on critical data will realize that conventional colocation facilities cannot assure the ultrahigh speed and low latency needed.

On the contrary, many on-premises facilities are not well-suited for purposes as far as IoT data storage management is concerned because they cannot house the specialist IT needed for it. Even an IT team often lacks room for expansion as capacity requirements increase. High-performance computers have sheer magnitude, GPU-based processing power, associated cooling technology, and high energy consumption; all of these need specialist facilities that are fireproof and weatherproof with seamless connectivity to the cloud and support dynamic power consumption.

Meeting Data Demands

Providing a customized facility with potency to meet IoT data demands is not on the list of many manufacturers due to the high costs. What is required is a way of providing centralized data center capabilities locally without the need to build a customized facility that promises HPC processing.

This has not been possible so far because of financial constraints, complicated project management needs, and excess deployment times. Nevertheless, the IoT handling complexities in manufacturing are about to resolve, and all credit goes to the disruptive approach to edge data center infrastructure. In the absence of cost-efficient and possible means of delivering HPC at the edge, IoT data will stay untapped regardless of the precision or elaborateness of the associated embedded sensors. Edge data center infrastructures must acclimate to the transforming data processing landscape.

Proper Storage for Successful IoT

We all know that the Internet of Things is the future of technology, and everything will be connected and automated in the coming years. From health wearables to equipment location tracking in hospitals, smart houses to smart towns, IoT is already offering many things that make our daily life more comfortable. Still, there is a lot more to discover in IoT. This means the storage system needs to be improved because the value of any IoT-derived data has a short span and becomes worthless if the connected storage management infrastructure fails to keep pace with the continuously-changing data. IoT investment becomes an expensive white elephant if proper measures are not taken.

https://www.iotforall.com/iot-data-flooding-industry-and-manufacturing

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