Blockchain and its Applications in Industry 4.0 9811987297, 9789811987298

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Studies in Big Data 119

Suyel Namasudra Kemal Akkaya   Editors

Blockchain and its Applications in Industry 4.0

Studies in Big Data Volume 119

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are reviewed in a single blind peer review process. Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH. All books published in the series are submitted for consideration in Web of Science.

Suyel Namasudra · Kemal Akkaya Editors

Blockchain and its Applications in Industry 4.0

Editors Suyel Namasudra Department of Computer Science and Engineering National Institute of Technology Agartala Tripura, India

Kemal Akkaya Department of Electrical and Computer Engineering Knight Foundation School of Computing and Information Sciences Florida International University Miami, FL, USA

ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-981-19-8729-8 ISBN 978-981-19-8730-4 (eBook) https://doi.org/10.1007/978-981-19-8730-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Currently, industries are transforming from Industry 3.0 to Industry 4.0. Industry 4.0 is the fourth industrial revolution, where the uses of sensors, Internet of Things (IoT), cloud computing, blockchain technology, Artificial Intelligence (AI), and many other advanced technologies are integrated to increase productivity. Most importantly, the internet is used in every aspect of this revolution. There are many advantages of Industry 4.0, such as improved productivity, improved efficiency, flexibility, agility, better user experience, and many more. There are some challenges too like trust, traceability, security, reliability, transparency, etc. These challenges can be addressed by adopting blockchain technology. Blockchain is a novel technique that allows a radical way of transaction among several entities, such as businesses, individuals, and machines. This emerging technology improves trust, transfers value, supports transparency, and improves the security of stored data. Blockchain has many applications like healthcare, finance, IoT, data storage, decentralized cryptocurrency, and many more. Nowadays, it is considered as the heart of Industry 4.0 and the digital economy. Blockchain technology has the potential to be applied and integrated with all the technologies of Industry 4.0 in such a way that the current business transaction process is changed and drives the new business models for the benefits of circular economy and sustainability. A supply chain management system by using blockchain technology can support transparency at each stage of communication, thus, it improves trust. As it is the early stage of blockchain, researchers, academicians, and other professionals are facing issues to use blockchain technology in Industry 4.0. This edited book discusses many applications of blockchain technology in Industry 4.0, including the integration of AI, IoT, and big data with blockchain for Industry 4.0. Chapters “Introduction to Blockchain Technology” and “Introduction to Industry 4.0” discuss the fundamentals of blockchain technology and the introduction to Industry 4.0, respectively. Chapter “Blockchain in Supply Chain Management” represents the uses of blockchain technology in supply chain management, while the chapter “Government Applications and Standards to Use Blockchain” deliberates upon government regulations, protocols, and standards to use blockchain in Industry 4.0. Chapter “Application of Blockchain in Mining 4.0” discusses the v

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applications of blockchain technology in Mining 4.0. Chapters “Integration of Data Science and IoT with Blockchain for Industry 4.0”, “Innovations in Blockchain Using Artificial Intelligence”, “Blockchain and Artificial Intelligence for Business Transformation Towards Sustainability”, and “Blockchain in Big Data for Agriculture Supply Chain” deal with the integration of blockchain technology with some advanced technologies, namely IoT, AI, and big data, for Industry 4.0 and the agriculture supply chain. Finally, the chapter “Novel Smart Homecare IoT System with Edge-AI and Blockchain” represents a case study of blockchain technology in Industry 4.0. Agartala, India Miami, USA

Suyel Namasudra Kemal Akkaya

Contents

Introduction to Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suyel Namasudra and Kemal Akkaya

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Introduction to Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahatsham Hayat, Vivek Shahare, Ashish K. Sharma, and Nitin Arora

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Blockchain in Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . Shivangi Surati, Bela Shrimali, Himani Trivedi, and Payal Chaudhari

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Government Applications and Standards to Use Blockchain . . . . . . . . . . . Sondra Skelaney, Hadi Sahin, Kemal Akkaya, and Sukumar Ganapati

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Application of Blockchain in Mining 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 P. K. Kunhahamed and Sonu Rajak Integration of Data Science and IoT with Blockchain for Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Pranav Gangwani, Alexander Perez-Pons, Santosh Joshi, Himanshu Upadhyay, and Leonel Lagos Innovations in Blockchain Using Artificial Intelligence . . . . . . . . . . . . . . . . 179 Shipra Swati and Mukesh Kumar Blockchain and Artificial Intelligence for Business Transformation Toward Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Dina Darwish Blockchain in Big Data for Agriculture Supply Chain . . . . . . . . . . . . . . . . . 257 Jenita Thinakaran, Sujni Paul, Beulah Christalin Latha Christudas, and Grasha Jacob Novel Smart Homecare IoT System with Edge-AI and Blockchain . . . . . 293 Tri Nguyen and Tuan Nguyen Gia

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Editors and Contributors

About the Editors Dr. Suyel Namasudra has received Ph.D. degree from the National Institute of Technology Silchar, Assam, India. He was a post-doctorate fellow at the International University of La Rioja (UNIR), Spain. Currently, Dr. Namasudra is working as an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Agartala, Tripura, India. Before joining the National Institute of Technology Agartala, Dr. Namasudra was an assistant professor in the Department of Computer Science and Engineering at the National Institute of Technology Patna, Bihar, India. His research interests include blockchain technology, cloud computing, IoT, and DNA computing. Dr. Namasudra has edited 4 books, 5 patents, and 70 publications in conference proceedings, book chapters, and refereed journals like IEEE TII, IEEE T-ITS, IEEE TSC, IEEE TCSS, IEEE TCBB, ACM TOMM, ACM TOSN, ACM TALLIP, FGCS, CAEE, and many more. He has served as a Lead Guest Editor/Guest Editor in many reputed journals like ACM TOMM (ACM, IF: 3.144), MONE (Springer, IF: 3.426), CAEE (Elsevier, IF: 3.818), CAIS (Springer, IF: 4.927), CMC (Tech Science Press, IF: 3.772), Sensors (MDPI, IF: 3.576), and many more. Dr. Namasudra has participated in many international conferences as an organizer and session Chair. He is a member of IEEE, ACM, and IEI. Dr. Namasudra has been featured in the list of top 2% scientists in the world in 2021 and 2022, and his h-index is 26.

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Dr. Kemal Akkaya is a full professor in the Department of Electrical and Computer Engineering with a joint courtesy appoint in the School of Computer and Information Sciences at Florida International University (FIU). He received his Ph.D. in Computer Science from University of Maryland Baltimore County in 2005 and joined the Department of Computer Science at Southern Illinois University (SIU) as an assistant professor. Dr. Akkaya was an associate professor at SIU from 2011 to 2014. He was also a visiting professor at the George Washington University in Fall 2013, a faculty fellow at Airforce Research Lab in Summer 2020 and visiting faculty at University of Florida Nelms Institute of Connected World in 2021. Dr. Akkaya leads the Advanced Wireless and Security Lab (ADWISE) in the ECE Department and serves as the program director for the newly developed Bachelor’s degree in IoT. He is also acting as the research director for the FIU’s Emerging Preeminent Program in Cybersecurity, which is a university wide interdisciplinary program. His current research interests include security and privacy, Blockchain, Internet of things, and cyberphysical systems. Dr. Akkaya is a senior member of IEEE. He is the area editor of Elsevier Ad Hoc Network Journal and serves on the editorial board of IEEE Communication Surveys and Tutorials. Dr. Akkaya was the general chair of IEEE LCN 2018 and TPC Chair for IEEE ICC Smart Grid Communications. He has served as the guest editor for many journals and in the OC/TPC of many leading network/security conferences including IEEE ICC, Globecom, INFOCOM, LCN and WCNC and ACM WiSec. He has published over 220 papers in peer-reviewed journal and conferences with more than 14,000 citations and Google h-index of 46. Dr. Akkaya received FIU Faculty Senate Excellence in Research Award in 2020. He has also received “Top Cited” article award from Elsevier in 2010. He also holds eight patents. More information about his research and lab can be obtained at http://web.eng.fiu.edu/kakkaya/ and http:// adwise.fiu.edu/.

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Contributors Kemal Akkaya Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA Nitin Arora Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, India Beulah Christalin Latha Christudas Department of Digital Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India Payal Chaudhari Department of CSE, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Dina Darwish Faculty of Computer Science and Information Technology, Ahram Canadian University, 6th October City, Egypt Sukumar Ganapati Department of Public Policy and Administration, Florida International University, Miami, USA Pranav Gangwani Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA Ahatsham Hayat University of Madeira, Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI), Funchal, Portugal Grasha Jacob Govt. Arts and Science College, Nagalapuram, Tamil Nadu, India Santosh Joshi Applied Research Center, Florida International University, Miami, FL, USA Mukesh Kumar Department of Computer Science and Engineering, NIT Patna, Patna, India P. K. Kunhahamed Department of Mechanical Engineering, National Institute of Technology Patna, Bihar, India Leonel Lagos Applied Research Center, Florida International University, Miami, FL, USA Suyel Namasudra Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India Tuan Nguyen Gia University of Turku, Turku, Finland Tri Nguyen University of Oulu, Oulu, Finland Sujni Paul Faculty of Computer Information Science, Higher Colleges of Technology, Dubai, UAE Alexander Perez-Pons Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA

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Sonu Rajak Department of Mechanical Engineering, National Institute of Technology Patna, Bihar, India Hadi Sahin Department of Electrical and Computer Engineering, Florida International University, Miami, USA Vivek Shahare Department of Computer Science and Engineering, Indian Institute of Technology, Dharwad, India Ashish K. Sharma Department of Computer Engineering, Bajaj Institute of Technology, Wardha, India Bela Shrimali Department of CSE, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India Sondra Skelaney Department of Public Policy and Administration, Florida International University, Miami, USA Shivangi Surati Department of CSE, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India Shipra Swati Department of Computer Science and Engineering, NIT Patna, Patna, India Jenita Thinakaran School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India Himani Trivedi Department of CE, LDRP Institute of Technology and Research, Gandhinagar, Gujarat, India Himanshu Upadhyay Applied Research Center, Florida International University, Miami, FL, USA

Introduction to Blockchain Technology Suyel Namasudra and Kemal Akkaya

Abstract Blockchain is a novel decentralized technology that is used to share, replicate, and synchronize data across different geographical locations. It guarantees a trusted transaction in any untrustworthy environment. There is no central administrator or central authority to control all the data-related aspects of blockchain technology. A blockchain network depends on the consensus algorithm that must be agreed upon by all the entities for any new transaction. There are numerous advantages of blockchain, such as security, trust, open source, traceability, transparency, and many more, which make it very popular to apply in different sectors. This chapter first covers all the technologies behind blockchain. Then, some fundamental aspects of blockchain, such as types of blockchain, features, advantages, and disadvantages have been discussed. Many applications of blockchain technology in several sectors are presented in this chapter in order to demonstrate its functional value. Finally, some challenges in the adoption of blockchain technology have been discussed, which can be beneficial for researchers doing their research in blockchain. Keywords Distributed ledger technology · Peer-to-peer network · Consensus algorithm · Merkle tree · Smart contract

1 Introduction The growth of emerging technologies, such as cloud computing, edge computing, Internet of Things (IoT), and big data, needs novel approaches to manage decentralized and distributed systems. As all these technologies work over the internet, security issues are continuously increasing due to the presence of many attackers S. Namasudra (B) Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India e-mail: [email protected] K. Akkaya Department of Electrical and Computer Engineering, Florida International University, Florida, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_1

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and malicious users. Moreover, nowadays, the enforcement of verifiable and trusted services is of paramount importance because the size of data and vulnerable devices is increasing. Blockchain technology is one of the many digital technologies available today, which helps businesses to achieve their goals by solving the above-mentioned issues. It is an immutable and distributed ledger of transactions [1]. Here, the transactions are organized in chronological order to support the users to track the transactions without using any central authority or record keeper. An operation or transaction in an untrusted decentralized environment (i.e., blockchain network) is secured by using various advanced consensus algorithms. The main features of blockchain are distributed database, enhanced security, immutability, decentralized systems, and faster settlement. Here, the details of the distributed database are kept in various computer networks or systems by using a Peer-to-Peer (P2P) network in which each computer is connected to other computers without any central authority. In a blockchain network, each computer is called a node, and each node is connected to its previous node. The public-key cryptography is used in blockchain to digitally sign the transactions by using private and public keys [2]. Both the keys are mathematically correlated to each other, and the sender utilizes his/her public key for signing and encrypting a message or data, and the designated receiver uses his/her private key to decrypt the data or message. A smart contract in blockchain allows the implementation of any contract or operation automatically by using a computer program under some predefined conditions [3, 4]. It is one of the important aspects of any blockchain network. Ethereum blockchain is popular because of its smart contract as it provides unlimited processing capability. The primary concept of blockchain was introduced in 1991. At that time, a chain or block of data with a digital signature was first used as an automated ledger that signed the documents to assure that attackers and malicious users are unable to tamper with the documents. In 2008, Satoshi Nakamoto has first proposed the concept of blockchain for digital currency, i.e., Bitcoin [5]. It is based on Distributed Ledger Technology (DLT), and blockchain was first implemented in 2009. To make Bitcoin a decentralized cryptocurrency, the concept of blockchain is used. Blockchain also supports authentication and verification of transactions of Bitcoin. Subsequently, many digital currencies are also emerged by using blockchain technology, such as Ripple, Litecoin, Ethereum, and many more. There are three generations of blockchain. The first generation, i.e., Blockchain 1.0, was the introduction of the DLT to enable a blockchain network for digital currency [6]. This was basically the network of Bitcoin. Most of the features of blockchain that are used nowadays were not available in Blockchain 1.0. During the initial stage of the blockchain network, researchers have explored more areas and concentrated to build rules by which transactions can be validated. Blockchain 2.0 was introduced in 2014 [7]. The introduction of Ethereum was a huge achievement of Blockchain 2.0. Here, programmers were allowed to develop applications and smart contracts. This helped to proliferate the use of blockchain in various business applications. However, there were two major challenges in Blockchain 2.0: (i) transaction

Introduction to Blockchain Technology

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speed and (ii) communication among blockchain networks. Then, the third generation of blockchain, i.e., Blockchain 3.0, was introduced with much processing power and the ability to interact a blockchain network with many blockchain networks [8, 9]. Now, in Blockchain 3.0, researchers are trying to execute many blockchain transactions in a second. However, blockchain may need to have a data scaling mechanism as the data size in each node is increasing day by day. Currently, companies are concentrating on the interoperability aspect of blockchain. Oracle, IBM, and SAP have worked collaboratively to develop an interoperable blockchain network [10]. This interoperable blockchain network seamlessly communicates across enterprises. Blockchain technology revolutionizes the entire ecosystem of computing by bringing its applications to financial institutions. Nowadays, blockchain is very popular in both academia and industry because of its applications in many sectors, such as internet of things (IoT), cloud computing, pharmaceutical sector, healthcare, banking, and many more [11–23]. Blockchain is mostly used in supply chain management systems as it records details of each transaction among different entities. In the latest report of Fortune Business Insights [24], it has been mentioned that the size of the global blockchain technology market in 2017 was USD 1,640.7 Million, and it is projected to reach USD 21,070.2 Million by the end of 2025 with a compound aggregate growth rate of 38.4%. Gartner predicts that the size of the blockchain technology market may exceed USD 3.1 Trillion by 2030 [25]. World Economic Forum believes 10% of the World’s Gross Domestic Product (GDP) might be used in blockchain by 2027. However, blockchain is still in its early stage and it is expected that blockchain will bring all the enterprise trading partners together into one single platform by using Enterprise Resource Planning (ERP). It is expected that by 2023, blockchain technology will support the movement of goods and services globally with USD 2 Trillion. Despite the advantages of blockchain, there are some disadvantages also, such as complexity, scalability, network size, transaction speed, energy consumption, and many more. The main motivation of this chapter is to present all the fundamental aspects of blockchain technology because fundamental knowledge is must required to apply it in any domain. The structure of the block and block generation process along with the entire process to execute a transaction have been discussed in detail in this chapter. As the popularity of blockchain technology has been increasing in different domains, many applications of blockchain technology in several domains are discussed in detail in this chapter. Moreover, issues or challenges to applying blockchain technology in different domains are also presented, which can be very useful for researchers, academicians, and industry professionals. The rest of the chapter is organized into different sections: Sect. 2 discusses the technologies that are used in a blockchain network. Sections 3 and 4 deal with types of Blockchain and features of blockchain, respectively. Some advantages and disadvantages of blockchain are discussed in Sects. 5 and 6, respectively. Section 7 presents many applications of blockchain in different fields. Then, challenges in the adoption of blockchain have been given in Sect. 8. At last, the chapter is concluded in Sect. 9.

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2 Technologies Behind Blockchain Blockchain technology is based on DLT, which is immutable in nature. When a user in a blockchain network wishes to execute a transaction, a request for that transaction is stored in the ledger, and the copy of that ledger is publicly available to all other users present in the network. All the users verify the requested transaction in a node and when all the users verify the node as authentic, the node is added to the network. If anyone wants to alter or hack the transaction, more than 51% of the nodes of the network must be compromised which is almost impossible. In this section, all the technologies behind blockchain technology are discussed.

2.1 Block Structure A block has two parts: (i) block header and (ii) block body. As mentioned earlier, each block is linked to the previous block or parent block and forms a chain. The first block, i.e., the genesis block does not have the parent block. Here, the hash of each block is recorded in the header of the respective block.

2.1.1

Block Header

A block header contains all the information about the block. There are six attributes of a block header: (i) block version, (ii) Merkle Tree (MT) root hash, (iii) Nonce, (iv) nBits, (v) hash of the previous block, and (vi) timestamp [26]. • Block version: A block version consists of several authentication rules of the blockchain network, which must be followed by all the nodes. • Merkle Tree (MT) root hash: MT is a data structure that is used to store a single hash value for all the transactions instead of storing different hash values for different transactions. It is also known as a binary hash tree. MT combines hash values of transactions in pairs, and hash values are combined for all the transactions and a root is achieved called MT root hash. MT can be applied in a large dataset to make the authentication process of each transaction efficient. In an MT, if any hacker or malicious user changes the hash value of any transaction, the MT root hash is changed. Thus, it supports immutability. An example of MT is shown in Figure 1. Here, pairs of hash values are calculated in a bottom-up manner. Each leaf node of MT holds a hash value of transactional data. As MT is binary, it requires an even number of leaf nodes. If there is an odd number of transactions, the hash value of the last transaction is duplicated to form an even number of leaf nodes. As shown in Figure 1, block 1 contains four transactions, namely TR1 , TR2 , TR3, and TR4 . The Hash Value (HV) of the respective transactions is stored in the leaf nodes, i.e., HV1 , HV2 , HV3, and HV4 .

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Fig. 1 Merkle tree

MT root hash

HV5

•

• • •

HV6

HV1

HV2

HV3

HV4

TR1

TR2

TR3

TR4

Then, HV1 and HV2 are combined to compute their parent HV5 , and HV3 and HV4 are also combined to compute their parent HV6 . Both these parent HVs are then combined to calculate the final ML root hash. SHA-256 hash function [27] is normally used to calculate HV. Nonce: A nonce is basically a random number of 32-bit. It can be used only one time and adjusted by the miners during the mining process. In Bitcoin, miners try to guess a valid nonce to calculate a block hash, which must meet particular requirements. The first miner, who finds a valid nonce that results in the block hash, has the right or power to add a new block to the blockchain network. The miner is also rewarded to add the block. nBits: It denotes the current difficulty, which is used for creating the block. Hash of the previous block: It refers to the 256-bit hash value of the parent or the preceding block. Timestamp: The timestamp refers to a Unix Time used to record the time, when a miner starts the mining process. The structure of a block is shown in Fig. 2.

2.1.2

Block Body

The body of a block consists of mainly two parts: (i) transaction counter, and (ii) transaction [28]. • Transaction counter: The transaction counter stores the number of transactions of a block. • Transaction: A transaction can be referred to as a communication between a sender and receiver to exchange an asset. In a blockchain network, there can be more than one transaction in a block. The size of a block and transaction decide the number of transactions that can be presented in a particular block. There are some attributes of a transaction: 1. Amount: Amount is the digital value that a sender needs to transfer. 2. Input: Input is basically the details of the digital asset, which desires to transfer. Here, the digital asset should be absolutely recognized and include values that are different from other assets.

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S. Namasudra and K. Akkaya Block 1 (Genesis block)

Block 2

Block header

Block header

Block Version

nBits

Block Version

nBits

Previous Hash

Timestamp

Previous Hash

Timestamp

MT Root Hash

Nonce

MT Root Hash

Nonce

Block body

Block body

Fig. 2 Structure of a block

3. Output: Output stores the entire details of the receiver’s account. It includes the value of the digital asset and the Identity (ID) of the receiver. In addition, the output includes some rules that the receiver must not violate to receive the related value. 4. Transaction hash or ID: Each transaction has a distinct hash or transaction ID that supports a digital signature based on public-key cryptography [29].

2.2 Block Generation A new block is actually a collection of transactions. In a blockchain network, a miner adds a new block to the network by using any special blockchain software that supports the mining process. Any electronic device that is used to run the software of the blockchain network is termed as a node, and there are mainly two types of nodes: (i) full node, and (ii) lightweight node [30]. (1) Full node: A full node maintains the information of the blockchain network. It forwards the data to all the nodes present in the blockchain network and guarantees that the newly added block is legitimate. A full node works as a miner and certifies that all the information of a block, such as MT root hash, hash of the previous block, etc., are valid. (2) Lightweight node: A lightweight node forwards the information or data to the full node for execution. It does not record the complete duplicate blockchain. These nodes are generally having less memory and less computational power, such as IoT devices [31].

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In a blockchain network, any node can recommend new transactions, and all the nodes of the network get information about the new transactions until they are added to a block. The miners keep a pool of all the projected transactions, and a miner adds the projected transaction to the newly added block after checking its validity [32]. As mentioned earlier, there can be many transactions in a block and if any of the transactions are invalid, the miner discarded the entire block. There are seven processes to generate a block in the blockchain network: (i) dispatch a token, (ii) broadcasting the transaction, (iii) selection of any transaction, (iv) mining the signature and creating a block, (v) adding a new block, (vi) verification of the signatures, and (vii) confirmation count. (1) Dispatch a token: When an entity tries to execute a transaction in the blockchain network, it dispatches tokens or crypto to another entity from his/her wallet application. (2) Broadcasting the transaction: When a transaction is executed, the wallet application broadcasts it for selection and confirmation by the miners. The transaction is then added to the pool of all the unconfirmed transactions. All these transactions wait in the pool until picked by the miner. (3) Selection of any transaction: Here, a miner picks an unconfirmed transaction from the pool to create a block. Each miner receives different computation problems that need to solve for the transaction. The miner validates a new transaction and records it on the global ledger. The average time of mining a block is 10 min. Here, the miner first checks whether the block can be added to the blockchain network or not through many characteristics of the blockchain network. For example, there is a predefined maximum block size. The maximum block size of Bitcoin is 5 MB. So, the miner checks the block size before adding any transaction to a block. The transaction with a high fee is taken into priority because of the high reward given by the blockchain network. Miners also can choose a certain threshold fee to select a transaction, so that the mining application only chooses the most money-making transactions. (4) Mining the signature and creating a block: A miner can group many transactions together to form a new block. Then, a signature needs to be added to the block by using the Proof of Work (PoW) consensus algorithm. PoW denotes that the miner already spent much resources and time to solve the computation problem. Each computation problem is very complex and requires much computational work, which results to consume a lot of resources. This process of solving a complex problem is called mining. A hash function plays a key role in the mining process. (5) Adding a new block: The miner, who first solves the computation problem and gets the output or signature, is eligible to add the transaction to the next block. Then, the miner introduces the newly added block, as well as its signature, to other miners. Once a new block with a transaction is confirmed, deleting, changing, or altering any information becomes computationally impossible because changing any information of the transaction changes the MT root hash. So, the change requested is declined by other nodes of the blockchain network.

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In the blockchain, presenting a fake block also results in a mismatch of data with other nodes of the network. If the CPU power is completely controlled by all the authorized nodes, it is impossible to force a fake block in the chain. Thus, the blockchain network becomes immutable. (6) Verification of the signatures: Here, the other miners use the hash technique to check the authenticity of the signature that is broadcasted by the respective miner. In this process, the miners calculate the hash of the data of the broadcasted block and match the hash with the signature. When it is matched, the miners give the confirmation of the block. A large number of validity confirmations makes it very tough for attackers or malicious users to hack the block. Now, the new block is prepared for addition to the blockchain network, and the information is broadcasted to all the nodes of the network. All the nodes store the information in their respective transactional data. (7) Confirmation count: When a new block is added to the blockchain network, each subsequent block that is added to the blockchain network after that new block is considered a confirmation for the new block. The miners need to restart from the third step, i.e., selection of any transaction when a new block is added to the blockchain network.

2.3 Consensus Algorithm The consensus algorithm is the backbone of any blockchain network. It is used for agreeing on different nodes to add a block to the blockchain network [33]. Most of the features like network accessibility, scalability, degree of decentralization, latency, throughput, security, network overhead, computational overhead, and storage overhead of any blockchain network depend on the consensus algorithm. Therefore, each consensus algorithm is able to address the requirements of the specific applications. In Bitcoin, the PoW consensus algorithm is used. Many consensus algorithms are being developed for different applications. Some consensus algorithms are briefly discussed in this subsection. (1)

Proof of work: Markus Jakobsson has first proposed the term “Proof of Work” in 1999 [34]. In PoW, all the nodes of the blockchain network try to solve a cryptographic hash function. Here, SHA-256 is used, which generates a fixed size hash value of 256-bit. To add a node or block by using PoW, the miners find a particular nonce to solve the cryptographic problem. This process is timeconsuming and mathematically hard because miners use brute force search to find the nonce that solves the problem. Here, the target nonce supports the difficulty of the network. In Bitcoin, the target nonce is labeled in such a manner that the mining process can take place every 10 min. Attackers or malicious users are able to affect a PoW-based blockchain network if they gain control over 25% of the computational power by selfish mining attacks. However, this attack does not affect the immutability of the blockchain network. PoW has been very popular for cryptocurrency for many years.

Introduction to Blockchain Technology

(2)

(3)

(4)

(5)

(6)

9

Proof of Capacity (PoC): PoC is almost similar to PoW. However, PoC relies on the capacity of the hard disk instead of relying on the miner’s computing power [35]. Therefore, it significantly saves more energy than PoW. In PoC, miners must store huge datasets, which is known as the plot for getting the opportunity to mine a new block. Thus, miners in PoC can increase the chance to add a new block by saving more plots. PermaCoin is one of the cryptocurrencies that uses PoC. In Poc, the block generation time is 4 min, and it has high latency. Proof of Stake (PoS): This consensus algorithm is a generalized form of PoW. In PoS, the nodes are referred to as validators. The validators validate an execution so that they can earn a transaction fee. In this consensus algorithm, there is no competition between the validators to solve the computational problem. A node is selected by using a lottery for mining a new block based on the quantity of stakeholders. The selected node uses the digital signature technique for proving ownership of the stake. Thus, much computational power is not required in PoS. However, there is a new problem as the node with the highest amount of stakes always gets the chance to add the new block. Thus, indirectly again it becomes a centralized network. Moreover, in PoS, if the selected node behaves critically, it has nothing to lose. This problem is also known as “nothing at stake”. In this consensus algorithm, all the coins are presented from the first day [35]. Delegated Proof of Stake (DPoS): DPoS is based on the concept of PoS. However, this consensus algorithm allows the stakeholders to choose a node by voting [36]. The chosen node is termed a witness, which is responsible to add a new block and get rewarded. During the election, N number of witnesses with the maximum votes by the stakeholders is chosen. Here, N is termed in such a way that at least 50% of the stakeholders consider that there is sufficient decentralization. There is an in-built mechanism in DPoS to detect a malicious witness. Bitshares, a cryptocurrency achieves high latency and throughput by using DPoS. Bisshares can process 100,000 transactions/second, and the average time to add a block is 1.5 s. However, the cost to build the blockchain network is higher. Leased Proof of Stake (LPoS): LPoS solves the centrality issue of PoS, and the working principle of LPoS is the same as PoS [37]. This consensus algorithm supports the nodes to participate in the verification of the new blocks by adding a novel leasing option. This leasing supports the higher wealth holders to lease their wealth or fund for a particular time. The nodes with low wealth or balance can take the leased fund and the chance to add a new block is increased. Then, they can share the wealth proportionally with the higher wealth holders. Thus, it makes the entire network decentralized. Proof of Activity (PoA): PoA is a consensus algorithm, which is based on the combination of both PoW and PoS [36]. Here, the miner tries to resolve a hash function for finding the new block as in PoW. However, the new block only contains the miner’s address and a header without any transaction, and then, the details of the transaction are added to the new block. Here, a set of

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validators is selected for signing the new block based on the solved block’s header to reach a consensus. This operation is executed by using PoS which is secure against many popular attacks. However, it experiences higher delay. (7) Casper: This consensus algorithm is based on PoS and it has been developed for Ethereum [38]. It slashes the entire attacker’s stakes and provides security against nothing at stake attacks. Greedy Heaviest Observed SubTree (GHOST) has been modified in Casper for the chain selection rule. In a blockchain network, if there is a network delay, the longest chain is chosen and the main chain is ignored. To solve this issue, GHOST has been proposed as an alternative approach to the longest chain rule of Bitcoin. Here, in GHOST, the heaviest subtree rooted at the fork is chosen in the chain [39] to achieve strong security and reduced delay. (8) Proof of Elapsed Time (PoET): Intel has proposed the PoET consensus algorithm, which is similar to PoW [40]. One of the important advantages of PoET is that it consumes less energy. In PoET, miners must solve a hash problem, and there is no competition among miners to add a new block. Here, the miner is selected randomly on the basis of random wait time, and the timer of the winning miner expires first. A trusted execution environment is used in PoET for verifying the correctness of the execution [41]. However, this consensus algorithm is completely dependent on Intel, which indirectly makes the blockchain network centralized. (9) Practical Byzantine Fault Tolerance (PBFT): In PBFT, all the nodes give the vote to add a new block to the blockchain network [42]. Here, a consensus is reached, when at least two-thirds of all the nodes in the network agree to add the new block. If there is one malicious node in a system that uses PBFT, there must be at least four nodes for reaching an acceptable consensus. In PBFT, the consensus is reached in very short time, and this consensus algorithm is more inexpensive compared to PoW. In addition, PBFT does not want to own assets like PoS for taking part in the consensus algorithm [35]. This consensus algorithm supports low latency, high throughput, and low computational overhead. Therefore, it is suitable for a private blockchain like hyperledger projects, which are controlled or managed by a third party. However, PBFT is not suitable for public blockchain because of its limited scalability. (10) Delegated Byzantine Fault Tolerance (dBFT): This consensus algorithm uses the same rules as PBFT and has similar features to PBFT. However, all the nodes need not participate to add a new block that supports achieving scalability, and some nodes are selected to delegate to other nodes in dBFT [36]. NEO, a cryptocurrency uses this consensus algorithm. However, the average latency of dBFT is 15 s to create a new block. (11) Stellar Consensus Protocol (SCP): David Mazieres has proposed SCP using Federated Byzantine Fault Tolerance (FBFT) to support micro-finance services [43]. This consensus algorithm is decentralized and it allows everyone to participate in reaching a consensus. SCP has low latency. It is the first Byzantine agreement-based consensus algorithm that allows users to select among several

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combinations of other users to reach a consensus. SCP supports robustness by using quorum slices. A quorum slice is a subset of the nodes that participate in consensus, and it assists a node in the process of the agreement. Quorum slices join the entire network in a similar manner as peering networks by binding together. There are two steps in SCP: (i) nomination protocol and (ii) ballot protocol. In the first step, the nomination protocol is executed and new values known as candidate values are proposed to make an agreement. New values are then delivered to all the users or nodes of the quorum, and then, each node gives a vote to select a unanimous single value among all the candidate values. In the next step, ballot protocol is initiated for federated voting to decline or accept the obtained value in the first step. Here, aborted ballots are ignored or discarded and the ballot of the current slot is selected for further processes. If the nodes are unable to reach a consensus, a ballot with a higher value is chosen, which is responsible for executing a new ballot protocol [44]. (12) Ripple: This consensus algorithm uses the FBFT consensus algorithm, and it is analogous to stellar. Ripple has been proposed for reducing the latency of a blockchain network [45]. Here, each miner uses a subset of the trusted nodes to reach a consensus. In the network, there are two types of nodes: (i) server nodes and (ii) client nodes. Server nodes are responsible for consensus protocol and client nodes are responsible for initiating a transaction, i.e., funds transfer. In Ripple, a Unique Node List (UNL) is contained in each server node, and nodes of UNL are utilized to reach consensus for new transactions. If at least 80% of the UNL nodes agree on any transaction, then the consensus is achieved. Ripple consensus algorithm is performed every few seconds for reaching consensus, and it is utilized for monetary purposes for enabling transactions. (13) Verifiable Random Function (VRF)-based consensus algorithm: In this consensus algorithm, there are some committee members, who are chosen randomly to participate in the consensus protocol [46]. Algorand, a new cryptocurrency, is one of the pioneers to use the VRF to perform cryptographic operations through committee members [47]. It has been developed to address some limitations of a blockchain network, such as decentralization, security, and scalability. In Algorand, a group of users, who are randomly selected by the VRF make a committee and this committee is responsible to approve a new block by using the private key of the user. Thus, this new cryptocurrency is fully decentralized. The selection of users is based on the account balance in their account. When the members of the committee are chosen, the committee uses a Byzantine agreement to reach a consensus to add the new block. Here, the attackers do not know the target users for attacking until and unless they participate in Byzantine agreement. Thus, it improves security. However, the randomness used to generate the VRF can be biased in Algorand. The latency of Algorand is less than a minute. (14) Elastico: Sharding is the process of dividing the transactional overhead among many small groups of nodes known as shards [48]. Shards work in a parallel manner to improve performance, and it was proposed to increase the scalability

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of a blockchain network [49]. Elastico, the first sharding-based consensus algorithm, has been proposed for permissionless blockchain. It combines PBFT and PoW for improving Bitcoin. In this consensus algorithm, the network is partitioned into several committees that execute shards (transactions). The number of the committee is linearly increased with the network’s computational power. There are some nodes in each committee that reach a consensus for a set of transactions (shard) by using Byzantine consensus. The PoW consensus algorithm is used to select a committee in each consensus round. Elastico increases the throughput and latency of Bitcoin. However, it also increases communication overhead. (15) Raft: Raft is a consensus algorithm based on voting, and it has been proposed to improve the Paxos algorithm in terms of understandability and implementation for practical systems. This Paxos algorithm resolves the consistency issue of Byzantine under some conditions. The efficiency of Raft is the same as Paxos [50], and both Paxos and Raft are non-Byzantine fault tolerance algorithms. There are two stages in Raft: (i) leader election and (ii) log replication. Here, the leader sorts the transactions, and a leader is selected by using a random timeout for all the servers. When a leader is selected, the log replication phase is started. In the log replication phase, log entries are accepted by the leader from the clients, and then the leader broadcasts the transactions in the network [51]. Corda is a blockchain network that uses Raft as the consensus algorithm. Raft has low latency and high throughput. However, the performance of Raft is fully dependent on the leader node. Therefore, in the worst case, if the leader node is an attacker or malicious user, the entire system that uses Raft can be destroyed. Table 1 shows comparisons among various consensus algorithms.

2.4 Blockchain Architecture In blockchain, there are mainly five layers: (i) data layer, (ii) network layer, (iii) consensus layer, (iv) excitation layer, and (v) application layer [52]. The data layer manages the entire structure of a blockchain network by using data block, chain structure, timestamp, hash function, Merkle tree, and public-key encryption. In the network layer, decentralized communication and Internet Protocol (IP) are managed. The network layer is mainly responsible for data communications (internode communications). The consensus layer is one of the most important layers of blockchain architecture as it directly controls the performance of a particular application. A number of consensus algorithms, such as PoW, PoA, PoS, PoET, etc., are executed in the consensus layer based on the users’ requirements [53]. The excitation layer manages the runtime setting of a blockchain network including issuing mechanism and allowing mechanism. This layer also deals with the platform of programming language, virtual machine, and smart contract for executing any tasks in an application. All the applications or use cases of blockchain technology like cryptocurrency, supply chain management, smart voting system, smart grid, etc.,

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Table 1 Comparisons among consensus algorithms Consensus algorithm

Decentralization

Scalability

Latency

Throughput

PoW

High

High

High

Low

PoC

High

High

High

Low

PoS

High

High

Medium

Low

DPoS

Medium

High

Medium

High

LPoS

High

High

Medium

Low

PoA

High

High

Medium

Low

Casper

High

High

Medium

Medium

PoET

Medium

High

Low

High

PBFT

Medium

Low

Low

High

dBFT

Medium

High

Medium

High

SCP

High

High

Medium

High

Ripple

High

High

Medium

High

VRF (Algorand)

High

High

Medium

Medium

Elastico

High

High

High

Low

Raft

Medium

High

Low

High

are grouped in the application layer [54]. Figure 3 shows the architecture of the blockchain.

3 Types of Blockchain A blockchain network can have a few nodes or millions of nodes across the World. There are mainly three types of blockchain: (i) public, (ii) private, and (iii) consortium, which are based on the arrangement of the network layer [55]. (1) Public or permissionless blockchain: In a public blockchain network, anyone can join the network as a node for mining and participating in the consensus protocol. Here, nodes need not take permission from other nodes to join, read, or write data into the network. Cryptographic rules can be applied in the public blockchain for writing any data, and transactions are verified by the miners. The public blockchain is fully decentralized, and there is no interference from any third party or developers. However, transaction speed is less in this type of blockchain. Bitcoin is a popular public blockchain network. (2) Private or permissioned blockchain: In a private blockchain network, participants cannot join randomly as it is a closed network. Here, reading and writing belong to an organization, and this type of blockchain network is generally structured by enterprises, which want to share their data with other enterprises. Participating nodes in a private blockchain are known to each other, and a few

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Fig. 3 Architecture of blockchain

Programmable currency

Programmable finance

Programmable supply chain

Application layer

Issuing mechanism

Allowing mechanism Excitation layer

PoW

PoS

PoET

Consensus layer

P2P network

IP protocol

Data transmission

Data verification

Network layer

Data block

Chain structure

Time stamp

Hash function

Merkle

Encryption

Data layer

nodes have high trust levels. Here, all the nodes need not verify the transactions. The consensus algorithm is not widely used in a private blockchain, and decisions are taken by the network owner. Moreover, all the rules and regulations regarding who gets what are predefined by the central authority of a private blockchain network. It is managed by the users using a contract, which is signed during their joining the blockchain network. Therefore, a private blockchain is faster than a public blockchain. Ripple is a common example of a private blockchain. Table 2 shows a comparison between different types of blockchain. Table 2 Comparisons among different types of blockchain Category

Public blockchain

Private blockchain

Consortium blockchain

Access

Anyone

Anyone cannot access

If all organizations allow

Environment

Untrusted

Trusted

Trusted

Throughput

Low

High

High

Transaction speed

Low

High

High

Transaction cost

High

Low

Low

Architecture

Decentralized

Partially centralized

Partially centralized or centralized

Efficiency

Low

High

High

Example

Bitcoin, Ethereum

Ripple, Corda

R3, Quorum

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(3) Consortium blockchain: This blockchain network has been developed to overcome the disadvantages of the private blockchain. Here, instead of the central authority, the blockchain network is governed by a group of members. Here, the consensus mechanism and process are controlled by a pre-selected set of nodes, who have the authority to decide all the aspects of a consortium blockchain network. Thus, there are more entities in charge, instead of a single entity in charge. Consortium provides the flexibility to bring in neutral partners or entities, which are required for businesses like government organizations, ports, banks, etc. As an example, one might imagine a consortium of 15 financial institutions, each of which operates a node, and at least, 10 financial institutions must sign every block for validation purposes. R3 is a very popular example of a consortium blockchain network.

4 Features of Blockchain Nowadays, because of the unique features of blockchain, it is used in various applications across different industries and use cases. Some of the important features of blockchain are discussed below: (1) Enhanced security: Blockchain technology supports enhanced security by decentralizing all the information stored in a blockchain network. Here, information cannot be tampered with, and the hash of one node is linked to the previous node; the changes in the hash of one node lead to changes in the hashes of all nodes. As already discussed, there are different types of blockchain networks. However, permissioned blockchain is mostly used in industries as it allows particular users to add new blocks to the network. (2) Decentralized network: Decentralization is one of the main characteristics of a blockchain network. It means there is no governing or central authority in a blockchain network. Here, a group of peer nodes controls the entire network by making it decentralized. Blockchain network places users in particular positions. So, users can directly use a blockchain network through the web and store their data on the blockchain network as the network does not need any central authority [56]. (3) Immutable: Data in a blockchain network is considered a permanent record of transactions. Once a block is added to the network, it cannot be altered or deleted. Here, every node of the blockchain network has a replica of the digital ledger, and every node checks the validity of a transaction when it is added to the network. In a blockchain network, if the majority of the nodes validate the transaction, it is then added to the public ledger. This ensures the trust of a blockchain network. (4) Transparent: Blockchain technology is designed to control users’ data transparently. Here, data blocks are available to every peer node and peer nodes can use the data as per their demand. This supports transparency, which breaks the barriers between organizations, systems, and people.

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(5) Distributed ledger: Independent computers or nodes are used in distributed ledgers to share, record, and synchronize transactions in their respective electronic ledgers. Here, data are not stored centrally like in a traditional ledger. Normally, a public ledger publicly provides all information regarding a transaction and its participants. Private or federated blockchain networks work in a slightly different way. Still, in these networks, many users or administrative people find many details of the ledger. That is mainly because the ledger is mainly maintained by all the users of the blockchain network. (6) Consensus: In a blockchain network, consensus algorithms are fundamental to the architecture of that network. These consensus algorithms help the network to make appropriate decisions based on the situation. If there are millions of nodes in a network, a consensus algorithm is essential for validating a particular transaction that supports running the entire blockchain network smoothly. Thus, all decisions of the network are directly or indirectly a winning scenario of that network. A consensus mechanism is one of the big strengths of DLT. (7) Faster settlement: Traditional banking systems are slow as these sometimes take days to complete a transaction. Compared to these traditional systems, blockchain technology supports a faster settlement technique. So, users can complete transactions in a short amount of time, which does not make the entire system slow. Although there are cases in which the network does not support too many users, as well as faster settlement, nowadays, blockchain is used for fast transactions, and it can be used to send money to peers. (8) Chronological data: As described earlier, blockchain technology is a chain of blocks. Here, each block stores information pertaining to a transaction and links to the previous block through a cryptographic hash. The subsequent block with the same hash value is connected to its previous block. These connected blocks in the network form a chronological chain of information, thus recording the transfer of ownership and establishing the provenance. (9) Smart contracts: Smart contract is one of the important characteristics of a blockchain network. In a blockchain network, these are programs, codes, or logics used for terms, conditions, or automated tasks of the network. Smart contracts are most useful in a permissioned blockchain. It helps in completing any settlement fast. Smart contracts can automate many tasks of a blockchain network.

5 Advantages of Blockchain Today, blockchain technology is one of the many available digital technologies that helps businesses in achieving their goals. The key advantages of blockchain technology are listed below: (1) Data integrity: In a blockchain network, the details of the transactions that are added to the network cannot be edited, i.e., information is immutable. In this way, a blockchain network supports data integrity, as well as high security.

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(2) Verifiability: Here, data is stored in a decentralized manner, thus all the users of the blockchain network can easily verify the correctness of the data using zero-knowledge proof. This zero-knowledge proof allows one entity to prove the correctness of any information of another entity without revealing the original content of data or file. (3) Decentralization: This is one of the main advantages of a blockchain network. As thousands of devices are used to store data in a blockchain network, the entire system, as well as data, is highly resistant to any kind of technical failure and malicious attacks. Most importantly, these devices are located on a distributed network. The distributed and synchronized data across all the entities make the entire system decentralized and efficient [57]. (4) Traceability: Blockchain technology is designed in such a way that it can create an irreversible audit trail, making it accessible and easy for tracing any information of that chain. (5) Security: A blockchain network is secured as each entity of that network is provided with a unique hash that is linked to the previous node. Also, the encryption technique of the blockchain makes it harder for hackers and attackers to hack the entire blockchain network. (6) Faster processing: Traditional banking systems take much time for processing and for completing transactions. However, after the introduction of blockchain technology, the speed of the transaction is increased to a great extent. It reduces the time by nearly to minute or even a second. (7) No third party interference: Nowadays, no financial institution or government has control of any cryptocurrency, which is mainly operated by using blockchain technology. This implies that there is no interference from the third party in a blockchain network [58]. (8) Provenance: Blockchain technology enables the registration of the transfer of ownership. The linked chronological connected blocks in the network help in establishing ownership and provenance. (9) Automation: The smart contracts and availability of data in every node reduce the complexity of the validation process. This in turn help in automating the process and improving the speed of transactions.

6 Disadvantages of Blockchain Along with the advantages mentioned above, there are also a few disadvantages of blockchain technology discussed below [59]: (1) Power use: The power consumption in a blockchain network during the mining process is comparatively high. This is mainly because when a new node is added to the network, there must be communications with all other nodes at the same time. Maintaining a real-time ledger is also one of the main reasons for power consumption.

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(2) Immaturity: Still, blockchain technology is in its early stage, so common people do not have much confidence in it due to the lack of knowledge. People are not ready for investing in it. However, several blockchain-based applications are performing effectively in different industries. (3) Time-consuming: To add a new block to the blockchain network, miners must compute a nonce value. In many cases, this computation process is very timeconsuming, as well as consumes many resources. It is desired to speed up the process of using blockchain technology in different industrial sectors. (4) Legal formality and standards: Nowadays, many countries have started to work on blockchain technology. However, it becomes an obstacle for Bitcoin to get accepted in many countries by their financial institutions. Moreover, since blockchain is still at its early age, there are no specific standards. (5) 51% attacks: Normally, a blockchain network is considered secure. However, there may be a few security attacks in a blockchain network, and 51% attack is one of the most common attacks. This attack happens, when an entity can manage more than 50% of the nodes of the blockchain network, which allows him/her to disrupt the blockchain network by purposely modifying or excluding the order of transactions. (6) Network robustness: All blockchain-based applications have an underlying business logic. This logic describes how the applications work in terms of business requirements, and it is a fixed logic that cannot be redesigned after developing the application. (7) Difficulty of development: Applying complex protocols for achieving consensus is very important. Here, a quick implementation process is not possible and sound knowledge of different programming languages is required to develop a blockchain-based application. (8) Storage: In a blockchain network, each block or transaction is added to the chain or network, which increases the database size, and the size of the ledgers grows over time. Currently, Bitcoin needs around 200 GB of storage. So, in many cases, it creates an issue for the users. (9) Scalability: Scalability is considered one of the main drawbacks of blockchain technology as the block size of a blockchain network is fixed. If the block size of any blockchain network is 1 MB, it can only save fewer details of transactions on a particular block.

7 Applications Many experts from industry and academia consider blockchain technology as one of the most important inventions since the internet [60]. The applications of blockchain technology initially were in the banking and financial sectors. However, slowly this got the attention of all other industries, and today, almost all leading industries are exploring blockchain technology to meet their business objectives. Integrating

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blockchain with ERP systems and IoT can drive a lot of applications in the supply chain management process.

7.1 Applications of Blockchain in Banking and Finance Blockchain technology has many applications in the banking and finance sectors like international payments, capital markets, trade finance, regulatory compliance and audit, money laundering protection, insurance, and many more. (1) International payments: Blockchain technology creates a tamper-proof record of sensitive information or data, which supports the use of this technology in international money and payments transfers. For example, Banco Santander started the first blockchain-based money transferring service in April 2018 called Santander One Pay FX [61]. This service utilizes Ripple’s xCurrent for enabling users to make international money transfers on the same day or the next day. Santander One Pay FX reduces many intermediaries required in such types of international transactions by automating the entire transaction process on a blockchain network. Here, blockchain technology also reduces the total cost of international transactions by eliminating the need for a bank to settle a transaction manually. (2) Capital markets: A system based on blockchain technology can also improve capital markets. In capital markets, blockchain technology can impact consolidated audit trail, faster clearing and settlement, and operational improvements. In 2013, Axoni, a startup was founded based on blockchain technology specifically for capital markets. Recently, a distributed ledger network is launched by Axoni for managing equity swap transactions. (3) Trade finance: Traditional trade financing methods have major issues because of the slow processing time, which sometimes interrupts the entire business and makes liquidity difficult for managing. Blockchain technology has the potential to rationalize trade finance deals. It supports enterprises to easily make transactions with each other beyond geographic boundaries. (4) Regulatory compliance and audit: As blockchain technology is secured, it can be used for useful auditing and accounting. This is because blockchain technology significantly reduces human error, as well as ensures data integrity. In addition, users or owners of the data can change any data or records, when they are stored using blockchain technology. Thus, blockchain technology can eliminate the work of auditors. (5) Money laundering protection: Due to the enhanced security feature of blockchain technology, it is suitable for combating money laundering. Here, a business can identify and verify the identity of its user, which directly supports “Know Your Customer (KYC)”. (6) Insurance: There are a lot of applications being explored in the insurance industry. In this insurance industry, blockchain technology can be used to reduce

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insurance fraud, streamline claims, boost security, and speed up authentication and claims. Here, smart contracts play a vital role to support these services. Through smart contracts, customers, as well as insurers, can manage claims and other insurance-related services in a transparent manner. For example, openIDL is a blockchain-based network, which is built on IBM’s blockchain to support the American Association of Insurance Services (AAIS).

