Chapter 2. IoT Sensor Devices and Generating Predictions
Editor’s Note: At Strata + Hadoop World in San Jose, in February 2015, Bruno Fernandez-Ruiz (Senior Fellow at Yahoo!) presented a talk that explores two issues that arise due to the computational resource gap between CPUs, storage, and network on IoT sensor devices: (a ) undefined prediction quality, and (b ) latency in generating predictions.
Let’s begin by defining the resource gap we face in the IoT by talking about wearables and the data they provide. Take, for example, an optical heart rate monitor in the form of a GPS watch. These watches measure the conductivity of the photocurrent, through the skin, and infer your actual heart rate, based on that data.
Essentially, it’s an input and output device, that goes through some “black box” inside the device. Other devices are more complicated. One example is Mobileye, which is a combination of radar/lidar cameras embedded in a car that, in theory, detects pedestrians in your path, and then initiates a braking maneuver. Tesla is going to start shipping vehicles with this device.
Likewise, Mercedes has an on-board device called Sonic Cruise, which is essentially a lidar (similar to a Google self-driving car). It sends a beam of light, and measures the reflection that comes back. It will tell you the distance between your car and the next vehicle, to initiate a forward collision warning or even a maneuver to stop the car.
In each of these examples, the ...
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