The industrial setup is seeing a significant increase in the amount of autonomous machinery with Industry 4.0. Not only are these machines providing human-like thinking capabilities, they are also revolutionizing the industry with their utmost precision and efficiency of operation. Edge sensors are an integral part of the industrial automation ecosystem. The edge sensors collect surrounding and environmental signals, sending them to edge data centers for monitoring and control of various parameters that affect operations. These sensors generate vast amounts of data that require monitoring for the identification of patterns while extracting important insights for further optimization.
With AI or Artificial Intelligence, ML or Machine Learning, and BDA or Big Data Analysis forming the base of Industry 4.0, the industry is treating data as the new gold. These tools process the data generated by edge sensors for efficiently managing and analyzing extensive processes. Enterprises use these tools to obtain insights into the working of processes, for recognizing patterns and looking for events associated with the industrial operation. The analysis helps with the further creation of algorithms that help in the optimization of machines and monitoring devices.
However, large computational power is necessary for processing the data that the sensors produce. The industry resorts to cloud computing, as data processing with the symbiotic support of the cloud, reduces the necessary investments. But this comes at the cost of higher bandwidth requirements and increased latency. On the other hand, applications like computational healthcare and self-driving cars require a faster response. Edge computing easily fills such gaps.
For the computation of data and remote monitoring, the Internet of Things happens to be a complete ecosystem of supporting devices and connected sensors. The cloud processes the enormous amounts of data the system generates. The cloud is simply huge data centers working round the clock, handling extensive amounts of data while being in connection with the internet.
The location of most of these data centers is in remote areas, as they need massive areas of land and cheap power to operate. This increases the bandwidth requirement and latency. Engineers are trying to solve this issue by placing smaller data centers close to the edge sensors, actuators, motors, etc.
Industries also use IoT to share data through unified analytic platforms. Industries usually deploy similar kinds of machinery, but use them in varied conditions of environments and load conditions. This generates various types of data, which when industries share them, can help build a robust ecosystem.
Companies can optimize their products based on shared local consumer data. This optimization can be in the hardware or in the software. Industries frequently conduct software optimization through the internet, while hardware optimization involves generating newer editions of the product. Collecting user data typically involves privacy and security issues. With edge computing, proper handling of local and distributed storage of data can help prevent huge tech giants from accumulating large amounts of private data. However, this makes data more prone to attacks from cyber-crooks.
Engineers typically collect and process the data collected from the edge sensors near the sensor itself. Sometimes, they transfer the data to centralized data centers or localized edge data centers for adding value.