7.2 Applications of Blockchain in Business Some of the applications of blockchain technology in businesses are discussed below: (1) Supply chain management: Applications of blockchain in manufacturing, distribution, and logistics are versatile and diverse. It has very useful applications if integrated with ERP and IoT devices. Blockchain technology can enable realtime tracking of goods, when they move, as well as change hands, throughout the supply chain process. It can also support safe, transparent, secure data sharing, and supply–demand visibility. Moreover, real-time trustable information to every member of the network can be provided by using a blockchain-based supply chain management process. (2) Healthcare: In a smart healthcare ecosystem, patients’ health-related data, such as age, gender, lab test reports, and medical history, must be always saved securely. As there are numerous patients in smart healthcare, this data should not be mixed with other non-sensitive data and stored separately. Recently, many researchers suggested many techniques to use a blockchain network to store patients’ data securely, which also supports to access the data of numerous patients without any privacy concerns [62]. (3) Real estate: Normally, a person sells his/her home or property every five to seven years. Thus, a person changes his/her home on average 12 times during his/her entire lifetime. Blockchain technology can be efficiently used in the real estate market to store such huge details. By using blockchain technology, the process to sell a home can be expedited by quickly verifying all the concerned details. Most importantly, it can reduce the chances of fraud, as well as maintains transparency in the entire purchasing and selling process [63]. (4) Media: In the communication and media industry, blockchain technology can be used for maintaining audit compliance, digital rights management, contract management, digital identity, and royalty tracking. Here, ownership of videos, music, and other related content can be smoothly managed by using a blockchain network. Nowadays, media companies are already using blockchain technology for reducing costs, eliminating fraud, and even protecting Intellectual Property (IP) rights. According to MarketWatch, the global market of the media and entertainment sector is estimated to reach around USD 1.54 billion by 2024. For example, Eluvio, which was launched in 2019, uses blockchain technology to distribute and manage premium videos for customers or users.

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(5) Energy: Blockchain technology is being explored in various areas of energy management, such as electricity distribution, energy trading, gas explorations, storage, and transformation. Here, it can be used for executing energy supply transactions.

7.3 Applications of Blockchain in Government A full fledge government can use blockchain technology to manage records, conduct elections, manage taxes, and many more [64].

(1) Record management: In developing countries like India, records of individuals, such as marital status, family details, birth date, death date, property, etc., are very important. Thus, managing such huge details is very difficult and important too. Sometimes, private institutions are also involved to maintain this information, and citizens must go physically to their local government offices for making any changes in their documents. This process is time-consuming and frustrating. Blockchain has much potential in such a scenario, where individuals’ data can be recorded in the blockchain and all the government and private institutions can be nodes referring to that data. This can ensure a transparent and secure system. (2) Voting: Blockchain technology can support the completion of the voting process easily, transparently, and securely. Here, if an attacker or malicious user wants to access the main system or server, s/he cannot be able to tamper with other nodes because of the tamper-proof feature of blockchain technology. In a blockchainbased voting system, each vote is associated with one ID, and creating a fake ID is almost impossible. Most importantly, government officials can easily count the votes, if a blockchain network is used. For example, Follow My Vote and Votem are two companies that support voting systems based on blockchain technology. (3) Taxes: Normally, the tax filing process is prone to many human errors. Blockchain technology can make the process of tax filing more efficient, where the information or data is stored on the private blockchain network. (4) Non-Governmental Organization (NGO): Currently, the number of NGOs is increasing rapidly, where people across the globe donate to charities. However, in many cases, they do not have any idea how their money is utilized by the NGOs. Here, blockchain technology can be used to efficiently manage a transparent system, so that all donors can see how their money is utilized. (5) Compliance/regulatory oversight: The majority of regulatory oversight stems from recordkeeping, however, the consequences of not maintaining records are inarguably much worse. Thus, compliance is non-negotiable for companies. A blockchain-based network is able to store updated records in real time, which is available to regulators and business professionals.

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7.4 Applications of Blockchain in Other Industries There are many applications of blockchain technology in various industries. Some of them are discussed in this subsection: (1) Tracking: In this digital era, in many cases, it is important to know about the manufacturing details of a product. Blockchain technology can be used to track the original manufacturing country of a product by using its features of transfer of ownership. Today, there is no reliable system which can provide real-time confirmation or information on the country of origin of a product apart from blockchain [65]. (2) Cybersecurity: The major advantage of blockchain in cybersecurity is that it eliminates the chances of failure at a single point. Moreover, blockchain technology also supports end-to-end transaction security. (3) Big data: Big data is referred to a large volume of both structured and unstructured data. As blockchain technology supports the immutable feature and every computer or node of the blockchain network verifies the data stored on it, blockchain technology is an excellent solution to store big data. (4) IoT: With IoT or sensor devices, blockchain technology supports a very powerful feature as it can capture various physical attributes that may be necessary for fault diagnosis [70]. (5) Agriculture industry: Many use cases of blockchain are being applied to the agriculture industry. It is mainly for tracing the product and transferring the ownership of agricultural products, such as coffee, palm oil, soya bean, and many more. This can help in maintaining the quality of the product, as well as can maintain safety. Researchers are also trying to trace the land of farmers through blockchain technology.

8 Challenges in Adopting Blockchain Most of the blockchain applications are in a proof-of-concept mode, as well real-time implementation phase. As it is in its early stage, blockchain technology faces many adoption challenges in business. (1)

Cyberattack: Many researchers have shown that blockchain technology is not secure against various cybersecurity attacks. In 2014, there was an attack on the Bitcoin network on MtGox, causing a loss of approximately USD 450 million. In 2016, there was an attack on distributed autonomous organizations resulting in a loss of USD 60 million. Similarly, Coinbase and Gate.io were also attacked. Here, hackers controlled the network’s computing power and rewrote transaction history [66]. There are many more such examples proving that blockchain technology is vulnerable to cyberattacks. However, these are mostly possible in permissionless blockchain, which is open to everyone. Permissioned or

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(3)

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(5)

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consortium blockchain is more secure as there is no access to this network outside of the approved nodes or users. Double spending: Double spending is referred to utilizing the same cryptocurrency in several transactions. Here, the time interval between the commencement and validation of two different transactions is exploited. In this case, the attacker or malicious user gains the result of the commencement or first transaction prior to the maximum announcement that the second transaction is invalid. Hence, it creates a major problem in a blockchain network. Data modification challenge: There are many scenarios, where users need to change the information of a blockchain network. In a blockchain network, it is impossible to do changes and it leads to forking. There are two types of forking, namely: soft and hard. Soft forking means when there is a change in code or protocol and previous blocks’ data is compatible with it. However, most of the time, changing a code or protocol leads to hard forking. The hard fork is a change in protocol, which is incompatible with the previous versions, and this leads to a lot of challenges. It is considered one of the key disadvantages of blockchain technology. Regulations of General Data Protection Regulation (GDPR): GDPR is based on the principle that the personal data of a person is given to a company or organization, which is the data controller. The data controller is legally liable to protect the data and subjects to the European Union (EU) data protection law [67]. These data controllers must comply with the GDPR’s obligations. GDPR adopted by the EU has certain guidelines, which are difficult to follow if blockchain technology is used. As already discussed earlier, blockchain is a distributed and decentralized network without a central authority of the system. However, this is conflicting with GDPR, as well as a major disadvantage, to using blockchain technology in the EU. High energy consumption: In a permissionless blockchain network, the miners solve a puzzle to add a new block to the blockchain network. In most blockchain networks, there are numerous miners, who are involved in computation, and only one miner wins the game or puzzle. The effort of other miners gets useless. This is highly inefficient because much energy is wasted during computation. Due to this reason, many users or organization do not show their interest in joining a blockchain network. If there is a mechanism that does not consume much energy, it could be much more efficient. Data compression: The nodes of a blockchain network need a mechanism to compress data before storing them on the blockchain network. This is mainly because the size of data is increasing rapidly. Many organizations are facing this issue, which must be solved at the earliest. Rate of transaction: Transaction Per Second (TPS) is the rate by which data in blocks are written in a blockchain network. Bitcoin network has 3–7 TPS and Ethereum has 10–20 TPS [68]. These numbers are not enough as millions of transactions are executed daily, which is also increasing exponentially. Thus, from the users’ or companies’ perspective, TPS should be always high, so that any company or organization can support a huge number of transactions daily.

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(8)

Cost: Energy consumption, technological complexity, and lack of blockchain developers are some major factors that make implementing blockchain technology costlier. Cost is a major factor in adopting blockchain technology in different sectors. (9) Compliance and regulatory clarity: In most cases, blockchain technology is not directly supported by any governmental laws and orders. There must be specified rules and standards defined by the government for different entities of a business, so that government can monitor all the activities of the business that uses blockchain technology. This inhibits the adoption by businesses, where the transactions are bound by legal processes and covered through smart contracts. Similarly, data of a blockchain network is lacking regulations, especially, if it is associated with any personal and financial data of users or customers. This discourages the adoption of blockchain technology in different kinds of businesses or organizations. (10) Interoperability: Two blockchain networks are not interoperable. There is no mechanism till date to make cross-chain transactions seamlessly. However, researchers are trying to combine two blockchain networks. Oracle, IBM, and other giant companies are collaborating with each other to make their blockchain networks interoperable and communicate with each other seamlessly. If it gets successful, many other blockchain solutions can also collaborate [69] for an efficient business.

9 Conclusions and Future Works Blockchain technology is one of the recent technologies that supports tracking the ownership of the asset by using a distributed ledger. It is a nightmare technology for data manipulators, cybercriminals, hackers, and attackers. However, it is challenging for the users to understand the technology that it offers. This chapter discussed the fundamentals of blockchain, such as its history, features, advantages, disadvantages, and many more. Here, the technical details of blockchain have been also discussed in detail. Blockchain technology has been used in many fields, such as business, copyright, digital identity, and many more. In this chapter, various applications of blockchain have been presented in detail. Moreover, many challenges in adopting blockchain technology in different industries are also explained. There are huge scopes to develop novel access control models using blockchain technology to improve the data security of the banking and financial sectors. In addition, power consumption issues at each node of a blockchain network can also be solved by developing novel techniques. Developing a complete standard of blockchain technology can be highly beneficial for all the entities to use it in different industries.

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Introduction to Industry 4.0 Ahatsham Hayat, Vivek Shahare, Ashish K. Sharma, and Nitin Arora

Abstract In this competitive world, businesses are constantly looking for options or environments that can give them access to real-time data and insights to make smarter, faster decisions about the business, which in turn can ultimately boost the efficiency and profitability of the entire operation. In addition, it should empower businesses to address potential threats and issues before they become more significant problems. Moreover, failing to adopt the technology of the Fourth Industrial Revolution (Industry 4.0) caused companies to fall behind, as their operations were not digitized enough to match competitors. Thus, to stay ahead of the competition, companies need to use Industry 4.0. Industry 4.0 is often used to point to business and chain manufacturing development. Artificial intelligence (AI), the Internet of things (IoT), Big data, and Blockchain are examples of Industry 4.0 technologies that have the potential to open up new opportunities in a variety of sectors, most notably the industrial and logistics industries. Blockchain can be incorporated to improve security, privacy, and data transparency for small and large enterprises. The chapter thus discusses the industry 4.0 concepts at length. It presents the topic’s background information and discusses the need for Industry 4.0. The chapter also briefly reviews the various related technologies and explores the role of Blockchain in Industry 4.0 and Blockchain for sustainable development. The chapter then presents some examples of what manufacturing may accomplish. It is realized that the chapter strongly assists A. Hayat (B) University of Madeira, Funchal, Portugal e-mail: [email protected]; [email protected] Interactive Technologies Institute (ITI/LARSyS and ARDITI), Funchal, Portugal V. Shahare Department of Computer Science and Engineering, Indian Institute of Technology, Dharwad, India e-mail: [email protected] A. K. Sharma Department of Computer Engineering, Bajaj Institute of Technology, Wardha, India e-mail: [email protected] N. Arora (B) Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_2

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the users in understanding the concepts and gaining insights into Industry 4.0. It is also discovered that the chapter facilitates users to familiarize themselves with the newest research on Industry 4.0 and identify future research directions. Keywords Industry 4.0 · Blockchain · Artificial intelligence · Bigdata · Supply chain · Smart factory

1 Introduction Manufacturers and producers, as well as related industries and value creation processes, are undergoing a digital transition known as Industry 4.0. Security systems, IoT, cloud computing, cognitive computing, and creating smart factories are all current developments in automation and data interchange in manufacturing technologies [1]. The concept emerged during the 2011 Hannover Industrial Fair to enhance German manufacturing using new technology like the Internet of Things (IoT) [2]. It refers to a more recent phase of the Industrial Revolution characterized by extensive interconnections, digitization, advanced analytics, and data collection and analysis. Investing in new technologies and techniques to increase manufacturing efficiency is just one aspect of Industry 4.0. It also involves changing how your whole firm operates and grows [3]. It is a term that is commonly used to indicate the development of business and chain manufacturing. Technological developments such as connectivity, service orientation, sophisticated materials, processing technology, and collaborative advanced manufacturing networks propelled the Fourth Industrial Revolution (Industry 4.0). This change impacts the whole value chain, from raw materials to endof-life reprocessing and the business and support services. It has brought the smart factory to life. It incorporates newer technologies that bring together the physical, digital, and biological worlds, touching all disciplines, economies, and industries. To enable fully independent decision-making systems, monitor resources, and operations in real-time, and enable equally real-time connected value creation networks, it is necessary to involve stakeholders earlier in the process and integrate vertically and horizontally. The goal is to make manufacturing and adjacent industries like logistics quicker, more effective, and more customer-driven while pushing beyond automation and optimization to identify new business prospects and models [1]. Optimized manufacturing via tuning and digitization, data for a real-time supply chain in a productive economy, growing business consistency via developed infrastructure and tracking options, and higher-quality products are just some of the benefits of automation and optimization in the manufacturing industry. One of the many benefits of Industry 4.0 is the ability to monitor and improve quality in real time. Other benefits include better working conditions and sustainability, personalization and customization for the ‘new’ consumer, increased agility, and the development of advanced capabilities. It involves innovations with digital technologies, including AI, Bigdata, CPS, IoT, Cloud Computing, Industrial Information Integration, and Blockchain technology. These can be suitably employed

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for executing Industry 4.0. Blockchain is an exciting new technology that is being widely used nowadays. Blockchain uses programs in a low-key and secure way and thus ensures a certain level of confidence. This is one of the main reasons that Blockchain has gained much recognition in a corrupt society [4]. It has significant potential in Industry 4.0, especially in the manufacturing and supply-chain domain. It can be integrated to enhance security, privacy, and data transparency for small and large businesses. Blockchain promises effective application of Industry 4.0. The use of Blockchain and its applicability and various possibilities in various fields, particularly in the manufacturing and supply- chain domain, has been highlighted in [5]. Authors have discussed different drivers, enablers, and associated capabilities of Blockchain technology for Industry 4.0 for insights and have found and studied fourteen noteworthy applications of Blockchain in Industry 4.0. These include fostering resilience, scalability, security, and autonomy and using Blockchain to timestamp sensor data. Decision-making is one such promising area where Blockchain is utilized. It forms an essential basis in manufacturing and supply-chain management. Any organization’s best guard against the unknown is an efficient decision-making process. Many businesses rely on supply-chain management professionals to build and implement decision-making procedures that keep them one step ahead of the competition in a constantly changing world [6]. The execution of a Blockchain protocol enhances individuals’ decision strategies and boosts the arrangement of desires and outcomes [7]. The significance of decision-making for successful businesses through a software tool has been demonstrated in [8–10]. Blockchain seeks to incorporate heterogeneous systems, handle commercial transactions and encourage the assets’ traceability, thus creating an optimized supply chain that can influence the global market [11]. Thus, Industry 4.0 leaves out a broader scope to explore and research to gain more insights on the topic; therefore, the chapter discusses the Industry 4.0 concepts at length. It highlights the significant applications and potential of Blockchain in Industry 4.0. It is recognized that the chapter enormously helps the users understand the concepts and gain insights into Industry 4.0. It is also realized that the chapter provides a strong foundation for researchers who wish to pursue research in this domain. The main contributions are: • This chapter provides a detailed discussion of Industry 4.0 to help the users understand the concepts and gain insights into Industry 4.0. • The study presents a detailed discussion on digital transformation, its significance, role, and the technologies involved, which extensively provides information on how digital transformation significantly influences the manufacturing industry’s future growth. • It discusses the problems, highlights the need for Industry 4.0, and discusses Industry 4.0 technologies. • It highlights the significant applications and potential of Blockchain in Industry 4.0.

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The remainder of the paper is formulated as follows. Section 1 describes the general introduction and background of Industry 4.0. Industrial Revolution and its evolutions from Industry 1.0 to Industry 4.0 are presented in Sect. 2. Section 3 discusses Digital transformation and its influence on the industry’s future growth. Section 4 highlights the need for Industry 4.0. Industry 4.0 for sustainable development is discussed in Sect. 5. Finally, the conclusions are presented in Sect. 6.

2 Industrial Revolution Industries have evolved and altered over time to serve better communities and economies. Manufacturing is no exception since our different activities’ pasts influence their futures. Hand-made items, such as food, clothes, and shelter, were the norm for millennia. As a result of technical improvements, the way people produce things has changed. The term “industrial revolution” refers to a significant change in manufacturing technology from earlier generations. New industrial technologies drastically changed people’s working conditions and lives. The beginning of contemporary manufacturing processes may be traced back to the first industrial revolution. On the other hand, industrialization patterns were determined mainly by present human requirements. For example, the growing usage of fossil fuels resulted in increased pollution. As a result, new trends emerged, allowing green industrial processes to flourish. Each stage of the manufacturing process represents a stride ahead in the industrial revolution, which has changed how people think about and work in the industry. Three industrial revolutions have already come and gone, and the fourth is in full swing. People eagerly await the arrival of the fifth. The revolution came in various industries, from Industry 1.0 to Industry 4.0.

2.1 Industrial Revolution 1.0 The first industrial revolution came during the eighteenth century, also known as mechanization; this century saw the primary means of manufacturing move from human resources to machine power. Also, in this revolution, daily-use products are produced by machines instead of humans, and the mechanized version achieved eight times the volume simultaneously. It all started in England in 1760, and by the end of the eighteenth century, it had spread worldwide [12]. This revolution represented the transition from rural and artisanal economies to machine-driven ones. This revolution primarily impacted the textile industry. The first plant was built in the eighteenth century, with British textile mills expanding over the country. Until then, most textiles were produced in private homes, while traders provided the required supplies for the operation. Workers would be allowed to create their schedules, making managing organization more challenging. Another industry that has been adversely affected by the significant drop in material costs and production

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time is the mining, glass, and agricultural industries. Among other things, the steam engine, the spinning wheel, and the water wheel transformed production and paved the way for today’s innovation [13]. Machines powered by water and steam were developed to help workers. The main drivers were water and steam, which helped workers mass-produce goods. The most significant breakthrough in enhancing human productivity was its application of it for industrial purposes. Thomas Newcomen, in around 1712, invented the first steam engine, but it did have lots of limitations, and initially, its mainly used for pumping water. Later, James Watt, in 1769, observed the flaw and modified Newcome’s model, which is considered the start of the first industrial revolution. Instead of using physical force, steam engines might be used to power weaving machines. Further enormous changes occurred due to developments such as railways and steamships, which helped people travel more in less time [14, 15]. Figure 1 shows some areas which saw rapid development during the first industrial revolution. Table 1 discusses the first industrial revolution’s features, advantages, and disadvantages. Fig. 1 Impact of first industrial revolution on various Industries

Table 1 Key highlights of the first industrial revolution Features

Advantages

1. First, manufacturing 1. Production level Increased machines were introduced 2. Increment in employment using steam and power plants opportunities 2. Steam trains were used for the transportation of goods

Disadvantages 1. Poor Working Conditions 2. Low wages for Labour 3, Child Labour

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2.2 Industrial Revolution 2.0 Technology has altered the globe in numerous ways, but perhaps no era has seen more transformations than the Second Industrial Revolution. This transformation, commonly referred to as the Technological Revolution [16], is taking place. From the late nineteenth century through the early twentieth century, this revolution saw fast scientific discovery, standardization, mass manufacturing, and industrialization. The ingenious notion of using electricity as a driving factor sparked the second industrial revolution. Faraday began to experiment with the concept of electricity. Transportation was powered by electricity. The first electric railroad was built in Berlin, Germany, in 1879. Electric streetcars replaced horse-drawn carriages in major European cities as early as 1880. Steam engines were phased out by the late nineteenth century, and engines driven by electricity took their place. Edison and Swan also had a significant contribution to the development of a light bulb that was suitable for domestic use. This together with the invention of the first practical commercial electrical generators in the 1870s, made public electricity possible. The Second Industrial Revolution is widely considered to have occurred between 1870 and 1914 (the start of World War I) [17]. Lighter metals, rare earth metals, new alloys, manufactured goods such as plastics, and new energy sources were previously unknown natural and manufactured resources that modern industry began to use. As a result of these advancements in machines, tools, and computers, the automated factory was developed. The telephone was created by Alexander Graham Bell in 1876, marking the beginning of the telecommunications revolution [18]. In 1913, Henry Ford revolutionized automobile manufacturing by introducing the assembly line. Because each employee just had to do one manual step, individual parts were created faster. Workplace communication has improved as well. Additionally, the typewriter was improved upon and came to be widely used. This was done by phone conversations and telegrams rather than letters, which helped speed up many economic operations. Transport progressed as well; the introduction of the plane at the turn of the twentieth century allowed people to fly across continents [19]. These innovations were the initial steps towards globalization and helped shape our world into what it is now. From now on, specialized personnel would be necessary. Limited availability of skilled workers has always been an issue, whether it was then or now. The Second Industrial Revolution was an era where a series of innovations showed in various industries like Telecommunication, Automobile, and Railways that changed society. Figure 2 shows some areas where significant technological advancements occur during this era. Table 2 discusses the second industrial revolution’s features, advantages, and disadvantages.

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Fig. 2 Impact of second industrial revolution on various Industries

Table 2 Key highlights of the second industrial revolution Features

Advantages

1. Use of Electric power for 1. New electric engines mass production replace old steam engines 2. Internal Combustion 2. Airplane was invented Engine was introduced in 3. Development of Railroads this revolution 3. Existing manufacturing methods are improved, like steel replacing iron in the buildings

Disadvantages 1. Starting of Unemployment 2. Stressful Lifestyle 3. Increase in pollution from the factories

2.3 Industrial Revolution 3.0 A big move from hardware to digitalization was ushered in by the Digital Revolution, also known as the Third Industrial Revolution, which began in the late 1900s with partial automation employing memory-programmable controllers and computers [20]. The emphasis was implicitly on automation through electrical engineering and information technology in the age of Industry 3.0. The Digital Revolution, like the Agricultural and Industrial Revolutions, signalled the start of the Information Age [21]. Industrial revolution 3.0, which reflects the quick pace of life and information technology, was created by computers and automation. From the seventeenth century

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forward, it is feasible to trace the origins of technology back to its inception. With their Analytical Engine, Charles Babbage and Ada Lovelace laid the framework for developing the intelligently programmed computer. Konrad Zuse, a German inventor, created the world’s first programmable computer, “Z3”, in May 1941. It was a prototype initially, but the modified version “Z4” was published at the ETH Zurich and is regarded as the world’s first commercial computer system [22]. These early computers were generally basic, bulky, and excessively huge compared to the computational power they could give, yet they set the foundation for a world. As a result, brisk development began because the development cycles became shorter and shorter. As previously stated, the period of the Third Industrial Revolution did not begin until the 1970s. More automated systems and computers, the advent of the Internet, and the discovery of nuclear energy were all part of Industry 3.0, which reflected the rapid speed of life and technology. Furthermore, the Programmable Logic Controller (PLC) is one of the era’s most influential inventions [23]. With the help of PLC, additional automated devices may be added to the manufacturing line without requiring much human interaction. Because of technological advancements, it is now feasible to automate the whole manufacturing process in a variety of sectors. It is inconceivable to picture a world without these technologies in today’s globe [24]. For the first time, personal computers (PCs) could be used at home or in the office thanks to significant advancements in computer technology and the introduction of the Internet. Various technologies from the second industrial revolution, such as typewriters, were supplanted by computer systems and other inventions from the third industrial revolution. Figure 3 depicts this age’s significant technological advancements, such as computer systems and the Internet. Fig. 3 Impact of third industrial revolution on various Industries

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Table 3 Key highlights of the third industrial revolution Features

Advantages

Disadvantages

1. Invention of the Internet 1. Development in education 1. Reduction of jobs for was one of the most and Information technology unskilled workers significant achievements of 2. Shift from analog to digital 2. Environmental Harm faster and more reliable increased this revolution 2. Shifting to renewable communication energy like solar, wind, etc

Table 3 discusses the third industrial revolution’s features, advantages, and disadvantages.

2.4 Industrial Revolution 4.0 Industry 4.0 is a set of principles that aim to increase productivity and mass production through innovative technology. It is driven by the following key enablers: The Internet of Things (IoT), artificial intelligence (AI), and Blockchain. These technologies are not only developed for Industry 4.0. Instead, they aim to combine these various industrial innovations to create an intelligent manufacturing process [25]. Using cyber-physical systems, Industry 4.0 allows factories to become autonomous and responsive. These components can communicate and act autonomously with each other. The Fourth Industrial Revolution demands that we can adapt to changes in the demand for our products and services. This is what makes the Fourth Industrial Revolution so important. The Internet is considered the most critical technology for Industry 4.0. Its ability to connect various entities and provide real-time information is key to the success of this movement [26]. The emergence of the IoT has enabled cyber-physical systems to communicate and collaborate. However, with the rise of cryptocurrencies and Blockchain, Internet-connected devices are more vulnerable to exploitation. Information and communication technology (ICT) is projected to play a vital role in achieving long-term industrialization. And ensure long-term economic, social, and environmental survival. So, markets, healthcare, smart agriculture, logistics, business, supply chain, and energy management have all seen significant increases in use as a result of the expansion of ICT in the past few decades. Security and privacy are important considerations for all the above applications when sending data using open channels such as the Internet [27]. Several security solutions and standards have been presented throughout the years to improve the security of the above-described intelligent applications. However, current systems either have a single point of failure or have high computational and communication costs, which makes them unsuitable for large-scale deployments. Furthermore, most existing security solutions only consider a few factors, such

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as scalability, dependability, data retention, latency, traceability, modifiability, and traceability. Blockchain technology is one of the solutions to solve the above problem. With a distributed platform, time stamp entry fraud is reduced, and user information is stored in a network-wide, immutable ledger using intelligent contacts. Blockchain helps reduce system costs by eliminating manual coordination and management processes between multiple isolated ledgers [28]. When it comes to blockchain technology, there are several advancements and additional benefits. Instead of software, algorithms, and automated robots, companies are increasingly debating themes like designing and manufacturing on-demand items, dematerialization, and eliminating intermediaries. Security and transparency will be affected by the combination of AI and Blockchain. In today’s industrial scenarios, Blockchain has the potential to tackle a wide range of issues. The broad usage of AI and blockchain integration is propelling Industry 4.0 forward. Cloud servers authenticate and link Industry 4.0 computing and storage resources. Figure 4 shows some key industry areas where Blockchain will play a vital role in their expansion. Table 4 discusses the fourth industrial revolution’s features, advantages, and disadvantages. Industrial revolution 1 started around the 1760s and focused on quality of life, and the critical moving factor of this industrial innovation was mass production. The second industrial revolution focused more on mobility and moving assembly lines that were introduced for the easy mobility of goods. Third industrial automation was the era of electronics and information technology; an electronics-based systemwas used for complex automation and communications tasks. The fourth industrial revolution comes with the idea of decentralization which helps digital manufacturers to grow more rapidly. Table 5 shows the advancement from industrial revolution 1 to 4. Fig. 4 Various key areas of blockchain for industry 4.0

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Table 4 Key highlights of the fourth industrial revolution Features

Advantages

Disadvantages

1. Introduction of new technologies like Blockchain, IoT, Cloud Computing, etc 2. Introduction of smart factories, intelligent agriculture, and intelligent healthcare system

1. Increased productivity and improved quality of life (robotics) 2. Enhanced decision-making with data-driven tools

1. Cybersecurity risk 2. Too much dependency on technology

Table 5 Roadmap of the first industrial revolution to the fourth industrial revolution Industry 1.0 to industry 4.0 Industry 1.0

Industry 2.0

Industry 3.0

Industry 4.0

Period

1760

1870

1970

2010

Key drivers

Mass production

Mobility

Digital electronics

Cyber-physical systems (CPS)

Technology

Water and steam power

Assembly line

Programmable logic controllers (PLC)

Smart automation and decentralization

Core industries

Mechanization

Textile industry

Electrical industry

Information industry

3 Digital Transformation The adoption of digital technology by industries is known as digital transformation. Its application often aims to increase efficiency, value, or innovation [29]. Thinking about how to change goods, processes, and organizations through new digital technologies is part of digital transformation. Digital transformation has garnered attention, and it is now in charge of converting the industry into a linked one [30]. It’s a process that necessitates a high level of personal awareness and brings all company areas together. Industrial manufacturing organizations are using new technologies to transition from mass production to more individualized production. In Fig. 5 [31], you can see how digital transformation in the industrial field combines various physical and digital technologies to accomplish manufacturing goals and intelligent manufacturing procedures that engage with customers. This includes automation, data exchange, Cloud Computing, Robotics, Big Data, Artificial Intelligence, the Internet of Things, and Blockchain. Digital transformation across vertical/horizontal value chains and firm product/service offerings drives Industry 4.0 in today’s business. Artificial intelligence, the IoT, machine learning, cloud computing, cybersecurity, and adaptive robotics, among other essential technologies required for Industry 4.0 transformation, produce significant changes in organizational business operations. Industry 4.0,

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Fig. 5 Core technologies used for digitalization in industry 4.0

with its autonomous cyber-physical production systems and intelligent goods, needs comprehensive methods and will result in long-term changes in industrial manufacturing [28]. Digitization has an influence on all global systems as well as the most current forms of civilization. There are several ways in which digitalization can be helpful. Digital transformation and presentation of information and communication are the classic ICT fundamental operations. It also includes the digital representation and modification of things, functions, processes, services, applications, and other digitalized environment components. The most complex image of digitalization is the digital transformation of systems and subsystems across society [30]. As one of the primary domains of application, digital transformation has a significant influence on the future growth of industry, particularly: • Digital transformation offers vast opportunities while also posing significant problems to the industry. • The industry’s digital revolution will cause massive structural changes in national economies. • Because of the enormous complexity, digital transformation needs shared and coordinated thoughts and activities. The primary goal of industrial transformation is to increase resource efficiency and productivity to increase organizational competitiveness [32]. Regardless of the enabling technology, the primary goal of industrial transformation is to boost organizational competitiveness by increasing resource efficiency and productivity. The present transition is distinct from earlier ones in that it affects core business processes and demonstrates the notion of intelligent and connected goods through service-oriented business models. The benefits of Industry 4.0, or the intelligent factory, have become increasingly essential; the digital transformation has significantly impacted the industrial sector. Once upon a time, industrial firms were on the periphery of digitization and information technology [33]. The following are the essential characteristics of intelligent factories in a related industry: • Automation: Industrial automation uses robots and software to operate machines and operations in numerous sectors. • Data connectivity: Connectivity in Industry 4.0 helps organizations build networks and optimize supply-chain procedures. Information and communications technology allow industrial process networks. Connecting warehouse systems, intelligent equipment, human employees, and manufacturing operations improve procedures and services.

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• Improved Data Insight: Digital information helps plants use their resources and run better. You have more information and can get it faster. Real-time reporting has benefits like better problem-solving, uptime, and higher productivity. Advanced digitization inside factories creates the Integrated Economy, which includes everything from the product generated to networked factories, supply networks, customers, and a few other stakeholders. Industry 4.0 [34] is projected to emerge by merging Internet technologies and other future-oriented technology, a paradigm shift in manufacturing output. Industry 4.0 is the next phase in transforming traditional factories into smart factories, which are more resource-efficient and adaptable to respond to ever-changing production requirements [35]. Gathering as much data as possible from various segments of the value chain is one of the pillars of Industry 4.0. Data collection, for example, should be done quickly and efficiently to be valuable in manufacturing. Data can be collected via systems that allow for the acquisition, storage, processing, and exchange of information with devices at factories or suppliers and devices held by clients. This shift allows for a slew of gamechanging concepts in sustainable industrial manufacturing [36]. To construct such an automated environment, you’ll need a connection to the Internet as well as a vast number of items; because such objects are not safe by design and are consequently vulnerable to attackers, hackers should be barred from joining botnets, patching their flaws, or updating them [37]. Many cryptographic approaches have been employed to secure sensitive information in communication and storage throughout the last few decades. These approaches were originally exclusively employed in information security systems but are currently used in various other applications [38]. Initially, the Blockchain was created for the Bitcoin cryptocurrency. The Blockchain, which underpins Bitcoin, is a decentralized data storage and transaction system. The concept of blockchain technology was first proposed in 2008, and it has sparked much attention since its fundamental qualities enable data integrity, security, and anonymity without the participation of a third party [39]. Blockchain’s role in Industry 4.0 has inspired dozens of new inventions. Blockchain enables the cyberphysical systems that make up intelligent factories to order a needed spare component autonomously and safely, enhance their production processes to cut energy usage, forecast future supply-chain errors before they happen, and many other advantages. Thanks to the promise of blockchain technology, based on the trust and security of all stakeholders, it may be possible to adapt to a new, optimized, adaptable, and effective business model. As a result, blockchain technology is an ardent supporter of Industry 4.0 [40]. From the manufacturer to the end user, the goods in an automated sector supply invaluable information for various issues. End users are interested in this; as a result, they place a high value on determining the exact traceability of the commodities. Furthermore, end consumers must be able to tell if the items they have been exposed to are genuine or counterfeit in any industrial context. Blockchain can enable various small and large industries to share their data more securely and transparently. Previously they used centralized servers to store their data which are costly to deploy and maintain. Blockchain is a decentralized directory that provides a way to power industries and a more transparent system that enables a

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more trusted and secure environment [28]. Blockchain is an immutable ledger that enables near-real-time data replication across a network of business partners. The method uses information previously stored in the company’s Enterprise Resource Planning system (ERP). It may now be accessed through a network of records that spans many businesses. Previously, most industries are using the internet or cloud services to share their data which is again not very secure. Industry 4.0 introduces smart factories like intelligent healthcare, smart city, and smart agriculture. Using Blockchain, they can share their data in a Peer to Peer (P2P) manner. Figure 6 shows the communication architecture of current and innovative industries under industry 4.0 using blockchain technology [41]. Blockchain technology will help in digital transformation in the industry. It can be integrated with multiple technologies like the Industrial Internet of Things (IIoT), Big Data and Data Analytics, Cyber-Physical Production Systems (CPPS), etc., to increase their performance in various industries. IIoT is nothing but deploying various sensors and machines with remote sensing capabilities in intelligent industries. Blockchain can help IIoT systems assist and perform decentralized transactions with a secure and transparent framework in various processing phases. For example, A credit-based payment structure facilitates quick and frequent secured energy trade [42]. It also gives semi-access or full access to the public, which is very important in some industries where data transparency is essential to trust a company. Sometimes IIoT devices alone cannot provide very complex security because of their computing capabilities, so if those IIoT devices use a blockchain network for transactions, it will help provide a more secure environment. Blockchain can also provide an integrity service framework to IIoT systems as it will help IIoT devices to guarantee that has not been changed or incorrect.

Fig. 6 Communication architecture of industries, a current industries, b smart industries for industry 4.0

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Innovative industries collect a massive amount of data from various devices, and it is imperative to check the reliability of that data before processing it. Blockchain can resolve the problem of data reliability and help big data techniques to process the data as big data and data analytics rely on correct data to make the right decisions. Blockchain can provide a shared interface that all the parties can use to interact with each other, and it can also provide a security mechanism by implementing data timestamping [43].

4 Need of Industry 4.0 A new industrial transformation is underway, one that will see the transition from a technologically focused to a technologically advanced state and depend on several different foundations, including the openness of information, support, and connectivity. It is necessary to have real-time data in the Industry 4.0 environment to maintain a seamless production and service system. In real-world applications, processing speed is the most crucial element; thus careful thought is essential. Blockchain technology is the most effective way to keep track of records and information, and it can solve huge problems. As a consequence, the goal of this research is to evaluate the function of Blockchain in Industry 4.0. For improved transaction efficiency in the business sector, it’s time for blockchain replenishment. Product lifecycle management is more accessible in Industry 4.0 thanks to blockchain-enabled sustainable production. This intelligent factory’s cutting-edge technology can contribute to data security efforts. This will thrive in production conditions with lower risks and give a higher level of process safety. However, before organizations embrace Blockchain for deployment, significant work with the platform must be completed. The degree of risk may be reduced, and because technology is constantly improving, top management must understand its positive impact on their business [44]. Industry 4.0 entails a higher level of trust and privacy. There is also some blockchain research connected to Industry 4.0. However, it is focused on a particular component of Industry 4.0 rather than the whole concept. To give a few examples, among the top-notch drivers and enablers used to construct blockchain technology for their specialized services from an industrial viewpoint are intelligent factories, innovative products, smart supply chains, and intelligent solutions, as shown in Fig. 7. In addition, these drivers and enablers can be further subdivided into categories depending on the usability of different innovative toolkits, which include robots and artificial intelligence, the Internet of Things, 3D printing idea, cybersecurity, and a cloud database; some of the topics covered [45].

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Fig. 7 Viewpoints of blockchain for industry 4.0

4.1 Capabilities of Blockchain Blockchain has several technology implementations and regularly presents innovative ideas. Interest in blockchain technology in the industrial sector has increased significantly over the last several years [46]. Several start-ups are investigating the use of blockchain technology for supply-chain monitoring and auditing, among other things. As a technology, Blockchain is initially just one technology that integrates all the cryptocurrencies that are used to anticipate future needs. After further examination, it is expected that significant advancements will occur on the Internet its selves. Technology has progressed inexorably, and blockchain technology has advanced dramatically. Because of this, Blockchain has been widely used as a distributed ledger system; this technology created a chain of information and collected data that were all verified and stored inside a single block. If necessary, these blocks are reviewed and attached to the transaction and knowledge string in the preceding blocks [47]. To fully realize the promise of Blockchain, a new style of thinking and an ambitious and flexible approach are necessary. Many leading companies in the manufacturing industry should make use of the inherent qualities of blockchain technology to repair sectors that were previously hurdles to technological advancement. Among them are the inherent security of blockchains, the impossibility of modifying data until it has been confirmed and processed in a block, and decentralized systems that make communication and responsibility more straightforward. A further advantage of blockchain technology is that it lowers the cost of transactional transactions. Intelligent contracts are placed alongside the ledger on the blockchain network, creating a decentralized ledger. Databases and traditional applications are available and may be used to solve most requirements when combined with blockchain technology [48].

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4.2 Need in Government The public administration is considering using blockchain technology to act as an official record for citizen-owned assets such as buildings, residences, and automobiles, among other things. Blockchains have the potential to enhance voting, fraud reduction, and back-office services such as buying. This concerns future commercial transactions, data management, and how choices are reached. Furthermore, in any industrial context, firms must keep track of how particular pieces function. In all of these instances, the Blockchain will ensure that the operations and acts of any organization can be traced back to their source. Also, important is the optimization of supply-chain management from beginning to finish, which involves commercial logic and data obtained from IoT sensors to ensure confidence and reduce the likelihood of fraudulent activity [45]. A real-world supply chain based on the Blockchain differs since it can reach an agreement among all parties. Data will become a significant asset in today’s industrial process management. It will eliminate the need for independent regulating agencies, increasing trust between customers and stakeholders and promoting trust among stakeholders [49]. There are systems to guarantee that processes are coordinated across multiple manufacturers and build such processes. Blockchain is employed in various markets and industries due to the growing number of enterprises [48].

4.3 Integration of System Blockchain technology can increase effectiveness and integrity across your whole supply chain. Applications are used by businesses in almost every industry to trace and locate data, verify validity and origin, carry out recalls, and expedite the distribution of goods. The data is encrypted in the Blockchain and is changed as it travels across the network, according to the protocol [50]. The appetite for blockchain applications is fast increasing across many sectors, with the automobile industry showing the most interest in using blockchain technology. With current production technologies, the supply chain will be able to function across a wide range of sectors and nations. As a result, the creative strategy to increase productivity and pursue events will become increasingly challenging to implement. Industry 4.0’s networked nature makes it simple to exchange copyrighted digital design data, improving manufacturing processes’ uniformity overall. Intelligent contracts carry out pre-programmed directives if a set of requirements previously agreed upon adhere. As part of the industry 4.0 industry, blockchain technology allows innovative organizations to decrease high transaction costs while significantly speeding up turnaround times [51]. In Industry 4.0, the relationship between the software, the computer, and the machinery is often quite substantial. It is essential to protect advanced industrial networks and simplified physical access systems as soon as possible since they

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are both at risk from cyberattacks. The conventional network isolation and central compliance methods are still in use. Using reliable shared key schemes, data-safety hash algorithms, and data-safety hash algorithms are required to replace cybersecurity models with rigorous, distributable, adaptive, and scalable models. Cryptocurrency Ethereum Blockchain is a distributed ledger system that provides edge security by enabling machine-to-machine and human-to-machine authentication, dependable data exchange, device lifecycle management, access control compliance, and self-sustaining operations [52]. Blockchain may give some practical options for establishing a solid fundamental strategy and technique to link the supply chain from source to customer. It would make it possible to recall a commodity in stock and at the customer’s location with extreme precision. As a result, distribution companies have a clear line of contact with or call back to the end-user. Until recently, it was not possible to establish direct communication. With Blockchain, it is possible to develop new customer loyalty techniques and automate the maintenance of vouchers. The reverse logistics industry, guarantee control, and product monitoring are a few of this technology’s other critical implementation domains [53].

4.4 Proper Management The promise of Blockchain may lead to the adoption of a new business model that is more effective, scalable, and optimized, with a particular focus placed on the preservation of Industry 4.0. As a result, Blockchain is an essential partner in developing the industrial sector. This technology further removes the need for a middleman, allowing companies to benefit and get more out of it. Using blockchain technology by businesses to more directly verify and execute safe transactions can be very beneficial. Arrangements including judges, bankers, dealers, and other intermediaries are theoretically feasible. The interactive nature of these solutions allows for data to be altered and then understood and validated by anybody further down the chain of command. Depending on the digital network, any computer linked to it may be able to witness a transaction that was begun on a single computer or network node [54]. Blockchain is an open and verifiable architecture altering how people think about trading value and products, contract enforcement, and data sharing. It is also changing how people think about financial transactions [55, 56]. Regarding Industry 4.0, the software is not only a provider, but also a mutual, secured transaction directory dispersed throughout a computer network [57]. One of the most significant benefits of blockchain technology is that it allows us to transcend the identities of people and objects inside supply chains and the actual items that move them. Since then, blockchain technology has made it easier to streamline the supply chain and the identification process in the pharmaceutical and healthcare industries. Participants in the medical profession were able to make payments using a cryptocurrency wallet, adjust the pharmaceutical design model, and give patients and medical practitioners an experience tailored to their needs while remaining trustworthy. The procedure

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of certificate record-keeping and authentication has been made easier thanks to this technology. It made use of verified open-source software [58].

5 Industry 4.0 for Sustainable Development In academic, corporate, and policy debates, as well as in the media, the concepts of Industry 4.0 and sustainability are gaining popularity. Despite the relevance of the issues, the link between Industry 4.0 and sustainability—which several authors have highlighted—remains a mystery. As knowledge about Industry 4.0 and sustainability continues to be expanded, especially regarding the effect of Industry 4.0 technology on sustainability practices and performance, this goal will be achieved [59]. Among other things, the concepts of sustainability and SD have gained widespread attention due to their proposed solutions for problems involving environmental issues such as energy conservation, climate change, and rural development, among others. Nortan [60] believes sustainability and social development are synonyms and are used interchangeably. Industry 4.0 has captured the imagination of people all across the globe during the past several years. To fulfil the continually expanding worldwide demand for capital and consumer products, while simultaneously assuring sustainability in its social, environmental, and economic aspects, contemporary globalization is confronted with a slew of difficulties in industry 4.0. In developed nations, the development geared towards the fourth stage of industrialization, which is referred to as “Industry 4.0”, primarily determines the nature of industrial value creation. To sum up, this can define industry 4.0 as follows: it is not only about manufacturing, but also about how various products, processes, programs, communities, and businesses function because of the involvement of artificial intelligence (AI), machine learning, hardware, software, and humans in the design and development of these products. With the growth of Industry 4.0, several potentials exist to realize and implement sustainable manufacturing practices [61]. The methods through which digitalization and Industry 4.0 technologies allow innovative products in the industry are still poorly understood, despite the growing importance of using Industry 4.0 technology for sustainability. A strategic roadmap explaining how firms may harness Industry 4.0 technology to include sustainability in creative practices will be developed to close the knowledge gap described above. A comprehensive assessment of existing literature was conducted to identify Industrial revolution 4.0 roles for achieving sustainability, and interpretative architectural modelling was used to develop the proposed roadmap. The findings provide intriguing insights into the uses of Industry 4.0 for long-term sustainability. The strategic roadmap that has been produced demonstrates that Industry 4.0 promotes sustained innovation via using 11 different functions. To effectively integrate multiple stakeholders, firms must increase inter-functional cooperation and use digital technologies and strategies to achieve Industry 4.0 success. Increased knowledge base and intelligent factory competencies are further enhanced by Industry 4.0, which also supports

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organizational qualities that are beneficial to global sustainability, such as environmental knowledge transfer, sustainable cooperation, and sustainable innovativeness. Industry 4.0, because of these functionalities, increases the potential for green process innovation and the ability to produce or reintroduce environmentally friendly goods affordably and competitively. The roadmap clarifies the complicated precedence connections across the sustainability roles of Industry 4.0. It has crucial implications for enterprises seeking to capitalize on Industry 4.0 sustainability impacts and govern a sustainable future [62, 63].

6 Applications of Industry 4.0 Industry 5.0 incorporates robots and innovative technology that help people perform better and be more intelligent. Universal Robots’ chief technology officer and cofounder, EsbenStergaard, believes that Industry 5.0 will change the factory into a place where creative individuals can come and work, leading to a customer and employee experience that is more personalized and human. By 2025, Industry 5.0 will employ chief robotics officers in over 60% of manufacturing, logistics, supply chains, agri-farming, mining, and oil and gas sectors. This chapter examined the manufacturing uses of Industry 4.0. Industry 4.0 in Manufacturing The manufacturing industry is now undergoing the Fourth Industrial Revolution. Sensors, software, connectivity, and big data analytics make it feasible for factories to become more efficient and adaptable due to digitalization. Businesses will find it simpler to shift their business models due to this. It was during the first industrial revolution that the invention of water and steam-powered engines occurred. This resulted in the development of the first structured method of producing things. This was followed by the discovery of electricity, which created the world’s first moving assembly lines. So, since the computer made it feasible for the Third Industrial Revolution, the revolution could occur. With the advent of Industry 4.0, it is now possible to build future factories using sensors, networking, communication, data, and artificial intelligence-powered analytics. The following are some examples of what manufacturing may accomplish. Smart Energy Consumption Automatic building management systems (ABMS) are networks of sensors, actuators, computers, and other devices linked by an IP backbone. This allows for monitoring energy consumption from machines, lights, HVAC systems, and fire alarm systems, among other things. As part of a larger dataset that includes weather forecasts, actual energy, and other utility tariffs, this data might offer a more accurate picture of a building’s total productivity and effectiveness and a much more accurate estimate of its energy consumption.

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This automated building management system, which monitors air quality, temperature, and lighting, has been deployed at a Schlumberger facility in France. The goal is to use energy more effectively while lowering expenses. Intelligent Lots Improved production facility operational efficiency is a significant driver of Industry 4.0 implementation. More innovative lots, characterized by intelligent information storage in goods and crates, are an example of supply- chain digitization that facilitates the deployment of just-in-time manufacturing. Intelligent lots are defined by smart information storage in items and pallets. The tracking and monitoring of containers, pallets, and roll cages before delivery and during storage in a warehouse may be accomplished via the use of Radio Frequency Identification (RFID) tags and sensors in combination with 3G, LoRa WAN, NB IoT, Wi-Fi, and Bluetooth connection. Information regarding location, inventory, and temperature may be obtained from this data, which can also be utilized to enable complex technologies such as automatic picking. A catalytic converter component manufacturer in the United States, NGK Ceramics, uses this technology to great success. The company has a 500,000-squarefoot facility that makes catalytic converter components. Using IoT-enabled monitoring, employees at the firm can keep track of pallets as they move across the plant, ensuring that inventory is constantly updated. Real-Time Yield Optimization Industrial assets may be connected via monitors, business applications, and telecommunications transmission to offer an accurate image of equipment operating at any given time. Thanks to computer vision and machine learning, this study might be pushed to real-time yield optimization in production settings, with outputs constantly re-calibrated to achieve optimal performance based on various criteria. This guarantees that industrial assets are constantly at their peak performance. For a cement kiln in Australia, ABB, an engineering powerhouse, employed realtime yield optimization to control and improve the process. The system simulated the actions of an “ideal” cement plant operator and made automatic adjustments to reach the goals. In addition to increasing kiln stability, this has decreased the energy needed to produce one ton of clinker. Routing Flexibility As organizations strive to become more responsive to changing client requirements, modern production necessitates increasing degrees of plant flexibility. As a result, the usage of flexible manufacturing systems, which employ IoT-enabled technology to help businesses become more responsive, is rising. Routing flexibility, for example, refers to a manufacturer’s capacity to cope with circumstances such as equipment outages to keep a component’s production going. This might be accomplished in various methods, including producing a specific item via many routes or continuing operations on multiple machines. Routing flexibility is achieved through the use of hierarchical job shop activity models that allow for dynamic modelling of

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manufacturing processes. This flexible method improves manufacturing flexibility by maximizing asset use and enhancing uptime. Machine Flexibility It is projected that machine flexibility will become an exciting development area as Industry 4.0 develops and manufacturing becomes more decentralized and automated. Standardized interface and intelligent infrastructure will enable a substantially more modular approach to industrial connectivity and automation, with modules that can be “plugged and manufactured” to allow for quick reconfiguration of production lines, pushing this trend further. For example, rapid information connections for robotic arms or high-level sensing devices that interface with Ethernet, eliminating the need for a traditional I/O module, are possible. This machine flexibility enables more dynamic production lines and more rapid and straightforward maintenance, benefiting the customer. Remote Monitoring and Control In a manufacturing facility, thousands of pieces of equipment work together to produce a final product. Consider how crucial it is to have visibility over all working processes when some manufacturing sites are part of a worldwide network. It is now possible to get this data in real time from anywhere. Engineers may use a tablet, laptop, or mobile dashboards to connect to networked systems and monitor individual assets. This remote monitoring and control may identify and eliminate bottlenecks and reduce waste. Varoc, a US automotive component maker, has been employing this machine health monitoring method, resulting in a 20% boost in total equipment effectiveness. Predictive Maintenance Automatically monitoring industrial equipment of various types may be accomplished by combining technology and data connections, with data analytics driven by machine learning being used to discover patterns and issues. Using failure trend monitoring, engineers may spot problems before they arise rather than relying on conventional calendar-based maintenance procedures such as periodic equipment inspections or the ‘if it isn’t broke, don’t repair it’ philosophy. Manufacturers may gain significant benefits by realizing the potential of entirely predictive maintenance regimens, particularly when it comes to mission-critical equipment, to avoid unexpected downtime and associated costs. This is especially true when it comes to mission-critical equipment. Gemu, a valve and control component maker, has been successfully using this strategy for many years, monitoring its manufacturing processes and finding and replacing any underperforming components before they break down.

