AI News, A methodology for solving problems with DataScience for Internet of Things - Part Two

A methodology for solving problems with DataScience for Internet of Things - Part Two

Many vendors like Cisco and Intel are proponents of Edge Processing  (also  called  Edge  computing).  The  main  idea behind Edge Computing is to push processing away from the core and towards the Edge of the network.

Edge devices may also include other features like: •    Apply rules and workflow against that data •    Take action as needed •    Filter and cleanse the data •    Store local data for local use •    Enhance security •    Provide governance admin controls The concept of a Data Lake is similar to that of a Data warehouse or a Data Mart.

For example – Reinforcement learning and time series – Brandon Rohrer How to turn your house robot into a robot – Answering the challenge – a new reinforcement learning robot Over time, we will see far more complex options – for example for Self driving cars  and the use of Recurrent neural networks (mobile...

For example, you can implement deep learning algorithms on mobile devices (Qualcomm snapdragon machine learning development kit for mobile mo....  So, even as I write it, I can think of exceptions!

This article is part of my forthcoming book on Data Science for IoT and also the courses I teach Welcome your comments.  Please email me at ajit.jaokar at  - Email me also for a pdf version if you are interested. If you want to be a part of my course please see the testimonials at Data Science for Internet of Things Course.

The Evolution of IoT Edge Analytics: Strategies of Leading Players

Thus, data that falls within normal parameters can be ignored or stored in a low cost storage and abnormal readings may be sent to the Lake or the in-memory database.

These vendors are positioning their servers as Edge devices by adding additional storage, computing power and analytics capabilities.

An airlines for example, the current Airbus A350 model has close to 6,000 sensors and generates 2.5 Tb of data per day,.

 Deployed through the  IoT Developer Kits, the end to end platform includes: the Wind River Edge Management System, IoT Gateway, Cloud Analytics, McAfee Security for IoT Gateways, Privacy Identity (EPID) modules, API and Traffic Management(based on Mashery) and possibly  with synergies with Cloudera where Intel is an investor.

For instance: the Access netfront browser used widely in set top boxes and Automotive applications, could also perform Edge analytics functions.

This brings the possibilities of deploying IoT analytics using web technology i.e. JavaScript engines based on node.js or PhantomJS.

Similarly, SAP has deployed features in HANA (in-memory database), which enables synchronization of  data between the enterprise and remote locations at the edge of the network.

On the hardware side, devices such as the Dell Edge Gateway 5000 series are built specifically for building and industrial automation.

More specifically,  this allows an analytics model to be created on one location(ex on the Cloud) and deployed to other parts of the ecosystem (ex on the Edge device or the actual sensor location itself – ex a windmill) using technologies like  PMML (more on this below).

Tutorial: Deploy Azure Stream Analytics as an IoT Edge module (preview)

Many IoT solutions use analytics services to gain insight about data as it arrives in the cloud from the IoT devices.

By processing telemetry streams at the edge, you can reduce the amount of uploaded data and reduce the time it takes to react to actionable insights.

In a production environment, you might use this functionality to shut off a machine or take preventative measures when the temperature reaches dangerous levels.

An Azure IoT Edge device: Cloud resources: In this section, you create an Azure Stream Analytics job to take data from your IoT hub, query the sent telemetry data from your device, and then forward the results to an Azure Blob storage container.

Once your Stream Analytics job is created in the Azure portal, you can configure it with an input, an output, and a query to run on the data that passes through.

Using the three elements of input, output, and query, this section creates a job that receives temperature data from the IoT Edge device.

A deployment manifest is a JSON file that describes all the modules that will be deployed to a device, the container registries that store the module images, how the modules should be managed, and how the modules can communicate with each other.

You should be able to watch the machine's temperature gradually rise until it reaches 70 degrees for 30 seconds.

If you created the IoT hub inside an existing resource group that has resources that you want to keep, delete only the IoT hub resource itself, instead of deleting the resource group.

To delete the resources: If you want to remove the IoT Edge runtime and related resources from your device, use the appropriate commands for your device operating system.

You then loaded this Azure Stream Analytics module on your IoT Edge device to process and react to temperature increase locally, as well as sending the aggregated data stream to the cloud.

Edge Analytics – The Pros and Cons of Immediate, Local Insight

A number of data scientists reached out to me about data storage and processing as discussed in my last blog around ‘IoT’.

Whether they should store or discard their enterprise data, and if stored, what is the best approach they can take to making that data a strategic asset for their company.

Many existing IoT platform solutions are painfully slow, expensive and a drain on resources—which makes analysing the rest extremely difficult.

It’s imperative that all data scientists tap into their swelling pools of IoT data to make sense of the various endpoints of information and help develop conclusions that will ultimately deliver business outcomes.  I am totally against discarding data without processing.

For example, sensors in train or at stop lights that provide intelligent monitoring and management of traffic should be powerful enough to raise an alarm to nearby fire or police departments based on their analysis of the local surroundings.

To transmit the live video without any change is pretty much useless.  There are algorithms that can detect a change and if new image is possible to generate from pervious image, they will only send the changings.

It is very important to understand that where edge analytics makes sense and if “devices” do not support local processing, how we can architect a connected network to make sense of data generated by sensors and devices at the nearest location.

Dell has built a complete system, hardware and software, for analytics that allows an analytics model to be created on one location or on cloud and deployed to other parts of the ecosystem.

An airplane system cannot afford to miss any data so all data should be transferred to be analysed to detect any kind of pattern that could lead to any abnormality.

The Internet of Things (IoT) and Analytics at The Edge

The Internet of Things (IoT) promises to change everything by enabling “smart” environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.).

But one of the key concepts in enabling this transition from connected to smart is the ability to perform “analytics at the edge.” Shawn Rogers, Chief Research Officer at Dell Statistica, had the following quote in an article in Information Management titled “Will the Citizen Data Scientist Inherit the World?”: “Organizations are fast coming to the realization that IoT implementations are only going to become more vast and more pervasive, and that as that happens, the traditional analytic model of pulling all data in to a centralized source such as a data warehouse or analytic sandbox is going to make less and less sense.

You speed up and simplify the analytic process in a way that’s never been done before.” So I asked Shawn and his boss John Thompson, General Manager of Advanced Analytics at Dell, to help me understand what exactly they mean by “analytics at the edge.” It really boils down to these questions: “At the edge” refers to the multitude of devices or sensors that are scattered across any network or embedded throughout a product (car, jet engine, CT Scan) that is generating data about the operations and performance of that specific device or sensor. For

It is what you do with that data that drives the business value, which brings us to… Are we really “performing analytics” (collecting the data, storing the data, preparing the data, running analytic algorithms, validating the analytic goodness of fit and then acting on the results) at the edges, or are we just “executing the analytic models”

This edge scoring capability enables organizations to address nearly any IoT analytics use case by executing the analytic models at the edge of the network where data is being created.” Okay, so we “execute” the pre-built modes at the edge, but we actually build (test, refine, test, refine) the analytic models by bringing the detailed sensor data back to a central data and analytics environment (a.k.a.

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