Industry 4.0 has significantly increased the amount of autonomous machinery needed in an industrial setup. These machines with human-like thinking capabilities are expected to revolutionize the industry with utmost efficiency and precision of operation. The industrial automation ecosystem incorporates several edge sensors, which collect the environmental and surrounding signals and send them to edge data centres for monitoring and controlling various parameters affecting the operations. These sensors generate a large amount of data which has to be monitored for identifying patterns and extracting important insights, which can further contribute to the optimization.
With AI/ML and BDA forming the base of Industry 4.0, data has been found to be the new gold. The data generated by edge sensors are processed by these tools in order to efficiently manage and analyze extensive processes. These tools help enterprises to get insights into the working of the process to recognize patterns and look for events associated with the industrial operations. The analysis further supports the creation of algorithms which help in machine optimizations and device monitoring.
To process the data produced, large computation power is required. Hence, cloud computing plays a significant role in data-processing with the symbiotic support of the cloud, resulting in lesser investment. But it comes at the cost of increased latency and bandwidth usage, thus affecting the overall functioning of the system. Furthermore, applications like self-driving cars and computational healthcare require a faster response. This is where edge computing is helping to fill the gap.
Evaluation of edge sensor data using IoT
The Internet of Things forms a complete ecosystem of connected sensors and supporting devices to ease the remote monitoring and computation of data. The huge amount of data generated is processed in the cloud, which is nothing but huge data centres working 24×7, handling massive amounts of data while being connected to the internet.
These data centres are generally located in remote areas because they require huge areas of land and cheap power availability. This in turn causes an increase in latency and more bandwidth usage. A solution to this could be placing small data centres about the size of small shipping containers near the edge that is near to the edge sensors, motor, actuators, etc.
IoT also helps to share data between industries through unified analytic platforms. Different industries deploy similar kinds of machinery and these machines are used in varied conditions of load and environmental conditions, hence generating various kinds of data. This data when shared among industries can help build a robust ecosystem.
Local consumer data helps companies optimize their products. These optimizations could be both software as well as hardware. The former is done Over-the-Air through the internet while the latter is done in new editions of the product. The collection of local user data involves security and privacy issues. Edge computing involves local and distributed storage of data in a manner which can help prevent huge amounts of private data from being accumulated by huge tech giants. But it makes data more prone to cyber-attacks.
What after edge sensor data is collected?
Data collected from edge sensors is either transferred to centralized data centres or to localized edge data centres or is collected and processed near the edge sensor/actuator itself. In either of the cases, value has to be derived from it. This data is fed into algorithms to identify patterns and provide insights into the process. Industries do the regular maintenance of their machinery in order to maintain maximum uptime of their machines and minimize breakdowns. Edge sensors are added to machines, which are prone to breakdowns, the data from these sensors can be analyzed to perform predictive maintenance and reduce downtime.
Autonomous vehicle manufacturers like Tesla use sensors and edge computing to quickly analyze data and perform corrective action based on real-time traffic data and also monitor and analyze the various systems present in the car. In healthcare systems edge computing with sensor data is used to gather and analyze data in order to provide a unified and complete picture of the patient’s health profile. Also, data from critical surgeries can be gathered and shared so as to improve procedures and also share information around the globe. In the agriculture sector edge sensor data is used to monitor the soil nutrients, water usage, and climatic conditions to predict harvesting. The data is also used to optimize and enhance the crop yield in the next cycle by predicting the best season and condition for growth.
Future of edge computing
With the development in the fields of private 5G networks, AI and ML, the future of edge computing is bright. By having more powerful processors, and better access to faster and more widespread networks through private 5G and smart edge devices powered by TinyML, the possibilities for new use cases would open up when all of these are combined with edge computing. In the field of energy where the world requires to save the available finite resources and prevent climate change, using smart edge sensors to monitor the use of these resources and using edge computing to provide sustainable solutions will become more affordable. Taking an example of a farm where centralized sensors and cloud computing were way too expensive, with edge technology this could become more affordable.
Edge computing has helped self-driven cars like Tesla to achieve new heights. The same concept could be used to build smart cities wherein other cars, buildings and structures could share information among themselves to create a smart ecosystem turning cities into AI-powered machines. Efficient edge computing is here with cheaper processors, improved storage and faster private 5G networks to access surrounding data for optimization purposes.
This article was originally published on EEWeb.
