IBM IoT - Products and Systems

IBM

IBM is a Niche Player according to 2020 Magic Quadrant of Gartner. 

The IBM Watson IoT platform is primarily delivered as a managed collection of cloud services in the IBM Cloud

The industrial base for Watson IoT centers on manufacturing and transportation enterprises.  Common use cases are predictive maintenance and asset monitoring. The industrial deployments are cloud-centric, with minimal examples of completely on-premises implementations. The Watson IoT platform is best-suited for cloud-centric deployments across a range of use cases spanning asset monitoring and process improvement.

Strengths

Watson IoT provides customers with a wide and deep set of functionalities to build IIoT-enabled business solutions.

Watson IoT analytics provides customers an easy-to-use graphical interface.

Cautions

Watson IoT has a more limited set of capabilities for edge deployments in factories and plants.

Deployment and ongoing management much more challenging for users.

http://www.ibm.com/internet-of-things/



2017
Download the August 2017 Report by Aberdeen on IoT and Analytics: Better Manufacturing Decisions in the Era of Industry 4.0

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http://www.ibm.com/internet-of-things/

http://www.ibm.com/internet-of-things/raspberry-pi.html

http://www.ibm.com/internet-of-things/iot-platform.html

http://www.ibm.com/internet-of-things/watson-iot.html


Cognitive IoT is the use of cognitive computing technologies in combination with data generated by connected devices and the actions those devices can perform.
http://www.ibmbigdatahub.com/blog/what-cognitive-iot?



https://watson.analytics.ibmcloud.com/product



Get started with Watson Analytics
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IBM wants to replace the spreadsheet with Watson Analytics
http://www.pcworld.com/article/2684332/ibm-wants-to-replace-the-spreadsheet-with-watson-analytics.html

IBM Bluemix

What is Bluemix
The cloud platform powered by the world’s most popular open source projects
http://www.ibm.com/cloud-computing/bluemix/what-is-bluemix/

Products on Bluemix

Compute
Network
Storage
Data and analytics
Watson
Integration
DevOps
Security
Application services
Mobile
Internet of Things

Bluemix and Internet of Things

Experience a fully managed, cloud-hosted service designed to simplify and derive value from your IoT devices. See how companies are using Watson Internet of Things to transform their business from the inside out.
How it all fits together
Connect your device, send data to our cloud, set up and manage your devices, and use APIs to connect apps to your device data.

Start with your device – whether it’s a sensor, gateway, or something else – and let us help you connect it with one of our recipes.
MQTT and HTTP

Your device data is always secure when you connect to the cloud using open, lightweight MQTT messaging protocol or HTTP.
IBM Watson IoT Platform

The hub of the IBM IoT approach – set up and manage your connected devices so your apps can access live and historical data.

REST and real-time APIs
Use our secure APIs to connect your apps with data from your devices.

Your application and analytics
Create applications within IBM Bluemix, another cloud, or your own servers to interpret data.

How much will it (IBM Watson IoT Platform)  cost?

The IBM Watson IoT Platform charges on three metrics.

Devices
The average number of devices you connect over the month. You get 20 free devices a month with each plan.

Data
The amount of data that devices exchange with the IBM Watson IoT Platform and associated applications.
You get free 100 MB data traffic a month with each plan (equivalent to 50,000 messages)
*assuming an average message size of 200 bytes

Storage
The amount of data stored in a historical database.
You get 1 GB free storage a month with each plan
http://www.ibm.com/cloud-computing/bluemix/internet-of-things/



Updated 6 December 2020, 20 September 2017,  20 September 2016,  24 March 2016

Siemens - IoT - SIMATIC IOT2000 - Mindsphere

Mindsphere

MindSphere - Industrial IoT as a service

Siemens MindSphere® provides you with the required transparency and data-driven insights needed to make the right decisions and strengthen your digitalization strategy.

Siemens MindSphere enables you to drive your business success by understanding the things that matter. You need to connect all of your machines and aggregate the relevant data into one system so you can perform concise and powerful analysis, optimize your processes, reduce costs, and accelerate your time to market. With the help of a strong and secure IoT solution that offers scalability, global IoT connectivity, and an easy application deployment process, you can make reasoned decisions. With MindSphere being built on Mendix, you can quickly create custom, low-code applications to accelerate the time-to-value for your industry investments. 