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Augmented Reality for MRO Maintenance experts fixing machinery in manufacturing units via well-instruction manuals are soon becoming obsolete. A range of information, such as computeraided statistics, schematics, and drawings, are shown in their field of sight. At the same time, workers go about their regular duties while wearing augmented reality headsets. This capacity is being driven by rapid developments in image recognition technology, computing power, wireless connectivity, and the Internet of Things, among other things. There is no doubt about the benefits of augmented reality-enabled maintenance: workers can do better work in less time and with minimal mistakes since they have instant access to accurate information that is presented in a comprehensible way. Several maintenance chores are performed at Festo’s European and American sites utilizing augmented reality headset glasses from Microsoft HoloLens, for example. Human–Robot Collaboration Compact, lightweight robots operate alongside people without needing safety cages to provide further flexibility in intelligent production environments. These collaborative robots, equipped with advanced motion, vision, and location sensors, can perform various tedious and repetitive tasks, enabling people to devote their time and attention to other activities. Cobots have been implemented at one of ThyssenKrupp’s vehicle suspension system manufacturers, performing machine tending, assembly, and product inspection. This enabled Thyssenkrupp to improve output and build its company when qualified employees were scarce. Remote Monitoring and Control Answers to two fundamental questions are provided by cloud-based remote monitoring. Who knows what happened to all of my possessions? What is the present state of their affairs? Workers in the production facility may save significant time thanks to the real-time information available and sent to dashboards on mobile devices. Workers who have immediate access to data on temperature, pressure, volume, energy consumption, loaded hours, and unloaded hours will be able to make better decisions based on more reliable information. This enhances the amount of time the system is up and running. Digital Performance Management Manufacturers can now monitor more than simply the functioning of their machinery thanks to digitalization. Through digital performance and operations management systems, other cost factors like materials and people may be examined and KPIs automated. A more precise method of cost allocation within an organization, and hence the opportunity to enhance cost estimates and overall financial performance, is provided by such an approach.

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Automation of Knowledge Work We are familiar with robots taking over the physical work humans formerly handled. Consider the alternative: what if automated technology were to take over some of the knowledge-based jobs that workers now do? McKinsey coined the phrase “depths analytics, delicate judgments, and new issue solutions” to represent the use of computers to do tasks that need “deep analysis, delicate judgments, and unique problem solutions,” according to the company. According to research conducted by the McKinsey Global Institute, knowledge job automation will be one of the top ten disruptive technologies in use by 2025. Specifically, business process automation in procurement, marketing, and customer service might significantly influence manufacturing, allowing workers in specialized vocations to be supplemented rather than replaced. Batch Size Industry 4.0 enables more customization as well as quicker and more flexible manufacturing. When taken to its conclusion, flawless factories should be capable of transitioning away from serial processes and producing a single item as rapidly and effectively as numerous components. With the capacity to produce ‘batch size 1’, you can save money, and businesses may attain new degrees of mass customization—effectively making to order for individual clients. The batch size one technique relies heavily on automation. Robotic arms, for example, are employed by Alp Stories, a customized beauty product maker, to begin creating its clients’ ‘beauty boxes’ as soon as an order is received. It is possible to configure the Motoman CSDA10F robots’ twin arms to choose and pack using various instruments and vise grips. The arms may be reprogrammed to learn new activities rapidly. With this approach to batch size 1—the capacity to create single units flexibly and economically—new solutions to stockpiles focused on more flexible need projections will be encouraged to emerge. Real-Time Supply-Chain Optimization In the age of Industry 4.0, supply networks are speedier, more flexible, and more transparent. Factories may have complete visibility of their incoming products, understanding each delivery’s specific position and quality, thanks to a combination of ubiquitous sensors and connections and big data analytics. Automatic handling is used to choose commodities and position them in the appropriate location in the warehouse; networks provide real-time information on production rates. The buying department’s feedback mechanism becomes more efficient due to this. Real-time supply-chain optimization has many advantages. For example, Hitachi has begun a thorough inventory and supply-chain optimization throughout its Asian manufacturing plants, decreasing items in stock, lowering logistics costs, and increasing logistics flexibility.

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In-situ 3D Printing The performance of 3D printing technology has increased substantially in recent years, with the most modern machines capable of reliably producing polymer and metal components. As a result, 3D printers have moved beyond their conventional prototyping functions to provide a versatile way of producing replacements or replacement components. Manufacturers may reduce the requirement for storage by opting for on-demand production of specific components at or near their plant, increasing supply-chain resilience and decreasing costs. At Siemens Mobility’s train production factory in Germany, for example, the firm has installed a computerized machining centre, which allows the industry to 3D print spare parts and tools on demand. Digital Quality Management When it comes to Industry 4.0, there has been much discussion, but what about Quality 4.0? It refers to the use of extensive data analysis to bring about a change in the way quality is evaluated. Because of the advancements in digital quality management, quality can now be connected across the production chain from source to delivery, rather than merely being assessed based on market integrity. According to Sparta Systems’ 3 Steps to Quality in the Cloud, transitioning away from previous evaluation criteria relying on physical documents and more towards implementing an online quality management process in the cloud could lead to reduced costs, greater compliance, and better compliance user experience for the organization. The company Cebos, a provider of quality software solutions, has published a case study detailing how it assisted Vishay Dale, a component supplier, in gaining better control over its internal documentation, resulting in a system that significantly reduced the time and effort required to complete engineering tasks. Therefore, both the product quality and the operating efficiency have been enhanced. Advanced Process Control Modern manufacturing plants include a complicated network of operating processes that govern material, temperature, and other factors. Advanced process control (APC) is increasingly employed to offer a single platform for procedure optimization, encompassing data gathering and analysis, and interactive modelling. The program can monitor feed rates, air temperatures, and powder moisture to optimize product quality and industrial production. An Aveva case study shows how spray dryer optimization for one client increased throughput by 10% and reduced specific energy usage by 8% while maintaining zero product quality violations. Statistical Process Control Statistics drive industrial production, and a wealth of data is accessible to offer insight into the overall quality of the product throughout the manufacturing process. This information is acquired in real time and shown on graphs with control limits that have been defined based on the capabilities of the process. Stumbling activities are

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characterized by deviations that occur outside of the boundaries and can potentially damage the quality of the product negatively. Bottled water company Nestle Waters has deployed Infinity QS statistical process control software to replace an inefficient paper-based data collection and analysis system, allowing it to get real-time insight across all 26 locations. Through the analysis of data patterns, the firm has been able to make more precise judgments on process changes in areas such as cap torque, resulting in more consistency in product quality. Data-Driven Design to Value The way discrete manufacturers get their products to market is changing due to the proliferation of sensors. Manufacturers may be able to produce better-performing things that are more closely related to their customers’ wishes if they attach sensors to prototypes and use the data gathered from testing to acquire a better sense of real-world operating circumstances. There is no reason why this method, known as data-driven design to value, should be limited to prototypes. Sensors on items in the field continue to offer operational data, allowing for product improvement over time due to the data. PTC is a pioneer in this market, having developed a case study that describes the tractor design requirements. Instead of making assumptions about things like the maximum weight that a tractor’s bucket should support, data-driven design to value is used to guarantee that the product is neither over-engineered (which adds time and cost) nor under-engineered (which increases time and price) (decreasing performance and customer satisfaction). Data-Driven Demand Prediction Different factors may have a substantial effect on the performance of manufacturers. Even the most meticulous planning and decision-making may be thrown off course by a change in customer purchasing patterns. This is where the statistics model comes in. It is a cloud-based forecast business intelligence application that offers a comprehensive perspective of growth in the future. The program uses global data, analytics, and expert services in industrial settings to identify potential dangers or opportunities to business performance across the financial, sales, marketing, and operations departments. According to Provider’s research, data-driven demand prediction may give reliable insight into future activities that can be used to change production levels or support expansion plans. Manufacturing organizations have reported an increase in demand prediction performance of more than 20%. Rapid Experimentation and Simulation Using software to accelerate experiments is not a new concept. This has been around for quite some time. Modern technology, such as 3D printers, is being utilized to significantly minimize the time required to bring a product to market. It is exceptionally well suited for ease of construction of components, with the result added back into

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the modelling and simulation process for further improvement. Previously, manufacturers were required to send each design evolution to their manufacturing department or outsource the process, which resulted in lengthy wait times for customers. According to the 4th edition of Sculptor’s The State of 3D Printing study, the primary purpose for firms that use 3D printing is to accelerate product development. Prototyping (55%), production (43%), and proof of concept models are the three most common 3D printing applications currently available (41%). Concurrent Engineering Concurrent engineering, also known as task parallelization, may be used to speed up the development of new products. However, if interactive systems are not in operation as part of the more significant Industry 4.0 ambitions, it may be impossible to coordinate such simultaneous tasks. As a result, technological advances from companies such as PTC could aid with the simultaneous process by giving the leading architect a ‘skeleton’ that can be used to guarantee that everyone will have access to the data they require and can operate on a single network. Engineers can work on multiple projects simultaneously without worrying about accidentally overwriting each other’s work. Each subassembly will change after the overall design has been established. This strategy was used in developing the KTM 690 DUKE motorcycle, which was built in just 22 months from idea to production—a 15% decrease in time-to-market compared to the previous generation model. Customer Co-creation/Open Innovation Industry 4.0 promotes open innovation and consumer co-creation. Other stakeholders, such as customers or suppliers, and local schoolchildren, may be asked to participate. Transparency and trust may be built while fostering lateral thinking. Consequences include more customer-centric solutions. Bringing a product to market quicker may also reduce the processing time. Valeo, a French automotive supplier, with its Innovation Challenge, an innovation competition that promotes the creation of unique solutions. The organization’s target audience is students worldwide. The event attracts high-potential workers from 80 countries and 1627 teams from 748 colleges, all competing for one of two $100,000 cash awards. Predictive Maintenance The servicing of industrial equipment is being transformed by digitalization. Sensors, software, and connection are used in specific after-care packages to allow machine manufacturers to analyze the operation of their goods in situ, allowing the client to save downtime by predicting issues before they arise. This IoT-enabled framework is always at the forefront of developing innovative products focused on services, for which the end-user essentially leases a product or a technology instead of purchasing it, eliminating a substantial initial capital expenditure. This service is based on key performance indicators (KPIs), including available

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uptime, and provides the producer with a better understanding of maintenance plans. Meantime, the service provider is paid constantly for their efforts. Remote Maintenance When a machine in a factory broke down, the in-house maintenance personnel had to either repair it or call the OEM for a service engineer. Remote monitoring and maintenance of in situ equipment allow OEM workers to do specific tasks without physically visiting the site. Software updates may be done safely and securely via the Internet, allowing equipment to be swiftly brought back online. Augmented reality headsets might also be utilized for remote maintenance, with plant workers receiving repair instructions in their line of sight. Plex Systems, for example, uses mixed reality remote maintenance to help customers and internal support workers get their equipment back up and running. Virtually Guided Self-Service Artificial intelligence isn’t just used in manufacturing to improve productivity, but also to improve customer service and after-sales support. Virtually guided self-service refers to the employment of virtual agents on corporate websites to assist clients with problem resolution.

7 Conclusion and Future Works Greater productivity and enhanced quality have always been the primary focus of any business, which can be accomplished by managing and optimizing all aspects of the manufacturing processes and supply chain. However, as businesses generate and use voluminous data, they must adopt new technology to their production facilities and business processes. Industry 4.0 is one promising technology that can alter how businesses manufacture, improve, and distribute their products via digital technologies such as Blockchain, the Internet of Things (IoT), cloud computing and analytics, artificial intelligence (AI), and machine learning. In this regard, this chapter has discussed the industry 4.0 concepts at length. A review of industrial revolutions from industry 1.0 to Industry 3.0 has been presented to facilitate a better understanding of the topic user. The chapter also discusses the digital transformation concept in context with Industry 4.0, its objectives, and various technologies that can be used to serve the purpose. A brief discussion on Industry 4.0 for sustainable development is also presented to show how it can contribute to circular economic objectives. It also highlighted the significant applications and potential of Blockchain in Industry 4.0. The chapter concluded with a discussion on Industry 4.0 in manufacturing and how it can bring benefit to manufacturing through some examples. It is recognized that the chapter will powerfully help the users understand the concepts and gain insights into Industry 4.0, its significance in increasing efficiency, value, or innovation, and how its implementation will transform manufacturing. It is also realized that the chapter

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will provide a solid foundation for researchers who wish to pursue research in this domain.

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Blockchain in Supply Chain Management Shivangi Surati, Bela Shrimali, Himani Trivedi, and Payal Chaudhari

Abstract Due to the expeditious growth in network and communication technologies, various industries such as healthcare, agriculture, supply chain and logistics, business, tourism, and hospitality as well as their various operations have undergone complete digitized transitions in industry 4.0. Supply Chain Management (SCM) and Demand Driven Supply Chain (DDSC) can be considered a core functionality of all these industries. Due to digitization in all the aforementioned industries and the related supply chain management, transparency, security, and privacy have become major aspects to be of concern. Innumerable open security solutions and research studies have been proposed in the existing literature and applied to enhance the security in variegated domains. However, the existing security mechanisms are either based on centralized architecture or they consume high computation power. To resolve these issues, Blockchain technology has been proven to be the best solution that depicts the new business aspects. Furthermore, Blockchain technology can address the additional requirements of supply chain management and demand-driven supply chain viz. scalability, robustness, data storage, network latency, auditability, immutability, and traceability. Moreover, the transparency and trust in the system can be achieved by Blockchain-based architecture without thirdparty intervention. Blockchain, as a distributed and peer-to-peer network technology facilitates immutable and transparent systems to satisfy the above-mentioned need of any industry including SCM. So, the discussion on the ecosystem of Blockchainbased supply chains as well as the demand-driven supply chain is emphasized in this chapter. In addition, the barriers and limitations to implementing Blockchain in supply chain management and demand-driven supply chain are explored along with its security aspects. Lastly, distinct case studies and schemes that discuss the

S. Surati (B) · P. Chaudhari Department of CSE, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India e-mail: [email protected] B. Shrimali Department of CSE, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India H. Trivedi Department of CE, LDRP Institute of Technology and Research, Gandhinagar, Gujarat, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_3

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success of Blockchain in supply chain management and demand-driven supply chain are presented. Keywords Blockchain · Supply chain management · Demand driven supply chain · Security · Industry 4.0

1 Introduction Supply Chains (SC) are one of the major paragon transitions in modern business management in industry 4.0 [1]. A network of consortium and their associated activities are generally carried out in a sequential manner, to deliver value to the customer is known as supply chain management in general terms. On the other hand, new demands of value-added services or products are generated in the market that reaches to the manufacturer and other stakeholders in SC, known as a demand-driven supply chain. Many SC and DDSC participants often have their own information systems, but connectivity between these systems is restricted due to centralized architecture. For example, enterprise resource planning systems. For SC entities to entrust their sensitive and important information to a single organization or broker, they must possess a high level of confidence [2]. This created a demand and need for such a technology that offered security along with the integration of all parts of SC as one. These all aspects are covered under the new growing technology named Blockchain. It is a peer-to-peer, decentralized and distributed network of nodes that share a public digital ledger to keep permanent records in form of a chain of blocks for all past and current confirmed transactions [3] as shown in Fig. 1. The usage of Blockchain is growing due to its immense potential for large-scale improvements in a variety of areas of the economy. Yet, its full prospects are not explored and realized. It has a wide scope of applications in the domain of SCM and logistics involving technologies such as Radio Frequency IDentification (RFID), Internet of Things (IoT), sensors-oriented technologies, telematics, barcodes, and many more [4]. It provides a distributed database where SC and DDSC stakeholders collaborate to generate, verify, authenticate, and securely store records such as product information, certifications, transactions, and data collected from sensors and other connected devices [5]. It enables the discrete parts of supply chains to integrate and work as one seamless unified system as shown in Fig. 2. A comprehensive literature review of the current studies on Blockchain in supply chains is studied in [6] that includes a brief discussion about the financial and business perspectives of Blockchain in supply chains. The classification of article topics, the concepts, and methods for adoption of Blockchain in SC, and citation analysis of the current state of the art are presented in [7]. A detailed survey of enhancements in Blockchain-based supply chain, research trends, applications in numerous domains, research challenges, and future scope is explored in [8] with an aim to address the current research issues. Furthermore, the latest survey of the taxonomy

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Fig. 1 Blockchain

Fig. 2 Blockchain in supply chain

of Blockchain implementation, in various domains of supply chains, its classification, the used Blockchain platform, and the issues are discussed in [9]. However, the existing survey/review of Blockchain in supply chain management has major concerns as follows: • The existing approaches of Blockchain in SCM are presented in brief and the related applications are not explored in detail [7]. • The applications of Blockchain in supply chain management and demand-driven supply chain management in multiple domains (finance, industry, agriculture, food, and healthcare) may not be covered under the same roof [6]. • The layered architecture of Blockchain-based SCM and the generalized sequence of data among the stakeholders through Blockchain implementation are not discussed in detail [8]. • To the best of our knowledge, review, architecture, and case studies/applications of Blockchain-based DDSC are not explored in the existing literature [6–9]. Thus, the study of both the types of supply chains and the integration of Blockchain in them are not discussed simultaneously in detail along with the heterogeneous real-life application scenarios in the existing relevant reviews or surveys. Hence, the detailed survey of the integration of Blockchain in SCM and DDSC management is required to be explored with the inclusion of a variety of domains. Moreover, the integration requires the study of efficient architectures, storage, and management

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of Blockchain-based SCM, and security concerns through various case studies and schemes. Based on the above observations, the contributions in this chapter are as follows: • Discussion about Blockchain in business SC: The transition of traditional SC into digitized SC and the relevant benefits are discussed. Due to the digitization, the need of Blockchain in Business SC is justified. Hence, the fundamental needs of Blockchain in SCs, the representative layered architecture, existing schemes, and case studies in industrial, financial, agricultural, pharmaceutical, and food SCs are discussed in detail. • Blockchain in SC security: The identification of potential vulnerabilities in SCM and the sequence of a process for the risk assessment are explained in detail. It includes elaborated discussions on smart contracts, asset tracking, secure as well as error-free order fulfillment, and cyber security concerns in business SCM. • Blockchain for demand-driven SC: The customer-oriented trends uplifted the concept of demand-driven supply chains in the market and business. Hence, the involvement of Blockchain in demand-driven SC and corresponding business scenarios are explored along with the recent case study of the COVID-19 demanddriven supply chain. • Barriers to implement Blockchain in SCM: Lastly, despite the benefits offered by Blockchain in SCM, various factors responsible for creating the barriers in the implementation of Blockchain in SCM are highlighted. The organization of this book chapter centers on the role of Blockchain and its essence in SCM. After the introduction in the first section, the second section consists of Business to Business (B2B) integration and connection that refers to electronic data transferred via the digital platform i.e. Internet, between various stakeholders. This section focuses on the importance of Blockchain in Business SCM, along with the current trends of digitization incorporated in SCM through real-world case studies. Further, the third section of the chapter discusses the security and confidentiality aspects of Blockchain at each separate level of SCM. The fourth section enlightens the knowledge on demand-driven SC with a thorough discussion on the systems, technologies, and processes that sense and react to real-time demand across a network of end users. It also includes a brief discussion on recent case studies. Lastly, the fifth section focuses on barriers and issues to implement Blockchain in SC. The conclusions drawn from this study and the future work that can be carried out in this domain are included in the sixth section.

2 Blockchain in Business Supply Chain A business supply chain includes the end-to-end process of covering the movement of the physical and correlated data of raw materials, prices, location, logistics, products, and capital. It manages and maintains various business processes and controls the overall performance of organizations. With this regard, four main components of

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Fig. 3 Arms of supply chain

SC are (i) Produce/Buy, (ii) Move/Transport, (iii) Store/Warehouse, and (iv) Sell as shown in Fig. 3. Each of the components represents the various processes and activities of SC [10]. These activities can be divided into two major processes [11]: 1. Primary/Core activities covering outsourcing, procurement, production/manufacturing, inventory, collaboration, distribution, and logistics. 2. Secondary activities covering various executions, controls, and planning viz. supply planning, product planning, sales and operation planning, demand planning, etc. A business supply chain consolidates a set of exploded and geographically distributed processes into an integrated system to achieve efficiency and profit in the business. The core processes and functionality of a typical business supply chain are illustrated in Fig. 2 in Sect. 1. The detailed answers of the questions “Why digitization in SCM?”, “How digitization in SCM?”, “Why Blockchain in business SC?”, “How Blockchain is integrated in SCM” are explored in this section. Not only that, numerous case studies/applications are presented to reinforce the presented concepts.

2.1 Digitization in Supply Chain System Geographically separated operations, management of logistics, satisfying customers’ requirements as well as real-time challenges create a high level of complexity for the traditional SC. The main aim of involving digitization in the SC is to reduce

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the complex execution of SC. Digitization can be defined as “an intelligent method integrated with the software and computer technologies like IoT, cloud technology and big data to collect, share and use the information for the appropriate process, decisions, operations and outputs”. Digitization brings paper-less and hassle-free operations in any industry such as agriculture [12], healthcare [13, 14], education, and many more. Digitization in the SC uplifts the business with more powerful opportunities. The following is the benefits of the digitized SC: • Pure automation: Automation at every stage of SC with a reduction in manual tasks. The physical and administrative workload of people is lessened as a consequence. Work automation optimizes the time of people at mandatory processes that required human interventions and supervision. Also, automation generates good revenue and profit by reducing human error and speeding up processes. For example, automatic invoice and goods dispatch order generations make the process and revenue generation fast. • Make the processes cohesive and connected: Managing the details through mail and spreadsheets consumes a lot of time due to dependencies of information. The tasks carried out through emails and papers may tend to human error and slow down the process executions. Digitization of the SC through a well-created system makes the process fast by reducing its dependencies of it. It eliminates the gap of physical connections and facilitates communication over the entire organization. • Speed up decision-making process: A digitized SC provides enormous data within less time compared to a traditional SC. Digitization and automation in SC processes generate and provide error-free data in real time that helps to make fast decisions. There are many industries like logistics and transportation that require fast decisions which can be achieved through pure automation. The layered architecture of the digitized SC is shown in Fig. 4. It depicts the workflow in a typical digitized SC through three layers. The layers involve all the processes and integration of every component of the business SC as follows: 1. Digitized Product Identification Layer: It is the lowest layer that highlights the various possible digitized smart products used by the business SC to facilitate digitization viz. Global Positioning System (GPS), RFID, sensors, barcodes, etc. 2. SC Data Layer: Layer-1 is connected to this layer by passing lots of data for automation, transparency, and integration along with very precise and accurate transaction details. The major processes of this layer are facilitating transparency (providing information like production time, cost, and location of products/items), integration with other existing systems like Enterprise Resource Planning (ERP) [15], Warehouse Management Systems (WMS) [16], Tracking Management System (TMS) [17] and storing transaction carried out among stakeholders. 3. Stakeholders’ Layer: It is the uppermost layer depicting various stakeholders of the digitized SC. In addition, the architecture depicts their interconnection.

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Fig. 4 Layered architecture of digitized SC

The digitized product identification layer and stakeholders’ layer are connected to the SC data layer. The data from the digitized product identification layer to the SC data layer is unidirectional, wherein, it is bidirectional between the stakeholders’ layer and the SC data layer. The bidirectional flow of data represents a bidirectional sharing of data. Thus, digitization over the past decades has changed the production and business style of various industries and the shopping styles of customers. These changes have raised the challenges and new needs in the existing digitized SCM such as the integration of Blockchain as discussed in the next subsection.

2.2 Need of Blockchain in Business Supply Chain Digitization introduced a very simplified, smooth, and efficient SC execution on the business side and a shopping experience on the customer side. The use of digitization facilitates customers to demand the customized products. Commonly, in any industry, supply chains of the system depend on the centralized management systems like enterprise resource planning systems for managing the information flow of supply chains. Such centralized systems are vulnerable to error, hacking, and corruption problems. The demand for tractability, traceability, and transparency about the cost and source of the products has increased by the customers day by day [18]. These ever-increasing requirements introduce new opportunities and significant challenges to businesses and current supply chains. These digitized supply chains have limitations with real-time demand management, risk management, traceability,

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and transparency. The following is the major needs of current digitized business supply chains: • Traceability: Traceability has become very crucial for supply chains to address various operations such as transportation due to the centralized system. It is very essential to evolve all the stakeholders and all SC processes in one place to make it traceable by each of the participants. • Trust: SC system covers various operations that may involve different and unreliable parties/stakeholders. Trust is a vital parameter in successful and effective SCM. Distrust among stakeholders affects the overall process of SCM and reduces process efficiency, profit and increases operational cost. • Transparency: The “transparency” refers to the availability of accurate information of products and processes to all the participating stakeholders [19]. The transparent system increases the trust among participants and thus, increases profit and reduces operational costs. • Immutable data sharing: In digitized SC networks, data and information are shared among many stakeholders and organizations using paper-based documentation. Important documents viz. various certificates, bills, insurance policies, authentication letters, and the other required letters are transferred independently or with the goods. In any circumstances, fraud is possible due to unreliable and vulnerable access. Hence, an immutable sharing system is required for the digitized SC [18]. The comparison of the traditional digitized SC and Blockchain-enabled digitized SC through salient parameters of SC is presented in Table 1. The comparison delineates the benefits of Blockchain-enabled digitized SC. It can be observed that the Blockchain uplifts the process of SC in various ways. The adoption of Blockchain technology in the SC can significantly attain the need of the current/traditional SC. The connection of various stakeholders with Blockchain is shown in Fig. 5. Integration with Blockchain technology uplifts the SC with improved security, privacy, efficacy, tractability, transparency, and transactional time and cost as discussed earlier. Following are the benefits of integrating Blockchain technology to the SC: Table 1 Comparison of digitized supply chain and Blockchain-enabled digitized supply chain

Parameters

Digitized supply chain Blockchain-enabled digitized supply chain

Architecture

Centralized

Decentralized

Data storage

Centralized

Distributed

Fault tolerance Poor

Strong

Trust

Low

High

Transparency

Low

High

Recovery

Low

High

Immutability

Low

High

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Fig. 5 SCM through Blockchain

• Advanced traceability: With the adoption of Blockchain technology, traceability within the SC process is improved extremely. It introduces a fully transparent and auditable path of all products/items moving in the SC network covering all of its phases. At the first phase, from manufacturers or producers, it collects the data from the IoT-based devices viz. RFID cards, sensors, barcodes, and GPS. A timestamp and location are attached to each product/item during its traversal in every phase from the manufacturer to the customer and that will create a transparent, accurate, and easy-to-accessible log file. An immutable property of the Blockchain and digital signatures of every stakeholder ensure the authenticity and ownership of data added to the Blockchain. Data stored in a Blockchain offers a full history of any products/items at any point of time in the entire SC process and thus, it reduces the change-in-view between the stakeholders [20]. • Improved transparency: Blockchain technology enables the stakeholders to view the SC process carried out by the other stakeholders at a particular time and place. The information of processes is stored and shared in the distributed shared ledgers that can be conveniently accessed by the participating authenticated stakeholders. The convenient access to this shared ledger facilitates the complete view of product flow between the stakeholders. The transparency through the shared ledger prevents, identifies as well as detects frauds and counterfeiting. Thus, a Blockchain-enabled SC helps businesses to control and manage every process smoothly which results in fast decision-making and profit.

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• Strong security: A Blockchain technology is built on strong cryptographic techniques. Use of hashing algorithm SHA-256 to secure blocks and digital signature mechanisms to authenticate stakeholders, it is almost impossible to hack the system with any kind of attacks and threats possible in centralized architecturebased systems. Thus, Blockchain technology provides a strong and secure way to maintain and manage log files in the form of a shared distributed ledger having all business activities and transactions of SC [21]. • Improved efficiency: One of the strong motivations behind using Blockchain technology for the business SC is to overcome the problems with the centralized systems. A centralized system requires a third-party intervention at many places in the process. For example, a bank or a digital wallet is required to complete the financial process and at many places, human interventions are required for decision-making. Wherein, with Blockchain technology, transactions are included immediately after they are validated by all the involved stakeholders and its transactional data are automatically synchronized to each stakeholder’s shared local copy. Moreover, the use of autonomous and self-executing Blockchain-based smart contracts makes the SC process faster, safer and convenient to maintain the quality of the business process. It reduces human intervention and errors, thus eliminates the need for third-party interventions [22]. • Enhanced trust: The distributed shared ledger enables transparency between the stakeholders. Every single process and transaction is available to every stakeholder creating a trustful environment between all. A Blockchain-enabled SC performs peer-to-peer communication that is controlled and managed with the strong cryptographic techniques, making the network most trustworthy. Stakeholders are authenticated by associated digital signatures. Thus, synchronized storing of data on time enhances the trust among SC stakeholders. • Easy compliance: A Blockchain-enabled SC facilitates the shared ledger storage having all the transactions with the related information like timestamps, location, product, and environmental conditions. These immutable, authenticated, and accurate records create the easy and smooth accessed flow for rules, regulations, and compliance [23]. • To remove tampering and misuse of data: In the centralized architecture, tampering and misuse of data are possible due to the third-party intervention at many places in the supply chain. The use of Blockchain removes the intermediary and third-party intervention and provides pure digitization using smart contracts and distributed shared ledger. Blockchain makes operational data secure throughout the entire global supply chain. • To get rid of transaction delays and charges: The supply chain fundamentally relies on the financial transactions. As an exceptional case in the existing SCM, the intermediary such as a financial organization may delay the processes and may apply processing charges that affect the overall processing of the system. This can be eliminated by the Blockchain-enabled supply chain. • To provide traceability and transparency: In any supply chain, when the product is moved from the manufacturer to the transportation, storage, retailer, and then to the customer, if the details are not maintained in a uniform way, tracking of the

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product becomes hard and transparency among the stakeholders is ruined. This results into problems and distrust. The distributed shared ledger technology of Blockchain helps in making information visible and available to all participant stakeholders and thus, it resolves the aforementioned issue. Streamlined performance: Blockchain enables a shared ledger of information among stakeholders. Various SCM operations are performed via smart contracts to improve the overall performance of the system and collaboration, communication, and connection among the stakeholders. In addition, Blockchain is also scalable and accessible worldwide. This indicates that the technology can quickly enable global relationships and communications. It is therefore the best option for a globalized competitive economy. Increased Automation: The likelihood of human error is greatly reduced because every step a product takes through the supply chain, is automated via Blockchain smart contracts. Deliveries may be made more quickly via increased automation as well as reduced inefficiencies and errors. Improved customer service and higher rates of client retention follow from faster delivery rates. Improved Collaboration: The need of intermediaries is one issue that frequently arises in the supply chain sector. By eliminating the intermediaries, Blockchain helps in saving time and money while also strengthening their connections. The usage of Blockchain in the logistics sector reduces error and breakdowns in communication, facilitating better collaboration between various providers in the global supply chain. Analytics: Blockchain is not merely a technology for storage. It also provides sophisticated ways to examine the uploaded data. It enables users to identify supply chain delays and can assist in making forecasts and predictions based on prior data. These data analytics are proven to be quite helpful for organizations looking to expand and reduce supply chain costs. Customer Engagement: Customer satisfaction is increased by using the analytics stated in the previous point. Retailers may utilize the Blockchain database to track the progress of their products during production and shipping, which helps to improve the performance.

The overall impact of Blockchain technology into the SC is summarized in Fig. 6. The figure depicts the fulfillment of the essential necessity of any SC system with the diagram. Thus, after discussing the answer of the question “Why Blockchain in business SC?”, the architecture of Blockchain-based SCM is presented in the next subsection that addresses the question “How Blockchain is integrated in SCM?”.

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Fig. 6 Summarized benefits of Blockchain in supply chain

2.3 Architecture of Blockchain-Based Supply Chain Management The layered architecture of Blockchain-enabled SCM is shown in Fig. 7. This layered architecture is inspired by the previously described architecture of the digitized SC (Fig. 4). A Blockchain-enabled SC using Blockchain technology is introduced through a Blockchain layer that is implemented with the distributed and decentralized peer-to-peer networks. The layered Architecture of Blockchain-enabled SC consists of four layers: 1. The lowest layer called the digitized product identification layer that controls, manages, and describes the usage of digital smart products viz. RFID, GPS, Barcodes, etc. 2. The next layer is the SC data layer that is integrated with the Blockchain technology for SC process management. 3. The Blockchain layer depicts the implementation of the distributed peer-to-peer network and various technologies such as Distributed Ledger Technology (DLT) [24], smart contracts [25], and digital identity authentication [26]. 4. Lastly, the uppermost layer is the stakeholders’ layer that connects the stakeholders of the digitized SC to the Blockchain layer.

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Fig. 7 Layered architecture of Blockchain-enabled supply chain

It is shown in this layered architecture how Blockchain technology integrates with both of its interconnected layers. Blockchain-Enabled SCM Systems (BESCMS) of any industry are interconnected through the Blockchain as shown in Fig. 5. To avoid non-repudiation in the system and to verify the authenticity of the stakeholders, the public-key cryptography technique (RSA algorithm) is used. The transaction will be encrypted using a private key of the sender stakeholder and the receiver stakeholder would verify the authenticity of the other stakeholder using the public key of the sender [27]. Following are the steps for the interactions among the stakeholders: 1. When any product/item is ready, the manufacturer/producer contacts the transport facilitator through the BESCMS with its identity, registration, and the other necessary details like order, time of pickup of the product, and invoice. 2. Transport facilitator authenticates itself through BESCMS, transfers the goods/products to the warehouse. 3. When a retailer wants to buy goods/products from a warehouse, it communicates with the BESCMS as well as bank with its identity, product code and product quantity along with its rate per unit. 4. Once the retailer’s identity is verified and the retailer has a sufficient balance (in the bank), then after, the required amount is transferred from the account of the retailer to that of the manufacturer.

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5. Once the payment confirmation is received from bank, BESCMS triggers the smart contract that informs the warehouse to transfer the required item in the specified quantity to the retailer and the warehouse transfers the required items to the retailer. 6. The transaction is added into the BESCMS. The existing schemes and success implementation of Blockchain in various SCM applications/case studies are discussed in the next section.

2.4 Existing Schemes In the SC operations viz. ordering of product, production, transportation, delivery, and consumption by customers, Blockchain has the capability to redesign and track these transformation steps efficiently [28]. It has restated the approaches of the value and information exchange through the Internet that leads to a new collaboration between the stakeholders in SC networks as shown in the sequence diagram (Fig. 8). In addition, the digitization of documentation, its management, and retrieval system has reduced the amount of work, cost, and the difficulty of processes. Thus, Blockchain can be considered, potentially, a supportive secure technology for the overall management of the design and functionality of supply chains [2].

Fig. 8 Sequence diagram of SCM in industry

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The existing schemes based on Blockchain in the supply chain are presented in this section.

2.4.1

Industrial Supply Chain

Scheme-1 A novel design scheme based on Ethereum Blockchain that provides an integrated platform to manage the information and various services provided by participants in the supply chain is proposed in [29]. A four-layered physical architecture is designed and implemented to efficiently store the mass data on the chain for the consortium Blockchain application. This scheme supports both types of the storage (on-chain and relational database) with an aim to significantly reduce the actual data stored on the chain as well as to verify the offline data. Apart from that, a cross-chain architecture is proposed to rectify the privacy problem in cross-border business and to solve the extensibility challenges resulting from interactions in Blockchain. The design of the system is mainly divided into five modules as per different tasks: system management to manage members and authority, tracing interface to retrieve and verify the information, process management to handle functional requirements for enterprises, reputation management to evaluate the order and to calculate reputation and cross-chain interaction to manage cross-chain transactions. The logical architecture of the system is classified into four layers (application, service, contract, and data) to bridge the gap between business requirements and Blockchain functionalities. The infrastructure of the interchain network and the maintenance of a chain structure are the core management aspects of the proposed cross-chain structure. Additionally, a data-driven reputation evaluation model is constructed to dynamically evaluate and calculate the participant’s reputation and a reputation value respectively to add intelligence in the supply chain management system. Scheme-2 Another Blockchain-based reliable, secure, and trusted scheme for supply chain management of an OaG (Oil and Gas) industries to execute the royalty transactions among various stakeholders is proposed in [30]. The proposed system model is categorized into three sections viz. landowner’s database, companies’ required data, and the transactional data (in the form of chains using smart contracts) executed between these two. For creating the smart contracts, two scenarios are considered depending on the number of participating landowners: Scenario-1 When only one landowner communicates with only one company for production. Scenario-2 When only one company communicates with N number of landowners for a specific production. Once the registration is successful, the creation and execution of smart contracts are divided into four independent phase: (1) Take grant from administrator, (2) Offer

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made to landowner from company, (3) Payment of initial bonus to the landowner, and (4) Payment of royalty to the landowner. The performance of this proposed scheme is compared with the existing royalty contract transactional model and it is observed that the proposed scheme has lower royalty percentage variation. Scheme-3 A UHV (Unmanned Aerial Vehicles)-based system is designed in [31] with an aim to automate the inventory procedures and to keep the track of the industrial items using attached RFID tags. To achieve decentralization in an adaptable and scalable organization, to strengthen the cyber security, and to give support to big data management and analysis, a Blockchain architecture having distributed ledger is customized. This distributed ledger stores, validates, and ensures the trustworthiness of the inventory details collected by UHVs and make them accessible to the parties interested in these data. The UHVs are equipped with Single-Board Computer (SBC) and a tag reader that collects the data from wireless identification tags attached with the items to be tracked. The SBC then takes these data for further processing and sends them to the ground stations through wireless communication. The ground stations send the data to the Blockchain through a Blockchain client and hence, the stakeholders of the inventory management industries (manufacturers, suppliers, operators, retailers) can also participate in this efficient, transparent, secured, decentralized, and trustworthy architecture. To prove the improved performance of the proposed architecture, the experiments are performed by considering a real industrial warehouse. An RFID reader -NPR Active Track 2 is carried by the UAV-mounted hexacopter. Two different Ethereum testnets Rinkeby and Ropsten are used for Proof-of-Authority (PoA) and Proof-ofWork (PoW) respectively. From these experimentation results, it is concluded that the proposed system obtains the inventory details faster than the traditional manual process. Scheme-4 To demonstrate the necessity of Blockchain in an industrial SCM, the Blockchaindriven transparent SC industrial network for aircraft’s parts is represented as a case study in [32]. The Blockchain technology is implemented at a particular hub that collects different parts of an aircraft from different hubs for assembling purposes. When any new part is procured at this hub, a transaction and assembly ledger will be updated in an entire network to specify that the received part with a unique id is utilized to assemble the aircraft. Thus, an assembly ledger and a transaction ledger integrated with each other will maintain the records of all the new products received and show the transaction details among the particular members respectively. Hence, for the products that are tempered or replaced, the manipulation process of the database will not be validated due to the mismatch in the identification of the product. Thus, the performance and usage monitoring of the parts of the aircraft are efficient using a decentralized Blockchain. This will also reduce the risk related to the availability of these parts in the black market.

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Agriculture-Food Supply Chain

A novel scheme, KRanTi, an efficient Blockchain-based credit system that facilitates the associated farmers to purchase the necessary agricultural high-quality raw product without the burden of instant payment is presented in [33]. This scheme helps the farmers to invest optimally in Agricultural Food Raw Material (AFRM) and assures quality for Agricultural Food Supply Chain (AFSC). A layered system architecture of KRanTi consists of three layers: the physical layer (includes the physical communication between the associated stakeholders), the Blockchain layer (includes secure data flow through Ethereum-based supply chains), and the Inter-Planetary File System (IPFS) layer (maintains the large data off-chain for cost reduction and to utilize optimum bandwidth). In the credit-based system, the farmers have to keep sufficient money in their wallet, however, the money will not be spent immediately for the payment of raw materials purchased by the farmers. Rather, when the distributors purchase supply from the farmers and pay them for the harvested crop, the part of that payment will be paid to AFRM providers, and remaining amount is stored in the farmer’s wallet. These transactions are securely handled by the Blockchain-based supply chains so that none of the stakeholders can modify any details. Additionally, the score-based system guides the customers to select agricultural food by the score assigned to them. Due to the quality assurance scheme embedded in KRanTi, the correlation in KRanTi outperforms the traditional system. In addition, the storage cost in the proposed scheme KRanTi is efficient as compared to that of the Ethereum BC that shows the cost-effectiveness of the proposed framework.

2.4.3

Financial Supply Chain

To support finance in SC, a Blockchain and IoT-driven platform for the Auto retail industry, BCautoSCF is introduced in [34] and the success stories of this platform are discussed. This platform is equipped with the main aspects viz. logistics or warehouse management, funding or credit service management, purchase order management as well as platform administration. Mainly, it is closely connected with the finance institutions to provide inventory financing and purchase order financing. Thus, the participants in BCautoSCF are as follows: • The finance institutions: The funding sources of the business having close collaboration with each other to support various operations, management, and an income source. In addition, the finance service is provided to the secondary distributors and various small-scale retailers to secure the goods supply. • The logistics service: This service provides reliable transportation with low transaction costs. • The warehouse service: This service decreases the vehicle delivering cost and to confirm the vehicle safety on the road.

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Along with these services, BCautoSCF plays the role of the fourth-party custody platform. It combines various flows from the above services to form time-stamped transaction records and store them in a shared ledger. In addition, the functionality hierarchy of BCautoSCF consists of underlying protocols, custody data, infrastructures, business management, third-party integration, and user interfaces. Thus, this platform provides much faster and more economical transactions using the time cost as the main factor.

2.4.4

Pharmaceutical Supply Chain

An interesting pharmaceutical scheme of supply chain management system based on drug recall using a hyperledger Blockchain ecosystem is presented in [35]. The proposed approach not only considers the forward chain for drug supply, but it also includes a backward supply chain for product recall (procedure of returning goods in case of defects viz. manufacturing defect, improper safety, incorrect labeling, or the expiry of product’s life). The drug recall is categorized into three classes— class one contains drugs that may cause adverse effects and lead to death, class two drugs having only health effects, and class three drugs are milder drugs considered for replacement. Each stakeholder (manufacturer, pharmacist, production unit, and distributor) is the member of the Blockchain network and maintains its respective details of the drug. The scheme is mainly beneficial to the manufacturers of the drugs to identify the defective drugs in the market through Blockchain platform. The implementation using Hyperledger Composer shows an easy implementation in the pharmaceutical domain.

2.5 Case Studies/Applications Various case studies/applications/use case scenarios of improvement in SC viz. security, transparency, trust, and efficiency through Blockchain are presented in this section.

2.5.1

Industrial Supply Chain

Application-1 The requirement of Blockchain in Industry 4.0 from the viewpoint of big data management and analytics in SC is investigated in [36] using a two-phase approach— an Action Research (AR) and case study research. The AR phase is further divided into three stages:

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• Stage-1: Problem diagnosis To develop big-data driven SCM and analytics, this phase reviews the existing literature (current and past) related to big-data analytics and SCM. This study reinstates the requirement of an efficient architecture to direct the stakeholders of supply chains and industries to adopt big data analytics. • Stage-2: Action plan Based on the outcome of stage-1, a high-level layered architecture is designed to adopt big data in SCM. The first layer is the infrastructure layer that includes the physical requirements of the implementation viz. software components, operating system, and hardware configuration. Next, the data layer handles the computations related to the structured and unstructured big data received from different sources. The third layer, the big data analytics layer, processes the data using artificial intelligence, machine learning, MapReduce, and prediction tools. The output of this analysis is recommendations and interactive visualizations, that can be explored to the industries and business organizations in visual forms generated by the fourth layer i.e. visualization layer. The last layer is the access control and security layer that is implemented to protect the privacy and sensitivity of the received information in big data. • Stage-3: Action taking To test the proposed architecture, two case studies are implemented by selecting two large companies—a 100-year-old engineering and manufacturing company as Case A and an international company in liquid chemical logistics services as Case B. The practical implications of adaption of big-data-driven Blockchainbased architecture in case A have some failure stories that teaches to adapt the new settings. On the other hand, case B has some success stories and systematic guidelines to adopt the high-level layered architecture. Application-2 The various industrial use cases and advantages of the recent frameworks integrating IoT and Blockchain technology in SCM are explored in [37] as follows: • Walmart: Walmart delivers a huge number of shipments to distribution retail stores around the nation each year using its own vehicles and third-party transportation. Each invoice includes multiple items that must be computed and accounted for individually. These contain the locations of each load’s stops, the number of gallons of gasoline consumed by the carriers, and temperature updates, among other things. As a result, the data is littered with inconsistencies, and most of the invoices require reconciliation. It’s worth noting that Walmart has conducted previous barcode technology studies, but they required a centralized server and registry and were thus unreliable. Walmart came up with a solution for SC by incorporating Blockchain in it to create a fully autonomous system for management, starting from manufacturing or production of product, transportation, tracking, billing, and payment. Walmart, indeed interested in advancing food SC visibility and traceability, introduced two food-based systems. Pork from Chinese supermarkets and crops from Latin

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America are being tracked using the technology of Blockchain. In the event of a foodborne illness epidemic, the provenance data can be utilized to accurately track back the origins of the food, as all the information is stored back then in the chain of blocks. • Alibaba: Alibaba, AusPost, Blackmores, and PwC have formed a coalition to tackle food fraudulent activity employing Blockchain technology. The Food Trust Framework, that will be supported by Australia Post and Blackmores will give the information from their current SC, will assist strengthen the authenticity and transparency of food shipments from Australia to China. Alibaba has been sued in the past for illegal watches sold by partners on the platform. To avoid future legal concerns such as infringement and financial damages, Alibaba’s long-term ambition is to use Blockchain to assure retail goods origins. 2.5.2

Financial Supply Chain

Another application of Blockchain in financial SC is discussed in [38] by considering financial organizations or buyers as important roles in SCF Supply Chain Finance (SCF). The purpose is to improve the efficiency of the SC and value creation for all the participating stakeholders. The theoretical framework of SCF-based Yinuo Finance in China is explored as an in-depth case study. The data is collected from one successful application of Yiuno Finance (a small rubber company, R) via conducting interviews with different representatives of Yiuno Finance, supplier company R, core company, and bank. The participants identified for this value-creation process are core companies (target customer), multilevel suppliers, and financiers. Next, the practices of service providers in this SCF are classified into standardization, visibility and automation, mutual confirmation, and risk control. Lastly, the operant resources (IT, human knowledge, and skills) streaming in this supply are products, capital, and information. Thus, this research study added foremost contributions in SCF, S-D (ServiceDominant) logic, and B2B concepts. The findings in this work prove that the working capital can be optimized and financial presentation can be promoted effectively using Blockchain-driven data exchange and collaborated business organizations. Furthermore, the Blockchain system facilitates IT-related liquidity ratios by compelling strategy of real-time transaction dealing, status update, and clarity to monetary transactions tracking, reducing expenditures, and improving customer satisfaction, thereby significantly increasing the performance of SCF services by providing value creation across all stakeholders involved.

2.5.3

Agricultural Supply Chain

The application of public Blockchain-driven double-chain architecture in the agriculture sector is proposed and analyzed in [39]. Before designing the double chain structure, two applications of Blockchain in the SC of agriculture viz. (i) adaptive

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rent-seeking along with matching between supply and demand of resources and (ii) decentralized collective maintenance and consensus mechanisms are discussed in brief. The public Blockchain in agriculture with double chain architecture is designed by considering four criteria: (i) Influence of business in the industry (the motivation and influence of core enterprises), (ii) Market share of products, (iii) Business reputation (to decide partners), and (iv) Business ideas and management concepts. The double Blockchain platform is divided into two supply chains: • User information chain: – It collects and stores the records related to transactions. – It ensures the integrity and authenticity of transactional data through digital signature (public key cryptography) and immutability by the usage of Merkle Tree. • Transaction chain: – It collects and stores user information of the agricultural organization in the public service layer. – It ensures the integrity, authenticity, and privacy of the personal details of the participating entities through the Merkle Tree structure. The experimental results show that the proposed Blockchain architecture enhances adaptive rent-seeking and matching mechanism for public service platforms. Furthermore, it improves trustworthiness of the public service platform and the complete effectiveness of the application apart from security, privacy, and transparency.

2.5.4

Food Supply Chain

One of the domains that is getting notable attention of Blockchain technology is the food distribution industries. The applicability of the Blockchain technology in the production as well as SC distribution system for eggs, by considering farm, and consumer as end-to-end stakeholders in the Midwestern USA-based company, is explored in [40]. The case study for Blockchain-driven traceability of the food SC is currently being experimented with the Bytable Inc., a company based on Blockchain food traceability. This project is motivated due to the affecting factors viz. compliance in the food industry, food safety and recall impact, condition monitoring for compliance, product quality, and food fraud as well as rising ethics concerns. The Proof of Concept (PoC) is developed to collect and store as much data as possible describing the farms from where the eggs are collected, the processes at the packing facility, tracking process, and continuous monitoring of the eggs during tracking. The end consumers can search all the data by means of QR codes on the egg cartons that maintain the quality of eggs supplied. The Blockchain-driven architecture is developed that utilizes various entities and concepts viz. smart contract(s), validator(s), docker, REST, API, react, microservices, nodeJS, and IoT. The different stakeholders are given different read access levels of

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Table 2 Blockchain-based supply chain: startups/companies Sr no.

