Guest blog post by ajit jaokar
Often, Data Science for IoT differs from conventional data science due to the presence of hardware.
Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing).
Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)
Hardware will increasingly play an important role in Data Science for IoT.
A good example is from a company called Cognimem which natively implements classifiers(unfortunately, the company does not seem to be active any more as per their twitter feed)
In IoT, speed and real time response play a key role. Often it makes sense to process the data closer to the sensor.
This allows for a limited / summarized data set to be sent to the server if needed and also allows for localized decision making. This architecture leads to a flow of information out from the Cloud and the storage of information at nodes which may not reside in the physical premises of the Cloud.
In this post, I try to explore the various hardware touchpoints for Data analytics and IoT to work together.
Cloud integration: Making decisions at the Edge
Intel Wind River edge management system certified to work with the Intel stack and includes capabilities such as data capture, rules-based data analysis and response, configuration, file transfer and Remote device management
Integration of Google analytics into Lantronix hardware – allows sensors to send real-time data to any node on the Internet or to a cloud based application.
Microchip integration with Amazon Web services uses an embedded application with the Amazon Elastic Compute Cloud (EC2) service. Based on Wi-Fi Client Module Development Kit . Languages like Python or Ruby can be used for development
Integration of Freescale and Oracle which consolidates data collected from multiple appliances from multiple Internet of things service providers.
Libraries
Libraries are another avenue for analytics engines to be integrated into products – often at the point of creation of the device. Xively cloud services is an example of this strategy through xively libraries
APIs
In contrast, keen.io provides APIs for IoT devices to create their own analytics engines ex (smartwatch Pebble’s using of keen.io) without locking equipment providers into a particular data architecture.
Specialized hardware
We see increasing deployment of specialized hardware for analytics. Ex egburt from Camgian which uses sensor fusion technolgies for IoT.
In the Deep learning space, GPUs are widely used and more specialized hardware emerges such asIBM’s synapse chip. But more interesting hardware platforms are emerging such as Nervana Systemswhich creates hardware specifically for Neural networks.
Ubuntu Core and IFTTT spark
Two more initiatives on my radar deserve a space in themselves – even when neither of them have currently an analytics engine: Ubuntu Core – Docker containers+lightweight Linux distribution as an IoT OS and IFTTT spark initiatives
Comments welcome
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Image source: cognimem
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