IoT, or edge computing, consists of the use of small sensors and computing devices at or near points where data is generated for immediate action to be taken. One of the best examples is a driverless car, where these devices help identify obstructions to apply the brakes quickly. We may not have the luxury of moving the data to the cloud for analysis, and then the instruction coming from the cloud to the car. Those extra milliseconds can cause the crash, assuming in the first place that an Internet connection to the cloud exists.
Ruchir Dixit, Country Manager for Siemens EDAThat said, many of us already have Edge devices. “My wristwatch is capturing my heart rate, how many calories I have burned. For heart rate, the chip sends a light pulse through my skin and collects its reflections from the blood vessels, and then measures the difference whether a pulse occurred or not. Another device in my watch, based on a gyrometer, is estimating how many physical movements my hand has made, to decide whether it can be counted as a step I have taken,” he said. said.
But there are less visible ones, such as in a factory where, say, dust can affect a robot’s ability to pick up an object by creating a vacuum. “An edge device on a robot can determine whether the vacuum manufacturing capability is robust and reliable,” Dixit said. or in dangerous places. Governments and organizations, Dixit said, are placing sensors and shore instruments close to the volcano and deep in the oceans to predict when the volcano is likely to erupt (based on the thermodynamics of the soil around it), or When a tsunami can occur (depending on unusual water movements).
A volcano, or an oil rig, or indeed, many other places even in large cities may not have fiber optic cables to transport data. This is where 5G becomes important to rapidly receive alerts from sensors to initiate necessary action.
Edge computing is also important because it is too expensive to move everything to the cloud, and puts a heavy burden on bandwidth. The cost of transferring data from device to cloud is 20 times higher than the cost of transferring data from device to edge. In one factory, Sahu said, a camera generates 2,000 terabytes of data per day. But out of this only 1 TB is valuable, the rest is useless. AI on the edge, he said, can filter out the waste, and send only what the cloud needs. “In home automation, mass transfer of data to the cloud will also involve serious privacy concerns,” Sahoo said.
Siemens and Intel are collaborating closely on various aspects of these technologies. Siemens EDA (Electronic Design Automation) is a supplier of the software and hardware tools Intel needs to build its chips for AI and IoT. “The chips require advanced software to help us design them and we provide them,” Dixit said. These chips Intel develops, in turn, are used in a host of Siemens businesses – wind turbines, oil and gas equipment, electric trains, factory automation, in health analysis.
Sahoo said that EDGE requires system talent, software talent, semiconductor talent, application talent. “India has most of it. Our semiconductor talent keeps getting better and better, but demand exceeds supply. This is where we need to pay more attention.”
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