IoT Analytics Gateway

IoT Analytics Gateway

Utilizing the "Serverless Framework" we are efficiently collecting anonymous sensor and usage data from over 20,000 connected devices.

Artificial Intelligence, Internet of Things

  • 0 Collaborators

  • 0 Followers

    Follow

Description

Creation of a scaleable and efficient Analytics Gateway to collect anonymous usage data was achieved leveraging a "Serverless" Architecture. The "Serverless" Framework allows the construction auto-scaling, event driven components deployable to many cloud platforms such as Amazon Web Services, Azure and Google Cloud Platform or as the business case requires, deploying as easily to your own private cloud (Intel Private Cloud). Currently servicing sensor and usage data to over 20,000 live connected IoT devices deployed across the U.S., we are able to collect real-time, anonymous statistics for only pennies per device over the service life of the device.

Extremely Low latency (<100ms) and minimal downtime from connectivity disruption is achieved from this service model.

Exploration of usage of Analytics data with leading Machine Learning platforms: Caffe2 (on AWS), Apache Spark BigDL, Cloud AI (Google) & Watson (IBM).

Topics explored: * Serverless - https://serverless.com/ * Caffe2 - https://caffe2.ai/ * Google Cloud AI - https://cloud.google.com/products/machine-learning/ * Apache Spark BigDL - https://software.intel.com/en-us/articles/bigdl-distributed-deep-learning-on-apache-spark * IBM Watson Machine Learning - https://datascience.ibm.com/features#machinelearning * Intel Private Cloud - https://www.intel.com/content/www/us/en/cloud-computing/private-cloud-solutions.html

Medium paul langdon

Paul L. created project IoT Analytics Gateway

Medium f885cba7 348e 40bc 941e 683e6e727ffe

IoT Analytics Gateway

Creation of a scaleable and efficient Analytics Gateway to collect anonymous usage data was achieved leveraging a "Serverless" Architecture. The "Serverless" Framework allows the construction auto-scaling, event driven components deployable to many cloud platforms such as Amazon Web Services, Azure and Google Cloud Platform or as the business case requires, deploying as easily to your own private cloud (Intel Private Cloud). Currently servicing sensor and usage data to over 20,000 live connected IoT devices deployed across the U.S., we are able to collect real-time, anonymous statistics for only pennies per device over the service life of the device.

Extremely Low latency (<100ms) and minimal downtime from connectivity disruption is achieved from this service model.

Exploration of usage of Analytics data with leading Machine Learning platforms: Caffe2 (on AWS), Apache Spark BigDL, Cloud AI (Google) & Watson (IBM).

Topics explored: * Serverless - https://serverless.com/ * Caffe2 - https://caffe2.ai/ * Google Cloud AI - https://cloud.google.com/products/machine-learning/ * Apache Spark BigDL - https://software.intel.com/en-us/articles/bigdl-distributed-deep-learning-on-apache-spark * IBM Watson Machine Learning - https://datascience.ibm.com/features#machinelearning * Intel Private Cloud - https://www.intel.com/content/www/us/en/cloud-computing/private-cloud-solutions.html

No users to show at the moment.

No users to show at the moment.