Name of company

Location

Blockchain-based supply chain concepts

1

SyncFab (https://syncfab. com/)

Northern and Southern California

End-to-end multi-tier manufacturing supply chain middleware solution secured by Blockchain, supplier incentives, and smart payments

2

CargoCoin (https://thecar gocoin.com/)

London, UK

Secure transport platform and payment provider

3

EverLedger (https://everle dger.io/)

London, UK

A platform providing a secure and permanent digital record of products’ origin, characteristics, and ownership

4

Skuchain (https://www.skuchain. com/)

San Francisco, California

Currency agnostic Blockchain for global trade

5

Huawei technologies (https://www.huawei. com/en/)

Over 170 countries and regions

Supply chain finance and supply chain tracing

6

De Beers (https://www.debeersgr oup.com/)

Multinational

The Blockchain platform, tracr, for verifying the authenticity of diamonds

the stored data using a private key and a proxy micro service or without a key using a public proxy client service with fewer privileges. The results and findings show that the cartons can be reliably traced back, to the details of the suppliers and the date when the cartons were collected. Thus, Blockchain technology has the capabilities to improve the effectiveness and profitability of majority of the businesses by providing a platform that can create, manage and distribute the ledgers as per requirements. Various startups related to Blockchain-based supply chains are presented in Table 2. The previous section discussed about Blockchain in supply chains. The security aspects of SC are discussed in the next section through integration with Blockchain to enhance security.

3 Blockchain in Supply Chain Security With a vast network of business communities across the world, an SC commonly traverses business policies and national boundaries. These connections make the SC more vulnerable and can cause it to disrupt. Many sources of SC risks are shown in Fig. 9.

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Fig. 9 Risk sources for supply chain

The business analyst must have to do the risk assessment accompanying the SCM. Such risk assessment is necessary to identify potential vulnerabilities in SCM and to prepare its mitigation techniques. As per [41], the risk assessment in the SC can be done with the following sequence of the process: 1. Identify the participants/stakeholders in the SCM: These participants are represented as nodes in the SC network. These nodes are responsible to create the SC contracts. 2. Draw the flow of SC to represent each transaction and its related information: Mapping the arcs of the network with the related data (e.g., cargo, container, capital, documents) provides indications of the probable risk points and their significant consequences. This traversal also helps to detect the possibilities of cyber attacks. 3. Do the assessment of vulnerabilities: Classify the risks based on the severity of risk consequences. It is also beneficial if the level of risk can be accompanied with the probability of such risks that can occur [42]. 4. Once the risk classification is done, then prepare for risk prevention and risk mitigation strategies. Based on the severity of the risk identified, its mitigation policies should be given the priority and a deadline. Each policy includes a set of actions, step-by-step implementation procedures, the possible outcomes, and the side effects of these plans on the regular SCM process. For example, to locate the risks of potential security violation, the security personal may implement an RFID system. Such systems accumulate the data regarding vehicle motion automatically. The security personnel collect these data, match it with previous transaction documents (e.g., shipping documents, manifests) and in result, identify the suspicious move in the incoming cargo for red flags [43]. 5. Only the implementation of a plan is not enough. Every plan should be periodically monitored to measure its effectiveness and influence on the security of SC.

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It is also noteworthy to check the feasibility of the plan with the given deadline, and frequently assessing the milestones it reaches. The continuous assessment of the risk mitigation plan is also necessary to adapt to the changes required in the mitigation strategy. This poses one more requirement of the design and implementation of a monitoring and controlling plan for the risk mitigation strategies. The outcome of this phase comprises the development of relevant performance metrics. Since the last few years, with the introduction of Blockchain technology, a new era for the secure SCM has been started. The Blockchain technology performs a notable role for each of the process described above. It is capable to prevent security flaws and to strengthen connectivity in the SC network. Due to its distributed ledger and network verification procedures, Blockchain technology is attack-resistant and tamper-proof. It facilitates automatic accountability because the append-only mechanism of distributed databases of transaction history can be shared across the entire peer-to-peer network. This distributed transaction history maintains the permanent footprints. A Blockchain network can also be depicted as a network comprised of nodes and arcs. This makes the Blockchain technology perfect to be projected on the traditional SC architecture. Thus, the Blockchain technology proves itself to be most suitable to identify both the organizational and network risks included in the SC. Despite of introducing Blockchain technology as a solution to mitigate the SC risks, it is also required to find out some SC events where the risk impact can be decreased, followed by the design of the possible solutions to improve the SC robustness. These solutions should fulfill the below-mentioned objectives in order to enhance security: 1. Mitigate the physical risks: Let’s say there are certain shipping routes on the sea where there is a high probability of piracy or the seaports where there is a higher ratio of labor strikes, then avoid the usage of such routes in SC. 2. Decrease the affected area due to interruptions in SCM: For example, one may think about keeping the buffer stock for extra safety, deployment of hedging techniques in transportation to withstand fuel price climbs, and reimbursement plans through insurance coverage. 3. Increase the sustainability against the SC interruptions: Decrease the response time for handling unexpected events such as natural calamities or change in Infrastructure 4. Remove the bad habits arising from traditional business etiquette: Because satisfaction with the current culture or organizational customs may suppress a pioneering idea for risk management in the SC. The professional working for the solutions in the SC should be open-minded and can welcome the new technology concepts such as Blockchain technology. Traditional risk management’s bad habits are generally the result of a set of rigid concepts and beliefs that market trend is always logical, predictions are trustworthy, accidental events are uncommon, and buffering is the only operative strategy to control risk. Accidents, robbery, natural calamities, and terrorism are examples of physical, apparent hazards that may be mitigated using traditional risk management techniques.

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Fig. 10 Conventional risk management attributes versus Blockchain-enabled risk management attributes

These cures are more reactive than proactive, focusing on damage management after the event has occurred. Cyberattacks, computer hacking, forging, misinterpretation, and contract fraud are all unseen hazards faced by many organizations that depend only on traditional risk management solutions. Here Blockchain technology plays a great role to mitigate these unseen risks. It helps to eliminate the previously unattended risks which were not identified by the SC team members (e.g., vendor, client, finance organization) in ordinary commercial transactions, thanks to the careful vigilance over the P2P network. In more general terms, Blockchain technology allows its users to take advantage of several layers of protection. A comparison of conventional risk management attributes vs Blockchain-enabled risk management attributes has been shown in Fig. 10. Considering all issues discussed so far, the concrete applications of Blockchain to enhance security in SCM are discussed subsequently.

3.1 Smart Contracts Contract creation is one of the primary phases that initiates the process. Contractual clashes as a consequence of scams, confusions, or performance failures, can damage the SC activity for an extended period of time. The preparation of a smart contract is one of the acceptable solutions that has recently been put forward. The smart contract is a computer protocol intended to simplify, validate, or impose contractual duties through the insertion of contractual policies (e.g., security bond, deliverables, description of property rights) in the computer system and then automating contract execution [44]. It shows that smart contracts are not only for defining contractual agreement policies as was done for traditional contracts, but they also possess the ability to enforce these commitments without any human intervention. Smart contracts are self-validating and self-executive contracts. The automation of the contract life cycle brings the advantages of compliance enhancement, risk mitigations, and improvement of overall organizational efficiency [45]. With smart contracts, one can convert the traditional contract lines into computer code followed by its storage and duplication over the computer systems participating

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Fig. 11 Advantages of Blockchain in SCM

in the SC network. This contract is then invigilated by the systems in the network running the Blockchain. In particular, smart contracts help you in fund transfer, property management, healthcare sector [46], stock exchange, etc. in an apparent and non-competitive way while excluding the intermediary. This allows smart contracts to execute themselves at a reduced transaction time and cost. By integrating the IoT into the Blockchain, one can easily detect and prevent contract fraud also. In addition, as shown in Fig. 11, the reliability over the asset transfers performed by the contract can be enhanced due to the common database shared and confirmed by the majority of network participants.

3.2 Asset Tracking The biggest advantage gained through Blockchain in SCM is to track the assets. Once the assets are listed on the Blockchain, whether they are physical entities or virtual items, their ownership cannot be modified without the concern of the owner. Because of the immutable nature of the Blockchain ledger, it is infeasible for an adversary to do any malfunctioning with the ownership or with the transaction details. The distributed ledger which creates the backbone of the Blockchain technology is transparent to all participants and embeds all the transaction records in it. This allows one to trace the asset back to its origin. As a consequence, Blockchain not only thwarts fake transactions but also facilitates the tracking of assets as they move within the SC. The treasury department of the USA has already taken the initiative to utilize Blockchain technology, for tracking and monitoring the physical asset transfer in real-time using the digital records stored in the distributed ledger of Blockchain [47]. Around the world, in the global market Blockchain technology can also be used to track shipments. Its ability to trace and track the assets on route helps to mitigate the risk of financial loss in the SC network. One of the largest shipping companies, Maersk, has successfully run a trial of Blockchain proof of concept for 20 days to

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Fig. 12 The order completion procedure in SCM

track its goods. The use of cryptographic signatures in Blockchain makes it harder for anybody to do any malfunctioning with shipping labels or drop any shipments during export. These all together simplify the global trade with an increased level of confidence and transparency in the SC [48, 49].

3.3 Secure and Error-Free Order Fulfillment The Blockchain facilitates the storage of digital records of customers. With the ease of accessibility of digital records, Blockchain can speed up order fulfilment processes across the SC with a prompt response for customer credit confirmation, inventory status check, finance verification, shipment status notification, and a transparent process throughout the order fulfillment. Blockchain-aiding SCM is shown in Fig. 12. Blockchain gets success in decreasing the order fulfillment errors and also speeds up the process by powering the entire order fulfillment procedures with an increased level of accuracy and security. Furthermore, because the Blockchain ledger is public and visible to any participant of the SC network (e.g., vendor and client), the order fulfillment visibility is increased and hence the chances of failure in order fulfillment are reduced.

3.4 Cyber Security For the past decade, cybercrime has climbed at a high pace [50, 51]. In the stretched SC network, the growing threat of cybercrime can impair SC activity. Even though several remedies such as antivirus or malware software, password protection, threat alerts, etc. are ready to combat the problem, cybercrime remains a constant menace.

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Another potential option is to adopt Blockchain technology that can eliminate the possibility of a single point of failure. The strength of Blockchain lies in its endto-end encryption and transparency through shared distributed ledger. A Blockchain is an effective tool for limiting the danger of cybercrime and hacking due to its immutable nature and the fact that every computer enrolled on the P2P network is able to check and verify the information stored on the ledger at any point in time. In particular, due to the secure nature of Blockchain technology, Blockchain proves itself a strong candidate for accounting and payment audits such as cargo payment audits, and international fund-transfer audits. The integrity of a transaction record is assured because once a record of a transaction is confirmed and stored in the ledger, no one including the participant of the transaction can change the accounting record. After discussing the roles of Blockchain SC security, how the Blockchain in business SC makes a way towards the evolution of Blockchain in demand-driven SC is discussed in the subsequent section.

4 Blockchain for Demand-Driven Supply Chain The supply chain in various manufacturing companies involves the end-to-end transactional process of information, inventory, and money. However, in this competitive era, it is essential to match with the dynamic, cost-effective, and demand-based product requirements from the customers. Hence, a demand-driven supply chain, also known as a Demand-Driven Supply Network (DDSN), is developed with an aim to sense dynamic demands of customers, suppliers, and middle layers and to respond in real time by utilizing appropriate technologies and procedures. The four competencies for the demand-driven supply chain are demand creation, demand sensing, demand shaping, and demand response. Apart from that, the nature of demands from the customers is changed due to the significant use of e-commerce techniques and tremendous growth of onset technologies viz. IoT, social networking, online transactions and communication, 3D printing, drones, etc. For example, weather prediction supports in ensuring an adequate amount of supplies at inventory levels viz. umbrellas, shoes, sunscreen, and woolen clothes at the real time or immediate demands received from the consumers. In addition, the manufacturing and delivery speed can also be improved using these technologies that help in fulfilling the services as per dynamic demands. The difference between the supply chain and the demand-driven supply chain is shown in Table 3. However, the transformation to demand-driven supply chain does not certainly exclude the results of history or forecasts. The patterns derived from the historical data or forecasts are still frequently used to derive definite parameters in a demanddriven approach. After setting these parameters and their dynamic maintenance, all the productions are driven based on real-time demands of consumers. Hence, the role of Blockchain in a demand-driven supply chain and a case study are discussed in this section.

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Table 3 Difference between supply chain and demand driven supply chain Parameters

Supply chain management

Demand driven SC (DDSC)

Working

Driven by suppliers

Driven by customers’ demands

Behaviour

Linear, one-dimensional, and static

Dynamic (constantly changing) and multi-dimensional

Decision speed

Nearly constant

Rapid as per changing demands in the market

Technique

Sequential push

Demand-pull

Elasticity

Lesser than DDSC

Greater than Supply Chain

Response time

Slower due to replanning in case Quicker to in-demand changes of any changes and real-time

Drivers

Production, Inventory, Transportation, Location, and Information

Customers and technologies

Services

Based on historical and forecasted data or sales patterns

Based on sharing and gathering requirements or demands in the market, temporal models and analytics

Performance measures

Functional productivity and cost Customer service, product availability and margins

Response to changes

Challenging to accomplish as the principal calculation is rigid

Flexible based on an enhanced supply network with reduced response time

Applications

Demand uncertainty is negligible

Higher uncertainty in demands

Inventory

Massive or high

No need initially or low

Communication

Poor

Better

4.1 Role of Blockchain in Demand-Driven Supply Chain DDSC fundamentally focuses on the behavior of customers, hence, the connection between the suppliers and consumers should be decentralized, anonymous, trustworthy, transparent, and democratic. The key benefits of Blockchain are consumerowned identity and its management on Blockchain. The stakeholders can have held on their personal details and they can put limitations on the type and amount of data shared. The suppliers must concentrate on consumer-owned identity management to build the healthy relationships with the customers in the flexible, automated, and self-repairing demand-driven supply chain. The consumers’ satisfaction and great experiences play important roles in building relationships between each stakeholder of the Blockchain-based demand-driven supply chain. Two different scenarios are observed for the demand-driven supply chain management. Scenario-1: When the customers put demands to the retailers or directly to the manufacturers (Fig. 13), the roles of the Blockchain in this DDSC are as follows:

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Fig. 13 DDSC management using Blockchain (Scenario-1)

• The smart contracts are created at the time of customer’s registration that store the real-time data viz timestamp, product details, and delivery details. • These details are tampered-proof and hence, cannot be altered by the stakeholders or external entities. • The details of the origin of the product (manufacturer) and the timestamp of the product’s manufacturing are stored through Blockchain, hence, the authenticity of the source/provider can be verified in the sensitive demand-driven applications, for example, medical supplies. Scenario-2: On the other hand, the demand-driven networks observe the behavioral sense of customers and/or the market to create the DDSC [52]. The roles of Blockchain in DDSC processes as demonstrated in Fig. 14 are as follows: • Product: A new product’s idea gets evolved from the department such as research and development of an organization/institution, where it undergoes designing, hence a new idea gets ready to be shaped into a customer-delivered product or a service. • Supply: This invokes the demand to the supplier for the required raw material along with deciding the best manufacturing processes and deciding the logistics to bring the product to the customers and/or market. • Demand: Once the product is created, the organization focuses on creating demand in the market, among the consumers. Once the demand is created the products

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Fig. 14 DDSC management using Blockchain (Scenario-2)

are started to get manufactured. Here, Blockchain gets involved maintaining the transactions of the created demand and hence, enabling the immutability of data, which is sensed through the market. Along with that, enabling Blockchain technology helps maintaining the timestamp of the sensed data/information as well as ensure the authenticity of data origin. After presenting two scenarios for DDSC, the scheme exploring the demands generated during COVID-19 and the application of Blockchain in fulfilling this demand-driven supply chain is discussed in the next subsection.

4.2 Existing Scheme of DDSC in Healthcare The recent application of Blockchain in SC and waste management for COVID19 health-oriented equipment and supplies in the healthcare domain are studied in [53]. Specifically the demand for the COVID-19 vaccine has been generated in the market currently, due to which the requirement of DDSC is created. The registered stakeholders in this SCM are COVID-19 medical equipment manufacturers, distributors, COVID-19 testing centers and hospitals, medical waste shippers, medical waste treatment facilities, regulatory authority, distributed storage, and smart contracts. The overall SC follows the following steps in this application (Fig. 15): • The need of a COVID-19 vaccine has generated the demand in the market. Hence, invoking a demand-driven supply chain. The demand reaches to the stakeholders like distributors and manufacturers. • The stakeholders register themselves through the registration of smart contracts and they are assigned a unique identifier. • The order manager smart contract triggered by the distributor assists the caller for acceptance/rejection of the order of demanded medical supplies.

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Fig. 15 Sequence diagram of DDSC in healthcare

• The authentic details of the demanded medical supplies and equipment produced by the manufacturer are stored in the Blockchain platform. After notification from the distributor about the receipt of the order, the manufacturer promotes the distributor as a new owner of the supplies. • Next, the COVID-19 testing center/hospital places the order of medical equipment to the distributor. In response to that, the distributor prepares and ships the supply lot (with new labels) to the testing center and transfers ownership to it. • After using medical equipment for COVID-19 testing, the waste is shipped to the medical waste treatment facility by the registered shippers through the shipment management smart contract. Not only that, the state of the waste bag whether it is opened or not along with the location of the waste bags are continuously monitored and stored on the Blockchain to ensure the waste bags are tampered or not as per COVID-19 protocols. The Food and Drug Administration (FDA) can verify the same using the provided data. The proposed approach ensures the use of genuine equipment for treating COVID-19 patients, the proper disposal of used equipment, cost-effective procedures, and the applicability accompanied with generosity in diverse use cases. Lastly, from the above discussions, the difference between the Blockchainbased supply chain and Blockchain-based demand-driven supply chain is shown in Table 4.

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Table 4 Comparison of Blockchain-based supply chain and Blockchain-based demand driven supply chain Parameters

Blockchain-based supply chain

Blockchain-based demand-driven supply chain

Event

Triggered by manufacturer

Triggered by customers/market

Application type

Batch processing

Real-time

Communication

Asynchronous

Synchronous

Dependency

No dependency among stakeholders

Demand-driven dependency between stakeholders

5 Barriers to Implement Blockchain in SCM Blockchain seems to be a perfect SCM platform from the perspective of many market leaders. Popular firms like Walmart have already included the strength of Blockchain into their SC operations as it enables the new functionalities like cross-industry integration and better user authorization. While bringing the advantages and strengths, Blockchain also faces certain challenges as follows for its incorporation into the current SCM as shown in Fig. 16. • Extent of Implementation: The decision-making of whether the solution of Blockchain should be implemented at all levels of SC or up to a certain extent only. The implementation of Blockchain in the entire SC at the initial level is difficult as it requires a lot of resources. This decision becomes crucial of using Blockchain in the complete SC of any industry. For example, Blockchain can be integrated also with some sections of SC such as delivery tracking of SC. Fig. 16 Barriers to implement Blockchain in SCM

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• Lack of Qualitative Data: Immutable is one of the major and distinguishing features of Blockchain, but the data in SC are entered by the human which are prone to make errors, hence, which may lead to partial or complete failure in the production and maintenance of clean data. • Scalability: It means the ability to satisfy and revert to users’ demand even if the size of input increases. Immutable being a major feature of Blockchain transactions causes the new blocks to be appended in the existing chain, thereby leading to grow the network as every node/block has to keep a copy of it. As SCM has many modules involved, Blockchain when implemented in it, causes the network to become increasingly large and complex. Hence, it becomes difficult when the system is highly scaled in the physical world as mostly Blockchain is not being tested in large real-world systems of SC. Therefore, the unpredictability of upcoming challenges of implementing Blockchain in SC increases [54]. • Performance and Efficacy: Blockchain uses high computation power as well as high bandwidth which are expensive and not easily accessible. Hence, this drives Blockchain to use an affordable or low-cost validation approach which might lead to centralization at some point in the end of the process, hence losing its indigenous benefits and core purpose. Secondly, SCs incorporate the use of IoT and sensor-enabled technologies which have low storage capacity along with computation resource thereby creating the barrier for better performance of Blockchain. Due to the chain structure, it takes a considerable amount of time to process each transaction. Therefore the latency period of Blockchain enabled SC operations are high [55]. • Legal and Governance: Blockchain provides the huge level of privacy and security by restricting the stakeholders control on Blockchain. Also, a private Blockchain infrastructure restricts the third-party intervention such as government or regulatory bodies that creates a barrier for government to verify the legal norms and regulations. As Blockchain provides the huge level of privacy and security, it becomes possible for it to prevent government’s interference. This can lead to the government, thereby imposing more strict regulations [55]. A contract might stipulate both, the supplier and its suppliers join the buyer’s SC Blockchain, allowing the buyer to have a better understanding of its SC [56]. The capacity to keep up with and engage in this expanding field may provide a barrier for low-level suppliers and sub-suppliers, limiting their ability to compete for specific contracts [54]. • Interoperability and Collaboration: It might happen that the value-added service provider or the supplier are participants in more than one SC. These SCs may have their own systems as well. The supplier is unlikely to like being confronted with multiple Blockchain designs from various SCs. Since the Blockchain networks would then have to communicate among each other, this might result in dispersion and increased complexity. Normalization and standardization may be necessary, however there are currently no compatibility technical standards for both SCM and Blockchain [55]. • Operational Adoption: Every member or participant has to involve when Blockchain is implemented in SCM. But, information sharing is a crucial point

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where organizations may consider it as a competitive benefit over others. Hence, they may not be highly motivated with the implementation of Blockchain in SCM that can again lead to the usage of the traditional manual approaches in SCM [55, 57]. • Knowledge and Proficiency: Implementation of Blockchain in SCM becomes a challenging task as it requires a sufficient amount of skill, knowledge, and expertise. The growing digitization has been affecting SC operations thereby demanding high level of expertise and knowledge to adapt and manage it successfully [58]. Deficiency in proficiency and expertise can lead to a reduction in the effectiveness of Blockchain benefits in SCM [57]. • Metamorphosis in SC: The traditional SCs and their operation used legacy methodologies till now. Hence, this becomes a challenge in the industry of SC to integrate with Blockchain as these traditionally working SCs had an absence or negligible level of easy and direct interfaces [58]. The continuous developments and growth happening in technology create a huge barrier in the system of SC [57]. • Pecuniary Barrier: In long term usage of Blockchain in SC can lead to incur high costs. It becomes difficult for smaller organizations having their SCs comparatively to manage the finance and budget that is required for maintenance and traceability of the product or value-added service. Therefore, the implementation of Blockchain in SC comes with a huge challenge for the availability of financial resources with the organizations [59].

6 Conclusions and Future Work With the increased demand of security, trustworthiness, and transparency in supply chains and demand-driven supply chains, the use of Blockchain technology has increased in different domains/organizations. However, due to the decentralized nature of Blockchain as opposed to traditional centralized supply chain architectures and the supporting technologies, many issues and challenges have been raised in integrating Blockchain with the existing supply chains. Hence, the updated architectures, requirements, security aspects, and barriers in applying Blockchain in supply chains are explored in detail in this book chapter. The aim of this chapter is to help the researchers and various organizations to dive into these integrated fields by means of discussions based on heterogeneous case studies/applications. In future, the research can be carried out to improve the barriers in implementing Blockchain in the supply chains, specifically scalability, knowledge, resources required, etc. The underdeveloped or developing countries face the challenges of Internet and the relevant resources to update their supply chains. Further, the application of Blockchain in demand-driven or real-time supply chains is also an issue to be addressed in future.

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Government Applications and Standards to Use Blockchain Sondra Skelaney, Hadi Sahin, Kemal Akkaya, and Sukumar Ganapati

Abstract This chapter examines the public sector applications of blockchain technology in government. Blockchain has found quick adoption in the public sector with versatile uses across several domains. We survey these extant and potential uses of blockchain in this chapter. Governments mediate or undertake a plethora of transactions which require transparency, security, and integrity in the long term. Blockchain technology has a strong potential to fulfill these requirements for governmental operations. We highlight the technology’s application in transforming various such government functions. Exemplary governmental uses of blockchain include those related to real estate, digital identity, infrastructure management, safety, and emergency management, and smart contracts. These cases demonstrate how the blockchain technology’s applications transcend those of the traditional use in cryptocurrency. We posit that the blockchain technology can potentially enhance efficiencies in the delivery of public services securely across several domains, but the realization of these uses requires more institutional learning and acceptance of the technology. Keywords Digital government · Public services · Smart contracts · Real estate · Self-sovereign identification · Infrastructure management

S. Skelaney (B) · S. Ganapati Department of Public Policy and Administration, Florida International University, Miami, USA e-mail: [email protected] S. Ganapati e-mail: [email protected] H. Sahin · K. Akkaya Department of Electrical and Computer Engineering, Florida International University, Miami, USA e-mail: [email protected] K. Akkaya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_4

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1 Introduction In this chapter, we examine the adoption of blockchain technologies by government agencies. As a newly emerging technology, blockchain technology holds several prospects for new areas of application in government due to its inherent technological characteristics of immutability, security, and transparency that are useful for the public sector. Blockchain offers a trustworthy infrastructure for decentralized governance by enabling safe and secure transactions which ensure safe recordkeeping of sensitive information needed by public agencies to perform critical services. It is a simple and efficient method of preserving the integrity of public records while reducing the cost and threat of attacks and security breaches that are common in centralized data repositories. Indeed, with the rapid evolution of blockchain technology over the last decade, the scope of the blockchain applications in government has also increased dramatically across different departments [1]. Blockchain technology is already being used for digital payments/currency, land and property transactions, electronic voting, secure identity management, and supply chain management. The emergence of blockchain technology marked not just the launch of a new technology, but the creation of a new infrastructure upon which countless projects and services can be built. Like the emergence of early roadways led to the creation of gas stations, mechanic shops, lighting grids, tolls and bridges, traffic signs and signals, road mapping, increasingly more advanced automobiles, and a host of regulation and legislation, we can imagine a cascade of experimentation, building, and regulatory initiatives are similarly occurring digitally on blockchain infrastructure. Stakeholders across industries and disciplines are building myriad solutions to some of our most pernicious problems, however, the rapid adoption of blockchain is not without its problems. On one hand problems arise from the speed of the adoption itself, where organizations (i.e. procedures and bureaucracy) and regulations have not been able to quickly adapt to blockchain technology. On the other hand, the inherent technological strengths of blockchain raise paradoxical constraints on its use. The decentralized ledgers of blockchains do not necessarily have a clear advantage over traditional digital record-keeping methods. In many cases, the government applications are pilot cases that have not scaled up well empirically [2]. The limited scaling up is arguably due to the collision between the utopian techno-centric imperatives of blockchain with the social realities of governance. Even though innovative and disruptive, blockchain technology may not lend itself to resolving governance issues that are complex and multi-faceted. Emerging criticisms of blockchain adoption in government show the dualistic nature of the technology’s strengths as well as limitations in the public sector [3–5]. We need to carefully parse the domains in which blockchain technology could be useful and effective to delineate a realistic potential of the technology. It is in this context that this chapter surveys the extant and potential blockchain applications in government. The survey is done with the intent of demarcating the domains of government applications where the blockchain technology could be well adapted and offer

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enhanced value. We also need to comprehend the public sector contexts where the blockchain may have potential application technologically but could face constraints due to governance realities arising from bureaucracy, regulations, procedures, or other social elements. We posit that the realization of blockchain technology’s use requires more institutional learning and acceptance of the technology. Our survey of government blockchain applications is not comprehensive in encompassing all domains. Rather, we examine selected exemplary applications with illustrative empirical cases. The intent is to provide a conceptual delineation of the domain where blockchain adoption adds value to the public sector, and where the technology adoption faces constraints. Our survey covers four domains: property/land management with blockchain-based cadastral recordkeeping; identity management with blockchain-based self-sovereign identities; infrastructure and safety management with blockchain-based security features; and blockchainbased smart contract systems. These domains provide sufficient richness and diversity in terms of the government applications of blockchain. Examination of these domains provides interesting insights into the delineation of the use of blockchain in government applications. The main contribution of the chapter in the context of Government 4.0 is to highlight the diverse range of empirical applications of the blockchain technology in the public sector. The survey shows both the prospects and the constraints of the technology’s use. Based on this survey, the chapter highlights the institutional and legal framework for enabling blockchain adoption in government. The rest of the chapter is organized as follows. Section 2 gives an overview of the blockchain’s prospects for government agencies, drawing on the inherent strengths and weaknesses of the technology. Section 3 gives the empirical applications of blockchain technology across four domains, namely: real estate, self-sovereign identity, infrastructure and safety, and smart contracts. Section 4 highlights the institutional and legal framework that can enable the adoption and implementation of blockchain technology in the public sector. Section 5 concludes.

2 Blockchain in Government 4.0 Blockchain is a distributed ledger technology (DLT) for securely recording transactions in a decentralized network. It is unlike a traditional ledger where transactions are stored in a centralized computer repository managed by a single trusted entity. A blockchain entails shared data structures across a network of computer nodes so that the ledger is maintained by the decentralized network. As the name implies, a blockchain organizes data into blocks, which are chained together in an append-only mode by the computer nodes. No one node has sole access or custody of all the data, and many nodes work synchronously to verify and add the data to the chain. Therefore, the blockchain removes the need for a single trusted intermediary entity to record the existence, characteristics, and provenance of information, creating a new type of management in the decentralized network. The nodes of the distributed

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network, each holding a copy of the ledger, synchronize in real-time using a consensus protocol such as proof of work or proof of stake. The consensus protocol ensures data integrity across the network. The blockchain introduces a new paradigm of decentralized management that is contrary to hierarchical traditional governance, especially in the public sector. In the traditional hierarchical systems, public agencies or other third parties would need to maintain separate records and require periodic reconciliations. This hierarchy is replaced by a decentralized computer network that relies on code-based consensus mechanisms, without the need for human intervention. The codes are open-source, transparent, and community-driven within the network [6]. Each participant has the latest verified data in the blockchain as any change made in one node is reflected among other nodes in the network [7]. As the data in blockchain are in append-only mode and distributed across a network, the blockchain data are immutable: when a data block is added to the chain, no one—not even the system administrator—can change the data without unraveling the chain of information blocks that had been subsequently added, a near impossibility. While blockchain technology is evolving quickly, it still faces technical concerns for their application in government. Although blockchains are immutable, they are also anonymous or pseudonymous. This implies that, for some applications, an additional layer of verification and authentication needs to be created and added to the security architecture, which could potentially add to the blockchain’s vulnerability for hackers to exploit. Other hallmark features of blockchain—transparency, privacy, and security—could also be at odds with each other. Transparency is valuable, except when one needs privacy. Ensuring a greater degree of privacy, however, could also result in the blockchain’s susceptibility to security and nefarious uses. For example, most ransomware demands require payments in cryptocurrency which provide privacy to the hacker, potentially shielding them from legal prosecution, at the cost of the data security of the public organization. Moreover, while blockchain’s immutable quality makes it corruption-resistant, it also makes it impossible to correct normal human errors. The record will exist in perpetuity, and any errors that occurred cannot be corrected by editing a record, only by adding a new one. Blockchain ledgers can be either open or closed [8]. Open blockchains (also referred to as public or permissionless blockchains) allow anyone to become a miner (agent maintaining a node in the network) in the blockchain process. Miners have the incentive to participate since they are rewarded with tokens like cryptocurrency. Miners provide their own resources such as hardware and electricity to identify a specific and unique hash value to secure the transaction [8]. The mining process, however, consumes large amounts of computing power (and therefore electricity) to maintain up-to-date records. In the public sector, closed (or private, permissioned) blockchains are more common. They operate in an environment where participants are already known and vetted. This removes the gaming behavior that could otherwise occur in an open blockchain [9]. For the public sector, building systems on a private permissioned blockchain managed by a consortium of actors provides a better configuration to ensure privacy for massive quantities of highly sensitive information.

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Blockchain’s distributed ledger technology is especially useful for public agencies that need to store vast amounts of records. Blockchain facilitates a distributed system which makes it easier to reconcile transactions across internal and external functions, thereby saving resources. It could dramatically reduce the costs of recordkeeping and ensure transactions are taken in near real-time [10]. Public agencies oversee many transactions across various fields, such as real estate, health, transportation, etc. There are several prospects of putting these transactions on the blockchain where records can be maintained with a high degree of integrity. Since there is no need for a thirdparty intermediary to verify ownership, or act as a custodian to the records, it reduces overall costs and increases data integrity. Moreover, as the data are maintained by the network, failure by any one node does not compromise the data. For example, a natural disaster that disables an entire city and disrupts the power supply to its blockchain mining nodes would have a negligible effect on the integrity and access to the records from anywhere else as the network would remain intact. These features of blockchain are particularly useful for maintaining secure and immutable public data.

3 Blockchain Implementation in Government 4.0 In this section, we highlight a selection of exemplary uses of blockchain in government to highlight its potential. The survey provides a broad indication of how blockchain technology finds empirical application in government. We highlight blockchain’s uses for real estate records with respect to land cadastres and property management; self-sovereign identity management; infrastructure and safety management; and smart contracts. The experience of the implementation of the blockchain technology shows both the prospects and the limitations of the adoption of the technology in government.

3.1 Real Estate: Land Cadastres and Property Records Land transactions require stable and reliable government data. Typically, the individual property level and cadastral information are held by specialized public agencies dealing with the land (e.g., property appraiser’s office, registration office, etc.). Secure property rights mean that the owner has clear title deeds and the rights to transact (i.e., buy and sell) the property. Whenever these transactions occur, the government records also need to be updated to ensure a smooth and secure transfer of property from one owner to another. However, procedures for enabling the transfer of immovable property have developed historically over many years. The specific forms of these transfers vary from country to country with various levels of government intervention in the property transfer. Governments maintain an interest in the property transfer for many reasons. Accurate records are required for levying property

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taxes in a transparent way. Infrastructure services such as water supply, electricity, and other commercial services are also dependent on providing clear documentation of the ownership deeds. Governments are generally the authoritative arbiters of providing modern deeds and title registration systems to establish private property ownership. Lack of clear property rights results in intractable legal disputes that last for a long time. If the property rights are unclear, they could stymie land transactions, stifle the economy, and potentially lead to small- or large-scale conflict. Blockchain technology provides an attractive technological solution for governments to manage property and land transactions. As immutable systems, blockchain provides a stable method of recording land transactions that are tamper-proof. Illegal transactions can be controlled by implementing the blockchain-based cadastral systems. Blockchain systems are also transparent, which is another important requirement for property transactions. The ownership of property needs to be public and transparent so that there are no disputes with respect to the ownership. Property is a durable asset that holds significant economic value for both individuals and governments. Hence, there is a high degree of interest in maintaining the integrity of records. The public should also trust that the transactions that they undertake are safe and sound. Blockchain technology offers several prospects in this regard. As a decentralized automatic system, blockchain can enable the records to be automatically updated every time a transaction occurs. The records can thus be current and updated in real time with every transaction. Implementation of applications using blockchain guarantees the quality of digital data that is being used. The transparent process with integrity could also speed up title transfer execution, use of title as a collateral, and reduce overall transaction time. Land title registries are sources of corruption in many countries. Secure blockchain-based digitization has the potential to mitigate the corruption by removing the need for the middle person to record the transactions. Traditional property relations in society could be replaced by or supplemented with blockchain models [17]. Blockchain technology offers a secure method of establishing a digital identity for each property. Current property records systems are based on Geographic Information Systems (GIS) that are spatial methods of identifying parcels (and other physical infrastructure). These data are maintained in superfluous ways by multiple agencies across different jurisdictions, based on their purpose. For example, postal services may record property in a distinctive way from that of the land registration departments. Identifying property using blockchain could provide a uniform way of recording property data across different jurisdictions. Each property may have multiple attributes like property id, property number, owner details, transaction amount, mode of payment, last transaction occurrence, special conditions, and other ancillary information as required by the jurisdiction and the department. These data need to be transparent as well as secure to trace the history of ownership and prevent fraudulent transactions. Blockchain-based applications enable stringent identity verification based on public key cryptography. Expensive processes involving land title companies to verify land ownership can be eschewed by both sellers and buyers if the public trust grows with respect to the use of blockchain technology as immutable systems with a high degree of integrity.

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Sweden is among the early adopters of blockchain for recording property transactions. The Swedish government agency, Lantmäteriet, pioneered the blockchain technology for conducting real property sales. Lantmäteriet is the Swedish state’s central agency for mapping, cadastral, and land registration records. In 2016, it partnered with the Swedish telecom company (Telia) Company and a blockchain startup (ChromaWay) to explore blockchain’s use for enabling real estate transactions more efficiently. Although Lantmäteriet had computerized much of the process of land cadastral information since the 1970s, the legacy land transfer process still depended on traditional methods of paper transactions and physical signatures. The process took over thirty steps. The manual process of recording the land transactions in each step took a long time and the overall land record change lagged behind the land transactions significantly. The Lantmäteriet could record a transaction only when it received the appropriate documents of sale such as the title registry application, bill of sale, mortgage application, etc. The property records methods were thus creating inefficiencies for the property market. The implementation of the blockchain-based system reduced the number of manual steps needed for a property transaction by almost a third. It implied greater transparency of the property transactions in real time. Relevant parties such as the banks, land registry, owner, and the seller could view the status of a transaction at any time during completion. The blockchain-based system simplified the land transaction process while also increasing the efficiency in the property market transactions. A major challenge for implementing the system was that the legal systems to enable other digital aspects were not fully developed (e.g., electronic signatures for property transactions were not still legally acceptable). Consequently, even if the blockchain-based land records systems offer efficiency, their overall effectiveness for efficiency in property market transactions depends on the legal ecosystem enabling digital transactions. Yet, many other local governments around the world have adopted similar systems for land records management.

3.2 Digital Identity Management: Self-Sovereign Identity One of the most fundamental public services that are performed by governments today is the registration of individuals’ identity and citizenship status. Most traditional personal identification systems are currently centralized, such that a public agency (such as the motor vehicles registration department) is mandated to collect and maintain custody of the sensitive information related to a person in a centralized database. Legally recognizable personal identification is crucial for any individual to access government services, purchase property, open a bank account, pay taxes and conduct other transactions that require the secure verification of the individual’s credentials. One’s identity is sovereign to an individual. No other individual is provided the same identification, and its loss implies that the person cannot undertake a variety of normal activities that require verification. The most basic of daily operations in the public space, such as driving on roads or flying in an airplane, require

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proof of identity to maintain public security. The use of identification credentials is so ubiquitous in daily life that most people don’t leave home without them, and the use of them is often taken for granted. The lack of secure and verifiable identification leads to significant problems for people. Many victims of identity theft have experienced the sudden loss of access to public services, finances, and normal daily activities that take place when one’s credentials have been hijacked and exploited by others. Misuse of one’s identity can lead to years of negative credit ratings and subsequent legal and administrative hurdles to overcome. Currently, most people possess physical government-issued identification in the form of a driver’s license, passport, and/or card to authorize work or access government benefits. Additionally, many people also access services online through a variety of digital profiles linked to their official identification information. These centralized digital identities lack adequate security to prevent identity theft. The anti-forgery design elements embedded in tangible identification documents do not exist online, making identity theft a problem that may go undetected for months. When individuals are required to create new credentials for each new service they access on the Internet, it results in countless usernames and passwords to remember, hundreds of personal records stored on an agency’s server, and greater vulnerability to hackers. As users experience an increasing number of data breaches leading to identity theft in companies that have custody of their personal information, there is a growing discomfort in sharing personal information. Users are also becoming more aware of the nature of how companies are using and selling personal data for profit, leading to a growing distrust in identity custodians [11]. On the other hand, centralized systems are burdened with continual updating of attributes and identifiers as users’ identity profiles change over time, and public agencies are similarly constrained due to the high costs of maintaining the centralized identity systems. At the extremes, the World Bank estimated that in 2018 nearly one billion people in the world and 50% of women in developing countries have no identification, preventing them from accessing critical public services and government benefits and rights, including the right to vote [12]. On the other hand, policies such as “Know Your Customer” (KYC) place heavy regulatory burdens of time and manual labor on financial companies to ensure the identity of their customers. The need for universal access to identification credentials on top of the need to alleviate the high costs of centralized data systems and the vulnerabilities of personal information stored in such databases have provided the imperative for developing alternative secure identification systems. Unlike centralized identity, self-sovereign identity (SSI) or decentralized identifiers (DID) refer to blockchain-based identification management systems that are designed for users to own and store their own identity-related information, attributes, and credentials, and then share them only when a third party requesting the information is granted access by the user. These decentralized systems are more secure and fraud-resistant solutions to identity thefts. Once a user has completed the authentication process and credentials are created, data exchange can occur only at the control of the user without the need for a central intermediary to validate claims and without the danger of sensitive information being stored and controlled by multiple

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parties [13]. Access to select identification credentials is also time-bound and revocable by the identity owner at will. The immutable, transparent, and decentralized nature of blockchain creates additional security and protection from identity theft and fraud. Authentication of transactions through consensus mechanisms prevents corruption by a sole authority, and transparent real time transactions allow users to verify the records that were created, including details such as when and who created the record. A decentralized identification management system eschews centralized hierarchy systems in favor of a non-bureaucratic and horizontal form of cooperative governance that more adequately represents the needs of all stakeholders in the development of its protocols. Blockchain is an ideal infrastructure for the maintenance of identification credentials. It is flexible in that it is agnostic to the type of transaction being performed, and the technology can be implemented in various ways to exchange information, tangible and intangible assets, and reputations [14]. However, care must be taken to deploy identity projects on blockchain in an ethical and equitable manner that increases security and reduces vulnerabilities. Christopher Allen, a pioneer in conceptualizing self-sovereign identification systems, outlined ten principles in a framework he introduced in “The Path to Self-Sovereign Identity” [15]. They are as follows: 1.

Existence. Sovereign identities should be based on human beings who have an independent and individual existence. The SSI provides public access to some part of the “I” that exists. 2. Control. The independent user is the sole authority over their identity information. It should be their prerogative to create, update, or hide their identity information as they see fit. 3. Access. There must not be any barriers to information retrieval for the individual; no gatekeepers or hidden data. A user should always be able to retrieve all of the claims to their data. 4. Transparency. The systems used to operate the network of identities must be transparent in how they are managed and updated. Algorithms should be free, open-source, reputable, and as independent as possible of any specific architecture. 5. Persistence. Identities should ideally live forever, or as long as the user wishes. 6. Portability. Information and services should be transportable and not held by a third-party entity, even a trusted entity, because entities can disappear and may not be trustworthy. 7. Interoperability. Identities are of little value if they only work in limited niches. They should be as widely usable as possible and therefore need to transcend platforms and applications. 8. Consent. Individuals must provide express permission for others to access their data, and consent can be withdrawn at any time. 9. Minimization. Claims to data should request only the absolute minimum amount of data necessary to perform the task. 10. Protection. Rights of the user should be protected in the case of a conflict between the needs of the identity network and the rights of individual users.

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Algorithms should be censorship-resistant and force-resilient and run in a decentralized manner [15]. The above principles prescribe the conditions under which the user should have greater control over personal identification information to be truly self-sovereign. However, this framework provides a broad conceptual framework with little technical guidance for developers. As the decentralized identifiers could exist in different formats, for example, technical standards would ensure enhanced interoperability [13]. In July 2022, the World Wide Web Consortium (W3C), published the recommended standards for decentralized identifiers. The W3C defines a decentralized identifier as “a globally unique persistent identifier that does not require a central registration authority that is generated and/or registered cryptographically.” Its recommendations outlined “DID syntax, a common data model, core properties, serialized representations, DID operations, and an explanation of the process of resolving DIDs to the resources that they represent” [16]. In addition to technical development, governments need to contend with several ethical and practical issues in adopting blockchain-based self-sovereign identity systems. At the core lies a need for deeper reflection on the ethical boundaries of the normative concepts of “identity” and “sovereignty” so that the systems do not violate the intent of decentralized identity. If the result of the creation of decentralized identity systems leads to another form of identity capture by third parties, then its purpose is defeated. Ishmaev cautions that “a proper moral evaluation of any technical implementations cannot be carried out in the vein of a naïve technological determinism” [17]. The decentralized identity was originally conceptualized as a response to the dependence of society on a communications infrastructure that is monopolized by a few big tech giants that rely on centralized hubs of data collection “built around mandatory identification, mandatory trust assessment, and scrupulous record keeping of past behavior for individuals” [17]. Present big tech business models rely on creating dependence on their platforms, hoarding personal data, and use it in identity profiles for financial gains. The core blockchain technology of decentralized systems is aimed at enhancing privacy and security and avoiding data capture. There should be no expectation that mainstream adoption in its most sovereign form will be seamless or easy, as it would require the private sector to release its hold on highly profitable data. At the very least, one can imagine that companies will actively work to influence the development of decentralized identity systems and all applicable regulations in a manner that still allows them to capture and profit from data transfer. Ishmaev posits that clarity around the meaning of identity and sovereignty will help maintain the integrity and main purpose of decentralization in the development of these systems. At a basic level, widespread adoption requires decentralized identity systems to be user-friendly. Users need to trust and see value in the system’s ability to protect their information, and it needs to be easy to use. Users would need access to affordable hardware with features they are already familiar with, such as a smartphone with reliable connectivity. The technical requirements for access may be untenable in the poorer areas of the world if costs are prohibitive [13]. As users are responsible for

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storing their own identity information either locally or on a distributed server, they also need to be responsible for maintaining their private and public keys in order to retrieve and grant third-party access to their data. People generally fall along a wide spectrum of desire and ability to self-manage their identity, and many are likely to prefer a custodian holding their keys, especially if that custodian can provide some level of support, accountability, or insurance against loss or damage. Wang and De Fillippi discuss the “use of blockchain technology and biometrics as a means to ensure the “unicity” and “singularity” of identities, and the associated challenges pertaining to the security and confidentiality of personal information” [13]. Unicity (only one person assigned to an identifier) and singularity (only one identifier assigned to an individual) are necessary attributes for digital identities. They propose alternative approaches to SSI where blockchain-based credentials are globally portable and not dependent upon any one government but may include government-issued credentials or biometric markers. Merging biometrics with SSI is a possible solution to the problem of users losing their private keys and passwords associated with their identification, or needing access to hardware or online connectivity, and has potential use for vulnerable people such as refugees and those experiencing homelessness who require identification to access life-saving services. There are problems with the use of biometrics as private keys, however. As biometric data is uniquely tied to an individual, political refugees may be easier to identify, putting them at risk. Data such as fingerprints that are left everywhere may effectively be public information and could potentially be lifted or stolen. Facial recognition can be tricked with photos and videos, and while iris scans are more difficult to circumvent, contacts have been known to trick them. Bodies are also subject to change with age, injury, or deliberate alteration. More exploration should be done on ways to best solve key and global portability problems while not placing the privacy and security of people at risk. The biggest challenge in changing established identity ecosystems, however, is that it requires cultural and regulatory change at the fundamental level to achieve mass adoption. Governments tend to move slowly with regard to legislative changes and infrastructure modifications, especially in large, complex political systems. In addition, decentralized systems could conflict with existing regulations regarding data privacy and confidentiality, causing more friction in adoption. Legislative changes in democracies necessitate public demand for the services. Achieving a critical mass of citizen advocates would require a great deal of education to change our cultural reliance on third-party custodians to handle identity information and bring about the awareness necessary for people to understand and demand the benefits of selfsovereignty. Currently, practical examples of the technology that are available to learn from are rare. Estonia and Switzerland, however, provide promising use cases that demonstrate the potential of decentralized identification management. Estonia launched a national digital identification system in 2002 for its 1.3 million citizens. After large-scale cyber-attacks disrupted the system in 2007, Estonia adopted blockchain technology to prevent future attacks on its registries. The country’s blockchain-enabled e-ID system provides digital identification to every resident and citizen at birth. The system is overseen by the Police and Border Guard

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agency and was developed by a consortium of private companies. The registries operate on a private, permissioned blockchain. The e-ID system is a common method for Estonians to access all their services such as health records, digital prescriptions, school records, property records, and police information. Residents can open and access bank accounts, charge electric vehicles, access parking and public transportation, pay taxes, and vote using their e-ID. The mobile e-ID also provides a digital identification card and a digital signature that are accepted nationally as legal documentation. The adoption rate for using e-ID to access many of the available services is nearly 99% [18]. Since 2014, international entrepreneurs can also obtain e-residency to start businesses in Estonia from anywhere in the world in just a few minutes. The e-ID system is interoperable with the open-source X-Road data exchange platform, whereby all outgoing data are digitally signed and encrypted, and all incoming data are authenticated and logged. Data can be exchanged between public agencies, citizens, and the private sector while ensuring privacy and data security with decentralized storage. The e-ID system has proven to be resilient despite severe cyber threats, such as those in 2017 when the services were disrupted for a while [19]. Switzerland undertook policy initiatives to adopt national blockchain-based digital identification in 2021. It aims to implement a state-operated e-ID digital identity infrastructure based on the foundational principles of data protection and privacy, self-sovereign identity, the minimization of data exchange, and decentralized data storage. A new e-ID law is expected to launch in 2023 [20]. The federal government will be responsible for issuing the e-ID and will operate the core infrastructure, as well as provide an app for smartphones to manage the ID. Anyone with a national identity card will be able to apply for the e-ID, and its use will be free and voluntary. Users will be able to use e-ID for basic services such as proving age when buying alcohol or ordering criminal records online, but eventually the infrastructure may develop and roll out in stages to accommodate the storage of a variety of official documentation from state and private actors such as confirmation of residence, diplomas, tickets, and membership cards. The Federal Council intends for the policy to be “technology-neutral” in order to respond to technological changes in an agile way and adhering to international standards so the e-ID card can be recognized and used in countries outside of Switzerland. Other self-sovereign identification systems proposed as experimental designs have the potential to be used in government sectors such as Sung and Park’s proposed application of SSI to the delivery of public services in the Korean government [14]. The idea behind this proposed system is that by storing and managing personal information on the blockchain and providing mobile apps to customers, users can log in or retrieve previously authenticated personal information without having to go through an authentication process. Since users do not need to go through the verification process every time, it is expected that they will be able to access only the necessary personal information more quickly and conveniently without having to deal with unnecessary details. In addition, the blockchain-based operation of a public service effectively increases the transparency and reliability of that service and reduces the social costs caused by personal information leakage. The authors conclude that while blockchain-based identity management systems can significantly

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improve transparency, accountability, and reliability in the user control of one’s own data while reducing the time and cost associated with delivering public services and increasing administrative efficiency. While introducing the new technologies in government requires a complicated, time-consuming process, there is significant interest in continuing to explore how to best achieve the benefits of adoption. While the Estonian and forthcoming Swiss experiences are promising, and there is clear momentum building for research and experimentation for use in the public sector, broader adoption of blockchain-enabled decentralized identity systems face challenges. First, Estonia is a small country and scaling up could be technologically challenging. The cyber infrastructure required for decentralized identification for larger countries such as the United States could be distinctive. Second, political conditions need to be ripe for mass adoption. If only a small portion of the population uses the system, it will fail in its goals of increasing efficiency and accessibility. Third, there needs to be adequate trust in the government and the willingness by the people in democratic nations to allow their governments to issue digital identification cards from birth and establish protocols for decentralization in quickly changing technological ecosystems. Despite these adoption challenges, Estonia’s experience shows the potential for decentralized identification systems for broader use. The success of such identification management will ultimately depend upon the ability of the novel systems to provide benefits to both the government and the end-user (citizens) [11].

