Why Amrita IoT Middleware (AIoTm)
Different?
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Easy to Use QUI (Query User
interface) to Query your Devices
Anywhere,Anytime to Acquire Real
Time Status or Status Over a Period
of Time.
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AD2 - Amrita Dynamic Dashboard to
define Your Data Directly and there
after Monitor your Devices with no
Programming Skills.
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Extra Security to your Device Data
Through Topic Based Authentication
in Shared Message Queue.
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AGway, an Indigenous Gateway that
will Ensure Secure Communication
Between AIoTm &Connected
Devices.
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Existing Devices can Push Data in
their Own Native Data Formats with
no Changes to the Existing Device
Firmware or Translators of any Sort.
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PChartZ Layer, A new Protocol
Characterization technique to build
a really thin, easy to upgrade device
communication layer.
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EventJ, A new Concurrent
programming language for IoT
Applications.
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A Full Blown SDK Made Available to
Make your Application Development
Effortless.
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Bridged Pipeline Concept to Speed
up
Protocol Communication.
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AIoTm Stack
Read MoreConnect Everything for Better World
Read MoreAmrita INTERNET of THINGS Middleware
Secure, Scalable, Interoperable IoT Middleware
Recent Works
IoT Enabled Inhouse Devices






Processing Streaming Sensor Data
Through ABDF
One of the major Advantage of AIoTm is that users can directly push their streaming sensor/device data for processing onto the in-built analytics framework ABDF (www.abdf.in). It is also possible for the users to query their IoT enabled devices/sensors on the fly to get current status. Users need not have to do programming, instead can use dynamic dashboard facility to define the data streams and then monitor the data generated. it is also possible for them to define thresholds and configure alerts. Data is pushed onto the secure message queues with topic based security enabled. This will ensure that right people get visibility to those data sets through appropriate authentication techniques, despite having to share the same queue with other users.
The whole idea of building ABDF was to make Data Mining more accessible to those spectrum of users outside the range of Data scientists. ABDF is intended to narrow the gap between regular BI and Data Mining world. Besides all a novice user will struggle to build a mining processing pipeline incorporating all the required elements to ensure that the output is reliable. ABDF have built in Algorithm Templates that can help build any mining process flow easily. One can then reshape the template to fine tune the results. A well integrated visualization engine will make life a lot easier for its users in visualizing the result sets.