The Internet of Things (IoT)

What's more: With the numerous applications, services, and closed-loop digital twin capabilities of Mindsphere, you are able to reduce costs and accelerate your time to market by connecting your assets and harnessing the wealth of your data.

For more detailed information on development capabilities, visit mindsphere.io

Industrial IoT as a Service: Enable global access of cloud-based applications and solutions that quickly scale, expand, and integrate based on business needs.

Industrial IoT Solutions Fast: Access industrial-based applications to get immediate value from the wealth of data collected from the Internet of Things.

Industrial IoT Data in Context: Access new insights by combining and analyzing IoT data with information from PLM, CRM, ERP, SCM, SLM, and MES systems.

Integrated Edge to Cloud: Perform advanced streaming analytics at the edge or in the cloud to fast-track insights for critical and non-critical processes.

Closed-Loop Digital Twin: Collect live performance data from production lines as well as from connected products to create a fully-functional, closed-loop digital twin.

https://www.plm.automation.siemens.com/global/en/products/mindsphere/internet-of-things-iot.html

Industrial IoT Services 

Connect assets and upload data to the cloud 

Collect, monitor, and analyze data in real-time 

Gain insights that improve efficiency and profitability 

Take advantage of apps and solutions that solve real problems

https://siemens.mindsphere.io/en

Connect and Monitor

Connect to get the key data from your assets and systems

The Connect and Monitor packaged solution helps manufacturers connect critical assets, gain complete operational transparency and take action to optimize asset performance and health. Benefit from the pre-packaged solution and smoothly start your MindSphere® journey.

https://www.dex.siemens.com/mindsphere/solution-packages/connect-and-monitor

The MindSphere Connect and Monitor Solution Package include the following:

MindAccess IoT Value Plan

Connect your entire fleet to MindSphere, securely send and store IoT data, and visualize and analyze connected assets with MindAccess IoT Value Plan.

Visual Flow Creator

Build your work flow to create rules, define key performance indicators (KPIs) and trigger actions, such as email notifications, if defined threshold values are exceeded.

Video

MindSphere : How to create dashboards in less than 10 minutes with Visual Flow Creator.

Premiered on 25 Dec 2019

https://www.youtube.com/watch?v=94_Ivf9itPQ

Visual Explorer

Create customized, advanced data visualizations and dashboards from complex data sets with this browser-based solution that utilizes Tableau®.

Key Services

Implementation: Implementation services involve connecting your data sources to MindSphere, including data collection, agent onboarding and data model configuration.

Support: Ongoing support and consultation to guide you through your digitalization journey and help you get the most out of your MindSphere solution.  Receive support to create rules/KPIs in Visual Flow Creator and to develop dashboards utilizing Visual Explorer.

https://www.dex.siemens.com/mindsphere/solution-packages/connect-and-monitor

Analyze and Predict

Modern analytic tools to better understand and improve your processes

The Analyze and Predict packaged solution gives manufacturers operational transparency to optimize maintenance and to predict and prevent unplanned asset downtime. Benefit from the pre-packaged solution to analyze your data and unleash the value of MindSphere®.

The MindSphere Analyze and Predict Solution Package includes the following:

MindConnect Integration

Combine existing databases, enterprise systems and cloud data sources with data collected on the shop floor to enable full contextual analysis of critical assets.

Predictive Learning

Build models using machine learning techniques to help predict future asset performance and optimize product quality.

Visual Flow Creator

Build your work flow to create rules, define key performance indicators (KPIs) and trigger actions, such as email notifications, if defined threshold values are exceeded.

Visual Explorer

Create customized, advanced data visualizations and dashboards from complex data sets with this browser-based solution that utilizes Tableau®.

Key Services

Implementation: Implementation services involve connecting your data sources to MindSphere, including data collection, agent onboarding and data model configuration.

Basic Enablement: Benefit from the experience of our experts to utilize and maximize the benefits of the MindSphere components.

Success Management: Ongoing support and consultation to guide you through your digitalization journey and help you get the most out of your MindSphere solution.

Configuration: Receive support to create rules/KPIs in Visual Flow Creator and to develop dashboards utilizing Visual Explorer.

Analytics consulting: Monthly review of the configurations of Visual Flow Creator and Visual Explorer to improve gained data insights.

