It is interesting to see what level of daily consumer problems are being solved by IoT. Varieties of use cases from simple inventory management to fully automated self driving cars are some examples of IoT.
Let us see how the ecosystem of IoT looks like. In my view – IoT ecosystem consists of 4 main pillars to it –
- IoT Devices – These are primarily any sensors that could collect the data and send it to cloud and also respond to commands from cloud.
- IoT Infrastructure – Seamless connectivity to ensure that an IoT device is always sending across data and able to receive data.
- IoT Cloud Storage – There will be billions of records to store. Cloud storage helps in dumping the raw data.
- Data Processing – Without ML/AI – IoT is totally incomplete. From the millions of data collected, ML/AI helps in identifying the useful data and act upon it.
- Client Devices – Devices where the analyzed data will be displayed. Also waiting for the manual approval to send the command (Instructions to IoT devices by applying ML/AI on collected data) to IoT devices. Not so far from now – these kind of devices might vanish.
Machine learning is so important in IoT because just by connecting n number of devices to internet does not solve any problem to customer unless and until the devices can respond to each other.
Lets see some use cases with good and VoW experiences in IoT –
A better Machine to Machine intelligence can be achieved by focusing more on processing the collected data. Equally it is also important for all the device to stay connected to the network and always stay connected. Data flow between machines should be real time to achieve greater IoT experience and success.
Share your thoughts on what other elements matter in IoT!
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