3.3 Infrastructure and Safety Management Infrastructure Management: The introduction of blockchain provided new opportunities for the protection and management of critical infrastructures such as power and water. The main rationale for utilizing blockchain to manage critical infrastructure is to enhance the security of systems with the deployment of decentralized intrusion detection mechanisms. Traditionally, centralized intrusion detection mechanisms are vulnerable to instability due to having single points of failure. In many cases, these mechanisms are controlled and managed by government entities. With regard to infrastructure safety and management, two key functions require attention: data sharing and trust management. Data sharing is a critical concept in intrusion detection systems where information has to be exchanged between multiple entities that may belong to different units. The establishment of the trust is a necessary prerequisite for ensuring smooth data sharing because a compromised node may generate false alerts that can destabilize the intrusion detection process [21]. Data shared through blockchain, however, resolve the trust problem within complex networks. This work posits that blockchain can prevent ransomware types of attacks to critical infrastructure so that the data can be replicated in multiple locations. This idea of blockchain-based data sharing has been proposed for smart grid systems aka SCADA (supervisory control and data acquisition). Sunny et al. [22] propose a mechanism for the protection of SCADA systems based on blockchain

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technologies. Their proposed framework monitors SCADA infrastructure using resource-constrained devices and builds a secure distributed data storage for missioncritical data. In this way, the framework removes the need for a central hub for managing a distributed infrastructure and also enables entities to collaborate. Blockchain is also very much an ideal solution for peer-to-peer (P2P) energy management and sharing among electric providers [23] as it can provide trust, transaction management, traceability, and data sharing among the vendors. In this setup, a consumer can act as a producer (i.e., prosumer) and will be able to generate revenue on its excess energy by selling it to other consumers such as neighbors. In this way, it is hoped that prices will go down because there will not be a need for a middleman. Furthermore, more people will be incentivized to install renewable energy generators. Blockchain has a critical role here because of the lack of the middlemen. This is because prosumers can rely on smart contracts that come with blockchain to place bids or execute purchases instead of going through a central utility. Payments will be real-time as soon as the conditions for purchase are met. There are many projects that are already utilizing blockchain-based P2P energy trading [24–26] Transportation infrastructure also aims to benefit from blockchain technology in various ways. While these benefits are mostly applicable to the logistics industry where there is a need to exchange data, goods, and services among different units [27], there are also many proposals to be able to use blockchain for data sharing among vehicles, road-side units and toll collection [28]. The main advantage is the establishment of trust so that the privacy concern of drivers can be addressed. Nevertheless, real-time operations and the scalability of the transactions still pose challenges in the deployment of these solutions. In the United States, sixteen critical infrastructures have been designated as necessary for national security, economic stability, public safety, and public health and wellbeing [29]. The Department of Homeland Security (DHS) manages nine of these critical infrastructures including emergency services, the chemical sector, commercial facilities, the manufacturing sector, dams, government facilities, information technology, nuclear facilities, and the transportation sector. There are many use cases where blockchain is being considered to assist DHS oversight of these infrastructures. Examples include “ensuring the authenticity and integrity of videos and photos from cameras, sensors, and Internet of Things devices; enhancing and facilitating international trade and customs processes; facilitating and securing passenger processing; and mitigating forgery and counterfeiting of official licenses and certificates” [30]. Safety Management: Safety information regarding community events, disaster response, traffic accidents, and other incidents can be stored in blockchain for digital forensics investigation purposes. One such example is a blockchain-based system investigated by Cebe et al. [31] that collects and stores information from vehicles and their vicinity to help in determining who is at fault when there is a traffic incident. This is particularly important for insurance companies and law enforcement as the cause of an incident is not always clear to observers or impacted parties and can be misrepresented by lack of technical knowledge, the desire to avoid consequences, inadequate memory recall due to psychological trauma or physical incapacity due to

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the incident itself. Having clarity on the cause helps law enforcement gather accurate information in an investigation and helps insurance companies accurately and expediently complete claims. The idea is to form a consortium blockchain that will include a number of stakeholders who will feed data to this blockchain. The stakeholders for this blockchain could be law enforcement, insurance companies, manufacturers, service providers, and the vehicle itself (i.e., its on-board smart unit that can communicate with the other cars and road-side units) [1]. In particular, the data coming from the vehicle is crucial since it may indicate whether one of the parts of the vehicle is malfunctioning. Any information received from other vehicles can also be traced from data collected from their on-board units to assess the reasons behind accidents. Note that once this information is written to the blockchain, it cannot be changed and thus its integrity will be protected. For emergency response, there are also recent proposals to use blockchain technology. One of such studies is reported in [32] where a multi-agent collaborative emergency management mechanism is proposed through blockchain. The idea is to move from a centralized emergency management system mainly based on government control to a multi-agent-based mechanism that includes other parties such as non-profits and the public. Sharing information among multiple diverse stakeholders is crucial for the success of emergency efforts. In this regard, the authors present an “alliance chain” which is much like a permissioned blockchain in which multiple stakeholders participate without any hierarchy. Accessing the blockchain will provide traceability and transparency while making contracts (utilizing the smart contract capability of blockchains) more accurate and faster with reduced costs. Indeed, a similar idea was already put in practice by The Miracle Relief Collaboration League (MRCL) [33] which is a Texas-based non-profit organization. MRCL was looking for ways to address the challenges of emergency management, such as inadequate communication and information asymmetry, in order to connect needs with resources in natural disaster relief efforts such as hurricanes and floods. Eventually they collaborated with a company called Chainyard to implement the idea on IBM’s Hyperledger platform [34]. Note that there are many other works that recommend systems with blockchain [35, 36].

3.4 Smart Contracts Smart contracts can be stand-alone applications of blockchain but are also the underlying mechanism, or permissions layer, that facilitates action in each of our previous examples. Smart contracts are digital contracts where terms of the agreement between the parties are written into computer codes. They are deployed on blockchain networks, and the terms are automatically enforced when the required conditions are met [37]. The transactions triggered by the smart contracts cannot be reversed or modified. Thus, using smart contracts parties can enter into an agreement without an intermediary or an external enforcement mechanism. Smart contracts also allow users to run new functions and applications on top of blockchain without

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disturbing the network [38]. Therefore, they bring much flexibility to blockchain use; any kind of exchange that is programmable into software can be implemented on a blockchain network. The automatic execution of the code can reduce transaction costs by automating complex government services [39]. Smart contracts find applications in various domains such as finance, healthcare, insurance, and supply chain [40]. Smart contract-based applications have been implemented in the public sector to increase transparency and accountability by reducing principal-agent problems. Principal-agent problems occur when a person or an entity delegates tasks to an agent for a specified period, but the principal cannot be sure that the agent will act in her best interest [41]. This is due to information asymmetry between the principal and the agent. First, the principal does not know the true preferences of the agent. She does not know how committed the agent will be to the policies set out by her. Second, the principal delegates a task to an agent either because she does not have the necessary expertise to perform the task or she does not have the time [42]. As such, the principal may not be able to efficiently or effectively monitor and evaluate the progress of the agent. Monitoring an agent’s actions is not costless for the principal. Any method that decreases monitoring costs such as performance indicators, incentive payments, and output targeting could reduce the principal-agent problem [43]. In the public sector context, the principals are citizens and agents are the political actors and the bureaucracy. Citizens’ ability to control and monitor political actors and bureaucracy is quite a complex political process [44, 45]. Smart contract-based applications can mitigate some of the agency problems in this delegation chain. Automatic executions through code reduce the cost of monitoring. First, all transactions on blockchain are open to the public and are recorded in an immutable ledger. Citizens could reach any public record regarding a specific service with relatively low cost. Second, smart contracts could reduce bureaucratic discretion and possible corruption by codifying the rules and regulations into computer codes. Once smart contracts are deployed on a blockchain network, the conditions of the contract or the actions it initiates cannot be modified. Bureaucrats cannot overwrite the code. Finally, citizens may not have enough time and resources to examine all public records on the blockchain. Automatic “red flags” could be embedded into the smart contracts where there is potential for bureaucratic shirking or rent-seeking behavior. Bureaucratic processes that involve repetitive tasks can be automated through the smart contracts. These include license and permit applications and renewals, property registration, tax filing, and payment and allocation of social benefits. In the last three decades, many of these services have been moved to online platforms both at federal and local levels. However, most of these services remain to be basic and have limited interactive capability [46]. Smart contracts could increase the number of online government services by allowing the design of more complicated tasks for online use. Tasks that have multiple steps can be linked to each other through separate smart contracts. Successful completion of certain criteria or failure to satisfy them could trigger processes with different smart contracts. Even if a process involves several departments and external agencies, the hierarchical workflow can be codified into smart contracts. Some of these processes could, of course, be implemented

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without blockchain and smart contract technology. However, these processes can be fully interactive and automated with smart contracts. The automation significantly reduces transaction costs and bureaucratic workload. Three smart contract applications exemplify the implementation of the smart contracts in the public sector. These cases show how smart contracts are applicable in relatively complex processes and highlight the attendant challenges. The first case is that of the Danish Syddjurs Municipality located in the eastern coast of Central Denmark with about 44,000 people [47]. The municipality implemented a smart contract-based blockchain application for a social welfare program to distribute benefits to parents who experience loss of earnings due to long-term care of their children. The process included three steps. In the first, the municipal caseworker has a guidance meeting with the parents to establish the family’s eligibility to get benefits and home care for the children. In the second, the caseworker collects information on lost earnings. In the third step, the caseworker decides the amount of benefit that the family should receive. This process is reviewed every six months, when the steps are repeated from start to end. The municipality reviews the social benefit rates every year; lost earnings and payouts are recalculated accordingly. Citizens could challenge any decision throughout the process. If the caseworker agrees to make the requested changes, the process continues with a modified plan. If the caseworker insists on her decision, the case goes automatically to the Appeals Board which could accept the initial decision, modify it, terminate the service, or return the decision for reconsideration. Each task is considered as a decision, data gathering or processing, or payment. These tasks, including the appeals, are put into smart contracts which are then deployed on the Ethereum blockchain network. Since the data on cases include sensitive information, only the hash of the data is stored on the blockchain. Each actor–citizen, case worker, or Appeals Board member–could check the information with their private keys and interact with the application through a web interface. The transparency ensures that the welfare programs are fairly enforced across different families. The second case is that of public procurement. Indeed, governments worldwide spend a substantial amount (nearly a third of their budget) in procuring goods and services from vendors. As such, the contractual and acquisition processes are quite prone to corruption, where political favoritism and bureaucratic discretions could be used for personal gains [48]. The openness and immutability features of blockchain make it an attractive tool for combating corruption in public procurement by enhancing government transparency and accountability. Thus, in Colombia, the government has been planning a blockchain-based public procurement application called the Transparency Project to combat corruption. It was designed by the partnership group of the Inter-American Development Bank (IBD), the Office of Inspector General of Colombia, and The World Economic Forum (WEF) [49]. The project was scheduled for a public-school program that provides breakfast and lunch to underprivileged students. The school meals program had traditionally been beset with corruption, wherein the funding was pocketed by politicians, officials, as well as

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contractors. Food materials were also being sold at inflated prices or were not delivered at all. The Transparency Project envisioned blockchain-based software implementation for the procurement process. The process includes the selection of vendors; initial and final tender offers; bidding period; first and second evaluations; calculation and publication of scores, and the final decision. Blockchain’s use helps to improve the transparency, fairness, and competitiveness of the bidding process. Political and bureaucratic favoritism is controlled by making a vendor publicly commit to contract terms and selection criteria before the bidding process. The blockchain-based bidding process would be transparent and tamper-proof since competing vendors cannot modify their bids after submission. The auction prices and vendor evaluation are automatically recorded and are easy to audit by the public. The record-keeping system is transparent for the public to monitor the procurement actions and to flag risks in real-time. The application generates ‘red flags’ when modifications to the initial offers are made at the last minute and when the selected winner is different from the automatically calculated one. Monitoring authorities like the Inspector General’s Office can thus investigate potential corrupt activity before the auction concludes. Blockchain helps in monitoring since the information on deliveries is available to key stakeholders (e.g., parents and teachers). Participants, such as teachers, could use the system to report on meal deliveries and quality in real-time. Improving observation in the delivery process and enabling stakeholder monitoring and engagement could improve both the formal and informal accountability of contractors. The third case is that of TradeLens, a blockchain-based global supply chain information sharing and document tracking platform, created by the collaboration of Maersk and IBM. There are many actors involved in the supply chain including sellers, carriers, and governmental agencies. The multi-actor and multi-jurisdictional character of cross-border transportation leads to several inefficiencies, including redundant documentation, shipment delays, and uncertainties. TradeLens focuses primarily on inefficiencies related to documentation, lack of uniform standards, and lack of coordination and collaboration between supply chain participants. Therefore, it provides a platform where the full journey of a container can be tracked from origin to destination. It also automates five of the most-used documents applicable to cross-border shipments: bill of lading, packing list, certificate of origin, commercial invoice, and export certificate [50]. Supply chain participants, including government agencies, could access these documents once they are uploaded to the system at the port of the origin. TradeLens uses a permissioned blockchain based on Hyperledger Fabric. Therefore, participants have control over their data, and only authorized participants have access to shared information. Blockchain technology assures that any changes in the documents are recorded, and all versions are tracked [51]. Moreover, with self-executed smart contracts TradeLens has the potential to automate activities including payments [50]. In December 2016, TradeLens was successfully piloted on a trade lane transporting fresh-cut roses from Kenya to Europe [50]. Since then, other big players of the shipping industry such as MSC and CMA-GGM also participated in TradeLens [52]. The implementation of the above projects has faced several issues. In the first project, the Danish municipality raised practical procedural concerns about

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immutability and costs. Since blockchain does not allow any changes to the records, governments cannot fix errors in the smart contracts after deployment. Procedural errors that cannot be reversed, especially if the automatic allocation of resources is involved, were significant concerns. The municipal government partners also did not perceive significant gains from the implementation as the cost outweighed the benefits. Transparency Project has also faced implementation hurdles due to integration and regulatory problems. The main challenge has been the compatibility and integration of the application with the existing e-government procurement system (SECOP II). It required a time-intensive development process and long negotiations with the software provider. The implementation was delayed due to the mayoral elections, and practical concerns also affected the implementation. Vendors cannot be anonymous before the bidding evaluation phase since they must reveal their identities when they pay the transaction fees. The transparency of blockchain, in itself, thus posed a bottleneck for the anonymity of the vendors. Moreover, anyone could put comments and raise flags about the bidding process, similar to other anonymous feedback systems online. These records are permanent and honest vendors do not have a recourse if they are targeted with malicious comments. Although these issues can be addressed from a technological standpoint, implementing these also require legal and institutional changes for the organizational implementation. Technology alone cannot solve the practical and procedural challenges. Finally, although TradeLens has significant potential in reducing inefficiencies, the shipping industry has been slow to adopt this new technology. There are several reasons: first, supply chain participants are reluctant to invest in a new technology as they are uncertain about blockchain’s future [50]. Instead, they continue to use their own software programs locally in their own organizations. Second, they have difficulty trusting that the information shared on a blockchain-based system will remain private and secure. Lastly, only a few governments have participated in TradeLens and accepted digital documents created on the blockchain. This prevents participants from fully realizing its potential and discourages new participants. In effect, smart contracts hold the potential to improve efficiency and accountability among government agencies. However, they could also face implementation challenges. Although immutability is a desirable characteristic, certain processes could have a justifiable need to alter or erase records. A partial solution could be hybridization, the implementation of permissioned blockchain that could provide more discretion for government agencies to monitor and control the records. Providing such discretion however sacrifices the decentralized and open structure of the blockchain. Second, the legal framework for implementing blockchain-based applications has not yet evolved. The technology is quite novel and the legal systems necessary to enable the technology adoption have lagged behind. Without legal clarity, government agencies cannot take a leap of faith and implement complex blockchain applications. Third, although smart contracts could automate several government services and therefore reduce costs, the initial costs to learn, implement, and update smart contract-based applications could be discouraging for many government agencies.

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4 Institutional and Legal Framework to Implement Blockchain in Government 4.0 Blockchain technology is a relatively new technology that has quickly evolved since 2009. Its adoption has grown in the public sector across a wide range of government applications. Blockchain can offer efficient, decentralized, and secure solutions for various purposes. This chapter provided a review of a select set of applications in the public sector. Although there is a wider range of blockchain’s applications, the domains highlighted in this chapter show the potential growth of the technology’s application. Clearly there are significant prospects for the adoption of the technology. We are still in the early years of the adoption of the blockchain for government applications. The chapter also shows the likely constraints in the adoption of the technology in the government sector. The examination shows the dualistic nature of the technology’s strengths as well as limitations in the public sector. We need to carefully parse the domains in which blockchain technology could be useful and effective to delineate a realistic potential of the technology. It is in this context that this chapter is useful in highlighting the potential challenges for blockchain applications in government. The realization of blockchain technology’s use requires more institutional learning and acceptance of the technology. Public organizations need to be ready to adopt the technology to achieve efficiency. At the same time, legal ecosystems also need to be developed for enabling the adoption and implementation of blockchain technology. The cases highlighted in this chapter show the prospects as well as the limiting factors for the adoption of blockchain. Putting land cadastral and real property information on blockchain can reduce the number of steps required for effecting property transactions. The blockchain records can be in real time, while also offering immutability, security, and transparency in the transaction records. People involved in the transactions can have real time visibility of the current state. Decentralized identity management systems with the self-sovereign identity offers prospects of providing identity that can be controlled by the individual. The case of blockchain enabled Estonian e-ID system shows that the system has been quite resilient. The ID system facilitates a one-step verification process for obtaining the various government services. At the same time, the case of Estonia could be unique as larger countries have to face the challenges of scaling up significantly. Other countries also need to have the appropriate political and legal framework to enable the adoption of such identification systems. Use of blockchain in critical infrastructure and emergency management brings obvious advantages in terms of eliminating the risk of a centralized architecture while enabling secure data sharing which is one of the pressing needs of the current government entities. Some of these services are already realized in the case of P2P energy trading to scale up the energy stakeholders. Others will still need some more efforts to gain trust of the government entities/users to give up their control over the data and get guarantees in terms of management quality of the used blockchains as

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well as the risks associated with the exposure of the sensitive data in case of public blockchains. Lastly, the case of smart contracts also shows that automated transactions can provide efficiency for government operations. Smart contracts are code-based and self-executable. They trigger when the appropriate conditions are satisfied. They can be implemented across a wide range of government services and operations. The Colombian case of using blockchain for transparency in procuring school meal vendors shows that the technology’s transparency can indeed have the potential for curbing corruption. However, governments also face practical and procedural challenges in implementing the blockchain technology. Public agencies need to realize the benefits of the use of blockchain-enabled processes vis-a-vis existing organizational processes. From an institutional perspective, public sector organizations still have to learn blockchain technology to implement it within their contexts. In this respect, there needs to be more awareness of the appropriate areas of applications of the technology for its adaptation [53]. The strength of the blockchain is its high degree of security and transparency. These strengths are already realized by governments. Governments have indeed been using blockchain technology in critical areas where security and effectiveness are required [54]. However, implementing the technology requires organizational readiness as well. As the above examples show, blockchain applications need to be customized to ensure a fit with the requirements of administrative processes to realize the technology’s benefits [55]. With respect to the legal framework, the laws and policies surrounding government processes have not adapted as quickly as the fast-paced evolution of the technology. The policy environments need to be conducive to the adoption and implementation of the technology. Laws need to enable the adoption and use of blockchain technology across the different application domains [56]. As the cases of the real estate transactions and smart contracts show, they are complex processes with many parties involved. All the parties need to have trust in the blockchain system to participate [57]. An enabling legal ecosystem that is supportive of the blockchain’s use would provide social trust in using the technology.

5 Conclusion As a rapidly evolving technology over the last decade, blockchain technology holds a wide range of potential prospects across different public sector applications. This chapter has highlighted these prospects across several major areas, namely real estate, digital identity, infrastructure management, safety and emergency management, and smart contracts. These cases demonstrate how the blockchain technology’s applications transcend those of the traditional use in cryptocurrency. As the chapter demonstrates, blockchain technology can enhance efficiencies in the delivery of public services. These uses are presently limited as they face institutional challenges. Legal systems need to enable the use and application of the technology in the public sector.

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Hence we posit that the realization of the blockchain’s potential uses requires more institutional learning and a conducive legal framework.

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Application of Blockchain in Mining 4.0 P. K. Kunhahamed and Sonu Rajak

Abstract The mining sector is one of the important sectors of the economy and is a major contributor to the growth of an economy. Many industries like iron, steel, petroleum, natural gas, fertilisers, cement, glass, ceramics, etc. depend on the mining sector for their basic raw materials. Thus, mining can be considered as a catalyst for other industries growth. Due to increased rivalry, environmental and governmental legislation, and regulations, the operating environment of mines today is more complicated than ever before. This is because many businesses rely on the mining sector for their primary raw materials. Rising energy costs, access to energy, increase in demands, change in customer demands, reduced ore grades, social and geopolitical risks, strict labour rules, cost control, etc. are some major challenges of the mining industry. To take the competitive advantage in the global mining industry, industries are now taking more risks and facing uncertainty to meet their demand and it has become increasingly apparent that technology will play a growing role in the coming future. The fourth industrial revolution or Industry 4.0 is about automation and digitisation of the manufacturing environment and revolutionising the way companies manufacture, optimise the operation, and distribute their products. To extract the full potential of Industry 4.0 movement, there are many technologies like artificial intelligence and machine-to-machine (M2M) communication, internet of things (IoT), Blockchain, data analytics, etc. The Blockchain is an emerging technology that plays a crucial role to bring transparency, security, and trust in all transactions among different entities of the mining industry like production, transportation, and distribution. Blockchain records information in such a way that makes it impossible to alter or hack. Blockchain is a digitally distributed and decentralised ledger of transactions that is immutable in nature. This chapter examines the use of blockchain in Mining 4.0. The study highlights the challenges, opportunities, and requirements of blockchain technology to handle the risk and mining.

P. K. Kunhahamed · S. Rajak (B) Department of Mechanical Engineering, National Institute of Technology Patna, Bihar, India e-mail: [email protected] P. K. Kunhahamed e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_5

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Keywords Industry 4.0 · Mining 4.0 · Blockchain · Machine-to-machine (M2M) communication · Internet of things (IoT) · Supply chain management

1 Introduction With 1531 operating mines, mining is one of the important industries in India. India produces as many as 95 minerals which include 4 hydrocarbon energy minerals (coal, lignite, petroleum, and natural gas), 5 atomic minerals (ilmenite, rutile, zircon, uranium, and monazite), 10 metallic, 21 non-metallic, and 55 minor minerals [1]. Figure 1 shows the major coal deposits and iron deposits distributed among the different parts of India. In Fig. 1, we can see that the major coal deposits are in the states of West Bengal, Jharkhand, Chhattisgarh, and Madhya Pradesh [2]. Also, Major Iron Deposits are in Karnataka, Andhra Pradesh, and Goa. Figure 2 shows the Non-Metallic mineral deposits distributed among the different parts of India. In Fig. 2, we can see the Lime Stone, Mica, Dolomite Asbestos, Gypsum, etc. deposits in the different states of India. India is the world’s second-largest coal producer and the fifth largest country in terms of coal deposits. And India Targets 1.2 bn Ton Coal Production by 2023–24. Coal Production Increases by 29% to 66.58 Mn Ton during April 2022. The mining industry generates a lot of data and their entire operation depends on data like geological and economic data which will help to decide whether the operation/mining is worthwhile and feasible. Another type of data the mining industry

Fig. 1 Geographical distribution of mines in India, coal and iron deposits

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Fig. 2 Geographical distribution of mines in India, non-metallic minerals

depends upon is environmental data which helps in monitoring and mitigating the impact on the environment. The third type of data which mining industry depends upon is the financial data. Properly collecting, storing, and communicating these data is vital as any failure in the same may result in a huge loss of capital, health, and safety of workers; trust of stakeholders; etc. Thus, the right technology tools are required for the handling of data. Moreover in recent years due to the adoption of digitisation and networking in the mining sector, there are increased numbers of smart devices which are being used in Mines and these smart devices generate enormous amounts of data. But as these devices and data are not linked properly in a conventional system, there is always doubt regarding the data whether the monitored data provided is real or un-tampered with. The other bottlenecks with the conventional system are bandwidth efficiency, transparency, scalability, and data security. Efficiency in the mining industry can be improved multifold with better access to accurate, real-time information. But with conventional systems, the sharing of information is carried out through phone calls or emails, usually these data are not recorded properly, and sending documents and information through email is cumbersome and insecure and most probably the information becomes out of date as soon as it is sent. The Fourth Industrial Revolution is removing the lines between the digital and physical worlds. Industry 4.0 is a current trend of automation, data analysis, and data exchange in the manufacturing sector [3] Fig. 3 shows the evolution of the first industrial revolution to the fourth industrial revolution, i.e. from mechanisation to sophisticated cyber-physical systems. Industry 4.0 is characterised by four fundamental technologies which are applicable along the value chain of all industries and they are as follows:

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INDUSTRY 4.0 INDUSTRY 3.0 INDUSTRY 2.0 INDUSTRY 1.0

Mechanization, steam power, weaving loom

Mass production assembly line, electrical energy

Automation computers and electronics

Cyber physical system, IoT, blockchain, networks

Fig. 3 Evolution of industry 4.0

• Connectivity, data, and computational power: Technologies involved Sensors, IoT, Cloud technology, and Blockchain. • Analytics and intelligence: Technologies involved Advanced Analytics, Artificial Intelligence, and Machine Learning. • Human Machine interaction: Technologies involved Augmented and Virtual reality, Automation, robotics, and chatbots. • Advanced engineering and Technologies involved Additive manufacturing like 3D printing, Renewable energy, and Nanoparticles. With the introduction of industrial revolution 4.0, there is the need for improved connections, intelligence, and merging of data for autonomous decision-making and this will in turn help predict possible outcomes or problems beforehand. Mining 4.0 refers to the transfer of fourth industrial revolution principles into the mining industry. Fully automated mines like smart mines and highly sophisticated processing facilities are the main result of Industry 4.0. To ensure production runs smoothly with Mining 4.0, the status of machines as well as real-time data regarding mining activity should be available and accessible all the time. In short, Mining 4.0 fully depends on real-time information from integrated IT and operational technology systems and uses the data to foresee issues and take immediate precautionary measures [4]. The improvements in connections intelligence and merging of data from different data points will help in underground safety by predicting possible accidents and the quality of mine products. Some of the enabling technologies of Industry 4.0 are Blockchain,

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the Internet of things (IoT), advanced analytics, Blockchain, AI, and ML (Artificial intelligence and Machine learning). Blockchain:—Blockchain can be considered as a ledger which is shared and immutable and this ledger will help in recording transactions and tracking assets of a business network [5]. In a Blockchain, data is gathered in groups called blocks, and blocks include sets of information. Blocks have specific storage capabilities; after the date gets entered, the blocks are closed and are linked to previously filled blocks thus forming a chain known as Blockchain. The main objective of blockchain is that once the information is recorded and distributed, it cannot be altered, deleted, or destroyed. Internet of things (IoT):—A network of wide variety of physical objects like computing devices, sensors, mechanical and digital machines, or anything which has the ability to transfer the data without requiring human-to-human or human-tomachine interaction has unique identifiers. These web-enabled objects communicate with similar objects and take action based on information sent between them. This will help in the automation of processes thus reducing the labour costs and mistakes that occur due to manual intervention. The introduction of IoT reduced wastage and improved service and increased transparency at all levels. IoT enabled businesses to have better access to information about their own internal systems and products, as well as more flexibility in making changes as a result. To make use of the potential of IoT manufacturers are integrating sensors into the parts of their products. This enables businesses to identify when a component is likely to fail and replace it before it results in damage. Businesses may also utilise the data produced by sensors for improving the efficiency of their products, supply chain, and production process. Advanced analytics:—Analysis of all those data generated in a value chain using complex techniques to predict events and forecast trends. Finding correlations and relationships and extracting predictive information from the data collected from different sources/points are the key to advanced analytics. Different techniques used in analytics are data mining, machine learning, pattern matching, forecasting, visualisation, semantic and sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, and neural networks. To keep pace with extremely competitive, quick-changing markets, the customer analysis and predictive analytics statistical modelling, etc. are helping businesses to make better decisions. Advanced analytics used in the right way will help the companies to predict the future, reduce risk, and anticipate the problems. AI and ML (Artificial intelligence and Machine learning) can be considered as intelligent software solutions. The ability of computers to generally mimic human reasoning and carry out tasks in real-world settings is referred to as artificial intelligence. ML is a subset of Artificial intelligence. The foundation of machine learning is the notion that objects should be able to learn from their experiences and adapt. ML is the study of creating and implementing algorithms that can learn from previous examples. If certain behaviour has occurred in the past, with ML we can forecast whether or not it will occur again. Many companies are combining two or more of the

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above technologies to get maximum output. Like in the mining industry from exploration to processing and transportation, AI with the help of data collected through IoT helps in streamlining operations, cost reduction, and improving safety in the mining industry. Out of the above-depicted technologies, one of the important technologies of Mining 4.0 is Blockchain [6].

2 Blockchain A Blockchain is a digital ledger of transactions and each block in the chain contains interconnected transactions. Blockchain stores and protects digital data and record. With Blockchain technology, every user in the network has the latest updated copy of timestamped and validated ledger. In case of any change or addition of transaction, all user nodes are notified about the change and all nodes have to verify the change thus creating a new block in the chain. A validated and published block cannot be changed. As all nodes/users have the latest copy of ledger, third-party verification of transaction is not required. Figure 4 flow chart shows the working of Blockchain with different stages.

2.1 Differences Between Blockchain and Traditional Database • In Blockchain technology, transactions are distributed to all participants and any change requires approval from all participating users on a network and it happens in real time, this in turn increases transparency, trust, efficiency, and security of the whole process among participants. • Blockchain databases are secured with encryption and transactions require approval from all parties thus network can flag false entries which will cement the integrity of data. Thus, it can be said that Blockchain is a far more secure and trustworthy database, as it’s shared.

A transaction is initiated

Data packed in a block

The block added to chain

Fig. 4 Flow chart of how blockchain works

The block sent to user nodes

The update is distributed

Verification and Approval by users

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There are two types of Blockchain, one is open Blockchain and the other is private Blockchain. Open Blockchain is the idea for Bitcoin and other cryptocurrencies (the most widely used implementation of Blockchain). Open Blockchain permits anybody with a correct computer program to read the contents of the database and to write new data to the database. The Second type of Blockchain is private Blockchain. In a private Blockchain, there is a distinct network with a few participants, in a value chain the participants might be from different organisations. The data entered into the Blockchain is validated by a set of rules, rules which are established by an agreement or contract. The advantage of private Blockchain is that it makes current practices more efficient by providing real-time data to multiple parties involved in the network, thus improving transparency.

2.2 Significances of Blockchain Easy tracking of spare Parts/machine Upkeep data: Regular service of spare parts is of much importance in any industry for reducing downtime. Data regarding the servicing like when the servicing was done, who has done the servicing, which all parts were replaced and the frequency of replacements, and the most repetitive faults which are resulting in downtime are of much importance for the auditing process as well. As Blockchain is an immutable database with timestamped data and which requires approval from all participants for changing, the data stored in Blockchain makes the auditing process and risk, straightforward and certain. In case the local spares manufacturers/suppliers and OEM spares manufacturers/suppliers are connected on the same network, once the requirement of spare parts or maintenance arrives the same shall be known by the concerned party and they can act immediately without any further input from any other people, thus reducing the downtime. Figure 5 explains about the use of Blockchain results in Improved Inventory visibility, knows where the required spares are, and can connect with the right person to expedite the process. Contract Administration: This is advantageous for contracts where more parties are involved and there are time-limited elements and the fulfilment of the contract depends on performance. Thus, Blockchain technology is good for contracts like maintenance or equipment contracts and labour contracts. Human intervention in contracts may result in miscalculations mistakes and trust issues and most importantly this increases costs associated with manpower. With Blockchain technology execution of contracts is automated and it keeps track of progress and also increases trust between stakeholders. Figure 6 explains about Blockchain network in Contracts management. Blockchains are still perceived with mistrust and companies approach Blockchain with caution. But in reality, Blockchain provides security and transparency in business transactions and is extremely helpful in creating the provenance of essence

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Fig. 5 Inventory visibility for mine and mill maintenance

Fig. 6 Blockchain network in contracts management

(materials), securing mining data, and effectively managing the supply chain thus providing assurance, transparency, and traceability.

2.3 Provenance of Essence (Materials) Blockchain records each and every transaction and is immutable and has clear asset ownership since the time the asset first appears in a transaction on the Blockchain. This will ensure the prevention of data theft, unauthorised usage, duplication of asset,

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and mis-selling of high-value assets and intellectual properties and will help secure data. Blockchain provides a transparent, traceable database to store data. This implies that there is no tampering and is transparent and principled. With respect to the mining industry, all supply chain transactional data from mine to smelter get saved in the Blockchain database; the data not only include weight, quantity, and grade, but also provenance information. These data related to mine products (primary and secondary) are encrypted and stored in Blockchain decentralised network. The advantage of blockchain database is the proof of mineral integrity. Primary data is the data related to mine products like data regarding quality grade location, etc. and secondary data is the details of the handler. These primary and secondary data are stored in form of a ledger and timestamped. The information in blockchain is updated every time the material moves and relevant information regarding the mine details, etc. get recorded. After the product is ready to sell, the transfer of ownership is immutably written on Blockchain in the form of a transaction. Finally, the distributors, retailers, and buyers are shared with complete process detail of all handlers, pictures, characteristics, and dimensions. In a Blockchain-based network, the asset can be used to keep track of assets in the form of transactions that can be updated. And every update is broadcasted to the network and gets approval from nodes and thus creating immutable trail of information.

3 Supply Chain Supply chain transactions are recorded in the Blockchain database. In the supply chain for tracking of material digital tags, serial numbers, bar codes RFID tags, etc. are used [7] Data recorded on blockchain can incorporate properties of the item, location of items, etc. [8] In a mineral or metal supply chain, the following properties are recorded in Blockchain: • • • • • • • •

Weight of the item; Quantity available; Grade of the product; Images of the material; Mineral fingerprints; Ownership of the material; Bills of lading; Store and transfer locations.

By entering the data into Blockchain, it is ensured that the data is entered according to already agreed terms and validation mechanism, blockchain would ensure the same by pointing out abnormalities in data if entered wrongly. This would help to identify problematic supply chain data within minutes. From the traceability tools, Blockchain has different features which make them superior, they are as follows:

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• Requirement of Consensus: In Blockchain all members within the network have to agree over the data recorded on the database. In the metal and mineral industry, the data also include provenance data and relevant certificates. This will help to build the trustworthiness of the company to the companies involved in downstream and upstream and they can include these data in their reviews. • Immutable and un-corruptible data: Once the data is added to the blockchain effectively, it is timestamped, approved, and connected to the remaining data in the chain and this will reduce extortion of any data from the chain. • Decentralised control: Blockchain provides decentralised control which means the framework cannot be controlled by a single person or system. The data is stored in databases on a server after which it is self-executed. These decentralised versions improve trust among participants. • Sharable, but encrypted supply chain information: Once the data is entered and approved by all participants in the network, any third party like auditors’ insurers’ shareholders, investors’ downstream suppliers, logistics providers, etc. can be given access in real time. This direct access to real-time information can help improve the efficiency of the supply chain [11, 13]. In certain Blockchain setups, access from the third party is restricted to avoid concerns regarding confidentiality. Moreover, the company can retain the confidentiality of sensitive information by sending encrypted proof of the fact instead of actual data. • Scalability: The Blockchain system is scalable that means once all the participants agreed and the system is established, n number of users can be on-boarded onto the Blockchain platform thus ensuring scalability. • Costs reduction: Blockchain can result in cost reduction and the most important cost reduction factor is a paperless system. It reduces the burden of audit as real-time consensual data is available more easily. The flow of minerals along the supply chain is still complex and there involves a lot of manual processes whether it is traceability of minerals and ores, rights management, regulatory compliance, or trade finance. Once a mineral is extracted from the mine, it moves between varieties of parties, and each one of them has to generate and verify a certain number of paper documents. That inefficiency in itself not only costs the mining industry a significant amount, but it also creates multiple opportunities for fraud, because there isn’t visibility and data transparency.

4 Machines-to-Machine Communication by Using Blockchain Direct connection between devices utilising wired or wireless communications is known as machine-to-machine (M2M). Industrial instrumentation may enable machine-to-machine communication, allowing a sensor or metre to transmit the data it collects (such as temperature, inventory level, etc.) to application software that can utilise it (for example, adjusting an industrial process based on temperature or placing

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orders to replenish inventory) [9, 10]. A distant network of machines would formerly carry out such communication by sending data to a central hub for processing, which would then be sent onto a device like a personal computer. A system of networks that sends data to personal appliances has replaced more contemporary machine-to-machine communication. Machine-to-machine communication is now faster, simpler, and consumes less power because of the global spread of IP networks. New business prospects for suppliers and customers are also made possible by these networks. Machine-to-machine technology’s primary goal is to collect sensor data and send it via a network. In contrast to SCADA or other remote monitoring tools, M2M systems frequently leverage open networks and access techniques, including cellular or Ethernet, to make them more affordable. Sensors, RFID, a Wi-Fi or cellular communications link, and autonomic computing software designed to assist a network device in interpreting data and making choices are the core elements of an M2M system. The data is translated by these M2M applications, which can start predefined, automatic activities. Telemetry is one of the most well-known forms of machine-to-machine communication and has been used to send operational data since the turn of the past century. Telemetrics pioneers initially transmitted performance measurements obtained from monitoring devices in faraway locations using telephone lines, and then radio waves. Telemetry is now used more frequently in items like heating units, electric metres, and internet-connected appliances than it was in pure science, engineering, and manufacturing before the Internet and enhanced wireless technology standards.

4.1 Applications and Instances of M2M Remote monitoring frequently makes use of machine-to-machine communication. When a particular product is running short, for instance, a vending machine can send a message to the distributor’s network or a machine to request a refill. M2M is essential to warehouse management systems (WMS) and supply chain management as it makes asset tracking and monitoring possible. Utility firms frequently employ M2M devices and applications to monitor workplace conditions like pressure, temperature, and equipment status in addition to harvesting energy sources like oil and gas and billing customers via Smart metres. The following are the top advantages of M2M in addition to being able to remotely monitor devices and systems: • Save expenses by reducing equipment upkeep and downtime; • Increased sales by showcasing fresh business options for in-field product servicing; • Enhanced customer service through proactive equipment monitoring and maintenance performed before failure or only when required.

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5 Applications of Blockchain in Mining 4.0 Introducing blockchain technology brings with its transparency, reliability, scalability, and traceability to mining supply chains and improves the traceability of minerals and ores, rights management, regulatory compliance, or trade finance. Blockchain can be used to track materials in the mining value chain [12, 13]. Most mining companies have a fragmented value chain with transactions spread across multiple parties. Similarly, in the case of mining products, there are intermediaries parties such as miners, processors, refiners, distributors, and retailers and the customer is not sure about the quality and transparency of the final product. In order to introduce transparent and fair trade of mining products, there should be an immutable record of all handlers and such record should be verified by the other nodes/participants. This can be only done if all parties are integrated on a single decentralised network maintaining all the records. Presently, such networks can be launched using Blockchain technologies only [14].

5.1 Secure Mining Data All type of data related to mining is extremely sensitive and many of it has to be kept as confidential. With Blockchain technology, the data is secure, transparent, and accessible [15]. The sensitive data which require security includes higher level decisions regarding acquisition, resource/ reserve estimation, mine design, and planning process. Blockchain also removes the middleman by acting as a conciliator between two companies [16]. Through smart contracts, companies can change information and documents between them and an external mate to confirm that information is correct and transparent.

5.2 Challenges of Implementing Blockchain in the Mining Industry 1. Lack of adoption: To get the maximum advantage from blockchain broader adoption of the same is required, this means not only the organisation but also the suppliers should also adopt blockchain, without which the effectiveness as well as scalability of Blockchain is limited. 2. Skills gap: Blockchain adoption is still in the nascent stage and is an emerging technology; this requires skills required for the development of Blockchain, and uses of Blockchain are scarce. 3. Inefficient Technological Design: Blockchain has many advantages but not deploying it properly may endanger the true efficiency of it. Poor implementation or deployment may be due to a lack of technical know-how or due to an

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error in coding or process. These loopholes in programming or implementation may help hackers gain access to the system thus risking the confidential data of a company. 4. Trust among users: Trust among all the players involved in Blockchain is necessary for the successful implementation and working of any Blockchain. But it is seen that there is a lack of trust among users which may even hamper the widespread adoption of Blockchain. There are two kinds of trust which is required for the success of Blockchain, one is the organisation’s trust on the security of technology and another is the trust of other parties on a Blockchain network. 5. Lack of central financial resources or cost: Lack of financial resources is another hurdle for the implementation of blockchain. A significant investment is required for hiring software developers, who develop the blockchain, and another financial outlay is to procure high-end systems which can process a huge quantity of data in a small amount of time, training overall administration of blockchain is also costly, and for many companies, the expenditures may be prohibitive. As there is no universal standard that enables communication among different types of blockchain systems, separate Blockchain/versions may not work together. Many organisations develop their own systems with varying versions/rules, etc.; unless different blockchains are not integrated together for communicating with each other, the true potential of blockchain cannot be reached. The interoperability of Blockchain means the ability to share, see, and access information across different Blockchain networks without the need for a central authority.

5.3 Security and Privacy Challenges of Blockchain Blockchain is believed to be very safe and secure; there is data privacy and security challenge in front of the blockchain as it has a huge collection of important data. Any error in code or loosely implemented blockchain system, etc. may pose a security risk to the entire organisation.

6 Conclusion This decade will likely see a variety of ways that the Blockchain grows and expands. One of the key components of Mining 4.0 is digitalisation, which enables businesses to gain efficiency in many areas, from management and technology consulting to mining strategy and solutions. This Blockchain holds potential for many businesses and can be really beneficial in addition to other things. These days, banks use technology to speed up transactions and cut down on associated costs. Blockchain implementation is not just limited to the financial system; it also offers information. This

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permanent ledger attests to the commodity being produced using the proper procedures and resources, and that the procedure has received approval. Blockchain ensures reliable and effective data sharing, and it also creates an unalterable database of all communications sent and received by all connected smart devices. Another fantastic use of Blockchain is identity protection. Because it is manipulative, this technology enables users to create their own secure and reliable digital identity. People would be able to use their Blockchain identities for a variety of things, from simple activities to programmes, software, or signing digital signatures. Blockchain might be the answer to simplify this phase by giving smaller businesses and providers a reliable source of high-quality transactional knowledge. Real-time data regarding mining activity should be available and accessible all the time to ensure production runs smoothly. Blockchain is a digital ledger of transactions and each block in the chain contains interconnected transactions. Every user in the network has the latest updated copy of timestamped and validated ledger. As all nodes/users have the latest copy of the ledger, third-party verification of a transaction is not required. The advantage of blockchain database is the proof of mineral integrity. Information in blockchain is updated every time the material moves and relevant information regarding the mine details, etc. get recorded. By entering the data into Blockchain, it is ensured that the data is entered according to already agreed terms and validation mechanism. In the metal and mineral industry, the data also include provenance data and relevant certificates. This will help to build the trustworthiness of the company to the companies involved downstream and upstream. Once the data is entered and approved by all participants in the network, any third party can be given access in real time. Blockchain can be used to track materials in the mining value chain. It improves the traceability of minerals and ores, rights management, regulatory compliance, or trade finance. Trust among all the players involved in Blockchain is necessary for the successful implementation and working of any Blockchain. There are some limitations and concerns that arise from this study that provide opportunities for further research. For example, the book chapter lists a few obstacles to Industry 4.0’s deployment in the mining sector; other obstacles may emerge in the future to the sector’s seamless adoption of Industry 4.0. The adoption of the blockchain along with other reliable technology or methods for data security, supply chain visibility, and transparency is possible in the future.

References 1. Mining Sector. https://www.indiabudget.gov.in/economicsurvey/ebook_es2021, p 300 2. Geographical distribution of Mines. https://www.drishtiias.com/to-the-points/paper1/miningsector-in-india 3. A. Raj, G. Dwivedi, A. Sharma, A.B.L. de Sousa Jabbour, S. Rajak, Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. Int. J. Prod. Econ. 224, 107546 (2020) 4. J. Lööw, L. Abrahamsson, J. Johansson, Mining 4.0—The impact of new technology from a work place perspective. Min. Metall. Explor. 36(4), 701–707 (2019)

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5. Blockchain. https://www.ibm.com/topics/what-is-blockchain 6. T. Bartnitzki, Mining 4.0—importance of industry 4.0 for the raw materials sector. Artif. Intell. 2, M2M (2017) 7. Mathiyazhagan, K., Mani, V., Mathivathanan, D., Rajak, S.: Evaluation of antecedents to social sustainability practices in multi-tier Indian automotive manufacturing firms. Int. J. Prod. Res. 1–22 (2021) 8. S. Namasudra, G.C. Deka, P. Johri, M. Hosseinpour, A.H. Gandomi, The revolution of blockchain: state-of-the-art and research challenges. Archiv. Comput. Methods Eng. 28, 1497–1515 (2021). https://doi.org/10.1007/s11831-020-09426-0 9. R.Y. Zhong, X. Xu, E. Klotz, S.T. Newman, Intelligent manufacturing in the context of industry 4.0: a review. Eng. 3(5), 616–630 (2017) 10. S. Namasudra, P. Sharma, Achieving a decentralized and secure cab sharing system using blockchain technology. IEEE Trans. Intell. Transp. Syst. (2022). https://doi.org/10.1109/TITS. 2022.3186361 11. S. Rajak, K. Mathiyazhagan, V. Agarwal, K. Sivakumar, V. Kumar, A. Appolloni, Issues and analysis of critical success factors for the sustainable initiatives in the supply chain during COVID-19 pandemic outbreak in India: a case study. Res. Transp. Econ. 101114 (2021) 12. P. Sharma, N.R. Moparthi, S. Namasudra, S. Vimal, C.H. Hsu, Blockchain-based IoT architecture to secure healthcare system using identity-based encryption. Expert. Syst. (2021). https:// doi.org/10.1111/EXSY.12915 13. S. Rajak, P. Parthiban, R. Dhanalakshmi, Selection of transportation channels in closed-loop supply chain using meta-heuristic algorithm. Int. J. Inf. Syst. Supply Chain Manag. (IJISSCM) 11(3), 64–86 (2018) 14. D. Agrawal, S. Minocha, S. Namasudra, A.H. Gandomi, A robust drug recall supply chain management system using hyperledger blockchain ecosystem. Comput. Biol. Med. 140 (2021). https://doi.org/10.1016/j.compbiomed.2021.105100 15. Y. Zuo, Making smart manufacturing smarter–a survey on blockchain technology in Industry 4.0. Enterpr. Inf. Syst. 15(10), 1323–1353 (2021) 16. C.T.B. Garrocho, E. Klippel, A.V. Machado, C.M.S. Ferreira, C.F.M. da Cunha Cavalcanti, R.A.R. Oliveira, Blockchain-based machine-to-machine communication in the industry 4.0 applied at the industrial mining environment, in 2020 X Brazilian Symposium on Computing Systems Engineering (SBESC). IEEE, November (2020), pp. 1–8

Integration of Data Science and IoT with Blockchain for Industry 4.0 Pranav Gangwani, Alexander Perez-Pons, Santosh Joshi, Himanshu Upadhyay, and Leonel Lagos

Abstract The field of the Industrial Internet of Things (IIoT) is developing rapidly and gaining abundant attention throughout our society. The vast volume of raw information that these IoT devices generate opens the door for an evolving field that draws useful insights from raw data. This area of study that deals with the extraction of information and draws insights from IoT data using various algorithmic and scientific methods is called data science. This field is in high demand among various industries and academic institutions. However, due to the lack of fundamental security technology, vulnerabilities and security risks are emerging in these IoT devices. Therefore, there is a need to secure and store such extracted data and insights, since this information can be sensitive and must be tamper-proof. Blockchain technology is being used as a decentralized and distributed approach to ensure the security requirements of IoT, enhance IoT and IIoT development, and as an immutable storage system. However, scalability and transaction fees are the major challenges that restrict the use of blockchain technology for IIoT. This chapter introduces a Directed Acyclic Graph (DAG)-based blockchain called IOTA, which overcomes these major challenges of the traditional blockchain to enable seamless integration with IoT devices. Furthermore, three IIoT applications are proposed and elaborated, consisting of (1) Device Identity Management, (2) Sensor data anomaly detection using Artificial Intelligence (AI), and (3) Security of IIoT data using IOTA Tangle. P. Gangwani (B) · A. Perez-Pons Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33199, USA e-mail: [email protected] A. Perez-Pons e-mail: [email protected] S. Joshi · H. Upadhyay · L. Lagos Applied Research Center, Florida International University, Miami, FL 33199, USA e-mail: [email protected] H. Upadhyay e-mail: [email protected] L. Lagos e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_6

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Keywords IoT · Blockchain · IOTA · Industrial IoT · Machine learning · Identity management

1 Introduction The Internet of things (IoT) is a collection of objects or “things” that includes various machines and devices such as sensors and actuators [1]. These devices sense or gather data from their surroundings, transmit it to other devices over the Internet, and can automatically analyze or act upon the collected data without the need for human interaction. For example, a smart home environment [2] is equipped with numerous sensors that may perform specific actions such as automatically turning on or off electrical appliances to enhance efficiencies, improve the lives of people, and conserve electrical energy. Every year the total number of IoT devices increases drastically, with this number estimated to reach 75 billion worldwide by 2030 [3]. In contrast to IoT, IIoT is widespread machinery that frequently relies on heterogeneous technologies. Due to the proliferation of IIoT devices with numerous protocols [4] for communication, processing capabilities, and operating systems, these heterogeneous technologies encompass a complex environment to enable data collection from various sources under different connectivity conditions. The challenges associated with IIoT [5] are increasing as the total number of devices deployed in industrial environments continues to grow. Among the many challenges that confront IIoT technology, the major ones include security, anomaly detection, and Identity Management (IM). These heterogeneous IIoT devices generate and collect massive amounts of data through the deployment of a multitude of sensors that are prone to the possibility to obtain anomalous or faulty data. This can be attributed to various causes such as device malfunctioning or tampering. This malicious data can cause issues for IoT systems/industries and may provide incorrect insights. Many times, due to temporal patterns and high-dimensional data structures such as images and text data, these anomalies may be difficult to detect. Hence, there is a need to timely detect these anomalies in the data for maintenance and to prevent losses. Anomaly detection [6] is a technique that identifies abnormal data or data that deviates from its historical pattern. The detection of such data is critical in a variety of applications, which includes medical diagnosis [7], fraud detection [8], and intrusion detection [9]. Furthermore, detecting anomalies from a huge amount of data is becoming a requirement for several new applications. Additionally, the results of anomaly detection using Artificial Intelligence (AI) and the massive volume of data collected by these IoT devices must be stored in a distributed and decentralized manner. While traditional databases and centralized storage can become a single point of failure, there is a need to store the huge amount of IIoT data in a distributed ledger such as a blockchain that is decentralized and solves the issues of traditional and centralized storage systems. Therefore, there is a need to integrate IoT and data science with blockchain technology to overcome the major challenges of IIoT.