Data Science consulting: Development and ongoing review of built models using the MindSphere Predictive Learning environment.

https://www.dex.siemens.com/mindsphere/solution-packages/analyze-and-predict

Digitalize and Transform

Build and operate powerful targeted applications to transform your business

The Digitalize and Transform packaged solution helps manufacturers build powerful, targeted applications for internal use and for selling to customers, enabling the development of new services and business models. 

The MindSphere Digitalize and Transform​​​​​​​ Solution Package includes the following:

MindAccess DevOps Plan

Develop and operate MindSphere applications to strengthen your own digitalization strategy, and sell applications to customers.

https://www.dex.siemens.com/mindsphere/solution-packages/digitalize-and-transform

Atos Apps for Mindsphere

https://atos.net/en/solutions/industry-4-0-the-industrial-internet-of-things/atos-apps-siemens-mindsphere

Siemens Manufacturing Data Platform

https://www.thefuturefactory.com/blog/32

Digital Manufacturing Platforms - Siemens & SAP

Siemen integrated MES with Simatic Products

https://www.engineering.com/PLMERP/ArticleID/16921/SAP-Siemens-PLM-and-the-Race-to-the-Manufacturing-Cloud.aspx

2016

PC Based Automation

http://w3.siemens.com/mcms/pc-based-automation/en/industrial-iot/pages/default.aspx



The intelligent gateway for industrial IoT solutions

SIMATIC IOT2000

As part of Industry 4.0, networking of production and office IT continues to expand. Production data is collected and evaluated in the cloud to optimize production. Networking of existing plants is a major challenge in this regard, because the machines from different manufacturers and on different technological levels often do not speak the same data language. The solution is often time-consuming and complex retrofitting in these situations.
An intelligent gateway that harmonizes communication between the various data sources, analyses it and forwards it to the corresponding recipients, is a solution that can be easily implemented in these scenarios. It can be used to implement production concepts even for existing plants that are prepared to face the future

Updated  5 Dec 2020

First published on 28 March 2016

Internet of Things - Bibliography

Top IoT Systems and Components Vendors





September 2014

Designing the Industrial Internet of Things



June 2014

How to Connect Anything to the Internet

5 Oct 2020

3.1.2015

Top IoT Systems and Components Vendors

IIoT Platforms Gartner

By 2025, 50% of industrial enterprises will use industrial Internet of Things (IIoT) platforms to improve factory operations, up from 10% in 2020.

Market Definition/Description

Gartner defines the IIoT platform market as a set of integrated software capabilities to improve asset management decision making within asset-intensive industries. IIoT platforms also provide operational visibility and control for plants, infrastructure and equipment.

IIoT Platforms

The IIoT platform  cost-effectively collects higher volumes of high-velocity, complex machine data from networked IoT endpoints. The IIoT platform also orchestrates historically siloed data sources to enable better accessibility, and improve insights and actions across a heterogeneous asset group through specialized analysis of the data.

The IIoT platform:

Monitors IoT endpoints and event streams

Analyzes data at the edge and in the cloud

Integrates and engages IT and OT systems in data sharing and consumption

Enables application development and deployment

Can enrich and supplement OT functions for improved asset management life cycle strategies and processes

The IIoT platform, in concert with the IoT edge and through enterprise IT/OT integration, prepares asset-intensive industries to become digital businesses. Digital capabilities are achieved by enhancing and connecting their core business with customers, suppliers and business partners.

The IIoT platform software that resides on and near devices — such as controllers, routers, access points, gateways and edge compute systems — is considered part of the “distributed IIoT platform.”

The platform provider must exhibit demonstrable value in terms of integration and interoperability with such applications, which include:

Enterprise asset management (EAM)

Computerized maintenance management systems (CMMSs)

Fleet management

Condition-based maintenance (CBM)

Manufacturing execution systems (MES)

Maintenance, repair and operations (MRO)

Product life cycle management (PLM)

Application portfolio management (APM)

Field service management (FSM)

Building management systems (BMSs)

IIoT Platform Capabilities

The IIoT platform  is composed of the following technology functions:

Device management — This function includes software that enables manual and automated tasks to create, provision, configure, troubleshoot and manage fleets of IoT devices and gateways remotely, in bulk or individually, and securely.

