Big Data for IoT:

Analytics from Descriptive

to Predictive to Prescriptive

Saturday, November 4, 2017 at GCU

Mark Goldstein, International Research Center

PO Box 825, Tempe, AZ 85280-0825, Phone: 602-470-0389,

markg@researchedge.com, URL: http://www.researchedge.com/

Presentation Available at http://www.slideshare.net/markgirc

© 2017 - International Research Center

Arizona Chapter

IoT Overview and Ecosystems

See also: Internet of Things Innovations & Megatrends

Presentation to the IEEE Computer Society Phoenix

On December 14, 2016 at http://bit.ly/2hLXjPT

IoT Overview and Ecosystems

IoT Computing Platforms and Sensors

IoT Gateway and Network Connections

IoT Application Arenas

• Consumer and Home Automation

• Wearables

• Healthcare and Life Science

• Retail and Logistics

• Industrial

• Smart Buildings

• Smart Cities and Environment

• Transportation

IoT Security and Privacy

IoT Standards and Organizations

IoT Data Applications and Business Models

Next mega IoT update scheduled for 12/13/17 at DeVry University Phoenix for the

IEEE Computer Society Phoenix (http://ewh.ieee.org/r6/phoenix/compsociety/)

Source: Teradata Corporation

Internet of Things Basics

Source: Postscapes (http://postscapes.com/)

Sensor Cluster Trends for Mobile Phones

(Inertial Measurement Units)

AMS AV-MLV-P2 is a volatile organic compounds (VOC) gas sensor which can detect

alcohols, aldehydes, ketones, organic acids, amines, aliphatic and aromatic hydrocarbons.

Source: Postscapes (http://postscapes.com/)

IoT Technology Data Rate and Range Needs

Source: Rohde & Schwartz

Wi-Fi Ecosystem is Undergoing Change

http://www.wirelessdesignmag.com/article/2016/05/now-80211ac-wave-1-rolled-out-whats-next-wi-fi

Why the Present 802.11 Technology is Inadequate:

• Absence of power-saving mechanisms: The energy constraints of sensor networks are not considered in the current IEEE 802.11 standard.

• Unsuitable bands: Due to their short wireless range and high obstruction losses, existing Wi-Fi bands require the use of intermediate nodes, adding complexity to the network.

IEEE 802.11ah Requirements to Support M2M Communications:

• Up to 8,191 devices associated with an access point (AP) through a hierarchical identifier structure

• Carrier frequencies of approximately 900 MHz (license-exempt) that are less congested and guarantee a long range

• Transmission range up to 1 km in outdoor areas• Data rates of at least 100 kbps• One-hop network topologies• Short and infrequent data transmissions (data packet size approximately 100 bytes and

packet inter-arrival time greater than 30 s)• Very low energy consumption by adopting power saving strategies• Cost-effective solution for network device manufacturers

IEEE 802.11ah Wi-Fi Approach for M2M Communications

http://www.ieee802.org/11/Reports/tgah_update.htm

http://www.techrepublic.com/article/802-11ah-wi-fi-protocol-for-iot-solves-two-m2m-problems/

Potential 5G Services Bandwidth & Latency Requirements

Source: GMSA, Heavy Reading

Source: Postscapes (http://postscapes.com/)

https://www.abiresearch.com/pages/what-is-internet-everything/

Source: IDC & Peplink 2015

IoT Vision

Source: TE Connectivity

IoT Adoption Landscape

https://f5.com/labs/articles/threat-intelligence/ddos/the-hunt-for-iot-the-rise-of-thingbots

Source: IEEE Spectrum 10/16

IoT Solutions Architecture

Source: TechBeacon (https://techbeacon.com/4-stages-iot-architecture)

Internet of Things Solutions Framework

Future X Network Enabling a New Digital Era

Source: Bell Labs Consulting

Big Data Overview

Source: HP

Why the Internet of Things Matters

The Data-Value Pyramid

Source: Russell Jurney/Data Syndrome 2017

Source: LNS Research

Source: HP

Internet of Things Data Value Chain

Source: Navigant Research

Elements of a Cognitive/AI Software Platform

Source: LNS Research

Adoption Across the Analytics Spectrum

IoT Data Platforms, Tools,

and Big Data Analytics

Predix delivers the industrial intelligence you need to transform your operations

and generate new revenues. Combining sophisticated asset modeling, big data

processing, analytics, and applications, Predix provides the IT foundation for

tomorrow’s industrial operations. Predix lets you deploy processing and analytics

power to control edge assets in real time or analyze big data in the cloud using

the secure Predix connectivity and execution environment.

https://www.ge.com/digital/predix/

https://www.ge.com/digital/predix/

Microsoft Azure IoT Suite Overview

Source: VDC Research

https://www.microsoft.com/en-us/cognitive-toolkit/

https://github.com/microsoft/cntk

CNTK can be included as a library in your Python, C#, or C++ programs, or used

as a standalone machine learning tool through its own model description

language (BrainScript). In addition you can use the CNTK model evaluation

functionality from your Java program.

CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you

can either choose pre-compiled binary packages, or compile the toolkit from the

source provided in GitHub.

AWS IoT Architecture

Source: VDC Research

AT&T Dedicated IoT Starter Kit for Amazon Web Services

https://starterkit.att.com/

https://news.microsoft.com/2017/10/12/aws-

and-microsoft-announce-gluon-making-

deep-learning-accessible-to-all-developers/

IBM Watson’s System Architecture

http://www.mesalliance.org/2017/11/02/ibm-expands-watson-data-platform-help-unleash-ai-professionals-scn/

IBM Watson Analytics Editions and Pricing

https://www.ibm.com/watson-analytics/pricing

The Analytics Solutions Stack

Source: Intel Corporation 2017

IoT Challenges and Cisco Jasper/Tele2 Solutions

Google’s Serverless Cloud IoT platform

Google Cloud IoT is a comprehensive set of fully managed and integrated services that

allow you to easily and securely connect, manage, and ingest IoT data from globally

dispersed devices at a large scale, process and analyze/visualize that data in real time, and

implement operational changes and take actions as needed. Device data captured by Cloud

IoT Core gets published to Cloud Pub/Sub for downstream analytics. You can do ad hoc

analysis using Google BigQuery, easily run advanced analytics and apply machine learning

with Cloud Machine Learning Engine, or visualize IoT data results with rich reports and

dashboards in Google Data Studio. https://cloud.google.com/solutions/iot/

From Data Warehouses to Data Lakes:

Mission-critical information is quickly moving from databases to data lakes, from

structured to unstructured data and from millions of transactions to billions of

interactions. Qubole can help you transition from a legacy, on-premises data

warehouse to an elastic, open source data lake in the cloud.

https://www.qubole.com/

https://www.zoomdata.com/

Tealium’s Universal Data Hub

Source: Tealium 2017 (https://tealium.com/)

TensorFlow™ is an open source software library for numerical computation using

data flow graphs. Nodes in the graph represent mathematical operations, while

the graph edges represent the multidimensional data arrays (tensors)

communicated between them. The flexible architecture allows you to deploy

computation to one or more CPUs or GPUs in a desktop, server, or mobile device

with a single API. TensorFlow™ was originally developed by researchers and

engineers working on the Google Brain Team within Google's Machine

Intelligence research organization for the purposes of conducting machine

learning and deep neural networks research, but the system is general enough to

be applicable in a wide variety of other domains as well.

TensorFlow™ has APIs available in several languages both for constructing and

executing a TensorFlow graph. The Python API is at present the most complete

and the easiest to use, but other language APIs may be easier to integrate into

projects and may offer some performance advantages in graph execution.

https://www.tensorflow.org/

Big Data Infrastructure Priorities

Data Analytics Market Challenges and Innovations

Data Analytics Key Vendors and Products

IDC’s Cognitive Systems Ecosystem

IoT Big Data Wrapup

Source: MIT Sloan

Source:

CompTIA

Modern BI and Analytics Platforms

Internet of Things (IoT) Maturity Mode

Source: TDWI

The Five Levels of Analytics Maturity

Source:

Logi Analytics

Advanced Analytics Maturity Path

Source: Intel Corporation 2017

Big Data and Analytics MaturityScape

Source: IDC 2015

Source: Gartner (July 2017)

Emerging Technologies Hype Cycle

Big Data for IoT:

Analytics from Descriptive

to Predictive to Prescriptive

Saturday, November 4, 2017 at GCU

Mark Goldstein, International Research Center

PO Box 825, Tempe, AZ 85280-0825, Phone: 602-470-0389,

markg@researchedge.com, URL: http://www.researchedge.com/

Presentation Available at http://www.slideshare.net/markgirc

© 2017 - International Research Center

Arizona Chapter