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Blockchain technology [10] was introduced in 2008 [11] by Satoshi Nakamoto and has become a disruptive and significant technology for Industry 4.0. Blockchain is a distributed ledger or database which is decentralized, immutable, and operates in a peer-to-peer manner without the need for any intermediary or central authority. The above-mentioned features of blockchain ensure that the data is secured, authentic, and immutable once it gets stored on the blockchain. Using this decentralized blockchain technology to store IIoT data logs can be useful, particularly, if transparency and immutability are required, such that every participant will be able to view the data without modifying it. However, blockchain technology has certain limitations, which restrict its integration with the IoT [12]. For instance, the size of a block in the blockchain is limited to 1 MB and the mining time is about 10 min. Additionally, scalability is a major challenge for many types of blockchain technology [13]. This hinders its application for IIoT systems where scalability is important. Furthermore, there is also a possibility of selfish mining in the blockchain, where miners can achieve higher rewards than their allocated amount. Therefore, to meet the current demands of the IIoT systems, there is a need for a distributed ledger that can seamlessly integrate with IIoT and overcome the limitations of the traditional blockchain. The IOTA distributed ledger was introduced in 2015 [14], and it uses a data structure called the tangle that is a DAG in contrast to the chained block structure of blockchain. IOTA was designed to address the resource limitations of IoT devices. Hence, IOTA overcomes the limitations of blockchain, particularly the transactional throughput, which makes it the most suitable choice for IIoT systems. In this chapter, IoT refers to IIoT unless specified otherwise. This chapter highlights three IIoT applications that can be used in securing IIoT data, providing trust among the devices by identifying each device in the network, storing, and securing the IIoT data in a distributed database. The first application focuses on the Identity Management of each device within an IIoT system. Sensors are identified using various hardware features and their unique IDs in an implemented testbed that mimics an actual IIoT system, employing a hardware-based approach called MAC Address-based Identity Management (MAIM). Secondly, anomalies are detected on the aggregated IIoT data using Artificial Intelligence (AI) techniques. Machine learning models [15] are developed and trained that can predict and identify abnormal data patterns. Finally, a solution is presented to secure the IIoT data using the IOTA distributed ledger to provide immutability and authenticity. This chapter demonstrates the manner that IOTA’s features, such as immutability, decentralization, and its distributed nature, can benefit the storage of IIoT data. Each application is conducted within a use case of a thermal power plant as the IIoT system. The chapter’s contribution can be summarized as follows, with regard to its application in a thermal power plant scenario: • Propose a hardware-based application called MAIM for device identity management. • Provide an application that uses machine learning algorithms to efficiently detect anomalies collected from the sensors.

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• Present an application that uses blockchain, specifically the IOTA distributed ledger, to provide security, immutability, and authenticity to the sensor data. The remainder of the chapter is structured as follows: Sect. 2 elaborates on the fundamentals of data science. Section 3 discusses the details and fundamentals of IoT. Section 4 provides detailed background on the IOTA distributed ledger. Section 5 presents the challenges to implement blockchain technology in data science and IoT. Section 6 describes how blockchain technology is integrated with data science and IoT. Section 7 explains the applications of blockchain technology with data science and IoT for Industry 4.0. Finally, the chapter concludes with Sect. 8.

2 Fundamentals of Data Science Data science, in general, is an interdisciplinary study that involves the collection of data, data cleaning, and feature extraction techniques to draw meaningful/useful insights [16]. It also involves performing advanced data analysis [17] in manipulating the data and building machine learning models that can make data-informed predictions. The field deals with vast datasets that are huge, consisting of varying formats that are made available in data repositories or on the Internet. By utilizing hybrid math and computer science models, data science aims to answer real-world challenges such as setting the price of rent for various hotels and houses. Many data science techniques and tools have been developed to solve more challenging real-life problems. This is due to complex datasets and their properties such as heterogeneity, size, and structure, which require more sophisticated solutions. Statistical machine learning [18] is a popular subfield of these techniques that are utilized and implemented for a variety of tasks involving data science.

2.1 Artificial Intelligence (AI) Artificial intelligence is intelligence displayed by devices [19] instead of natural intelligence inherently found in animals and human beings. According to computer science, AI research can be defined as devices also known as “intelligent agents” which sense their surroundings and execute actions [20] that will improve their chances of succeeding at a specific objective. The term “artificial intelligence” is used when a technology imitates “cognitive” tasks, for instance, “learning” and “problemsolving.” Successfully interpreting human speech, playing various tactical games (for example, StarCraft and chess [21]), voice assistants, military simulations, analyzing complicated data, and self-driving cars are all capabilities that are now categorized as AI. Figure 1 depicts the relationship between AI, Machine Learning, and Deep Learning.

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Fig. 1 Relationship between AI, machine learning, and deep learning

2.2 Machine Learning Machine learning is a subset of AI that enables computers to learn without being explicitly programmed. Machine learning [22] arose from the evaluation of computational learning theory and pattern recognition in AI, and it entails the research and development of algorithms that are trained and used to predict data. By producing data-driven predictions [23] or conclusions based on sample inputs, these algorithms avoid adopting rigidly static program instructions. Similarly, computational statistics focuses on generating predictions with computers that are intricately connected to and often overlap with machine learning. Computational statistics provides the field with theory and is linked to mathematical optimization. Some real-life examples that use machine learning include spam detection [24], speech recognition [25], detection of fraudulent credit card activities [26], intrusion detection in networks, image recognition, and different product recommendations. Machine learning approaches are divided into four learning categories, as depicted in Fig. 2. • Supervised Learning In supervised learning [27], the machine learning algorithms use labeled data [28], which means that the input to the algorithm and the predicted outcome are known. Consider the example of a dataset about wine color, with the data labeled either “Red” or “White.” A set of inputs is passed to the machine learning algorithm along with the corresponding target or labels to predict. The algorithm, according to the features of the dataset and the type of problem that needs to be solved, computes the required machine learning technique/model such as regression, classification [29], gradient boosting, and prediction. Patterns may also be utilized by supervised learning to predict label values on unlabeled supplementary data [30]. Commonly, supervised learning is used in applications where predictions are needed or can be done based on

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Fig. 2 Types of learning

past data. The supervised learning algorithm trains the machine learning model [31] using past data, and then the trained model is applied to present data to predict future events. For example, supervised learning [32] can be used to detect if a transaction with a credit card is fraudulent or not based on past transactional data, or it can also predict which customer is going to file an insurance claim. Some examples of supervised learning algorithms include k-nearest neighbors (KNN), Support Vector Machines (SVM), and Decision Trees. • Semi-supervised Learning Semi-supervised learning is another type of machine learning [33] that is a blend of unsupervised and supervised learning and consists of data that is partially labeled. This type of learning aims to overcome both supervised and unsupervised learning’s limitations [34]. To categorize test data, supervised learning requires a massive amount of data for training, which is a time-consuming and costly operation. Unsupervised learning, however, utilizes data that is not labeled, and according to the similarity of the data points, the algorithm clusters the data by using methods such as clustering or the approach of maximum likelihood. The major limitation of this learning is that it cannot accurately cluster an unknown data sample. Therefore, to address these concerns, semi-supervised learning [35] was proposed by researchers which can predict an outcome accurately with a small amount of trained data. A major example of semi-supervised learning is Natural Language Processing (NLP), which uses a combination of supervised and unsupervised algorithms such as KNN and Long Short-Term Memory (LSTM). • Unsupervised Learning Another subfield of machine learning is unsupervised learning [36], which uses unlabeled data to make predictions. The unlabeled data is passed as input to the unsupervised learning algorithm [37], where the algorithm clusters that data or tries to find a pattern in it. The trained algorithm must interpret the input data provided to it and the aim is to divide the data into various clusters. Transactional data that is found in transactions/events is a suitable example of a dataset where unsupervised learning

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excels. For instance, unsupervised learning algorithms [38] can contribute to the identification of segments in transactional data. These segments can be created based on similar attributes of customers, which can further be employed for targeted marketing campaigns to treat each customer according to the clustering results. Additionally, the algorithms could also identify the major features that differentiate customer segments from each other. Agglomerative clustering, Probabilistic clustering [39], and Isolation Forest are some examples of unsupervised learning techniques [40]. These algorithms are often employed to make recommendations, establish cluster topics of text, and in the detection outliers. Some examples of unsupervised learning include Autoencoders, LSTM, and clustering algorithms such as K-means clustering. • Reinforcement Learning Another type of machine learning technique is reinforcement learning, which is widely used for applications such as navigation, gaming, and robotics [41]. By applying reinforcement learning, the actions that yield the greatest reward are discovered by the algorithm through trial and error. Reinforcement learning contains three major elements: the agent which makes certain decisions based on the rewards and punishment, the environment which is the world of the agent where it can interact and live, and the actions which are what the agent does in different states. The objective of the agent is to maximize the expected reward [42] by selecting appropriate actions in a limited amount of time. If the agent follows a good policy, then the objective can be achieved quicker than expected. Thus, learning the best policy is the ultimate objective of reinforcement learning. Some examples of reinforcement learning algorithms include Deep Q Network (DQN) and Deep Deterministic Policy Gradient (DDPG).

2.3 Deep Learning Deep learning [43] is a subset of machine learning that focuses on artificial neural networks and machine learning algorithms with several hidden layers [44], consisting of individual neurons. Deep Learning is also called “deep machine learning,” “deep structured learning,” and “hierarchical learning.” These neural networks extract features and perform transformations by using a collection of many layers that contain non-linear processing units, where the output of previous layers is passed as input to subsequent layers. Deep learning techniques [45] can be supervised or unsupervised, with the most common applications being supervised classification and unsupervised pattern analysis. These deep networks function by learning many levels of data features or representations. Higher-level features are obtained from lower-level features, forming a hierarchical representation. Additionally, these artificial neural networks learn multiple levels of representation and are part of a greater machine learning area that focuses on learning data representations at various levels of abstraction.

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In a neural network, there are several tasks that neurons must perform. These neurons take information from other neurons, process it, and then generate an output. Many layers are present in a neural network [46] between the input layer and the output layer. The algorithm utilizes many layers for processing, which are composed of many transformations that may be linear or non-linear. The layers between them are known as hidden layers, which are responsible for processing the information that consists of linear or non-linear information depending on the input layers processed data. There are many interconnected patterns between the layers rendering fully connected neurons. Many different architectures of deep learning are being utilized in various applications such as language translations, computer vision, chatbots, automatic game playing, and self-driving cars. The type of deep learning architectures used in the above applications includes convolutional neural networks (CNN) [47], deep neural networks, recurrent neural networks (RNN), and deep belief networks. All these mentioned architectures have been proven to show cutting-edge results when applied in different application domains.

3 Fundamentals of IoT In this section, various fundamental concepts are discussed associated with IoT, encompassing its related protocols and technologies that are employed. The IoT architecture describes the essential IoT components and their layered architecture, including the position of the layers and their function in the IoT stack. Then, the reasons why there is a need to uniquely identify different IoT devices within an IoT system deployment are covered.

3.1 IoT Components The IoT ecosystem contains four major components that are described below: • Things: The physical devices that work and operate in the IoT ecosystem are referred to as “things.” Every device in this ecosystem of IoT must be able to communicate with the network or other devices. This communication can be performed using Internet Protocols (IP) or communication protocols such as Bluetooth or LoRa. For communication among the devices, there must be some amount of data to be transmitted. This data is most commonly sensor data that is collected through various sensors, such as temperature readings from a thermistor, a smoke detection sensor, and/or images taken from a security camera. The “thing” may also be instructed to execute certain actions such as delivering a specific piece of information, moving an actuator, or some control motor. The device must be able to recognize these commands, conduct related operations, and validate that

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the requested action is performed via the remote controller. Some devices such as gateways, switches, and routers are also referred to as network components or appliances; however, they are often categorized as “things.” Devices must be able to withstand the environments in which they are embedded, as well as have the requisite power, sensors, and communications to conduct their functions. • Data: The data component associated with “things” and how it represents the data collected from sensors or instructions/commands sent to the device has been defined. Since there exists an enormous number of “things” generating data, the size of the data generated can potentially be enormous. Before storing this raw data for further analysis or immediate processing, it must be cleaned, error-checked, and structured. This operation can be performed at the network’s edge, in proximity to the devices, or the data can be sent to a more decentralized storage such as BigchainDB for further analysis. BigchainDB is a decentralized database that contains the characteristics of blockchain. • People: The Internet of Things affects people in at least two diverse ways: as an agent who must work to make IoT operate and as a beneficiary of its effects. In most cases, people work as specialists in their specific fields. However, within IoT, there is a much greater interconnectedness between services and people communicating across various industries. People must accurately analyze the aggregated data and provide the most appropriate interpretation to make complete sense of the data. The entire IoT technology is built by people and their efforts, which provides many benefits to the customers or end-users. • Process: The advantages of efficient procedures, informed decision-making and control, and smart automation are achieved in the final component of the IoT ecosystem, which is the “process.” With the appropriate information at the right time, all the methods, techniques, and processes now employed in vertical industries such as logistics and manufacturing could be made more efficient. The fundamental idea behind the IoT process is to analyze data collected from sensors and deliver it to the right stakeholders, taking intelligent and appropriate actions to solve their organization’s business problem.

3.2 IoT Architecture The most conventional and most prominent architectural IoT model consists of three layers [48], namely the perception layer, network layer, and application layer. However, this three-layer model does not adapt to the advancement of the technologies within the IoT scope. Furthermore, the three-layered IoT architecture lacks the layers which must perform the data processing, analysis, and cloud computing. An IoT architecture consists of five layers and uses the features of the IoT ecosystem as a driver. This five-layer architecture includes additional layers which overcome the limitations of the conventional three-layer architecture. These five

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layers are shown in Fig. 3, such as the “perception layer,” “network access layer,” “network transmission layer,” “application support layer,” and finally the “presentation layer.” • Perception Layer - 1 The “perception layer,” also known as the “recognition layer,” is a layer that links the information world and the physical world. The key role of this layer is to sense or gather valuable data/information from its surroundings and convert the collected data to a digital form of information. To complete data collection, this layer employs different types of technologies such as bar code technology, radio frequency identification technology, positioning, information sampling, and sensor technology [49].

Fig. 3 IoT Architecture of five layers

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Moreover, this layer is made up of different technologies such as QR code, GPS, and physical “things” such as actuators, Wireless Sensor Network (WSN), and sensors. Additionally, the primary components of the “perception layer” include RFID readerwriters, RFID tags, two-dimensional code labels, various sensors, and cameras. As a result, the major duties of the IoT “perception layer” are to perceive the data, collect it, transform it into digital signals, and eventually transfer this data to the above layer in sequence. • Network Access Layer - 2 The “network access layer” is positioned above the IoT “perception layer” and primarily contains the network access gateway along with the base station node. The base station node, also known as a base transceiver station (BTS), is a piece of hardware equipment that assists in the wireless communication between a user device (such as a mobile phone) and a network. This layer provides the required protocols and technologies to communicate data to other devices and forwards the data to the above layers of the IoT stack such as the “network transmission layer” and “application support layer” [50]. When the nodes or devices of the “perception layer” have completed networking, they must upload data and deliver it to the base station. Once the base station node or BTS receives the information, the access gateway will establish a connection with the “network transmission layer.” The BTS then transmits information to every node in the perception layer only when the following conditions are met: (1) There is a need for the network layer and the application layer to downlink data, i.e., acquire data from a higher level of the network, for example, a satellite, and (2) The network access gateway has received data from the “network transmission layer.” Once this is done, the interaction and forwarding of data can be completed between the “perception layer” and the “network transmission layer” [51]. ZIGBEE, WIFI, Mesh, industrial bus, and Ad hoc are the most common network access methods today. These technologies enable initial processing, network accessing, and data collection via different cognitive tools. • Network Transmission Layer - 3 The “network transmission layer” is primarily utilized to transmit and exchange data and to provide the fundamental transmission networks for a variety of applications and services, such as satellite communication networks [52], mobile communication networks, optical fiber communication networks, and local independent private networks such as 5G networks. • Application Support Layer - 4 The “application support layer” provides data storage services, complete public intelligent analysis, data processing capabilities, and all types of intelligent application exchanging and sharing services. This is due to the support of middleware [53] and cloud computing technologies combined with information and database technologies.

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• Application Presentation Layer - 5 The “application presentation layer” develops various IoT applications based on the data processing capabilities of the “application support layer” and uses various technologies such as virtual reality, multimedia, and human–computer interface to build intelligent IoT applications [54], which are to be delivered to end-users.

3.3 Need for Identity Management One of the key issues in the field of IoT is Identity Management, i.e., identifying each IoT device with the help of its unique identity. In sensor networks, whether in industry or institutions, a network may contain a massive number of sensors sharing data. Therefore, authentication and identification of these sensors become of utmost importance, since these numerous sensors may record and share data that is private and confidential. IoT identity protection [55] faces many issues and challenges such as tampering, spoofing, and theft. For example, IoT devices communicate by using the TCP/IP model on a network, and by using these devices’ MAC address and IP address, they can be identified on the network at the Data Link Layer and the Network Layer, respectively. However, if these devices are identified based on their IP address, they can become vulnerable to IP spoofing attacks. IP spoofing is a threat in which a device can forge its network identity to impersonate another device to gain access to a specific network. Additionally, there are no clear definitions of how IoT devices or sensors can be uniquely represented, identified, accessed, or searched. Due to this, many IoT devices become vulnerable to various identity threats such as a Sybil attack, in which an attacker uses sensors or IoT devices with many illegal identities. In these types of attacks, an attacker attempts to acquire numerous unique IDs and uses them to operate different nodes, where the IDs may or may not be produced randomly. Therefore, numerous standard security methods must be evaluated to see if they are feasible and applicable in the IoT.

4 IOTA Distributed Ledger The IOTA distributed ledger is not only an open-source decentralized data communication protocol but also a cryptocurrency [56] that can be used by participants to exchange funds without the involvement of a third party. What makes the IOTA architecture unique is that it enables a distributed ledger without any blocks or mining and zero transaction fees. IoT devices, with their limited resources, can propagate their data across the IOTA network by making data transactions. This provides data

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immutability and allows participants to make payments in the form of cryptocurrency and exchange data without any overhead or fees. The key features of IOTA that overcome the challenges of blockchain and make it a better technology include zero transaction fees, high scalability, no mining, and zero blocks. The most prominent and popular types of blockchain, Bitcoin and Ethereum, contain chained and sequential blocks where each block consists of multiple transactions. IOTA, on the other hand, uses a Directed Acyclic Graph (DAG) [57] as its data structure, called the Tangle, as shown in Fig. 4. Every node or participant in the Tangle must randomly select and validate two transactions submitted by other members on the network before making their transaction. This removes the need for mining, as the transactions in IOTA are validated and approved by the participants themselves who wish to publish transactions. This is a major difference between IOTA and other blockchain types. In the blockchain, there are special nodes called miners who validate new blocks that contain a set of transactions. This process of validation is done by solving a complicated mathematical puzzle, called Proof-of-Work (PoW), that is undertaken with the miners. The PoW is a computationally intensive task where a miner must perform many computations which consume an extremely high amount of electricity. Miners solve the PoW to calculate a value called Nonce, which is present in the block header of the blockchain. The block header contains the following values: timestamp, block version, the hash of the previous block, Nonce, and Merkle Root. The role of the miner is to hash these contents of the block header by using a hashing algorithm, which is SHA-256 in the case of the Bitcoin blockchain. The output of this hashing algorithm must be either less than or equal to the target hash value set by the network. The miner continuously calculates the hash by incrementing the Nonce value by one,

Fig. 4 DAG structure of the Tangle

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on every iteration. Once the output hash value is less than or equal to the target value, mining is completed, and the Nonce that was used in the last iteration is the desired value that the miners solve for. Since the Tangle is a DAG, it can process micro-transactions or small transactions much faster than blockchain. Furthermore, since there is no mining in IOTA, the transactions do not need to wait or form into blocks like blockchain. Table 1 shows the comparison of IOTA with other distributed ledgers. The IOTA distributed ledger was designed for low-power and low-memory IoT devices. Since the architecture of DAG provides high scalability [58], it allows for seamless integration with IoT devices. The IOTA distributed ledger can manage high-frequency transactions which are required for IoT devices as they generate and collect data continuously. On the other hand, blockchain technology cannot provide a scalable solution for IoT since it involves mining. Due to mining, the scalability in the blockchain is drastically reduced as the process of solving the PoW can take a considerable amount of time and energy. This is not suitable for resource-constrained IoT devices as it will cause undesirable delays. For example, the time taken to append a block in the Bitcoin blockchain is approximately 10 min, which is not feasible for IoT devices. Moreover, the IOTA ledger does not have any transaction fees in contrast to blockchain, which provides an economical and easier solution for device-to-device communication in the ledger. IOTA provides a new type of transaction called data transaction, where the data can be user-defined or IoT data. By making these data transactions, data can be efficiently stored in the IOTA ledger without any charge. IOTA provides two data communication protocols called Masked Authenticated Messaging (MAM) [59] and IOTA streams. Both protocols have been designed to integrate with various IoT devices, allowing them to publish and store sensor data at a high frequency without any delays and with the added benefit of encryption. A data transaction with MAM and IoT devices is performed in the following way: • A sensor collects data from its surroundings. • The sensor sends this data to a data aggregator such as a Raspberry Pi, which is an IOTA client. • The client then chooses a MAM channel mode that may be public, private, or restricted based on the level of privacy needed. • The client then creates a data transaction using the MAM protocol which IOTA provides. • The MAM protocol encrypts the data and then signs it with the Merkle Tree Signature Scheme. • The client then sends this data transaction to the IOTA network where the full IOTA nodes perform the PoW and validate two previous transactions. • Once this is done, the sent transaction gets confirmed and the content of the transaction is stored in the distributed database of the IOTA network.

Permissioned network

1000 TPS

Vary

Distributed ledger Type

Scalability

Ease of integration

Easy

50–10,000 TPS

Public

Fast

Frequently

N/A

Quarterly

Update frequency

Yes

VeThor token

Varies

Yes

Smart contract availability

Block confirmation time

None

Cryptocurrency

VeChain

Transaction time Varies

Hyperledger Fabric

Feature

Table 1 Comparison of IOTA with other distributed ledgers

Easy

1500 TPS

Permissioned network

N/A

Fast

Quarterly

No

XRP

Ripple

Vary

N/A

Permissioned network

N/A

Fast

Minimal

Yes

None

R3Corda

Difficult

15 TPS

Public with private fork

12 blocks

Moderate

Frequently

Yes

Ether

Ethereum

Easy

1000 TPS

Public/private

N/A

Very fast

High

Yes

IOTA

IOTA

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5 Challenges to Implement Blockchain Technology in Data Science and IoT Blockchain technology has many benefits, and integrating it with other technologies such as IoT would be beneficial as well. However, today blockchain still cannot be seamlessly integrated with IoT, since many issues and challenges restrict it from meeting the current demands of the IoT system. These challenges can be summarized as follows: • The balance of security, power consumption, and performance The progress and development of technical applications on low-power and lowmemory devices such as IoT have been hindered by blockchain. This is due to the high computational power required to run blockchain consensus algorithms. The energy consumption of Bitcoin is comparable to the energy consumption of Ireland [60], which makes the integration with IoT devices difficult. It was informed in [61] that the total energy consumption of the whole Bitcoin network is more than the consumed energy of Colombia and Austria. The estimated power consumption of Bitcoin was more than 30 TWh in one year. This value exceeds the power consumption of many other European countries. Additionally, many researchers have disputed the performance of blockchain to store and manage IoT data. They have proposed and advised different techniques to modify the blockchain consensus which would lead to an increase in the throughput of transactions. For example, by removing PoW from the blockchain technology, the scalability can be improved while reducing energy consumption. However, the PoW consensus algorithms also prevent Sybil attacks [62] on the blockchain and make the blocks immutable. Therefore, the aim is to optimize blockchain operations to balance performance efficiency and security. • Throughput and concurrency issues IoT devices continuously generate and collect streams of data in different IoT systems, resulting in high concurrency. Blockchain technology contains various consensus algorithms and complicated cryptographic mechanisms that limit the throughput of transactions [63]. The blocks in the blockchain are cryptographically linked to each other through hashes. Every block contains the hash of the previous block. These blocks are hashed-linked since hashing is a one-way function. If something were to be changed in the blockchain, even if it is a minor change, then the hash produced would be completely different. Therefore, the network disapproves modifications in the blockchain ensuring immutability. The linking of blocks in a chain-like structure and the synchronization of new blocks among the blockchain nodes require a high amount of bandwidth, which when available can enhance the throughput. Hence, the main challenge is to enhance the scalability of blockchain to be compatible with the high-frequency transactions of various IoT devices. • IoT connectivity challenges To exchange IoT data with prospective stakeholders, IoT devices are anticipated to be connected to high computational storage and networking resources. IoT devices

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have limited resources that make them difficult to connect to blockchain technology [64]. At present, people rely on centralized client/server architecture to authenticate and provide connections to different nodes in the IoT network. • Managing big data on the blockchain Every node or participant in the blockchain network contains the same state of the entire distributed ledger. Once a block in the blockchain is confirmed, it is broadcasted through the entire peer-to-peer network so that each node appends that block to its local ledger. Although the blockchain provides decentralization and data immutability and removes the requirement for a third party, managing IoT data on the blockchain consumes a high amount of storage for every node [65]. This is due to the size of the blockchain that is growing as new blocks and transactions are being added. The nodes must have enough storage to store the state of the ledger. Hence, the main challenge is to manage the huge amount of IoT data in the blockchain and increase the data storage size for the nodes. • Preserving privacy and transparency The main benefit of using blockchain technology is its transparency of transactions and data that is on the blockchain, which can be beneficial for some application domains such as finance. However, certain fields such as electronic healthcare require confidentiality as it involves patients’ confidential data. Therefore, if blockchain technology is used for e-Health, then the privacy of the healthcare data may be compromised [66]. Many researchers have suggested the use of different access control mechanisms for IoT [67], which may provide a certain level of privacy. However, maintaining a balance between privacy and transparency in the blockchain is still an open issue. • Regulatory issues of blockchain in IoT Blockchain technology, as mentioned previously, has many features or characteristics such as anonymity, decentralization, automation, and immutability, which are proven to enhance the security of various IoT applications. However, when these properties are combined, they face different regulatory challenges. Blockchain offers data immutability, which means that the data on the blockchain is stored in the form of transactional blocks that cannot be modified or deleted. Furthermore, records cannot be inspected for privacy concerns before being published on the blockchain due to a lack of governance [68]. Governance in the blockchain means providing a set of rules for managing and implementing any type of changes in the blockchain cryptocurrency. When self-executing programs such as smart contracts are run on the blockchain, there might be certain outcomes that could violate certain laws. Many benefits come with the automation feature of blockchain; however, there can be some malicious errors in the program that may prevent the intended output of the smart contract. The existing rules and regulations of IoT are becoming outdated as the potential game-changer technologies, such as blockchain, are advancing.

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• Challenge of scalability for data science Data science [69] requires a huge amount of data to be analyzed and processed, and hence, the high scalability and transactional throughput must be supported by the data storage system. The potential of a system, network, or process to extend its performance by managing escalating demands is known as scalability. Blockchain technology offers numerous benefits that many other technologies lack; however, the main challenge that still needs to be solved is the issue of scalability, which hinders real-time trading [70]. As the blockchain ledger grows, the latencies associated with distributing data to all the nodes will increase. This impacts new participants or nodes as well as those that have been offline for a prolonged period and have not been updated. The scalability challenge of a large-scale decentralized system cannot be solved at the algorithm level. Many techniques have been implemented apart from the blockchain that contradicts the notion of decentralization in the blockchain. Consequently, many blockchain-based implementations are adopting a more scalable approach called sharding. Sharding is a mechanism for partitioning databases into smaller chunks which are called shards. The approaches mentioned above can improve scalability but may bring some security-related problems as well.

6 Integration of Blockchain with Data Science and IoT Blockchain technology and data science have been integrated with various IoT systems which is beneficial in different application domains. Smart manufacturing and supply chains are the major IoT applications where blockchain has been integrated and widely used. The integration of blockchain with data science and IoT can be done in an IIoT system. Consider the case of a thermal power plant as the IIoT system that is equipped with many sensors. The integration of these three powerful technologies is achieved in the construction of a data pipeline consisting of these three applications. These applications include sensor identity management, sensor anomaly detection using AI, and secure storage of sensor data using blockchain. This pipeline provides a way to integrate blockchain with IoT and data science, leading to high security and resiliency. Additionally, another application consisting of smart lamps is explained, which converges blockchain with machine learning and IoT to reduce downtime. Some of the existing applications and schemes are described below where Table 2 shows the comparison of the existing schemes: Mohanta et al. [71] identified the various security and privacy challenges that exist within each IoT layer in the heterogeneous IoT devices and networks by conducting a comprehensive review. To tackle these challenges and provide more security to the IoT systems, the authors proposed a method to integrate the IoT devices with blockchain technology. A case study was presented using the public Ethereum blockchain and various sensors acting as IoT devices. This case study

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Table 2 Comparison of existing schemes where blockchain, IoT, and data science were integrated References

Advantage

[66]

Provides a good case Performance metrics of the Integration of smart study using the Ethereum experiment are missing contracts, Ethereum blockchain blockchain, and IoT devices solved many data and device security issues such as identity management, data communication, data privacy, and integrity

Disadvantage

[67]

Provides a layer-based model for smart transportation and shows the exact layer where blockchain can be implemented

[68]

Uses a DAG-based Managing the blockchain to enhance the cryptographic keys performance of becomes an issue transactions

The experimental results of the proposed L2Sec protocol prove that it is compatible and performs well with constrained IoT devices to enhance security

[69]

The Hyperledger Fabric platform was combined with IoT and machine learning to achieve smart manufacturing

The security measures required for IoT devices were lacking

Integrated machine learning with blockchain to enhance the existing smart manufacturing process. Based on the experimental results, XGBoost gave the highest accuracy

[70]

The application of smart contracts made the processing of trading and assigning ownership intelligent

Scalability is a problem as the Hyperledger Fabric platform was not scalable enough for the IoT devices

Blockchain and smart contracts when integrated with IoT provide a solution to many lacking mechanisms such as lack of authorship, durability, and immutability of data

[71]

The system integrated Only the application blockchain, machine domain of farming and learning, and IoT to make agriculture was considered traditional agriculture smart

Only the domain of transportation was discussed and no other applications

Key finding

The proposed framework was applied to two real use cases to demonstrate how the integration of blockchain and IoT can enhance the process of transportation and logistics

The simulation results proved that blockchain can track the whole food lifespan, avoid food wastage, and detect and eliminate the causes of foodborne diseases (continued)

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Table 2 (continued) References

Advantage

Disadvantage

Key finding

[72]

The data management system integrated blockchain, IoT, and data science to analyze healthcare data. Also, scalability was improved by eliminating PoW

Although scalability was enhanced by eliminating PoW, however, the system became vulnerable. Malicious nodes can perform Sybil attacks and Denial-of-Service (DoS) attacks

The cryptographic schemes combined with blockchain and IoT ensured the security and privacy of medical IoT data

presented the integration of sensors with blockchain technology where the sensors were authenticated, and the sensor data was encrypted and digitally signed. Humayun et al. [72] proposed a framework that integrates blockchain technology and IoT devices to achieve smart transportation and logistics. This research focused on the field of transportation and logistics and how the integration with blockchain technology enhances various operations in this field. To showcase this, the authors proposed a layer-based framework called Blockchain for Smart Transportation and Logistic Framework (BCTLF) that integrates blockchain and IoT. Finally, the authors presented two real-world case studies where blockchain and IoT were integrated to enhance transportation and logistics operations. Carreli et al. [73] proposed a cryptographic protocol called L2Sec that communicates with the IOTA tangle to exchange and secure IoT data. L2 means that the proposed protocol runs on the second layer of IOTA which is the communication layer. Their proposed protocol was highly scalable and compatible with the IoT devices that were limited in power and memory. The authors set up an experimental testbed to evaluate their proposed protocol. The experiment evaluated the latency, memory consumption, and power consumption of the proposed L2Sec protocol. Shahbazi et al. [74] claim that there exist many management and data security issues in the field of manufacturing and production. To overcome these issues, the authors proposed the integration of blockchain and machine learning to secure the transactions on the proposed system and monitor the quality of the manufacturing process. The authors employed machine learning to achieve smart manufacturing that could detect the anomalies or faults in the sensor data collected from the manufacturing plant and perform quality control. The proposed system was implemented by using a private Hyperledger fabric blockchain, and different big data techniques were applied to manage and pre-process the data before applying machine learning algorithms. Teslya et al. [75] state that there exist some issues among various components of a production plant where trust is required for communication. To overcome these issues and establish trust among the IoT devices, the authors propose a method in which they integrate blockchain and IoT. To implement the proposed model, blockchain was integrated with a smart M3 data-sharing platform. Smart-M3 is an information interoperability approach for devices to easily share and access local

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semantic information. The proposed system used smart contracts to store and process data collected by the IoT devices among the different components. Awan et al. [76] proposed a model to enhance conventional agriculture to intelligent farming by integrating blockchain with IoT and data science. Their proposed model provided the ability to track the entire lifespan of the product, which enhanced the delivery process and customer satisfaction. This work aimed to ensure that the quality of the product is maintained at every stage of its cycle. Blockchain technology provided a history of the distinct stages of the product for monitoring purposes. The authors also applied machine learning techniques to predict crop growth and its health. Dwivedi et al. [77] proposed a data management system that integrates blockchain technology with IoT and data science to analyze healthcare data. The authors also used advanced cryptography to attain data privacy. Moreover, to make the blockchain adaptable to low-power IoT devices, the authors eliminated the PoW consensus algorithm. Additionally, to achieve anonymity, the authors proposed a lightweight ring signature scheme that allowed for anonymous transactions.

7 Applications of Blockchain with Data Science and IoT for Industry 4.0 The fourth industrial revolution or Industry 4.0 is transforming the way businesses create, improve, and distribute their goods. Engineers and manufacturers are incorporating emerging technologies into their production facilities such as blockchain, IoT, and data science. These technologies work with advanced software and sensors that are collecting data at a high frequency. This facilitates the making of intelligent decisions that could improve the manufacturing and the distribution process. Blockchain technology is being integrated with IoT and data science to create real-life applications for many industries. Specifically, four major applications where blockchain is applied to IoT and IIoT have been described. For the first three applications, a corresponding use case has been developed and implemented. The four applications include IoT identity management, sensor data anomaly detection, security of IIoT data using IOTA tangle, and smart lamps.

7.1 Device Identity Management Various research studies have been conducted that aim to uniquely identify sensors and IoT devices using hardware, software, and blockchain-based approaches. Most of these studies use network packets and Radio Frequency (RF) fingerprints at the hardware level to uniquely identify IoT devices. On the other hand, the softwarebased approaches usually employ machine learning and deep learning techniques to

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classify IoT devices. Furthermore, many software-based solutions include blockchain technology and cryptography to uniquely perform IoT identity management. Table 3 shows the comparison of the existing schemes for identity management. Kostas et al. [78] proposed a machine learning framework called “IoTDevID” which could identify unique IoT devices from the features that were contained within the network packets. Their approach was realistic and can be generalized with other software-based methods for identity management due to the rigorous feature selection and analysis they performed. They utilized openly available datasets, and their framework achieved excellent results and a high prediction accuracy rate. Their framework uniquely identified various IoT devices using general features and the behavior of devices at the individual packet level. This approach differs from most of the existing literature as the “IoTDevID” could uniquely identify the devices employing low-energy protocols such as ZigBee, Z-Wave, and non-IP-based techniques. Abbas et al. [79] proposed a technique that can identify IoT devices using their RF as a fingerprint. The authors applied machine learning classification algorithms to identify devices based on the device’s fingerprint. This fingerprint was created through the inherent RF emissions which are transmitted from these devices. The proposed technique was evaluated in which a huge 4G-LTE testbed was used that consisted of various IoT devices, and the RF fingerprint technique was employed to identify these devices. Chen et al. [80] presented a system of IoT identity management by combining Software-Defined Radio (SDR) and transfer learning. To identify IoT devices, the authors used the “Universal Software Radio Peripheral (USRP),” which is a range of radios that are defined by software. These radios were used to collect the RF signals and the signals were identified by employing transfer learning. Transfer learning is a machine learning research subject that focuses on storing and transferring information learned while addressing one problem to a different but related problem. An experiment was performed to evaluate the proposed system and produced highquality results, as transfer learning reduced the training time and produced high accuracy. Zhou et al. [81] presented three issues that arise with IoT identity management solutions that use RF fingerprinting. These three issues consist of fading interference, performance unreliability of long-term experiments that may produce inaccurate data, and verification of unauthorized devices. To tackle these three issues, the authors proposed three solutions using various algorithms. The authors proposed an algorithm called artificial noise adding (ANA) to overcome the first issue. The ANA algorithm used channel adaptation and regularization, enhancing recognition robustness. To tackle the second issue, the authors proposed an algorithm called Long-term Stacking of Repetitive Symbols (LSRs), which reduced the computed signal variance, improved identification accuracy, and provided stability. Finally, to solve the third issue and manage unauthorized devices, the authors proposed another algorithm which was a generative model of Gaussian Probabilistic Linear Discriminant Analysis (GPLDA). Bouras et al. [82] presented a blockchain-based system and related protocols for IoT identity management. The authors proposed a blockchain-based IoT identity

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Table 3 Comparison of existing schemes for IoT identity management References Advantage

Disadvantage

Key finding

[73]

Integrates machine learning with IoT to identify IoT devices using network packets and public datasets

Public datasets were used, and the approach was completely dependent on the datasets

Based on the performance evaluation of machine learning algorithms, Decision Tree performed the best for IoT identity management

[74]

Classification machine learning algorithms were used to identify devices and produce excellent results

Classification algorithms require sufficient datasets that must be labeled, and this becomes a major challenge

The proposed threshold-based detection algorithm could identify the IoT devices in a 4G-LTE network with high accuracy of 95.6%

[75]

The integration of transfer learning and the RF signals produced excellent results and identified various devices with high accuracy

The device identification based on the RF signals will become a major challenge if there exists an enormous number of IoT devices and the signals of these devices will contain noise too

The proposed RF signal identification along with transfer learning required less labeled data and the detection accuracy was more than 95%

[76]

Three issues in RF-based The complex multipath identity management fading channels were less were identified and three algorithms were proposed to solve these three issues

The proposed RF-based method produced an accuracy of 99.5% for node identification. The accuracy for the multipath fading experiments was 95.52%

[77]

The identity management The throughput of the framework integrated framework was low consortium blockchain and IoT devices to produce a lightweight approach for identifying IoT devices

The proposed blockchain-based scheme was evaluated based on its performance metrics such as latency. The results show that the proposed scheme is scalable for IoT devices and suitable for business adoption

[78]

The application of blockchain and smart contracts with IoT provided an efficient way to uniquely identify various IoT devices and track their ownership

The blockchain-based IoT identity management scheme proposed a concept called Bring Your Own Device Identity (BYODID) to allow users with a single unique identity to transfer from one enterprise to the other

Management of cryptographic keys becomes an issue with a vast number of IoT devices and the approach was only semi-decentralized

(continued)

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Table 3 (continued) References Advantage

Disadvantage

Key finding

[79]

The identity management scheme relies on a single shared key for confidentiality and key exchange is a major issue

The proposed blockchain-based identity management method meets the security requirements of healthcare data and has low latency in terms of performance

A private blockchain was integrated with IoT to efficiently identify medical IoT devices in the healthcare domain

management system that uses a consortium blockchain. The proposed system is lightweight, addressing various challenges such as throughput, security, and privacy that may arise with a centralized IoT system. Furthermore, the authors implemented a prototype of their proposed model and evaluated their approach in calculating various performance metrics, for instance, payload size, transactional throughput, and latency. Omar et al. [83] proposed a blockchain-based identity management framework that was semi-decentralized. This proposed framework created identities, transferred ownership, and provided identity portability among various IoT devices in the network. To validate the proposed approach, the authors used smart contracts to implement their proposed model. The major assumption on which their proposed approach is based is that the IoT devices will have a unique ID associated at the manufacturing stage providing for identification. Xiang et al. [84] proposed a user authentication scheme for IoT identity management, which is based on blockchain. The authors applied a private blockchain for their proposed approach to uniquely identify IoT devices in the healthcare domain. The proposed system fulfilled the necessary security requirements that must be ensured while managing healthcare data. The proposed system was evaluated by measuring the security standards and network latency. Employing blockchain for the tasks of authenticating users and management of keys provides the necessary features such as traceability, anonymity, and non-repudiation. Therefore, the use of blockchain in the proposed approach and the mentioned features solved the security and privacy problems related to healthcare data.

7.1.1

MAC Address-Based Identity Management (MAIM)

The MAIM system design mimics the design of an IIoT system, which involves a network of various IoT devices and sensors that interact with production systems to improve industrial operations. Productions today deploy a massive number of sensors to meet their operational requirements and solve real-life problems. The telemetry data collected from these sensors is then evaluated to detect anomalies and patterns by using certain analytic algorithms. This allows businesses to enhance and better understand their processes.

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Sensor data is delivered through wireless radio waves in areas where Wi-Fi solutions are deployed, and it is retrieved by one or multiple central ground stations. The protocol which is used to send this data is called Message Queuing Telemetry Transport (MQTT), and the data which is exchanged is in the form of tiny single packets that become massive when aggregated. This aggregated data is then analyzed and visualized using relevant tools. MQTT is a standard IoT messaging protocol that runs on top of the TCP/IP network stack and is designed for certain IoT networks that may be unreliable and with high latency and low bandwidth. The capabilities of MQTT make it a viable choice for delivering copious amounts of sensor data to analytics tools. The clients of MQTT are divided into two types which contrast with the conventional model of client–server, where a client directly interacts with an endpoint. There are two types of clients in MQTT: (1) the “publisher” who sends the data, and (2) the “subscriber” who receives the data. There is no direct communication between the “publisher” and the “subscriber” as they have no prior knowledge of each other. There is also a third entity in the MQTT protocol known as the MQTT broker, which functions as a validator. This broker routes data messages from the publisher to entities that are serving as subscribers. To implement the MQTT protocol, an MQTT broker must be installed on a computer to create a central base server. The application uses Mosquitto MQTT Broker as the broker since “Mosquitto” is a lightweight open-source message broker that executes various MQTT versions. The Mosquitto broker can be installed on a Raspberry Pi 4 with a Linux-based operating system called Raspbian. To set up and implement the broker, a client sensor is designed to publish raw sensor data to the broker. The client sensor is designed by using an ESP8266 board and one of the available sensors (temperature, gas, pressure, or vibration) is connected to the ESP8266 board. The ESP8266 board is upgraded with firmware to connect to the Wi-Fi and the MQTT broker. Once achieved, the collected data from the sensor is published to a specific topic as required. After the data is published on the specific topic, the broker (in this case the Raspberry Pi) as shown in Fig. 5 can subscribe to the topic and send the real-time data to the database by running a Python script on the Raspberry Pi. Once the broker has been successfully tested, the next step is to encrypt the data. This step is crucial because by default the MQTT protocol sends/receives data unencrypted. Therefore, this means if someone were to sniff the traffic in the network, they would see all the raw critical data being sent from the client to the broker. To solve this issue, an SSL (secure socket layer) encryption is implemented to encrypt all the data that is published/subscribed to the MQTT broker. To implement SSL, two different certificates (one for the client and one for the server) are created and signed using private keys. Once the certificates are created, then the MQTT broker is reconfigured to only accept data matching the certificates.

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Fig. 5 Data flow and MQTT components

7.2 Anomaly Detection of IIoT Data Using AI There has been significant research on detecting anomalies in sensor data either in real time or on aggregated data. Specifically, the focus is on anomaly detection of sensor data that is collected or managed in an IIoT system. The most appropriate and popular approach for detecting anomalies in any kind of data is using AI. In most of the approaches, researchers have used various machine learning and deep learning algorithms to detect anomalies in IIoT data where a massive number of sensors are deployed. Table 4 shows the comparison of existing schemes related to anomaly detection. Hill et al. [85] developed a method that detects anomalies on streams of environmental sensor data in real time. Their approach identified and detected patterns that were deviated when considering historical data. The proposed data-driven model was built using four machine learning and deep learning models namely Naïve Predictor, Nearest Cluster (NC) predictor, Single-layer Linear (LN) Predictor, and Multi-layer Perceptron (MLP). The model was autoregressive, scalable, and did not require any anomaly label. Furthermore, the authors presented a case study in which they used several data-driven approaches to detect anomalies in wind speed data collected from the Corpus Christi, Texas testbed. Han et al. [86] developed a method for anomaly detection which was based on multidimensional data and was driven by sixth-generation (6G) networks. The 6G cellular technology is the successor to the 5G technology. 6G networks will operate at higher frequencies than 5G networks, resulting in significantly increased capacity and decreased latency. The proposed model could characterize spatiotemporal correlations in heterogeneous datasets. To eliminate the effects of noise in the data and to aid anomaly detection, the authors trained an autoregressive exogenous model. Lastly, to enable a huge 6G IoT system and the identification of sensory devices, the proposed algorithm calculated a metric called Cumulative Coefficient of Value (CCoV). Hayes et al. [87] proposed a framework that could detect contextual anomalies and detect anomalies in two stages, i.e., content detection and context detection. The proposed framework was employed to detect real-time anomalies in sensor data streams using clustering. Their research minimized false positives and detected real-time anomalies due to the first step, i.e., content detection. Identification of anomalies that could be both content and context was possible due to the context detection algorithm. The authors evaluated their research using two real-world sensor datasets.

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Table 4 Comparison of existing schemes where anomaly detection was applied in IIoT systems References Advantage

Disadvantage

Key finding

[80]

The anomaly detection Only four anomaly algorithms were quickly detection algorithms were able to detect anomalies in applied environmental sensor data and a real-life application was demonstrated

The performance of the Linear Network (LN) and Multi-layer Perceptron-based detectors for error detection in test data was significantly enhanced by using the Anomaly Detection and Mitigation (ADAM) strategy. The MLP-ADAM detector performed best by using a 95% prediction interval

[81]

Training of an autoregressive exogenous model was done which eliminated the noise in the data

The total number of IoT devices was too low in the simulation experiment to relate to an actual IIoT system

The experimental results show that the proposed algorithm performs better than the conventional methods such as High Reliable Data Verification (HRDV) and Recursive PCA

[82]

Both types of anomalies, i.e., context and content anomalies were detected in sensor data using clustering

Only clustering algorithms were applied to detect anomalies

The proposed anomaly detection framework performed well and could timely detect anomalies when evaluated with real-world datasets

[83]

Anomalies were efficiently detected by using a combination of unsupervised deep learning algorithms and federated learning

The cloud aggregator in the proposed system could become a single point of failure

The experimental results prove that the proposed anomaly detection framework could timely detect anomalies and reduce the communication overhead by 50% when compared to the federated learning method

Liu et al. [88] proposed a framework for detecting anomalies in time series IIoT data, which was based on federated learning. The proposed framework enabled the decentralized edge devices to train the model for detecting anomalies, which enhanced the model’s ability to generalize. The authors proposed a model based on the combination of convolutional neural networks (CNN) and long short-term memory networks (LSTM) for achieving high accuracy in anomaly detection. This model prevented memory loss and the problem of vanishing gradient due to the units of CNN that captured crucial features. Finally, the authors proposed a mechanism

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for gradient compression which enhanced communication efficiency and was based on Top-k selection.