Integration — This function includes software, tools and technologies, such as communications protocols, APIs and application adapters, which minimally address the data, process, enterprise application and IIoT ecosystem integration requirements across cloud and on-premises implementations for end-to-end IIoT solutions. These IIoT solutions include IIoT devices (for example, communications modules and controllers), IIoT gateways, IIoT edge and IIoT platforms.

Data management — This function includes capabilities that support:

Ingesting IoT endpoint and edge device data

Storing data from edge to enterprise platforms

Providing data accessibility (by devices, IT and OT systems, and external parties, when required)

Tracking lineage and flow of data

Enforcing data and analytics governance policies to ensure the quality, security, privacy and currency of data

Analytics — This function includes processing of data streams, such as device, enterprise and contextual data, to provide insights into asset state by monitoring use, providing indicators, tracking patterns and optimizing asset use. A variety of techniques, such as rule engines, event stream processing, data visualization and machine learning, may be applied.

Application enablement and management — This function includes software that enables business applications in any deployment model to analyze data and accomplish IoT-related business functions. Core software components manage the OS, standard input and output or file systems to enable other software components of the platform. The application platform (for example, application platform as a service [aPaaS]) includes application-enabling infrastructure components, application development, runtime management and digital twins. The platform allows users to achieve “cloud scale” scalability and reliability and deploy and deliver IoT solutions quickly and seamlessly.

Security — This function includes the software, tools and practices facilitated to audit and ensure compliance. This function also establishes preventive, detective and corrective controls and actions to ensure privacy and the security of data across the IIoT solution.

2019

https://www.gartner.com/reviews/market/industrial-iot-platforms

Hitachi Again Named a “Visionary” in Gartner Magic Quadrant for IIoT Platforms 2019

https://www.hitachivantara.com/ext/gartner-magic-quadrant-for-industrial-iot.html

https://www.ptc.com/en/resources/iiot/white-paper/gartner-mq-for-iiot

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IBM

Google

Intel

Microsoft

Cisco

Apple

SAP

Oracle

Samsung

Hewlett Packard

Ericson

Amazon.Com

GE

Qualcomm

AT&T

Orange

Blackberry

Facebook

Dell

Verizon

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News

February 2016

http://www.ecommercetimes.com/story/83088.html


IoT Players

http://electronicsofthings.com/category/industry-players/

http://internetofthingswiki.com/iot-companies-you-must-know/653



5 Oct 2020
25 March 2016

Fundamentals of the Artificial Intelligence - Notes - Toshinori Munakata & Others

Book by Munakata is available with me.

Important Topics

What is artificial intelligence?

The Industrial Revolution, which started in England around 1760, has replaced human muscle power with the machine. Artificial intelligence (AI) aims at replacing human intelligence with the machine. The work on artificial intelligence started in the early 1950s, and the term  was coined in 1956.

AI can be more broadly defined as "the study of making computers do things that the human needs intelligence to do." This extended definition not only includes the first, mimicking human thought processes, but also covers the technologies that make the computer achieve intelligent tasks even if they do not necessarily simulate human thought processes.

But what is intelligent computation (AI) and what is not AI? 

Purely numeric computations, such as adding and multiplying numbers with incredible speed, are not AI. The category of pure numeric computations includes engineering problems such as solving a system of linear equations, numeric differentiation and integration, statistical analysis, and so on. Similarly, pure data recording and information retrieval are not AI. So processing of most business data and file processing, simple word processing and database handling are not AI.  

Two types of AI: A computer performing symbolic integration of (sin^2x)(e^-x)  is intelligent. 

Classes of problems requiring intelligence include inference based on knowledge,  reasoning with uncertain or incomplete information, various forms of perception and learning, and applications to problems such as control, prediction, classification, and optimization. 

A second type of intelligent computation is based on the mechanisms for biological processes used to arrive at a solution. The primary examples of this type or category are neural networks and genetic algorithms. These techniques are being used to compute many complex things using computers even though  the techniques do not appear intelligent, 

Although much practical AI is still best characterized as advanced computing rather than "intelligence," applications in everyday commercial and industrial settings have grown, especially since 1990.

As mentioned above, there are two fundamentally different major approaches in the field of AI. One is traditional symbolic AI. It is characterized by a high level of abstraction and a macroscopic view. Knowledge engineering systems and logic programming fall in this category. Symbolic AI covers areas such as knowledge based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. 

