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Internet of Things

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Nguyễn Gia Hào

Academic year: 2023

Chia sẻ "Internet of Things"

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Roman Słowiński Poznan University of Technology, Poland Leon Strous De Nederlandsche Bank, Netherlands General Program Co-Chairs. Mike Hinchey Lero-Irish Software Research Centre, Ireland Jerzy Nawrocki Poznan University of Technology Publication Chair Poland. Members (* are also IFIP IoT Domain Committee members) Jose Abdelnour-Nocera University of West London, UK.

Hamideh Afsarmanesh University of Amsterdam, Netherlands Carmelo Ardito* Universitàdegli Studi di Bari Aldo Moro, Italy Ioannis Askoxylakis FORTH-ICS, Greece. Soumya Banerjee Birla Institute of Technology, Mesra, India Ezio Bartocci Vienna University of Technology, Austria Juergen Becker Karlsruhe Institute of Technology, Germany. Srinivas Katkoori University of South Florida, Tampa, USA Bouabdellah Kechar University of Oran 1 Ahmed Ben Bella, Algeria.

Royal Holloway University of London, United Kingdom Peter Marwedel Technical University of Dortmund, Germany Maristella Matera Politecnico di Milano, Italy. Raja Naeem Akram ISG-SCC, Royal Holloway University of London, UK Maciej Ogorzalek Jagiellonian University, Poland.

The Four Essential Elements of Data Science

1 Data Science

1. A data science pipeline that shows that different capabilities are needed to turn data into value. It is about providing answers to known and unknown unknowns.1 The "impact" part of the plan refers to the impact of data science on people, organizations and society. The introduction of the Internet of Things (IoT) illustrates the critical role of data science.

The Internet of Things (IoT) depends on the entire data science pipeline shown in Fig.1. However, for data science, we are still in the phase where we are looking for the essential elements. This article uses "water" as a placeholder for the availability of different forms of data, "fire" as a placeholder for irresponsible uses of data (eg threats to fairness, accuracy, confidentiality,.

We also know that there are known unknowns; that is, we know there are some things we don't know. Donald Rumsfeld, February and Transparency), "wind" as a placeholder for how data science can be used to improve processes, and "earth" as a placeholder for education and research (i.e., the foundations of data science) that support it all.

Fig. 1. The data science pipeline showing that different capabilities are needed to turn data into value.
Fig. 1. The data science pipeline showing that different capabilities are needed to turn data into value.

2 The “Water” of Data Science

This can be generalized to storage and throughput and also applies to cost and speed.

3 The “Fire” of Data Science

The term green data science was introduced for breakthrough solutions that enable individuals, organizations and society to benefit from widespread data availability while ensuring fairness, accuracy, confidentiality and transparency (FACT) [2]. One might naively think that "fire" can be controlled by "water", but this is not the case. There is a need for new and positive data science techniques that are responsible (i.e., "green") by design.

Using the metaphor of "green energy": we should not be against the use of energy ("data"), but rather tackle the pollution caused by traditional engines. For example, discrimination-aware data mining [8] can be used to ensure fairness and polymorphic encryption can be used to ensure confidentiality.

4 The “Wind” of Data Science

RPA is an umbrella term for tools that operate on the user interface of other computer systems as a human would. Data science approaches such as process mining can be used to learn the behavior of people performing routine tasks. After the desired behavior has been "enacted", it can be "enacted" to deal with new matters intelligently.

RPA illustrates that data science will lead to new trade-offs between what humans do and what robots do [6,7]. These trade-offs are interesting: How to distribute work between given breakthroughs in data science.

5 The “Earth” of Data Science

This can also apply to education, on Coursera, for example, some American universities dominate data science education. This cannot be left to “the market” or solved with half-hearted legislation such as the European General Data Protection Regulation (GDPR) [5].

6 Epilogue

The story of the Allies' breaking of the German Enigma codes in World War II was first published in the 1970s. Even now, many of the details, especially regarding the critical work in the 1930s undertaken by talented and dedicated Polish codebreakers, remain largely unknown. The author is a member of IFIP WG 9.7 and is also Secretary of the Turing Welchman Bombe Rebuild Trust (TWBRT) which owns the Bombe replica completed in 2007 and is on display every week at the National Museum of Computing (TNMoC) located in Block H of Bletchley Park in the UK.

To the surprise of the French and British, the Poles announced almost immediately that they had cracked the Enigma several years earlier. However, as the German and Italian armies suffered setbacks, the attitude of the Spanish authorities softened. One of the most important events of World War II was the breaking of the German Enigma codes.

Common definitions and terminology are required and it is hoped that e-CF will provide at least part of the solution. These challenges along with government encouragement resulted in the development of cyber security frameworks. The aim of e-CF is to provide a common language which can be used to address the skills gap.

The strength of e-CF is that it relates to real life and the real labor market. GDPR may already be a good step in the right direction by returning control over personal data to the owner instead of the company. The higher it is in this picture and the higher it is in the value chain, the more diversification is taking place and the knowledge of the application area becomes essential.

Professionals at the end of the chain, in applications, will increasingly be confronted with the enormous opportunities of IoT. Working structure of the Alliance for Internet of Things Innovation (2018), a combination of horizontal and vertical working groups (WG). This draft version of the position paper was discussed at the IFIP IoT working conference at and in the IFIP General Assembly on 9/23/2018.

The outcome of these discussions will be processed in the final version of the position paper. The outcome of these discussions will be processed in the final version of the position paper. Users should be aware of the consequences of opt-in and opt-out choices.

IFIP's position is that users should be informed about the various aspects (benefits/risks) of the IoT-connected devices they use.

Fig. 1. Three wheel Enigma machine
Fig. 1. Three wheel Enigma machine

Hình ảnh

Fig. 1. The data science pipeline showing that different capabilities are needed to turn data into value.
Fig. 2. Moore’s law predicts an exponential growth of the number of transistors per chip
Fig. 3. The “water”, “fire”, “wind”, and “earth” of data science.
Fig. 1. Three wheel Enigma machine
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a) Liên kết sigma: liên kết hình thành do sự xen phủ dọc theo trục của 2 obitan, mỗi obitan chứa 1e với spin trái chiều. Liên kết pi: liên kết hình thành do sự xen