7.2.1

Anomaly Detection in Thermal Power Plant

Anomaly detection has been extensively performed on IIoT systems such as thermal power plants. Such power plants consist of numerous sensors that are attached to various components of the power plant. It is crucial to monitor these sensors and the data generated for predictive maintenance or fault detection. Anomaly detection can assist with fault detection or anomalies in these huge data-driven plants that supply power to a city, state, or an entire country. To implement the proposed approach, it is divided into two parts: (1) Data generation and (2) Model Prediction. • Data generation For the data generation, the main focus is specifically on a temperature sensor that is working inside the boiler of a thermal power plant. However, the methodology can be extended to other components as well. A Python script is developed that artificially generates temperature sensor data simulating an actual physical sensor inside the boiler. The script generates synthesized sensor data in the range of 540 °C to 570 °C. This generated data is labeled as “normal” as it was within the working range of the sensor. This entire “normal” data is stored in a CSV file for training purposes. The next task is to generate abnormal or anomalous data that must be outside the working range of the boiler. To achieve this, a Python script is developed that generated data outside the specified range using an offset value. This data is labeled as “malicious” as it is outside the range. Next, another CSV file is created where the “malicious” and “normal” data both are stored for testing purposes. Figure 6 shows the distribution of malicious data along with their respective timestamp. All the points with a red cross are anomalies and can be seen outside the sensor working range. • Model Building and Prediction The next main task is to apply machine learning algorithms for anomaly detection in artificially generated data. Before the algorithms can be implemented, preprocessing and cleaning the data are required for the machine learning algorithms which are

Fig. 6 Position of outliers

Integration of Data Science and IoT with Blockchain for Industry 4.0 Table 5 The three applied Algorithms and their accuracy

Algorithm

Accuracy (%)

One-Class SVM

98.75

Isolation forest

98.75

Elliptic envelope

93.37

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done in Python. The data is manipulated, and unnecessary columns are removed from the CSV files that are not required for machine learning. Then, three unsupervised anomaly detection algorithms are applied that are widely used in industries for anomaly detection. These three algorithms are One-Class SVM, Isolation Forest, and Elliptic Envelope. These algorithms are unsupervised, which means the predicted outcome is not known by the machine learning model. These algorithms are trained with only the “normal” dataset, and for testing or prediction purposes, the test data that contains both “normal” and “malicious” data is used. All three algorithms produced excellent results for detecting anomalies in sensor data, and among the three of them, One-Class SVM and Isolation Forest gave the highest accuracy. The accuracy score and the corresponding algorithm are shown in Table 8. Accuracy indicates how well the model performed in prediction and indicates the number of correctly predicted data points out of the total data points. Table 5 shows that if there are, for example, 100 total data points, then 98.75 out of 100 are correct in the case of One-class SVM and Isolation Forest. Our approach for anomaly detection produces excellent results and can be applied to other sensors and components of a fossil fuel power plant.

7.3 Security of IIoT Data Using IOTA Tangle Many research studies focus on the use of blockchain as a means of distributed storage for storing sensor data or IIoT data. Everything sent and stored in the blockchain is done through transactions, whether it is data or cryptocurrency transactions. However, many challenges must be addressed when employing blockchain technology. The majority of the challenges include throughput, scalability, mining, and transaction fees. These challenges hinder the integration with IIoT as the devices in the IIoT system are scalable and cannot sustain delays. Therefore, an application that uses a DAG-based blockchain called IOTA Tangle to secure sensor data is demonstrated. IOTA overcomes the performance limitations of blockchain without compromising its security standards and meets the current demands of an IIoT system. Table 6 shows the comparison of existing schemes where blockchain and IoT are integrated. Ayoade et al. [89] propose a system for managing IoT data that is decentralized and contains a mechanism for access control which was performed using smart contracts. The proposed system uses blockchain only to store the audit trail and hash of the IoT data. The raw IoT data was stored in an Intel SGX platform, which enabled a trusted execution environment. The use of blockchain and smart contracts provided

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Table 6 Comparison of existing schemes where blockchain was integrated with IoT to provide data security References Advantage

Disadvantage

Key finding

[84]

Integrates blockchain and IoT to provide an efficient data storage system where the raw data gets stored in trusted storage and its checksum is stored on the blockchain

The trusted environment used, i.e., for IoT data storage is vulnerable to Load Value Injection attack

The experimental evaluation of the proposed smart contract blockchain model shows that when the IoT data is hashed, the efficiency of gas consumption and transaction throughput is significantly enhanced as compared to data with no hashing

[85]

Integrates blockchain and IoT and uses the combined model to demonstrate a use case in a smart grid system

The performance evaluation lacks the metrics used to measure throughput such as transactions per second

The proposed decentralized model was applied and evaluated in a smart grid. The proposed method had low-energy management costs and transmission delays as compared to the traditional models

[86]

Proposed a system in which public blockchain stores the hash of the data collected by drones

The raw data collected by drones is stored on a cloud server which is vulnerable to cyber-attacks and modification

The proposed system called DroneChain was experimentally evaluated, and results show that the scalability improved with the increasing number of drones and data sizes

[87]

IoT sensors were integrated with IOTA and the MAM protocol which provided real-time data transfer from sensors to the blockchain without any scalability issues

The IOTA’s MAM protocol does not provide a solution to the cryptographic key exchange problem

The proposed method integrated the IOTA tangle and the XDK 110 multisensor. A web application was developed to visualize the sensor data and provide meaningful insights

the ability to enforce rules by multiple entities, ensuring security and privacy for confidential IoT data. Kumari et al. [90] point out some of the key issues in the distribution of data in an IIoT system and propose a system using blockchain technology called the decentralized model for IIoT (DMIIoT). Moreover, the authors analyzed their proposed distributed model and compared it with a centralized model by measuring various metrics such as the cost of managing energy, delays in transmission, and data load balance. Finally, a case study was presented in a smart grid system and the prototype was built using the Ethereum blockchain.

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Liang et al. [91] proposed a solution that uses blockchain technology and conventional cloud technology to ensure the security of the data, which is gathered and transferred by drones. The authors stored the raw data collected by drones in a cloud server and a digital receipt or hash of this data was stored on a public blockchain. This proposed architecture was evaluated to ensure the integrity, security, and resiliency of the data collected by drones. Silvano et al. [92] proposed a system by using the IOTA tangle and the Bosch XDK110 multisensory device in which the two components are integrated. The authors presented an application by using their proposed model. The Masked Authenticated Messaging (MAM) protocol was used to transfer and store data to the IOTA nodes from the XDK110 sensor. The application contained a dashboard that could visualize the data sent and fetched from the IOTA network. Finally, the authors presented a use case using an IOTA ledger to secure the data in an industry produced from a fine blanking machine.

7.3.1

System Architecture and Methodologies

A use case has been demonstrated by using the IOTA tangle to secure and store sensor data in a decentralized and distributed manner. The system ensures that the data stored on the IOTA nodes are immutable, private, and can be utilized for auditing purposes. The application is developed by implementing a private tangle network that contains three nodes connected in a peer-to-peer manner. For setting up these nodes, three Linux Ubuntu 18.04.1 servers are installed with Hornet node software on all three servers. Once the Hornet software is installed, these nodes are configured to make them interact in a peer-to-peer manner by creating a private tangle. To communicate with the private tangle, a client is required which must be an IoT device. To set up the client, a windows machine is used, and the “iota-client” Python library is installed. This library is the official Python library for IOTA and provides the required APIs and methods to query the IOTA nodes. The system architecture and the connection between various components are shown in Fig. 7. Two Python scripts called send_data.py and fetch_data.py to send and fetch data from the private tangle are developed. The send.py script loads the sensor data from the database that is collected from various sensors used which mimics a fossil fuel power plant. This data is prepared in the form of transactions to be sent to the private tangle. The send.py script sends the prepared data transactions to the private IOTA nodes for immutable storage. Once this is done, every node in the private tangle ensures that it is updated with the current state of the ledger. This means that every other node will have the same copy of the database as the node that is referred to by the client while sending transactions. This is due to the distributed and decentralized nature of blockchain and other distributed ledgers. To retrieve the data back from the private tangle, the fetch_data.py script is developed. In IOTA, every transaction that is sent and stored in the tangle is referenced by a transaction Id. The script queries the private tangle with a transaction Id and the

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Fig. 7 IOTA system architecture

nodes, in response to the query, fetch the transaction and send it back to the client along with the transaction timestamp. Our architecture and methodology provide security against replay and doublespending attacks. In replay attacks, if multiple transactions are made or sent using the same address, then the sender’s information such as the data sent and the access to its wallet can be compromised. To prevent replay attacks, a new address is calculated every time a transaction is made in the send_data.py script. The approach has high scalability and low latency, which is an important requirement of IoT and IIoT systems. This high performance is due to the development of the private tangle that contains three private full IOTA nodes. Private tangle computes the PoW faster than the public or test IOTA network, thus providing high throughput. The implemented system can be easily integrated into an IIoT system since all the performance and security requirements of an IIoT system are satisfied. Moreover, the use of a private tangle for IoT industries is strongly encouraged, as it provides high scalability and provides privacy and confidentiality within an organization.

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Fig. 8 Smart lamp working

7.4 Smart Lamps Consider a case of smart lamps, which contain their own unique identities stored on the blockchain, also known as blockchain-based identities. These smart lights interact with the blockchain by using smart contracts which automate the communication of these lamps with the blockchain. Therefore, these lamps function as autonomous operating entities without any controller. The lamps can be triggered to switch on by making micropayments directly through smart contracts. The lamps will switch on once a user or a company makes the micropayment as shown in Fig. 8. Micropayments in blockchain are transactions that contain a small amount of cryptocurrency that can be worth a fraction of a cent. All the intelligent lamps interact with the blockchain and, therefore, will store their data inside the blockchain. This data contains useful information about the performance, usage, and downtime of the intelligent lamps. By using machine learning and AI, anomaly detection and predictive maintenance of these lamps can be performed. If some of the intelligent lamps are extensively used, then more frequent maintenance of these lamps can be suggested. Additionally, AI could alert a maintenance crew if a fault is detected, and an immediate response is required. AI and machine learning will significantly reduce the downtime of the network. These lamps can be made available to investors since they can be tokenized as assets. As a result, investors can install and maintain these smart lamps on a large scale where they could earn revenue from them.

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8 Conclusion This chapter discusses in detail various disruptive technologies such as IoT, blockchain, data science, machine learning, and AI. It discusses how blockchain technology can be integrated with IoT and data science to provide security, resiliency, and privacy to IIoT data. This chapter proposes three applications, namely IoT identity management, sensor data anomaly detection, and data security in an IIoT system, where the use case of a data-driven fossil fuel power plant is shown. An additional application of smart lamps is explained which integrates blockchain with AI and IoT. All four applications utilized and integrated the three most evolving technologies, i.e., blockchain, IoT, and data science. Blockchain technology contains scalability and latency issues that are not suitable for scalable IoT devices. To overcome these issues, the IOTA distributed ledger is used, which is a DAG-based blockchain designed for IIoT devices that provides a scalable solution to meet the current demands of IIoT and Industry 4.0. The main requirements of an IIoT system are high performance, high security, low latency, and ease of integration. The four implemented IoT applications satisfy these main requirements of an IIoT system. Firstly, the presented IoT identity management solution can efficiently identify each IoT device or sensor in a thermal power plant. Secondly, the implemented anomaly detection approach for the IIoT system performs well and produces excellent results by timely detecting anomalies in sensor data of a thermal power plant. The third application uses the IOTA distributed ledger to secure IIoT data. This solution provides an efficient way to manage and secure thermal power plant sensor data on the DAG-based ledger. Finally, the fourth application of smart lamps is shown, which combines blockchain with data science and IoT to reduce the downtime of the smart lamps. The main motivation behind the application of blockchain is to significantly enhance the security of IIoT by adding an additional layer that would prevent adversaries to gain access to or bypass the IIoT networks. Furthermore, by leveraging smart contracts, blockchain technology can improve IIoT security by automatically updating the firmware of various devices to solve different vulnerabilities. Additionally, authenticity and trust among users in an IIoT system can be enabled by using a blockchain where each transaction will be digitally signed. Private blockchains provide all the features of a public blockchain and the additional component of confidentiality and privacy. This is important when sensitive IIoT data is being managed or stored in the blockchain to prevent access from unauthorized users. Therefore, there is a need to integrate blockchain with IIoT and data science to overcome the shortcomings of an IIoT system.

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Innovations in Blockchain Using Artificial Intelligence Shipra Swati and Mukesh Kumar

Abstract This cutting-edge technological era is assisted by many innovative projects like the Internet of Things, Cloud Computing, Artificial Intelligence, Augmented reality, Metaverse, and many more. Together, they resulted in a digital revolution called as the fourth industrial revolution, symbolised as 4IR or Industry 4.0. To increase people’s control over their personal data, these technologies must adhere to privacy regulations. Before initialising the interaction, every system authenticates users by means of a private/public key pair, a hardware token, third-party authentication software, or a username and password combination. Thus, the services accessed by users own their digital identity and are no longer in the control of users. Traditional identity management solutions rely on centralised certification authority (CA) to handle public key management and confirm the association of individuals with their individual keys. Consequently, they raise a number of security issues including the CA being the system’s single point of failure. In addition, with the current increase in users and the dispersed systems they utilise, maintaining public keys by a centralised CA is getting expensive. Additionally, centralised identity management solutions are incompatible and raise privacy issues. Self-Sovereign Identity (SSI) systems aim to address these difficulties by offering decentralised identity ecosystems that permit the registration and interchange of identity attributes and the propagation of trust across participating entities. Blockchain technology improves SSI security by allowing control of credential storage and disclosure. They increase data integrity, privacy, and interoperability. The study analyses how a blockchain-based decentralised identity management system can use SSI to deliver high-level security and transparency in Industry 4.0 environments. The effectiveness of Artificial Intelligence (AI) has also been witnessed in securing Identity and Access Management (I&AM) procedures.

S. Swati · M. Kumar (B) Department of Computer Science and Engineering, NIT Patna, Patna, India e-mail: [email protected] S. Swati e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_7

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Keywords Blockchain · Identity management · I&AM · Self-sovereign identity · Artificial intelligence · Industry 4.0 · Metaverse

1 Introduction Industry 4.0 has been collecting data from equipment and environments for years. This data is kept in the cloud and accessed using Amazon’s AWS or Microsoft’s Azure. On the opposite side is 3D gaming, which involves augmented/virtual/mixed realities (AR/VR/MR), and CAD. Combining the two creates an immersive environment for accessing IoT data in new ways. When we add remote collaboration to this environment, several people can view the same data simultaneously and engage digitally irrespective of their physical status. However, the real-time positioning systems may track a person’s position in the virtual 3D world, with their avatars and other information. According to Oztemel and Gursev [1], Industry 4.0 is the shift from machinedominated to digital manufacturing as shown in Fig. 1. This shift will allow implantable technologies, wearable Internet, cooperating and coordinating machines, self-decision-making systems, autonomous problem solvers, and learning machines. AI, biotechnology, robotics, IIOT, and Metaverse are also examples of future fundamental advances of Industry 4.0. It is estimated that, in 2023, these technologies will converge and the industrial metaverse will emerge. Amazon is seen embracing Industry 4.0 and the industrial metaverse. An example step in this direction is its product: AWS IoT TwinMaker, which combines its IoT product and 3D rendering technologies. Since Industry 4.0 uses a wide range of cutting-edge technologies as its foundation, it runs the risk of inheriting the defects and weaknesses of those technologies and the systems they underpin. Emerging technologies have been subject to security events

Fig. 1 Industry 4.0 enabling technologies

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like the theft of virtual currencies, the compromise of wearable devices, and the misuse of artificial intelligence to generate fake news. So, despite these promising and exciting indicators of Industry 4.0, creates new opportunities for crimes on private big data [2] because the personal data involved can be more granular and unprecedentedly widespread to build a digital duplicate of the real world. The security and privacy concerns are the primary problems that hamper its ongoing development. Second, because of how different technologies interact with one another, the consequences of current risks can be exacerbated and become more severe in virtual worlds. It may lead to the generation of new threats that do not even exist in physical and cyber environments, such as virtual stalking and virtual espionage [2]. Secure and effective identity management is a key part of Industry 4.0 security since it sits between user/avatar interaction and the providing of critical services; Management of huge data streams, pervasive user profiling activities, unfair outputs of AI algorithms, and the safety of physical infrastructures and human bodies are just some examples of the types of security breaches and privacy intrusions that could happen in the Industry 4.0. From an identity management standpoint, digital identities can be divided into the following types: • Centralised identity. It is a digital identity administered by a single organisation, like Gmail. • Federated identity. Numerous institutions or federations manage Federated identity. It reduces administrative costs for cross-platform and cross-domain identity and authentication and eliminates the need for users to continually type personal information. • Self-sovereign identity (SSI). SSI is a user-controlled digital identification. It lets users to share and associate personal information (e.g., username, education, and career information) to enable identity interoperability with their consent. Centralised identity management solutions may be vulnerable to single point of failure (SPoF) concerns and leakage hazards in Industry 4.0. Federated identity systems are semi-centralised, and a small number of organisations or federations are in charge of managing IDs. These organisations or federations could potentially face centralisation problems. Future Industry 4.0 construction will be dominated by identification systems based on SSIs [3]. Self-sovereign identity (SSI) is a framework and digital movement that recognises the individual’s right to own and control their identity without government or centralised authorities. Each person is now their own sovereign nation. SSI refers to individuals or organisations having sole ownership over their digital identities and control over how their data is shared and used, including who can access, refer to, and exchange identity components. Certain components of identification are issued by authorities (passports and driver’s licences), but the individual must consent to revealing their identities and any relevant data. An exact definition of SSI does not exist, although its underlying principles have been recognised. They are recognised as Existence, Control, Access, Transparency, Persistence, Portability, Interoperability, Consent, Minimisation, and Protection.

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Christopher Allen [4] conceived these guiding principles as a benchmark for SSI. These principles may be regarded as defining legal rules and guidelines for what makes a system an SSI foundation for legal certainty. To be really in charge of our personally identifiable information (PII) or selfsovereign, we must possess the cryptographic keys to our digital identity data. Blockchain is a uses public key encryption technique to boost security, which is decentralised, transparent, and builds confidence in online services. The development of data quantities, processing capacity, and sophistication and efficiency of Machine Learning (ML) approaches have enabled the use of Artificial Intelligence (AI) in organisations, notably in identity access and management, i.e., I&AM solutions. The objective of this study includes examining the application of AI to improve Identity and Access Management to address Industry 4.0 security problems. Hence, Blockchain and AI help to implement and comply with the standards of SSI; both of these terminologies are majorly investigated in this presented article. This study prompts two important contributions as follows: • First, it highlights the obstacles an Identity Management System (IdM) in Industry 4.0 must overcome and how Blockchain-based IdM addresses these challenges. Based on the challenges, uPort, Sovrin, and Civic are compared. A tiered SSI architecture model with actors, objects, components, and processes is then provided with a use case. • Second, from an AI perspective, this article explores how the extracted difficulties have been addressed by surveying ML apps for enhancing I&AM’s five processes: identity, authentication, authorisation, auditing/monitoring, and accountability. Additionally, future research directions are outlined. The chapter is organised as follows. In Sect. 2, we present the necessary background related to the definition of Industry 4.0 and its related security challenges. We highlight the I&AM processes, architectures, and related definitions. The next section (Sect. 3) explains the important role of blockchains in enabling IdM to overcome most of these challenges. Section 4 investigates the role of SSI ecosystems to handle the above challenges. Section 5 is about the architecture and main components of a blockchain-based SSI system and then a conception of a layered architecture model of blockchain-based SSI is given in Sect. 7. In Sect. 8, we focus on key concepts of machine learning and its major applications. We provide a state of the art of ML and DL techniques used in I&AM field in Sect. 9, and we also propose a new taxonomy for DL and ML in the I&AM process in Sect. 10. We finally conclude, in Sect. 11, with some main challenges and future research directions.

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2 Identity Challenges in the Context of Industry 4.0 and the Metaverse Identity management is crucial for providing services to large numbers of users/avatars in Industry 4.0. Users’ and avatars’ identities can be fraudulently taken or impersonated, and authentication problems with interoperability can arise between virtual worlds. User privacy, including geographical privacy, habit, living styles, and so forth, may be violated when engaging in digital lives in the metaverse. It includes data services like data perception, transmission, processing, governance, and storage. As shown in Table 1, the most significant identity management difficulties posed by Industry 4.0 during this lifecycle are listed below. Industry 4.0 identity management schemes should follow standard design principles to meet these issues. It includes scalability to huge users/avatars, node damage resilience, and sub-industry interoperability for the sake of authorisation. It should let identity owners own and govern their data under the umbrella of the Identity Management System (IdM). IdMs must prove entities’ identities to assure trustworthiness. Interoperability between existing IdMs is an issue because no single IdM is sufficient in all scenarios. An IdM must also allow user identity mobility and provide privacy by ensuring the absence of correlation between personally identifiable information (PII) and assigned digital IDs. However, IdMs struggle to retrieve keys and credentials easily and safely. Blockchain and AI may be a crucial enabler to empower smart self-sovereign identity in Industry 4.0 for free or low cost is required.

3 I&AM Background: Definitions, Architecture, and Evolution This section begins with a brief introduction to the identification and its management. The term “identity and access management” (I&AM) describes the procedures, tools, and rules that are used to administer digital identities and restrict how those identities are used to get access to resources. The use of information or data fusion [18] to produce fraud-resistant I&AM is the technique of combining numerous qualities.

3.1 Definition of Identity Management System Digital identity is made up of five different steps: identifying, authenticating, authorising, auditing/monitoring, and accountability as shown in Fig. 2. 1. Identification means being able to find out who is using a system or what programme is running on that system [19]. It is the process of giving a user identifier (ID) to a person, another computer, or a part of a network [20].

If a user’s identity is taken, his/her avatars, digital possessions, social ties, and even digital existence in the metaverse might be disclosed. Hackers can obtain users’ personal information (e.g., full names, SSNs, digital asset secret keys, and banking details) through hacked devices, phishing email scams, and stolen company customer data to conduct fraud and crimes (e.g., steal the victim’s avatar and digital assets) in the metaverse

An attacker can use impersonation to gain access to a service or system [5]. Attackers can spoof trustworthy endpoints and unlawfully access services via Bluetooth impersonation [6]. Hackers can infiltrate helmets or wearable gadgets to mimic the victim and acquire service access

As Industry 4.0 assimilates reality, human, physical, and virtual worlds are seamlessly merged into the metaverse, causing identity linkability difficulties [2]. Malicious player A can track player B by the name above his/her avatar and infer his/her real-world whereabouts. Hackers can also track individuals using hijacked VR headsets or glasses

Identity theft

Impersonation attack

Identity linkability in ternary worlds

In Industry 4.0 systems, sensitive user data from XR devices (e.g., helmets) is exchanged over wired and wireless communications. Unauthorised individuals/services should not have access to this data. Although conversations are encrypted and information is delivered confidentially, adversaries can still get raw data by eavesdropping on the specific channel and track users’ positions via differential attacks [10] and sophisticated inference attacks [11]

Privacy leakage in data transmission

(continued)

Avatar creation needs extensive user profile [2], including facial expressions, eye/hand movements, voice and biometric traits, brain wave patterns [8, 9], and surroundings. The Oculus helmet’s motion sensors and four built-in cameras track head direction and movement, sketch rooms, and track positions and environment in real time with submillimeter accuracy. If these sensitive details are hacked, attackers can commit serious crimes

Pervasive data collection

Trusted and interoperable authentication For users/avatars, rapid, efficient, and reliable cross-platform and cross-domain identity authentication is crucial [7]

Discussion

Challenge

Table 1 Identity challenges in Industry 4.0

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Discussion

Aggregating and processing enormous data from human bodies and environments is vital for creating avatars and metaverses in Industry 4.0. It may leak critical user information [12] resulting in a violation of user privacy and related rules like the General Data Protection Regulation (GDPR). Adversaries can deduce users’ privacy (e.g., preferences) from metaverse processing results (e.g., synthetic avatars)

Private and sensitive information (e.g., user profiling) stored in cloud servers or edge devices may also cause privacy difficulties. Hackers can derive user privacy via differential attacks [10] and DDoS attacks [13]

Different service providers in sub-metaverses need real-time user/avatar profiling to give smooth customised services (e.g., customised avatar appearance) in the metaverse. Malicious service providers can illegally gain data access by buffer overrun and altering ACLs [14]

User/avatar-related data might be intentionally revealed by attackers or unintentionally by service providers to assist user profiling and precision marketing. As the behaviour pattern, preferences, habits, and activities of avatars might reflect the true statuses of their physical counterpart, attackers can collect the digital footprints of avatars to assist in accurate user profiling and even unlawful operations [15]. Industry 4.0 technologies, such as the metaverse or AR, offer a larger third-person perspective of their avatar’s surrounds than in the actual world [16], which may infringe on other players’ conduct privacy without understanding. An avatar may stalk/spy on you by following your avatar and recording your digital traces, such as purchase habits, to assist social engineering assaults

As XR devices capture increasingly sensitive data, such as users’ locations and surroundings, accountability in Industry 4.0 is vital to maintain privacy compliance. Under centralised service providing design, auditing Industry 4.0 service providers’ compliance with privacy requirements (e.g., GDPR) can be clunky and time-consuming. In the new digital ecosystem of Industry 4.0, it’s hard for them to maintain transparency of regulation compliance during data management’s lifecycle [17]

Challenge

Privacy leakage in data processing

Privacy leakage in cloud/edge storage

Unauthorised data access

Misuse of user/avatar data

Threats to accountability

Table 1 (continued)

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Fig. 2 I&AM processes

2. Authentication is the process of linking an ID to a specific entity [21]. Passwords, physical keys, smart cards, passports, static biometrics (fingerprint, face, retina, and iris recognition), and dynamic biometrics can all be used to verify a user (voice, handwriting, and typing recognition). 3. Once we know the user is who he says he is, the authorisation process takes over to figure out what the user can and cannot do based on who he is. So, authorisation is the process of giving people permission to do certain things with restricted information. Authorisation has many models, such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Usage Control Model (UCON), Capability-based access control (CapBAC), and Organisational-Based Access Control (OrBAC) [22]. 4. Auditing is the process of keeping track of events and checking them to see if anything unexpected or illegal has happened or if someone has tried to do something like that. Monitoring is the control of the system that goes on all the time [23]. 5. Accountability is then being able to hold any user or programme that accesses a system responsible for what it does. It has many requirements, such as not being able to be denied, discouraging people from doing something, isolating problems, finding and stopping intrusions, and recovering from them and taking legal action. This process is defined as “the security goal that makes it necessary to be able to track the actions of a single entity” [24].

3.2 I&AM Architectures and Evolution Since the introduction of the Internet and the rise of digital services, I&AM systems have progressed to accommodate these ever-evolving IT systems. The following subsections provide an overview of key features of the major I&AM architectures.

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Phase One: Centralised

Administrative control is given to a single authority through centralised identity (administrative control by a single hierarchy or authority, ex: certificate authority). These authorities had the authority to accept or reject the applicants’ claims of identity as shown in Fig. 3. The core unit held the reins of power in this scheme. The expansion of Internet resulted in proliferation of websites causing identity division for users. The result was that users had to manage several identities across various websites [25]. Initial digital identity issuers and authenticators on the Internet were centralised authorities. Internet Assigned Numbers Authority (IANA-1988) and Internet Corporation for Assigned Names and Numbers (ICANN-1998) were organisations that arbitrated domain names and IP address validity. Then, starting in 1995, certificate authorities (CAs) took action to assist Internet commerce sites in establishing their identity. Some of these organisations developed hierarchies as a modest step beyond centralisation. Other organisations could be appointed by a root controller to manage their own hierarchies. Although they were building new, weaker centralisations below them, the essential power remained at the root. Unfortunately, giving centralised authorities online the power to govern a person’s digital identity has many of the same drawbacks as giving state authorities control over people’s physical identities: users are tied to a single authority who can confirm a false identity or even deny their own. Power is inherently transferred from the users to the centralised entities through centralisation. Another issue emerged as the Internet expanded and power grew across hierarchies: identities were becoming more fragmented. They grew in number as websites did, forcing users to manage numerous identities across

Fig. 3 Centralised I&AM architecture

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numerous sites while maintaining little control over any of them. Identity is still mostly centralised, or at most hierarchical, on the Internet today. Digital identities can be rented out to users or cancelled at any moment. They are held by CAs, domain registrars, and specific websites. However, during the past 20 years, there has also been an increasing movement to give individuals back their identities so that they can truly have control over them. One of the earliest cues towards what might develop into self-sovereign identity was provided by PGP (1991). In order to build confidence for a digital identity, it invented the “Web of Trust,” which gave peers the ability to introduce and verify public keys. In the PGP model, a validator might be anyone. The outcome was a strong illustration of decentralised trust management, but because it concentrated on email addresses, it still relied on centralised hierarchies. PGP was never widely used for a number of reasons. Other early ideas were published in Carl Ellison’s 1996 paper “Establishing Identity without Certification Authority,” which looked at the development of digital identity. He considered peer-to-peer systems like PGP as well as authorities like certificate authorities when establishing digital identity. He finally decided on a technique for transferring shared secrets via a secure channel to confirm an individual’s online identity. This made it possible for users to manage their own identities independently of managing authorities. Ellison also served as the project’s driving force. Its objective was to provide a less complex public infrastructure for identity certificates that would be able to take the place of the convoluted X.509 system. Centralised administrations were one possibility, but they weren’t the only ones. It was a start, but to fully advance self-sovereignty, a more radical reinvention of identity for the twenty-first century would be necessary.

3.2.2

Phase Two: Federated

To address the issue of identity fragmentation, Federated Identity (administrative control by multiple, federated authorities) was introduced, represented in Fig. 4; as a result, the administrative control is supplied by numerous, federated authorities [26]. To develop a standard for federated I&AM, Sun created the Liberty Alliance Project (2001). At the turn of the century, a number of commercial enterprises moved beyond hierarchy to debalkanise online identity in a novel way, marking the next significant development for digital identity. One of the first was Microsoft’s Passport programme from 1999. It envisioned federated identification, which would enable users to use a single identity across several websites. However, it made the federation virtually as centralised as conventional government by placing Microsoft at its heart. Sun Microsoft established the Liberty Alliance in response (2001). They opposed the concept of centralised power and established a “genuine” federation instead. The outcome, however, was an oligarchy because the power of centralised authority was now shared among many strong organisations. Balkanisation was a problem, but Federation fixed it by allowing people to roam freely across sites. Each website stood alone, nevertheless, as an authority.

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Fig. 4 Federated I&AM architecture

3.2.3

Phase Three: User-Centric

Figure 5 shows the User-Centric Identity (individual or administrative control across various authorities without necessitating a federation), which strives to provide users more control over their identities and enhance the user experience by putting them at the centre of the identity process. OpenID, OpenID Connect, and OAuth are examples of frameworks that adopt a user-centric strategy [27]. A user-centric scheme has changed centralised identities into interoperable and federated identities, with centralised control in the hands of the user himself: one has the option to consent or not to identity sharing, as well as choosing with whom it may be shared. The Augmented Social Network’s (ASN) (2000) plan for the next-generation Internet established the groundwork for a new type of digital identity. In a lengthy white paper, they proposed integrating “permanent online identity” into the Internet’s infrastructure. From the perspective of self-sovereign identity, their most significant advancement was “the idea that every person should have the right to determine his or her own online identity.” The ASN group believed that Passport and the Liberty Alliance were incapable of achieving these objectives because the “businessbased initiatives” placed an excessive amount of emphasis on the privatisation of information and the modelling of users as customers. The Identification Commons (2001-Present) initiated the decentralised consolidation of fresh work on digital identity. Their most significant contribution may have been the formation of the Internet Identity Workshop (IIW), the working group in collaboration with the Identity Gang. The IIW has promoted the concept of decentralised identity through a series of semi-annual gatherings for the past decade. The IIW community focused on a new phrase, user-centric identification, to fight the server-centric model of centralised authorities. The word implies that users are at the centre of the identity process. Initial talks of the topic centred on improving the user experience, highlighting the necessity to prioritise the user in the pursuit of an

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Fig. 5 User-centric I&AM architecture

online identity. However, the idea of a user-centric identity quickly grew to encompass the need for users to have greater control over their identities and for trust to be decentralised. OpenID (2005), OpenID 2.0 (2006), OpenID Connect (2014), OAuth (2010), and FIDO are some of the new methods supported by IIW for creating a digital identity. As often applied, user-centric techniques tend to prioritise user consent and interoperability. By adopting them, a user can debalkanise his or her digital self by sharing a single identity across many services. The user-centric identity communities had even loftier goals; they aimed to give people total control over their digital identities. Unfortunately, influential institutions co-opted their activities and prevented them from achieving their objectives. Similar to the Liberty Alliance, the final ownership of user-centric identities remains with the registering entities. OpenID is an illustration, where, theoretically, a user can register his own OpenID, which he can then use independently. However, this requires some technical knowledge; therefore, it is more likely that a casual Internet user may use an OpenID from one public website as a login for another. If the user chooses a trustworthy website, he can get many of the benefits of a self-sovereign identity, but the registration entity can revoke it at any time! Facebook Connect (2008) debuted a few years after OpenID, incorporating lessons learnt, and was consequently far more successful due to a superior user interface. Facebook Connect deviates even farther from the original user-centric objective of user autonomy. Initially, there is no choice of provider; Facebook is the only option.

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Facebook has a history of arbitrarily cancelling accounts, as the current real name dispute demonstrated. Consequently, individuals who browse other websites using their “user-centric” Facebook Connect identity may be even more susceptible than OpenID users to losing that identity in many locations simultaneously. It’s the central government all over again. Worse, it is similar to state-controlled identity authentication, but with a self-elected “rogue” state. In other words, user-centricity alone is insufficient.

3.2.4

Phase Four: Self-sovereign I&AM

I&AM systems have developed over time, and the next step beyond user-centric identification is to grant people full authority over their online personas (resulting in user autonomy). Similar to this line of thinking, the Self-Sovereign Identity (SSI: individual control across any number of authorities) paradigm was developed to provide individuals full ownership and control over their identities apart from any governing bodies. Actions in an SSI architecture are shown in Fig. 6. Depending on the circumstances, the claim issuer may employ a single or combination of user identity traits to issue the identity. When the user is in charge of their own identity, they may choose which pieces of that identity to share with the organisations that are dependent on them to correctly identify them [28]. Interoperable federated identities under centralised management have evolved from centralised identities thanks to user-centric designs that respect users’ wishes regarding how their identities are shared (and with whom). It was a big step in the right direction towards giving users full control over their own identities, but it was still just a step. User independence was necessary for the following stage. This is the essence of the concept of self-sovereign identity, which is gaining popularity in the twenty-first century. Self-sovereign identity goes beyond just recommending that users take the lead role in the identification procedure by making them the identity’s de facto decision-makers. In February 2012, developer Moxie Marlinspike

Fig. 6 Self-sovereign I&AM

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made a passing reference to “Sovereign Source Authority” in an article, which is often cited as the first published mention of identity sovereignty. People “have an established right to a ‘identity,’” he argued, but national registration undermines this autonomy. Open Mustard Seed, an open-source framework that places consumers in charge of their digital identity and data in decentralised systems, was developed by Patrick Deegan around the same time, in March 2012. Several “personal cloud” projects emerged at roughly the same period. The concept of individual sovereignty has grown in popularity since then. An organisation whose mission is to spread the idea of Self-Sovereign Identity, the Sovrin Alliance (2017, 2019 [29]), defines it as follows: In the digital realm, the concept of “Self-Sovereign identity” (SSI) refers to the growing consensus that people should be the sole custodians of their personal data and that governmental agencies should stay out of the business of regulating it. With SSI, people may be as open and trusting in their online interactions as they are in their offline ones. Self-sovereign identification (SSI) is an identity management solution that brings the same privacy and control to the digital sphere as we know it offline. With SSI, a person (or group) is in charge of their own digital identity and the credentials that go along with it. Because of SSI, the individual, and not an external administrator, is in charge of his or her own data. Stakeholders can utilise the credentials they have been provided and store them in their digital wallets thanks to the SSI identification system. Users are freed from the risky practise of disclosing personal information to dozens of databases every time they wish to try a new service or product. Because each person is now a sovereign nation with complete authority over their own identity, this concept is known as “self-sovereign identity.” An individual’s ability to manage their personal data and social connections is not limited. A person’s digital identity is now decentralised and cannot be taken away by any single institution.

4 How Does SSI Leverage Blockchain Technology? The blockchain is a distributed ledger that stores data in the form of transactions, guaranteeing the data’s indestructibility. Without relying on a single authority to store and verify transactions, blockchain technology makes it possible to register and keep track of resources. It can process the exchange of cryptographic keys in a fashion that is transparent, immutable, and trustworthy. Therefore, integrating blockchains into IdMs is likely to improve the effectiveness of these systems [30]. The blockchain network assures the execution of smart contracts, which can then apply data processing algorithms. As a result, blockchain-based IdMs can utilise smart contracts to facilitate novel governance models for certifying entities. Blockchain was designed to be an immutable database that is difficult to alter. Once key ownership has been verified, decentralised authentication can be set up. Blockchains are a robust non-repudiation instrument for any data in the ledger due to their distributed nature, event recording qualities, immutability, and irreversibility.

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Achieving a distributed consensus within the nodes ensures that the ledger state becomes practically immutable and irreversible after a certain period, even for smart contracts which enable the deployment and execution of immutable programmes, negating the need for a central authority to verify credentials. Since the state of the ledger and all transactions between entities can be confirmed by an authorised party, blockchain interactions are open and transparent. Blockchains and distributed ledgers are utilised by self-sovereign identification systems to facilitate the search of decentralised IDs without the need for a centralised repository. Although blockchains don’t magically fix online identity problems, they do make it possible for individuals and organisations to establish their credibility and authenticity through independently verifiable records. One example is adding a verified credential, such as a certificate, to an individual’s SSI file in the form of a digitally signed copy of the credential (stored in an identity wallet). This certification can be shown to any organisation, such as a prospective employer, that uses a similar form of self-sovereign identification. This establishes the identity holder as the conductor and unifier of a network of credentials that can be independently verified.

5 Blockchain-Based SSI Architecture The World Wide Web Consortium (W3C) has established two standards that are used in the construction of Self-Sovereign Identity. These standards are the Decentralised Identifiers (DIDs) and the Verifiable Credentials (VC). In this part, a high-level overview of the architecture of a blockchain-based SSI system is presented, which is illustrated in Fig. 7. This figure is a tiered model of the SSI architecture consisting of details of its important factors: actors, objects, components, and primary processes. 1. Actors: A blockchain-based SSI involves three actors: the identity holder, the issuer, and the verification. It contains all of the actors’ DIDs. • To get a verified Verifiable Credential, the Identity Holder must supply personal information to the Credential Issuer in order to confirm his identity. • This validation is acquired through the Issuer DID’s signature on the credential. This signed credential can be saved securely in the Credential Holder’s Wallet and linked to his DID. The Credential Holder has the option of presenting merely the assertion of the credential, the claim, as required by the Verifier without invalidating the Issuer’s signature. • The Verifier can then validate the identity and claim by comparing the DID signatures (included in the claim) of the Holder and Issuer to the blockchain. 2. Components: It is possible to secure one’s self-sovereign identity through the coordinated operation of the major components that are stated below: • Blockchain serves as a registration authority by replacing IdM’s registration authority. It allows the user to leave digital signatures and timestamps in a

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Fig. 7 Blockchain-based SSI architecture: components and process

decentralised public manner so that any authorised organisation can validate them. • The User Agent is a component (mobile app or browser) that conducts practically all DID actions, including its creation. It can store verifiable credentials locally or on an identity hub. The user agent mediates identity holders, issuers, and verifiers. • DIF universal Resolver [31] provides a single interface to resolve any decentralised identifier across ledgers and DID techniques. The system links the Universal Resolver to the DID technique to read the DID Document. Universal Resolver supports 30 DID methods (Sovrin, Bitcoin Reference, Ethereum uPort, Jolocom, Blockstack, and Veres One) [32]. Both Blockstack and uPort feature public profiles with names and photographs. uPort uses a smart contract, uPort Registry, to map user identification to hashes of claims kept off-chain in IPFS. The relying party checks claim fingerprints for integrity. Blockchain timestamps prevent secret alteration of claims and signatures. Blockstack used Amazon S3, Dropbox, and Google Drive. Even so, because these systems are highly redundant, it can be questioned if the user has full control over the privacy and security of their identity attributes. • Off-chain repositories are essential components since blockchains are public and unchangeable, and so these ledgers are not suitable for storing personal data. There are two ways to store claims and credentials: registry and nonregistry. Claims fingerprints are kept on blockchain in the registry model. For the Non-registry approach, credentials/claims may be held locally in the wallet by the user agent or a third-party custodian. This strategy makes it difficult

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to protect user rights including privacy, data ownership, and the right to be forgotten [33]. 3. Objects: In a blockchain-based SSI, Decentralised Identifiers (DIDs), Pairwise Pseudonymous, and Credentials are the three main items. • According to the W3C specification [34], a Decentralised Identity (DID) is a URI with three parts as shown in Fig. 8: the scheme “did:,” a method id (such as “ethr” for Ethereum or “btdr” for Bitcoin), and a unique identifier created by the DID method specification. In accordance with a given distributed ledger or network, this DID procedure specifies how a given DID and its corresponding DID document are generated, resolved, updated, and disabled. Formats like JavaScript Object Notation are resolved to the DID document (JSON). In SSI built on the blockchain, DIDs are used to track individual entities. • Pairwise-pseudonymous IDs are used to present oneself to other entities without revealing one’s primary identity, allowing users to retain some measure of anonymity. Sovrin, for instance, proposes the use of “microledgers” to generate DIDs that need not be recorded in the blockchain. They can only be disclosed privately between an identity’s owner and a trusting third party. The Bitcoin Enhancement Protocol describes hierarchical deterministic (HD) wallets, which can be used to generate untraceable identifiers (called offspring accounts) with a single master key. • An issuer creates a credential and assigns it a unique number, or identification. A claim is a statement about something, such as an individual’s age or the role they play in society, and the accompanying metadata. What makes a credential verifiable is the cryptographic evidence of its originator’s identity embedded in its claims and metadata. Internet credentials that are cryptographically sound, private, and machine-verifiable are defined by W3C [35]. 4. Processes: Registration, authentication, issuance, and verification are the four steps of the blockchain-based IdM lifecycle process. • To register a DID [36] on a distributed ledger and link it to one or more public keys requires a registration process. A pair of keys is created by the user’s agent. The public key, DID, and DID Document are then added as a proposed transaction to the blockchain. Next, the new information bloc (DID and DID Document) is validated, distributed, and written immutably across all the dispersed nodes using the built-in consensus mechanism. Fig. 8 DID syntax (W3C specification)

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• In the context of DIDs, authentication refers to establishing one’s rightful possession of a DID. Establishing ownership of a public key and its corresponding private key in relation to the DID is how this is established. The public key is recorded on the blockchain as a value of the identification. The term “Decentralised Public Key Infrastructure” has been coined to characterise this idea. • DID Auth is a system for verifying DID ownership cryptographically. The resolution of a DID to its corresponding DID Document is one method by which such authentication can be accomplished [37]. These loops depend on token and object transfers carried out by a mix of technologies including scanning QR codes, the HTTP protocol, biometrics [37], and near-field communication (NFC). The method and structure of the challenge vary as needs change. They may see a QR code or “Sign in with DID Auth” button on a website. A DID Auth could be embedded within a higher layer interaction, such as the exchange of Verifiable Credentials, to simultaneously demonstrate DID ownership and provide Verifiable Credentials for a transaction-specific purpose. • Each time credentials are distributed, the issuer updates the distributed ledger with the asset’s current state, a cryptographic proof of issuance, and a timestamp. Credentials’ status (e.g., whether they are active or revoked) may be modified in accordance with the rules in effect by the issuer or a party authorised by the issuer. It’s important to remember that the issuing of credentials may result in transaction charges for some blockchain-based SSI systems in order to write to these registries. These fees would be paid by the consumers. When a user offers credentials for verification, verifiers will check the blockchain for cryptographic proofs left by issuers. When conducting verification, it is important to make sure that all credentials presented are current and have not been revoked or suspended [38].

6 SSI Layered Model This section presents the layered model of SSI that should be taken into consideration when developing SSI-related solutions. Figure 9 presents a simple representation of the SSI layered model. 1. Applications Layer: This layer is devoted to apps that extend SSI through library integrations and API calls. This layer is required to incorporate a UI/UX layer. 2. Governance Layer: An SSI ecosystem must have a governance framework that ensures its security and reliability. This necessitates the development of governance frameworks that stipulate policies, requirements, principles, and standards [39]. 3. Credentials and Claim Layer: In this layer, all processes associated with the credential and claim lifecycle must be considered, including the request for

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Fig. 9 Blockchain-based SSI layered model

credentials/claims, their issuance and signature, their storage, their disclosure, their verification, their revocation, and their expiration. This layer defines ways for authenticating entities (individuals, organisations, devices, etc.) based on their DIDs. 4. DID Resolution Layer: DID Resolver converts a DID to its corresponding DID document. DID resolution can be accomplished using either a native resolution technique stated in a DID method specification or the DIF Universal Resolver. 5. DID Operations Layer: This layer should specify how a particular type of DID (corresponding to a particular distributed ledger) and its associated DID document are produced on the blockchain, how the DID is resolved to its associated DID Document, and how the DID can be modified or deactivated. The DID method definition [33] defines these CRUD actions. 6. The Blockchain Layer: It is a decentralised information ledger that complies with the DID standard and has an accompanying DID “method” that specifies how DIDs are anchored on the ledger. The DID technique can occasionally isolate the storage of the DID and the DID Document from the blockchain. The management of this store occurs outside of the blockchain.

6.1 Illustrative Use Case To show how the SSI architecture works, let’s look at a car loan application in the context of an IdM based on blockchain. In the scenario shown in Fig. 10, the Borrower is the Id holder, the Lender is the Relying Party, and the Borrower’s Employer is the Issuer of the Borrower’s Salary Certificate, which is considered a Verifiable Credential.

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Fig. 10 An illustrative scenario

1. First, the Borrower puts the IdM user agent on his mobile device. This agent makes a pair of public and private keys and then registers the borrower’s DID on the blockchain, as described above in the “Registration” section. 2. The borrower goes to the lender’s website and fills out the application: • The Lender sends the Borrower a QR Code challenge to prove their identity. • Based on the challenge, the Borrower sends the Lender a response that proves they have control of their DID (for example, cryptographic signature). This can be an HTTP POST response. • After getting the response, the Lender resolves the borrower’s DID and checks that the response is valid (for example, by using a public key object in the borrower’s DID Document to check the signature of the response). The challenge that the Lender sends may or may not include proof that they own a DID. After filling out the Lender’s application, the borrower may have to show a number of supporting documents, such as a salary certificate, a recent proof of address, a bank identity statement, and many others. The Borrower must show legal proof of the required documents in the form of signed Verifiable Credentials. 3. The borrower then logs in to his employer with his DID and asks for a salary certificate. 4. Once the Employer verifies that the Borrower is his employee, he gives the salary certificate to the Borrower, signs it with his DID, and records the status of this VC along with cryptographic proof of issuance and a timestamp in the decentralised ledger.

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5. The Borrower’s user agent stores that VC (the signed salary certificate) locally on the wallet and then shows it to the Lender. 6. To validate this VC, the Lender uses the blockchain to resolve the Employer’s DID and then checks the VC’s signature.

7 AI, Machine Learning, and Deep Learning: Background and Basic Principles A. Turing proposed the imitation game in 1950 to define intelligence [40] and subsequently John McCarthy introduced AI in 1956 at Dartmouth [41]. AI is any technology that seeks to replicate intelligence artificially using machines, and it can be categorised along two dimensions: reasoning and behaviour [42]. The earliest AI implementation (Expert systems) is straightforward to understand, while newer techniques, like Neural Network (NN) and Machine learning (ML), are more challenging to understand. ML focuses on creating efficient and reliable prediction algorithms by offering training and assessment sets to the models. NN strives to emulate the human brain and give a robust learning algorithm modelling system. It consists of connected artificial neurons termed “Perceptrons” [43], structured in layers: input, hidden, and output. The popular learning scenarios are Supervised Supervised learning (SL), Unsupervised learning (UL), Semi-supervised learning (SSL), and Reinforcement learning (RL). SL involves categorisation and regression, whereas UL is applicable for clustering and dimensionality reduction. RL uses rewards instead of output data to help algorithms learn from prior rounds. Real-time choices use this. The learner must choose between exploring unfamiliar behaviours to gather additional knowledge and using the information previously collected because the environment provides no long-term reward feedback [44]. SSL may be useful in case of the dataset being partially labelled. AI faced a serious barrier in its early days: while it quickly solved mathematical issues that were difficult for humans to solve, it was less adept with intuitive tasks (voice recognition, facial recognition, etc.). Each difficulty was broken down into a series of concepts, with each concept stated in connection to simpler concepts. Computers learned complex concepts by combining simpler ones. These connections form a multilayered graph. This is deep learning, which is a domain of AI as shown in Fig. 11. Deep learning performs sophisticated feature extraction automatically, unlike feature engineering [45]. Deep learning is making huge breakthroughs in solving issues that have resisted AI efforts for years. This is due to two main enablers: increased data volumes and computational power. Fully connected (FC) DNNs require a lot of data and processing because all output activations are a mixture of all input activations. We can eliminate some activation links to create a sparse layer. Deep Neural Networks (DNNs) come in many forms. DNNs are feedforward and recurrent. Each perceptron in one layer is coupled to every perceptron in the following layer in feedforward neural networks (FFNNs). FFNNs are famous for classifying

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Fig. 11 Difference between AI, DL, and ML

data [46]. Recurrent networks have hidden states and use previous outputs as inputs. Natural language processing and speech recognition use RNN models [47].