The second approach is based on low level, microscopic biological models and other computation procedures.  Neural networks and genetic algorithms are the prime examples of this latter approach.  These new evolving areas have shown application potential  from which many people expect significant practical applications in the future. There are relatively new AI techniques which include fuzzy systems, rough set theory, and chaotic systems or chaos for short. 

Neural networks:   A artificial neural network has neurons as the basic unit.  Neurons are interconnected by edges, forming a neural network. Similar to the brain, the network receives input, internal processes take place such as activations of the neurons, and the network yields output. 

Genetic algorithms: Computational models based on genetics and evolution theory and processes. The three basic ingredients are selection of solutions based on their fitness, reproduction of genes, and occasional mutation. The computer finds better and better solutions to problems mimicking the   

species evolution process.  

Fuzzy systems: It coverts discrete objects techniques like sets into continuous objects. In ordinary logic, proposition is either true or false, with nothing between, but fuzzy logic allows truthfulness in various degrees and truth a continuous variable. 

Rough Sets:  "Rough" sets means approximation sets. Given a set of elements and attribute values associated with these elements, some of which can be imprecise or incomplete, the theory is suitable 

to reasoning and discovering relationships in the data. 

Chaos: Nonlinear deterministic dynamical systems that exhibit sustained irregularity and extreme sensitivity to initial conditions. 

Further Reading 

For practical applications of AI, both in traditional and newer areas, the following 

five special issues provide a comprehensive survey. 

T. Munakata (Guest Editor), Special Issue on "Commercial and Industrial AI," Communications of the ACM, Vol. 37, No. 3, March, 1994. 

T. Munakata (Guest Editor), Special Issue on "New Horizons in Commercial and Industrial AI," Communications of the ACM, Vol. 38, No. 11, Nov., 1995. 

U. M. Fayyad, et al. (Eds.), Data Mining and Knowledge Discovery in Databases, Communications of the ACM, Vol. 39, No. 11, Nov., 1996. 

T. Munakata (Guest Editor), Special Section on "Knowledge Discovery," Communications of the ACM, Vol. 42, No. 11, Nov., 1999. 

U. M. Fayyad, et al. (Eds.), Evolving Data Mining into Solutions for Insights, Communications of the ACM, Vol. 45, No. 8, Aug., 2002. 

Four books on traditional AI (Symbolic AI) 

G. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Ed., Addison-Wesley; 2005. 

S. Russell and P. Norvig, Artificial Intelligence: Modern Approach, 2nd Ed., Prentice-Hall, 2003. 

E. Rich and K. Knight, Artificial Intelligence, 2nd Ed., McGraw-Hill, 1991. 

P.H. Winston, Artificial Intelligence, 3rd Ed., Addison-Wesley, 1992. 

The Artificial Intelligence domains are: game theory; knowledge acquisition and learning; automatic planning; perception; image and speech understanding; robotics; languages and development environments for artificial intelligence; knowledge representation; demonstration of automatic theorem; 

expert systems; natural language processing. (From  a research paper)

5 Oct 2020

21 Nov 2018

Top Interesting Books on Internet of Things (IoT)

Top 5 Data Science Trends for 2020
https://www.datasciencecentral.com/profiles/blogs/top-5-data-science-trends-for-2020

Trend 2. Rapid growth in the IoT
According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which is an exceptional growth of connected devices. Many of them are smart devices. We are already using many apps and devices that are functioning based on  IoT. Google Assistant or Microsoft Cortana allow us to automate the regular things based on IoT only., Businesses are investing in this technology, especially in smartphone development that uses IoT.

Internet of Things: Architectures, Protocols and Standards

Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri
John Wiley & Sons, 30-Aug-2018 - Technology & Engineering - 408 pages
This book addresses researchers and graduate students at the forefront of study/research on the Internet of Things (IoT) by presenting state-of-the-art research together with the current and future challenges in building new smart applications (e.g., Smart Cities, Smart Buildings, and Industrial IoT) in an efficient, scalable, and sustainable way. It covers the main pillars of the IoT world (Connectivity, Interoperability, Discoverability, and Security/Privacy), providing a comprehensive look at the current technologies, procedures, and architectures.
https://books.google.co.in/books?id=iERsDwAAQBAJ