The amount of data that goes up to the cloud is creating a data lake that continues to get bigger and bigger. I’m not adverse to this but what’s the point? How much data do you need or use? Which neatly brings me onto useless IoT applications. (I had to find a reason to get onto this…)
I recently read a review of a wireless kettle (and believe me before I instigate a wave of responses – “annoyed from Andover” anyone? I’m sure there are many perfect wireless kettles out there but honestly why can’t you just get off your chair and flip the switch?)…there are some applications that just don’t make sense to me.
I’ll share some feedback on getting this kettle going “On the first attempt to boil the kettle, it was hampered by a forced-debugging, which consequently caused its base station to reset. The kettle's base station appeared unable to connect with the kettle itself. On re-calibration it seems that it had never connected at all. Then the flaky WiFi connectivity required constant checking and resetting.” – all for a cup of hot water anyone? Now if that kettle had been monitoring the level of water and looping back into my system to pass up a local value of how much power it used to boil back into my home efficiency system then I get it. This is what I call sending insight versus just data.
Smart partitioning and embedding of algorithms in the sensors allows the most critical data to be interpreted in real time at its source. Algorithms embedded into intelligent sensors and in the cloud allow for interpretation beyond what can be done with silicon alone. In fact, this leads to the possibility for prediction and anticipation of future system behavior. Accelerating adoption of IoT solutions in mission critical applications is dependent on the ability to build secure systems – intelligent partitioning enables this.
In Industrial automation, active machine monitoring can transform factories by radically improving uptime efficiency. For example, accurate MEMS vibration sensors and algorithms to predict machine performance and reliability both locally for real-time optimization and intervention and in the cloud where information from multiple systems across multiple factories can be aggregated, analyzed and acted upon to improve productivity.
In mobile healthcare, clinical grade monitoring of vital signs and other wellness indicators are becoming increasingly important. For example, energy efficient optical sensors and ultra-low power MEMS sensors that reliably assess patient health in real-time using advanced algorithms. Another example is motion rejection algorithms which more accurately interpret heart rate from optical measurements at a patient’s wrist. Resultant information can then be transferred to the cloud for remote processing that may include more intensive data analysis used to derive further bio-markers and trends.
And that’s why you should use the cloud effectively (and why you should not have to wait hours for your kettle to boil…)
From Rant to Reality:
For intelligence at the edge you need low power processing. More information on the latest ultra low power MCU that enables 10 times system-level power savings.
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