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