Computer Science and Engineering Knowledge Center

Sunday, October 4, 2020

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

--------------------------

IBM

Google

Intel

Microsoft

Cisco

Apple

SAP

Oracle

Samsung

Hewlett Packard

Ericson

Amazon.Com

GE

Qualcomm

AT&T

Orange

Blackberry

Facebook

Dell

Verizon

--------------------

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.

Friday, March 13, 2020

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

Tuesday, November 20, 2018

Artificial Neural Networks - Introduction

Author: Marek Libra

Posted under creative commons from Knol

The Artificial Neural Network (NN later) is a topic in artificial intelligence methods and techniques. It was successfully applied in a wide range of problem domains like finance, engineering, medicine, geology, physics or control.

Neural networks are useful especially for solving problems of prediction, classification or control. They are also a good alternative to classical statistical approaches like regression analysis.

The artificial neural networks techniques were developed based on the model of biological neural networks. The biological neural networks are the basis of functioning of the nervous system of biological organisms. This inspiration is commonly known fact and it is mentioned in most of neural networks publications.

The NN is built from a large number of simple processing units called artificial neurons (called
just neurons later).

The interface of an artificial neuron stays from n numeric inputs and one numeric output. Some models of neurons consider one next special input called bias. Each input is evaluated by its numeric weight. The neuron can perform two operations: compute and adapt.

The compute operation transforms inputs to output. The compute operation takes numerical
inputs and computes their weighted sum. It performs a so called activation function to this sum
(a mathematical transformation) afterwards. The result of the activation function is set as a value
to the output interface.

The adapt operation, based on a pair of inputs and awaited outputs specified by the user,
tunes the weights of an NN for a better approximation of the computed output compared to
the awaited output for considered input.

The neurons in an NN are ordered and numerically signed (from N) according to the order.
    A lot of models of NNs are known. These models differs to each other by different usage of

    • the domain of numeric input, output and weights (real, integer or finite set like {0,1}),

    • the presence of bias (yes or no),

    • the definition of an activation function (sigmoid, hyperbolic tangents, discrete threshold,
      etc),

    • the topology of interconnected neurons (feed-forward or recurrent),

    • the ability to change the number of neurons or the network topology during the lifetime of
      the network,

    • the algorithm of the computation flow through the network over neurons,

    • the simulation time (discrete or continuous) or

    • the adaptation algorithm (none, back propagation, perceptron rule, genetic, simulated annealing etc.).

A good taxonomy of NN models can be found i.e. in [1]

More detailed general descriptions, which are formal and well readable, can be found in [2] .

References

[1] Šíma and P. Orponen. General purpose computation with neural

[2] David M Skapura. Building Neural Networks. Addison-Wesley, 1995

Source Knol: /knol.google.com/ marek-libra/artificial-neural-networks/5rqq7q8930m0/12#
Knol Nrao - 5193

Artificial Intelligence: The Basics

Kevin Warwick, Professor of Cybernetics Kevin Warwick
Routledge, 01-Mar-2013 - COMPUTERS
https://books.google.co.in/books?id=b16pAgAAQBAJ

2012

Artificial Intelligence: A Beginner's Guide

Blay Whitby
Oneworld Publications, 01-Dec-2012 - Computers - 192 pages
https://books.google.co.in/books?id=TKOfhnUhgS4C

2010

Artificial Intelligence: Foundations of Computational Agents

David L. Poole, Alan K. Mackworth
Cambridge University Press, 19-Apr-2010
https://books.google.co.in/books?id=B7khAwAAQBAJ

2008

Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More

Toshinori Munakata
Springer Science; Business Media, Jan 1, 2008 - 272 pages

This significantly updated 2nd edition thoroughly covers the most essential & widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, & chaos. The exposition reveals the core principles, concepts, & technologies in a concise & accessible, easy-to-understand manner, & as a result, prerequisites are minimal. Topics & features: Retains the well-received features of the first edition, yet clarifies & expands on the topic Features completely new material on simulated annealing, Boltzmann machines, & extended fuzzy if-then rules tables

https://books.google.co.in/books?id=lei-Zt8UGSQC

Updated 21 November 2018, 26 June 2016, 27 June 2015

Internet of Things (IOT) and Industrial Internet of Things (IIoT) - Research Papers, Books and Articles - Bibliography

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: A Simple definition by Vermesan (2013):

Internet of things is a network of physical objects
(Devayani Kulkarni's MS Thesis Internet of Things in Finnish Metal Industry, March 2018)

100+ Books on Internet of Things - IoT Books

IBM IoT - Products and Systems

Updated 2020 on 14 March 2020
20 November 2018