8 AI, ML, and DL in I&AM Field: The Main Challenges This section reviews the problems facing identity, authentication, authorisation, auditing/monitoring, and accountability. Figure 12 illustrates these problems. 1. Identification: First, secure resources must be identified. In the authentication process, the user’s claim to be a specified entity utilising a specific digital identity (identity attributes or credentials) is verified. Static or dynamic identity traits. One of the primary obstacles in the identification process is attribute retrieval. To efficiently complete the subsequent I&AM processes, the chosen collection of attributes must differentiate across identities. Another difficulty is the usefulness of dynamic credentials, such as ageing faces, which can alter user identity. Identity theft through phoney identity attributes threatens I&AM; fake identities cost the UK roughly 1.3 billion pounds annually. Presentation attacks concern I&AM, specifically face spoofing: an attacker utilises a video or photo to mimic an authorised user. 2. Authentication: In authentication, a user proves his identity. Online engagement in digital services leads to so many digital identities and passwords that managing them is difficult. Most reported breaches are due to weak or stolen passwords, making authentication open to hackers. Some biometrics alter over time or based on circumstance, boosting security. This affects biometric authentication’s reliability. Phishing is another authentication challenge. In this cybercrime scenario, bad actors try to gain personal information from consumers by building a counterfeit website.

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Fig. 12 I&AM challenges

3. Authorisation: After successful authentication, the authorisation procedure determines the user’s access to resources and operations based on his profile. ABAC is versatile and interoperable, making it ideal for current distributed I&AM systems. ABAC rules must be monitored and audited in continuously developing systems. Migrating from outdated authorisation models is difficult and timeconsuming. OAuth allows a third-party programme to access a user’s secure resources without providing credentials2. OAuth is widely used but vulnerable to Cross-Site Scripting (XSS). 4. Auditing and Monitoring: Auditing, a formal and disciplined way to evaluating and improving process effectiveness3, and monitoring, a continual effort, are both important to guarantee I&AM systems are performing as intended. Microservice architecture emphasises that modern systems are extremely dispersed and generate huge volumes of data. Due to data redundancy and volume, it’s expensive to transfer auditing data to a central node. The auditing process also faces a “needle-in-a-haystack” difficulty [41]. SIEM (Security Information and Event Management) solutions provide real-time analysis of security alerts and alert security analysts when vulnerabilities are found. Because SIEM systems can create gigabytes of data per day and hundreds of thousands of warnings per day, security analysts risk missing serious threats [29]. 5. Accountability: For an I&AM system to be efficient, all processes must be visible and the system must hold the user or programme responsible for each activity accountable. System administrators and IT security teams must trace all identities connecting to and using a system.

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Privacy is another problem, especially given that identification information is sensitive and deep learning algorithms require a lot of data, which poses a major challenge in its application to I&AM.

9 AI in I&AM: A State of the Art This section presents a literature analysis of AI solutions used for I&AM procedures in order to address the identified difficulties.

9.1 Identification The identification process can be improved through the acquisition of new knowledge, such as the learning of discriminative feature representations for individuals. An attribute person recognition (APR) network is proposed by the authors in [42]. Attribute recognition and identification are both handled by separate prediction components in this answer. The best accuracy compared to state-of-the-art algorithms was achieved in experiments on three major datasets (Market-1501, DukeMTMC-reID, and PETA). Presentation attack detection solutions work to identify forgeries in an effort to protect I&AM systems from compromise. To determine whether a face is real or fake, the authors of [48] employed a convolutional neural network (CNN-RNN) trained on a depth map (a 2D representation of the 3D shape of the face) and temporal information regarding face liveness. TensorFlow was used to implement the strategy, and the CASIA-MFSD and Replay-Attack datasets were used to test their efficacy. The lowest possible False acceptance was attained by the results.

9.2 Authentication There have been attempts to replace passwords with biometrics or other more secure, user-friendly, and efficient methods. More security can be attained using biometric authentication due to the fact that biometric identifiers are difficult to forge or steal. Using a mix of two-dimensional Gabor Kernels (2-DGKs), step filters (SFs), and polynomial filters (PFs), the authors of [49] offer a solution for the last two steps of the classic IRS (PF). They employed a combination of a radial basis function neural network (RBFNN) and a genetic algorithm to perform the matching and verification (GA), which achieved up to 99.99% accuracy on the CASIA-Iris V3 and UBIRIS.V1 datasets while taking up minimal processing effort. In spite of the fact that some biometrics, like fingerprints, fade away after a while, vein patterns generally don’t. For finger vein biometric authentication, the authors of

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[50] used a four-layer convolutional neural network (CNN) that fused convolution and subsampling layers. With a reported identification rate of 100% and an accuracy rate of up to 99.38%, finger vein identity recognition provides a more secure biometric authentication solution. Multi-factor authentication (MFA) is a method of increasing the security of authentication by employing more than one means to verify a user’s identity (in addition to a login and password). Two biometric features, coupled with pseudo and physical measurements, are used to create a multimodal authentication system, as proposed and implemented in [51]. A multilayer artificial neural network was trained in MATLAB utilising a total of six attributes. Keeping track of several user names and passwords can be a hassle; therefore, researchers have been studying adaptive and continuous intelligent authentication systems to improve the user experience and only require authentication on occasion. The safety of ID and account management is enhanced, and so is the user experience, with these systems. In [52], the authors propose a fresh approach using the unsupervised machine learning method Isolation Forest. For the purpose of determining what constitutes “typical” user behaviour, the system gathers information from users’ devices regarding their app usage and geographic location. The frequency of authentication requests can be optimised because experiments have shown that the system can properly tell the difference between the behaviour of an abnormal user and that of the device owner.

9.3 Authorisation Reinforcement learning applied inside a dynamic access control architecture can greatly improve the efficiency with which ABAC rules are maintained [53]: The authors employ reinforcement learning to supply an adaptive, self-tuning security policy. If a problem occurs frequently when Subject S1 tries to access certain resources, the algorithm may decide to blacklist S1 and revise the access control list accordingly. The model continually learns and improves the dynamic security policy by delivering feedback after each access attempt. Changing from a conventional access control system to an ABAC system is a time-consuming process. It is based on a machine learning algorithm for subgroup discovery. With this approach, we can build an effective ABAC model without resorting to either overly liberal rules or excessively broad rules. Rhapsody was tested in a real-world setting with data from a large institution and two logs provided by Amazon; the results demonstrate that it mines higher-quality policies than state-of-the-art approaches, ensures the brevity of mined rules, and prevents over-permissiveness. In [54], the authors describe a machine learning-based vulnerability detection model for the OAUTH2.0 protocol; the work models the system as a Finite State Machine (FSM), and data mining techniques are utilised to produce training datasets. The model was built with the use of a supervised machine learning (ML) algorithm for regression and classification issues known as the Gradient Boosting (GB) technique. The authors classified all the potential outcomes into three distinct groups: successful

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workflows, failed workflows, and failed undefined workflows. Different ML classification techniques (Random Forest, Naive Bayes, Decision Tree, K-Nearest Neighbours, Logistic Regression, and Support Vector Machine (SVM)) were compared to their own proposal. The results demonstrate that GB Classifier is able to find vulnerabilities in OAUTH2.0 with an accuracy of above 90%.

9.4 Auditing and Monitoring To accommodate modern systems, which are characterised by substantial redundancy and thus create a large volume of logs, a Data Provenance method for system auditing provides a comprehensive account of system operations. Winnower is a method introduced in paper [55] that uses grammatical inference to convert audit data into provenance graphs. By doing so, we can cut back on resources like storage and bandwidth without sacrificing the quality of the data provenance that helps us spot assaults. Five real-world attacks, including the ImageTragick, Ransomware, Inexperienced Admin, Dirty Cow, and Backdoor attacks, were used to put Winnower through its paces on a Docker Swarm cluster. Winnower improves query performance by two orders of magnitude and reduces log size by three orders of magnitude compared to the Linux auditing system auditd, which is responsible for writing audit records to the disc, and SPADE, an open-source software architecture for data provenance collecting. Large volumes of warnings are generated by SIEM systems. In [23], the authors introduce a user-centric machine learning system that utilises Multi-layer Neural Network (MNN) and Random Forest (RF) algorithms to aid in the correct decisionmaking process of which alert to handle, thus increasing the efficiency of the auditing process. This method does so by providing the security analyst with a risk score of a user, allowing him to prioritise those users who have received high scores. The framework was examined after one month of operation in Symantec’s SOC production environment. In order to measure effectiveness, the authors employ Model Detection Rate and Model Lift. A model’s “Lift” indicates by how many times it is preferable to use the model rather than not using one. With RF, 80% of genuine high-risk patients may be identified with just 20% of the highest forecasts, but with MNN, the average lift is above 5.5.

9.5 Accountability The notion of Explainable Artificial Intelligence (XAI) was developed to address the lack of openness in machine learning algorithms. Researchers at Google have been focusing on the development of datasets as a means to guarantee accountability, and they suggest that adopting best practises around visibility and ownership is essential for accountability. They created a strict system for developing datasets in

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[56]; each piece of documentation has an owner with certain responsibilities. Because of this, “Activation Atlases”4 have made it possible to investigate Neural Networks by rendering hitherto inaccessible layers of these systems accessible to human minds. To solve the problem of who is responsible for what, the authors of [57] propose SMACTR, a framework for internal algorithmic auditing. SMACTR is broken down into its five individual phases, which are as follows: Scope, Mapping, Artefact Collection, Testing, and Reflection. There is a separate collection of files for each phase. Despite the lack of real-world testing for SMACTR, its authors demonstrated its utility by imagining a business dealing with two fictitious customer projects. The framework will allow a thorough analysis of all possibilities and dangers in the case of a client operating in high-risk environments.

10 Discussion and Analysis AI has been shown to improve I&AM processes. It will be interesting to observe how well these solutions can use data in an Industry 4.0 setting. The subsequent content will compare the impact on solutions achieved using ML implementation to CPMAI, as given in previous Sect. 9. Identification The APR network implementation [42] adhered to the five first phases of CPMAI, and evaluation was carried out utilising around 5000 IDs. The execution time is anticipated to be 0.026 s per query, which is encouraging for the final CPMAI phase. Face Anti-Spoofing proposition [48] also went through the first five phases of CPMAI; an iterative approach could be proposed to reach model operationalisation. Authentication All three suggested solutions adhered to CPMAI’s five first steps. Model evaluation was carried out with a small number of user inputs: 4 for Multimodal authentication [58] and 81 for Finger Vein Recognition [59]. Diversifying the inputs is vital for challenging the model and identifying any noteworthy adjustments. In the case of IRS [60], the authors used a huge dataset to conduct the matching operation. None of these methods were operationalised during an industrial implementation; an avenue of research to get to this stage would be working on Continuous AI model iteration to attain acceptable execution times. It is equally critical to establish governance policies for such systems. Authorisation One of Rhapsody’s [61] intriguing goals is to mine ABAC rules using minimal logs; as a result, the authors did not do Data Preparation. Furthermore, businesses do not need to mine rules in real time, which is why Rhapsody skipped the CPMAI 6th phase.

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For the same reason as Rhapsody, the Dynamic Access Control [53] Framework did not go through Data Preparation. The authors did not complete the final two rounds of CPMAI model evaluation and operationalisation in this research. This could be a promising direction for future investigation. Because no acceptable OAuth-specific dataset was available for the OAuth 2.0 vulnerability detection model, the authors generated the initial test suite using fuzzing techniques and Microsoft’s Project Katana. Because this model has demonstrated its efficacy and has been intensively trained (213,840 occurrences in the training dataset), a future formal model can be used in an enterprise environment. Monitoring and auditing In [29], the introduced framework went through all six CPMAI phases before being implemented in the Symantec production environment and was shown to be efficient after one month of operation. Winnower was evaluated using a Docker Swarm cluster and five real-world attacks, making it a viable enterprise operational framework. Accountability Because SMARTC is a suggestion framework, the proposed solution for accountability cannot be deemed a proper AI project. CPMAI can be used in the case of Activation Atlas, although some phases are not applicable, such as model evaluation, because the goal is to explore the unknown internal data of DNNs. Table 2 outlines the primary issues faced by each I&AM process, as well as some major AI-based solutions provided in the literature and the outcomes gained by comparing their integration with that proposed by the CPMAI approach. CPMAI can be used in the case of Activation Atlas, although some phases are not applicable, such as model evaluation, because the goal is to explore the unknown internal data of DNNs. Challenges and future research direction One of the unfavourable biases affecting biometric authentication is domain difference, which refers to the situation in which a model is trained and verified in one domain but fails in another. For example, in the case of facial recognition, the decision may be hampered by clothes. To address this issue, a novel additive adversarial learning (AAL) mechanism has demonstrated its efficacy in both facial recognition and handwritten digit recognition [69]. Furthermore, a promising study of crossmodal matching between faces and voices was conducted in order to propose a CNN architecture capable of recognising a face using only the person’s voice (or vice versa). Personal data must be “adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed,” according to the General Data Protection Regulation (GDPR). One of the key disadvantages of DNNs is their reliance on big datasets to avoid overfitting during training [47]. Deep learning-based systems are quite greedy in terms of computational capacity; research is being performed in this area to alleviate barriers experienced by developers. Many businesses are developing low-cost AI-optimised chips. According to McKinsey, AI applications will generate $4–6 trillion in value every year.

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Table 2 ML implementation evaluation according to CPMAI I&AM process ML solution

CPMAI CPMAI CPMAI CPMAI CPMAI CPMAI Phase I Phase II Phase III Phase IV Phase V Phase VI

Identification

APR network [62]

Yes

Yes

Yes

Yes

Yes

No

Face anti-spoofing [48]

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

No

Finger vein [59]

Yes

Yes

Yes

Yes

Yes

No

Authentication Multimodal authentication [58]

Authorisation

Auditing and monitoring

IRS [60]

Yes

Yes

Yes

Yes

Yes

No

Rhapsody [63]

Yes

Yes

No

Yes

Yes

No

Dynamic access control [64]

Yes

Yes

No

Yes

No

No

Vulnerability Yes detection in OAuth 2.0 [65]

Yes

Yes

Yes

Yes

No

Winnower [55] Yes

Yes

Yes

Yes

Yes

Yes

ML framework Yes cyber SOC [66]

Yes

Yes

Yes

Yes

Yes

Accountability SMARTC [67] – Activation Atlas [68]

Yes

–

–

–

–

–

Yes

No

Yes

No

No

11 Conclusion This paper presents an in-depth survey of the fundamental concepts and challenges of Industry 4.0 and emphasises the importance of the Self-Sovereign Identity model to conceive transparent and user-centric I&AM systems that meet Industry 4.0 requirements for decentralisation, heterogeneity, and privacy. We’ve also leveraged Blockchain to construct a transparent, decentralised, trust-less SSI for Industry 4.0. It also highlights how ML and DL are utilised to improve and secure I&AM and concludes that Self-Sovereign Identity, driven by Blockchain and AI, is a viable approach for building identity in Industry 4.0, especially in the metaverse. However, some risks still persist that need proper handling. An important step in this direction is to comply with International standards by SSI. For instance, a regulation that specifies standards for the mutual recognition of electronic means of identity and signatures for communications between users and public sector institutions in the Member States. The General Data Protection Regulation (GDPR), which aims to

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protect consumers by giving them control over their identifying data, is also available. It covers the rights to accessibility, consent, data reduction, portability, and erasure. Blockchains, on the other hand, are used to store cryptography proofs and are immutable. Therefore, it is essential to take precautions to prevent the storing of personal information (PII) in the Ledger. Interoperability and portability in IdM are similarly unresolved for SSI systems and digital identification. Blockchain-based IdM integration may increase SSI adoption and may also improve user experience. Additional research required for describing the details of investigations is carried out in this domain.

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Blockchain and Artificial Intelligence for Business Transformation Toward Sustainability Dina Darwish

Abstract Artificial intelligence and blockchain are considered among the most wellknown technologies. Blockchain is a secure, distributed, and immutable database shared by all parties in a distributed network, in which transaction data can be recorded and is easy to audit. Artificial Intelligence represents today’s paradigm shift, and simultaneously promotes scientific progress and industry development. Combining these two technologies has many potentials, especially when they are related to companies, and the economy. A comprehensive overview of these two emerging fields, and the importance of their integration, is strongly needed. This chapter gives an overview of artificial intelligence technology, then, the importance of the integration of artificial intelligence and blockchain technologies is discussed. Also, the potential of their integration in the business field is discussed to transform businesses toward digitization, and enhance several processes, such as crossborder trade and transactions’ payments inside the business environment. Besides, this chapter discusses the effect of their integration on the circular economy for helping to reach a zero-waste ecosystem, on the alternative money system leading to the transformation of the current financial system, on the token economy in providing secured decentralized digital platforms, and finally, provides conclusions and future works about what are the expectations from their integration. Keywords Industry 4.0 technologies · Circular economy · Alternative money system · Token economy · Trade transactions · Sustainable development goals

1 Introduction Blockchain is a peer-to-peer technology that lets you simultaneously publish information to ledgers on several computers via a distributed network [1]. The blockchain is made up of a series of continually encrypted blocks that are connected to each other [2]. Digital signatures and cryptographic hashes can be used to record information D. Darwish (B) Faculty of Computer Science and Information Technology, Ahram Canadian University, 6th October City, Egypt e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_8

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on the blockchain [3]. Artificial intelligence (AI) is a branch of science concerned with computer observation, thinking, and learning abilities [4]. Artificial intelligence and blockchain are disruptive technologies predicted to transform current business practices. These new technologies affect foreign commerce operations. The digitalization of conventional foreign trade business procedures is a crucial component of establishing an intelligent trading matrix. While blockchain technology holds a lot of promise for future Internet systems, it also has a lot of technical obstacles, including scalability, development, latency, and transactions per second. Artificial intelligence continues to interact highly with almost every faction of society. Indeed, prediction, optimization, and automation are the major contributions of AI in several domains. Also, smart contracts can self-learn from previous experience in the network. The integration of both technologies provides many technical enhancements to several domains. There exists some research discussing the impact of the integration of AI with blockchain on business transformation, circular economy, the alternative money system, and token economy, but still, these researches are insufficient, and these topics need to be studied and discussed with more details. Regarding the impact of the integration of AI with blockchain on business transformation, some research is presented in the following section. Intensifying competition among companies worldwide has forced them to switch to more sustainable practices to assert themselves in the market [5]. Sustainable practices are becoming increasingly essential to ensure sensitivity to the internal global commitment to reducing greenhouse gas emissions, using appropriate resources, and efficient waste management [6] by businesses, academic researchers, and industry professionals. It is also crucial to understand that the contemporary industrial period is going toward digitization, which allows organizations to use information and communication technology (ICT) tools to reduce resource consumption. Disruptive technologies such as AI and blockchain that are part of Industry 4.0 (I4.0) will encourage manufacturers to develop new business models and opportunities [7]. Besides, the integration of AI with blockchain positively affects many aspects of the circular economy. Some research addressing this topic is mentioned in the following section. I4.0 digital technology has been known as a potential enabler of the CE business models [8]. The Circular Economy (CE) has also recently become more important with a cleaner production approach [9]. CE guarantees material recycling and improves resource efficiency. CE represents an emerging topic and is known as “a strategy aimed at reducing both new material inputs and waste emissions by closing the economic and ecological cycle of resource flows” [10]. Digitization is an important aspect of CE because it can integrate transparency and intelligence into assets and products. In the age of digitalization, businesses enhance their performance through the use of digital technology. The goal is to promote the advanced use of I4.0 technology to improve corporate performance and make it more sustainable. Blockchain technology and artificial intelligence are examples of digital technologies that promote transparency and traceability across a product’s life cycle [11]. Various researchers [12, 13] have proved that the integration of I4 and CE practices provides

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many sustainable benefits. In this context, it is critical to understand the potential of CE and sustainable business performance (SBP) in the digitalization era. Also, there is a need to address the effect of I4.0 digital technologies on the alternative money system and on the token economy. There exist some researchers that discussed the impact of integrating AI with blockchain on the alternative money system and the token economy in the following section. The challenges that fiat currencies and other financial market assets confront are the same challenges confronted by cryptocurrencies. According to a Business Insider Report [14] published in February 2022, conversational banking, anti-fraud detection, risk assessment, and credit underwriting are three areas where AI technologies are used in banking. AI software systems in the financial sector include financial chatbots and voice assistants that mimic real employees, build client relationships, and provide customized insights and recommendations. Also, AI is commonly used in intelligent trading systems to foresee changes in the value of currencies and the stock market. Additionally, machine learning is employed in anti-fraud detection tasks to study spending behaviors and trends and identify suspicious ones. Artificial intelligence in cryptocurrencies, like in financial services, reduces the risk of human error and expedites the trading process by forecasting the value of the currency or its rise and fall over time. The prediction and analysis of Bitcoin price movements involve the use of numerous statistical techniques and models. Multiple linear regression was employed in some research [15, 16] to model the relationship between the price of the Bitcoin cryptocurrency and numerous predictor variables. Google Trends, Wikipedia articles about Bitcoin [15], and Twitter feeds were used to gauge public interest in Bitcoin. A study [16] used data from Twitter and Google Trends to predict the prices of Bitcoin and Ethereum. The authors [16] discovered that a more reliable pricing indicator than tweet emotion is tweet volume. In a different study [17], the ability to predict Bitcoin prices using logistic regression (LR) was tested for autoregressive integrated moving average (ARIMA) and its several variants. The study [18] examined the stochastic characteristics of the top six wellknown cryptocurrencies at the time (Bitcoin, Ethereum, Ripple, Litecoin, Stellar, and Tether) and their relationship to six stock market indices using OLS regression and fractionally integrated ARMA (ARFIMA). The parameters (macroeconomic, technical trading indicators, etc.) that may have an impact on the trading behavior of various investor types (speculators, miners, informed traders, large professional investors, global traders, etc.) were identified by the authors [19] using the autoregressive distributed lag (ARDL) model. They classified investors into ten categories (six types of investors offering Bitcoins and four types of investors ordering Bitcoins). They also demonstrated that the volume of orders of a single category of investors had a significant impact on the Bitcoin exchange rate. Their investigation sheds further light on market speculators for bitcoin and the herd behavior exhibited by one cluster that results in speculative price variations. To identify fraudulent activity in the Bitcoin network, Monamo et al. [20] employed semi-supervised trimmed kmeans and k-means clustering based on attributes from the transactions graph. As examples of thefts and hacks, the trimmed algorithm detected 5 of the 30 wellknown anomalies, such as the Mt Gox, Linode Hack, and 50 Bitcoin (BTC) Theft.

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To comprehend the relationship between labels and predictor factors based on the clustering labels for outliers, the authors applied certain supervised classification models. The technique having the highest accuracy was the random forest. This chapter studies the impact of the integration of AI and blockchain in the business transformation, circular economy, alternative money system, and token economy, since these areas need to be addressed and analyzed in more depth. The contributions of this chapter are as follows: • Clarifying the technical enhancements resulting from the integration of AI with blockchain. • Illustrating the impact of the integration of AI with blockchain on business transformation and how it leads to more business-sustainable processes and practices in industries using real-life examples from corporations. • Illustrating the impact of the integration of AI with blockchain on the circular economy and how this results in more sustainable practices in the circular economy using real-life examples. • Giving focus on the impact of the integration of AI with blockchain in the alternative money system. • Providing a view about the impact of integrating AI with blockchain on the token economy. The rest of this chapter is organized as follows: the second section gives an introduction to artificial intelligence, the third section discusses the integration of blockchain with AI, the fourth section clarifies how blockchain and AI can lead to business transformation, the fifth section describes how the integration of AI and blockchain can make the circular economy sustainable, the sixth section explains how AI and blockchain can affect the alternative money system, the seventh section clarifies the influence of the AI and blockchain on the token economy, and finally, the eighth section provides conclusions and future works.

2 Introduction to Artificial Intelligence 2.1 Why AI Researchers realized at the end of the sixties that Artificial Intelligence was indeed a difficult field to manage, and the initial spark that brought the funding started disappearing. In the United States, the Automatic Language Processing Advisory Committee (ALPAC) report from 1966 and the “Lighthill report” from 1973 investigated the viability of creating a machine that could learn or be called intelligent. The two reports with the limited data available, combined with the lack of computational power, caused AI to fall into disgrace for the entire decade. However, in the 1980s, the development of “expert systems,” which were essentially examples of narrow AI, sparked a fresh wave of investment in the UK and Japan. These

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expert systems were able to imitate human expert skills in selected domains, and that was enough to start a new funding trend. During those years, the Japanese government was the most active player, and their haste to construct the fifth-generation computer prompted the United States and the United Kingdom to restart funding for AI research. When Massachusetts Institute of Technology’s (MIT) Cog project began work on a humanoid robot in 1993, and the Dynamic Analysis and Replanning Tool (DART)—whose funds were paid back by the US government since it had been received in 1950—and DeepBlue defeated Kasparov at chess in 1997, it was evident that AI had returned to the top. AI was not well acknowledged in 2012. A group of researchers presented extensive details on their convolutional neural networks that had won the first place in the ImageNet classification competition a few weeks before [21] at the Neural Information Processing Systems (NIPS) conference on December 4, 2012. Their work made the algorithm better. In less than two years, advancements in computer science brought computer classification to reach an accuracy of 96%, slightly higher than the human one (about 95%). The picture shows three major events that happened in the development of AI. One is that a company called DeepMind made a deal with Google in 2014. The second is an open letter with more than 8000 people’s signings from the Future of Life Institute. The third is a study on reinforcement learning released by DeepMind in February 2015. And also an article was published in Nature in January 2016 by DeepMind scientists on neural networks [22]. AI is a new way of thinking about machines that use computing power and algorithms to learn, reason, and make decisions. Artificial Intelligence is often misunderstood because it is difficult for the general audience to understand the technology; the investors are trying to mobilize lots of money, but they don’t know what kinds of companies are leading the market, and managers are trying to get the last AI software to improve their productivity. Artificial Intelligence is creating new opportunities for many areas of people’s lives. The meaning of Artificial Intelligence is going to be explained. “Artificial Intelligence” refers to machines that think and act like humans. Artificial intelligence is a computer program that can learn new things without being told what to do. Intelligence can be thought of as the ability to learn and solve new problems in a changing environment. In a world before people had learned to care about the environment, it was the attitude to foster survival and reproduction. A living being is then defined as intelligent if he is driving the world into states he is optimizing for. It is hard to imagine how well-developed machines would be able to do if they could have the same amount of intelligence as humans do. A human being uses physical observations to derive relationships between cause and effect in nature. An AI is driven by data without any knowledge of the underlying relationships. In this sense, it is “artificial” because it is not derived from the physical law but rather from pure data. Then, the description of what artificial intelligence is and what it means to us, can be simplified. In addition, there are two other notions that should be included in this AI definition: First, how does AI vary from and/or connect to other buzzwords (big data, machine learning, and so on); second, what functions must a system have in

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order to be classified as intelligent. Natural language processing, computer vision, Internet of Things, and robots all fall under the concept of artificial intelligence. As a result, AI serves as an umbrella term that encompasses a variety of content types. AI is comparable to a fully functioning human being. If AI and the human body are alike, they must both have brains that carry out a variety of tasks and are in charge of specific functions such as NLP and sight (computer vision). The body is made of bones and muscles, just like a robot is made of circuits and metals. Machine learning is the practice of programming machinebased algorithms. The Internet of Things is a way of connecting people and machines that allows them to both share and exchange data. It is the same way that the food consumed, the air breathed, and the senses are all connected. This is a very rough comparison, but it demonstrates how all of the terms connect to one another. Many individuals have various ideas on what properties a system should have in order to be considered AI. A narrow AI, which is focused on a single application or activity, improves over time by accumulating more data and learning how to make fewer mistakes. Deep Blue, a chess computer, is an example; however, this category encompasses any functional technology with a specified purpose. Because it is dedicated to certain tasks, a system may be regulated. Artificial General Intelligence (AGI) is going to be dealt with when software isn’t built to complete a specific task but can learn from an application and apply the same bucket of knowledge to other environments. It is technology as a product, not technology as a service, as in the narrow case. Google DeepMind is one example of a corporation attempting to develop self-driving AI systems. However, even DeepMind, a machine, cannot accomplish intellectual work as well as a human. Before getting there, there is a need to make a lot more progress in brain structure, brain operations, and computing power. Some people believe that developing a large number of narrow AIs will result in a super AI; however, this is not the case. It is not about how many specific skills a program can perform; it is about how well those abilities are integrated. This sort of intelligence does not require the assistance of a human expert. Still, it does have a limitation: it can only be attained by continuously streaming an unlimited quantity of data. The last step is referred to as Artificial Super-intelligence (ASI): this intelligence is significantly superior to human beings and is capable of scientific and creative thinking. It is characterized by common sense, social skills, and perhaps emotional intelligence. Although it is assumed that this intelligence will be developed by a single supercomputer, it is more likely that it will be created by a network or a swarm of several bits of intelligence. The path to the various stages is still debatable, and several schools of thought exist. Everything must be described in formal mathematical language, according to the symbolic approach, because all knowledge is symbolic, and the space for representation is limited. This way of thinking about the real world is difficult, and it comes with several computational problems as well as problems about where knowledge originates from. Statistical AI, as opposed to deductive logical AI, focuses on handling uncertainty in the real world [23], and it operates in the inference domain. However, it is unclear to what extent the human brain should be used as an example: a biological neural network appears to provide a good foundation for building AI,

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Fig. 1 Artificial intelligence classification

particularly when using Sparse Distributed Representations (SDRs) to handle data. The artificial intelligence classification is shown in Fig. 1. AI is important for many reasons, as follows: • AI is important because it enables, for the first time, the widespread implementation of formerly expensive human capabilities at low cost in software. Every industry may benefit from the efficiency and new prospects offered by AI. • AI technology is important because it lets software accomplish human capabilities such as thinking, reasoning, planning, communication, and perception more effectively, efficiently, and economically. • AI can now be utilized to perform more successfully general analytical tasks that have been handled by software for many years, such as finding patterns in data. • Most commercial sectors and consumer applications gain additional potential as a result of the automation of these skills. • Significant new goods, services, and capabilities include AI-enabled autonomous cars, automated medical diagnostics, voice input for the human–computer interface, intelligent agents, automated data synthesis, and improved decision-making. • Today, AI has a wide range of practical applications that help established industries improve their revenue and reduce costs. • The majority of applications will be found in sectors like financial services, retail and trade, professional services, manufacturing, and health care where a lot of time is spent acquiring and analyzing data. Applications for computer vision powered by AI will be particularly crucial in the transportation industry. As the potential of AI is realized, use cases are multiplying. There exist a lot of AI key application examples in eight industries: asset management, health care, insurance, law and compliance, manufacturing, retail, transportation, and utilities.

2.2 Advantages of AI AI is already being employed in a variety of commercial and industrial applications, such as automation, language processing, and production data analysis. This enables businesses to optimize their production processes, operations, and internal efficiency on a broad scale. AI operates using several computer programming rules that enable a machine to behave like a person and solve issues. Companies are interested in incorporating AI technologies into their operations because of the benefits it provides. The following are the advantages of using AI in the business sector:

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Processes are automated. Robots use artificial intelligence to automatically develop repetitive, routine, and process optimization activities without human interaction. It is really helpful for turning information into knowledge. It provides an updated and enhanced user interaction interface. It improves creative activities. AI relieves humans of routine and repetitive jobs, allowing them to devote more time to creative functions. It provides accuracy. Compared to humans, AI applications can offer greater precision. For instance, in industrial facilities, machines can now make decisions that were previously handled manually or observed without artificial intelligence. It lowers human mistakes. Artificial intelligence decreases shortcomings caused by human limitations. AI is utilized in certain manufacturing processes to identify tiny objects undetectable by the human eye using infrared sensors. It reduces the time spent on data analysis. It enables to analyze and use industrial data in real time. Predictive Maintenance. It enables the maintenance of industrial equipment depending on the times and circumstances of operation, hence increasing its performance and life cycle. Better decision-making both in terms of production and commerce. With additional details in a structured way, each person in charge can make choices faster and more efficiently. The management and improvement of production lines and processes. AI makes processes more effective and error-free, providing the organization with more control over its production lines. Better product quality and productivity. AI not only boosts machine productivity but also makes employees more productive and improves the quality of their job. With additional information, they can understand their task more clearly and make better judgments. Improved workflows. Deep learning technologies such as NLP and automatic speech recognition (ASR) have altered the way people operate. ASR enables attorneys to obtain transcripts of a three-hour deposition nearly immediately. Deeper data analysis. AI systems can process and analyze large volumes of data at incredible speed. They can uncover relevant information quickly, recognize patterns, make judgments, and give suggestions based on previous data, such as evaluating the efficacy of marketing materials and consumer preferences. 24/7 availability. Machines don’t take breaks for coffee or catch up with colleagues. Digital assistance solutions like chatbots are available to take customer inquiries at any time.

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2.3 Disadvantages of AI Some argue that Artificial Intelligence poses disadvantages. Especially if AI’s promise extends beyond merely mimicking human behavior. In terms of disadvantages, the following are some of the most prevalent that might arise in the corporate environment: 1. Data accessibility. For organizations aiming to develop value from AI at scale, data that is usually provided in isolation across firms or that is inconsistent and of poor quality presents a significant barrier. To get beyond this obstacle, having a clear plan from the beginning will be essential for rapid, well-organized retrieval of AI data. 2. It is expensive to implement AI. 3. The slow and expensive nature of software development for AI implementation adds to the challenges. Few competent programmers are available to develop software that uses artificial intelligence. 4. A robot is one of the applications of Artificial Intelligence with them, displacing occupations and helping to serve unemployment. 5. If machines are used improperly and the results are harmful to people, they could quickly result in disaster. 6. A lack of experts who are qualified. The lack of professionals with the knowledge and experience necessary for this kind of application is another obstacle to AI adoption at the corporate level. The presence of individuals with previous experience working on related projects is crucial in these circumstances. 7. The price and time required to implement an AI project. When considering whether to execute this kind of project, the cost of execution, both in terms of time and money, is an important factor. To obtain successful project outcomes, companies that require internal capabilities or are not familiar with AI systems may think about outsourcing installation and maintenance.

2.4 AI Applications AI has many applications. There is a list of Artificial Intelligence applications in different areas, as follows: 1.

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E-Commerce. Search engines for making recommendations are built with AI to help people better integrate with their customers. Based on their browsing history, preferences, and interests, these recommendations have been made. AI can reduce the likelihood of credit card theft by analyzing usage patterns. Education. The field of education is slowly being impacted by artificial intelligence. By producing and distributing lesson plans, audio and video summaries, and other learning materials, AI may contribute to a rich learning experience. Because of this, faculty members are now more productive and can concentrate more on students.

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Lifestyle. The way of living is significantly impacted by AI. Facial recognition technology with face filters is used by popular devices like phones, laptops, and PCs to detect and identify individuals in order to grant safe access. Numerous systems that are used daily to gather user data and provide individualized advice incorporate AI. 4. Navigation. Global Positioning System (GPS) technology may give users precise, timely, and thorough information, which can increase safety. Convolutional Neural Networks and Graph Neural Networks can be used concurrently. It makes users’ life simpler by recognizing the number of lanes and road kinds behind barriers. 5. Robotics. AI-powered robots that can detect impediments in their route using real-time updates might be employed to hold things in hospitals, industries, warehouses, cleaning offices, and inventory management. 6. Human Resource. AI assists in blind hiring. Applications may be assessed using machine learning software depending on specified factors. AI-powered systems may scan job candidate profiles and resumes to provide recruiters with an overview of the talent pool from which to pick. 7. Health Care. AI technologies are utilized in health care to build powerful computers that can detect diseases and recognize cancer cells. By examining lab and other medical data, artificial intelligence can help in the early detection of chronic disorders. By fusing historical data with medical knowledge, AI is utilized to create novel medication. 8. Agriculture. Artificial intelligence is used to identify nutrient deficiencies and soil faults. Applications for robotics, computer vision, and machine learning are used to accomplish this. AI bots may help harvest crops more quickly and in greater quantities than human employees. 9. Gaming. In video games, Artificial Intelligence can be used to create humanlike characters. The 2014-released Alien Isolation games use AI in pursuing the player at all times [24]. Two artificial intelligence systems, known as “Director AI” and “Alien AI,” are utilized in the game and are powered by sensors and behaviors that relentlessly pursue the player. 10. Automobiles. Autonomous vehicles are developed using AI. AI may be utilized to control the car in coordination with the camera, radar, cloud services, GPS, and control signals. By including features like emergency braking, blind-spot detection, and driver-assist steering, AI may enhance the in-car experience. 11. Social Media. Instagram uses AI to choose which posts to display in the Explore tab by looking at your likes and the accounts you follow. It can also be used to convert posts into several languages automatically. Using AI, Twitter suggests tweets to users based on the types of tweets they interact with. 12. Marketing. Using AI, marketers can provide highly targeted and tailored adverts using behavioral analysis, pattern recognition, and other techniques. Also, it helps in retargeting clients at the right time to get better results. AI-powered chatbots can evaluate the user’s language and answer in the same way that people do.

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13. Chatbots. Chatbots can understand natural language and reply to users who utilize the “live chat” option that many companies provide for customer service. Machine learning and chatbots work well together to create great websites and applications. They can successfully address client concerns, reply to basic inquiries, enhance customer service, and give 24/7 help. 14. Banking and Finance. The banking and finance sector was a pioneer in the use of Artificial Intelligence. Using AI bots, digital payment advisors, and biometric fraud detection techniques, a broader customer base can benefit from higherquality services. AI is frequently applied in banking to restructure businesses, reduce risks, and improve prospects. 15. Surveillance. Monitoring Tools for facial recognition that can be used for security and surveillance have been developed using AI. This enables automatic 24/7 working systems to witness the footage in real time, which might greatly improve public safety. Several countries use AI systems for surveillance purposes. 16. Entertainment. The entertainment industry mainly relies on the information gathered by consumers. AI technologies examine the contents of films frame by frame, identifying things to put suitable tags. This is done not just to provide correct recommendations but also to develop material that the majority of visitors would enjoy. 17. Space exploration. Numerous exoplanets, stars, galaxies, and most recently two new planets in our solar system have been discovered with the help of AI. AI systems are being developed to lower the risk to human life when exploring vast, uncharted areas of unknown space.

3 Integrating Blockchain with AI The thing about these AI and blockchain technologies is that they’re different. Up until recently, researchers had mainly focused on the unique applications of blockchain and AI in a variety of industries and businesses. Although the name “blockchain” was not stated at the time, it was originally discussed in the white paper Bitcoin: A Peer-to-Peer Electronic Cash System in 2008 [25]. Bringing AI and blockchain together, on the other hand, is a mixture of two completely distinct technologies. Nevertheless, connecting AI and blockchain could lead to great potential. In light of this, explainable artificial intelligence (XAI) research is gaining more attention from the research community [26]. As an exemplary application, the blockchain can therefore provide new potentials for increasing transparency [27]. On the other side, AI can assist in overcoming some of the difficulties that the blockchain faces as new technology. Besides, AI and blockchain will be used together and will work through their respective strengths. A platform for global employability [28] or a strategy that integrates blockchain and automated machine learning to deliver automated customer service [29] are some examples.

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The review of recent literature demonstrates that the opposite is also true, particularly for publications that discuss how AI affects and improves blockchain. There are currently few in-depth analyses or studies on the potential joint developments of blockchain and AI in the literature. Initial concepts were mentioned in publications from 2014 and 2015. The idea of combining blockchain technology and AI is still in its early phases of research. The literature on the role of blockchain in the context of AI was thoroughly reviewed by Salah et al. [30]. The blockchain consumes a lot of energy because it is a digital technology that relies on computers to function [31], but the technology could advance because artificial intelligence might be able to predict how long a transaction will take to complete. Group signature schemes [32] and ring signature schemes [33] were combined by Satoshi Nakamoto. On the basis of hash functions, Haber and Stornetta [34] suggested two methods for digital timestamping documents that make them impossible to forge. For instance, the concepts of digital signatures and public key cryptography were originally developed by Diffie and Hellmann in 1976 [35]. A blockchain consists of an ever-expanding collection of cryptographically linked data known as blocks. Each block [36] includes the cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree, where leaves represent data nodes). In order to be included in the hash, the timestamp confirms that the transaction data originated at the time the block was issued. Because each block contains information about the one that came before it, they form a chain, with each new block reinforcing the ones that came before it. As a result, blockchains are resilient to data manipulation since the contents of any one block, once recorded, cannot be modified unilaterally without impacting all following blocks. Blockchains are often used as a publicly distributed ledger on peer-to-peer networks, with nodes collectively adhering to a protocol to deal with and verify blocks. Although blockchain records are not unchangeable owing to the probability of forks, blockchains are secure by design and constitute a distributed computing system with high Byzantine fault tolerance [37]. A blockchain can safeguard title rights since it provides a record that needs a request and acceptance when properly set up to establish the trade arrangement. When someone initiates a transaction using a blockchain, the requested transaction is broadcasted to a peer-to-peer network consisting of computers, known as nodes; then, the transaction is verified by participants of the blockchain. Once verified, the transaction is integrated with other transactions to create a new block of data for the ledger; the new block is then added to the existing blockchain in a way that is permanent, and the transaction is completed, as shown in Fig. 2. Currently, blockchain is widely adopted in various fields, and has high expectations [38–41]. There are four kinds of blockchain networks: public blockchains, private blockchains, consortium blockchains, and hybrid blockchains. Blockchain is characterized by the following properties: immutable—this term describes the blockchain as an everlasting and unchangeable network. Distributed—for complete transparency, a copy of the ledger is present on every node in the network. Decentralized—because the blockchain network is decentralized, no single body will be in charge of making all the choices. The network is instead created and maintained by

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Fig. 2 A blockchain transaction

a number of nodes. Secure—each individual record on the blockchain is encrypted. Consensus—every blockchain has a consensus, which enables the network to make decisions quickly and fairly. Consensus is a decision-making technique that helps a network’s active nodes come to an agreement; faster settlement—compared to conventional banking systems, blockchain enables a faster settlement. On the other hand, AI is characterized by the following properties; centralized decisions based on given data and implemented algorithms, changing reactions according to the situation, probabilistic outcomes in some cases, and data and knowledge are centric and not decentralized across all parts of an AI system. Many of the shortcomings of blockchain and AI can be successfully addressed by combining two technical ecosystems. Virtually every organization that uses them benefits from the AI and blockchain combination, which is proving to be strong enough. AI algorithms depend on data or information for learning, inference, and decision-making. Machine learning algorithms work better when data are collected via a platform or data repository that is reliable, safe, trustworthy, and credible. Data can be stored and exchanged on the blockchain serving as a distributed ledger that is cryptographically signed, verified, and accepted by all mining nodes. High integrity and resilience are ensured when storing blockchain data so that it cannot be altered. The results of decisions made using smart contracts for machine learning algorithms may be relied upon and are unquestionable. Combining blockchain and AI can build a safe, immutable, decentralized system for the very sensitive data that AI-driven systems must acquire, store, and use. AI can efficiently mine a large dataset in order to generate novel scenarios and identify patterns in data behavior. Effectively removing errors and false datasets is made possible by blockchain. A decentralized blockchain infrastructure can be used to authenticate new classifiers and patterns produced by AI. Any business that interacts with customers can use this, including retail transactions. Blockchain infrastructure can be utilized to collect client data, which can then be leveraged to power marketing automation with artificial intelligence. Blockchain and artificial intelligence are joining to improve and secure data and information in a variety of industries, from media royalties and financial security to medical, personal, banking, trading, and legal data. For the purpose of running machine learning algorithms and tracing data that is kept on decentralized P2P storage systems, AI can profit from the availability of numerous blockchain platforms. These data are often generated by smart connected products, which can be anything from IoT devices to swarm robots

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Table 1 Blockchain, AI features, and their integration characteristics Blockchain features

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Immutable Decentralized technology Secure Distributed ledgers Consensus Faster settlement

AI features

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Centralized Changing Probabilistic Data, knowledge, and decision centric

Integration characteristics • Making decisions intelligent, trusted, and undisputed • Creating a secure, immutable, decentralized system for highly sensitive information • A large dataset can be efficiently mined by AI in order to generate novel scenarios and identify patterns in data behavior • A decentralized blockchain infrastructure can be used to authenticate new classifiers and patterns produced by AI • Marketing data stored on blockchain using AI • AI can be used for tracing and performing analytics and visualization for data that are stored on blockchain • AI and blockchain will provide a double shield against cyberattacks

to smart buildings, cars, and cities. The cloud’s capabilities and services can also be used for off-chain machine learning analytics and wise decision-making, and data visualization. Since AI and blockchain will act as a double barrier against cyberattacks, the integration of AI and blockchain has an impact on many areas, including security. Table 1 illustrates the features of Blockchain, AI, and their integration characteristics. The integration of AI and blockchain results in possibly the most trustworthy technology-enabled decision-making system ever created, one that is essentially tamper-proof and offers dependable insights and conclusions. The influence of AI on blockchain offers a number of advantages, including • Improved Security. Blockchain technology gets safer with the use of AI by enabling secure application deployments in the future. Blockchain-based data stored is very secure. Blockchains are well known for securely storing private and sensitive data in a diskless setting. Data in blockchain databases are digitally signed; therefore, the “respective private keys” need to be kept safe. This enables AI algorithms to operate on secure data, resulting in more reliable and credible decision-making. Additionally, AI algorithms are increasingly determining if financial transactions are fraudulent and require blockage or investigation. • Increased Confidence in Decisions. Any decision made by AI agents that are challenging for customers or users to comprehend and believe becomes dysfunctional. With the certainty that the records have not been tampered with during the

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human-involved auditing process, blockchain is well known for recording transactions in decentralized ledgers on a point-by-point basis. This makes it simpler to accept and trust the decisions made. Increased transparency and public confidence in the ability to understand robotic conclusions would result from the recording of an AI system’s decision-making process on a blockchain [42]. In a swarm robotic ecosystem, where the swarm can reach consensus in a completely decentralized way, the necessity for a third-party auditor can be avoided [42]. Making decisions collectively. Without the need for a centralized authority, distributed and decentralized decision-making algorithms have been deployed in numerous robotic applications. Robots make decisions by casting votes, and the results are decided by the majority. Since blockchain is available to all robots and every robot has the ability to vote in the form of a transaction, it may be used to verify the outcome of voting results. All robots in the swarm proceed in this manner until a swarm reaches a decisive conclusion. Greater Effectiveness. More than US$650 million has been invested by Deloitte [43] in developing future audit technologies, and the company has also created the AI-based Auvenir platform. It’s possible for intelligent systems to predict instantly which node will carry out a specific task first. Other miners might be able to stop working on that particular transaction at the same moment, lowering overall costs. Additionally, even with some structural limitations, better efficiency and lower energy usage can lessen network delays and accelerate transactions. Better Management. Human experts improve over time with expertise in deciphering codes. A mining technique powered by machine learning may do away with the need for human expertise because, given the right training data, it could almost instantly improve its skills. AI thus also aids in better management of blockchain networks. Higher data privacy standards in markets. Making private data secure always results in its sale, giving rise to data markets and model marketplaces. Markets now have simple, secure data sharing, which benefits smaller players. By using “Homomorphic encryption” algorithms, blockchain privacy can be further strengthened. The only algorithms that allow operations to be carried out directly on encrypted data are homomorphic ones. Enhanced Storage. When processed carefully by AI, very sensitive personal data can be stored on blockchains, adding value and convenience. An excellent illustration of that is smart healthcare technology that provides diagnoses based on medical records and scans. Cut Down the Probability of a Catastrophic Event. How can there be a guarantee that AI will thrive and continue to evolve despite all the benefits it will receive from interacting with blockchain technology and data democratization (as well as open-source software)? AI was developed in an open-source setting where data served as the actual moat. Decentralized Intelligence. When making wise high-level decisions that involve multiple agents to perform subtasks that have access to the common training data (e.g., in the case of supervised learning), different individual cybersecurity AI agents can be combined to provide fully coordinated security across the underlying

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Fig. 3 The benefits coming from the impact of integrating AI and blockchain

networks and to resolve scheduling issues. Figure 3 displays the benefits coming from the impact of integrating AI with blockchain.

4 AI with Blockchain for Business Transformation Exponential technologies are amazing and have the ability to enhance humanity, but as the advantages grow, so does the risk of “negative convergence.” To integrate blockchain and AI, a digital backbone for extended ecosystems can be created, an intelligent business network can be used, and the digital business components can be integrated by connecting the internal enterprise, the Internet of Things (IoT) connected devices, and the external business ecosystem. An intelligent business network helps businesses automate and track their data and is characterized as • Pervasive: Creates a digital backbone that enables the digital exchange of information and offers seamless integration into company systems • Connected: Enables a highly networked ecosystem of people, systems, and things with digital identities and cybersecurity • Intelligent: Provides a confident environment in which deep insights into supply chain performance can optimize business processes and learn to react more efficiently to different and unexpected situations • Trustworthy: Provides a single source of truth regarding the traceability of goods as they move through the physical supply chain and an archive of all digital interactions between a company and its trading partner community.

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Today, consumers are becoming more aware of technical tools, abilities, and language, prompting firms to embrace consumer-centric approaches. People are becoming less loyal to conventional financial service providers as technology gets increasingly consumed. According to a 2017 PwC Global Fintech Report, the report found that over 80% of incumbent financial institutions in the United States and Europe in 2017 [44] saw their current business at risk from the introduction of disruptive new technologies. With their extensive experience and reach, these major platforms [45] pose a serious threat not only to small fintech companies but also to large, established institutions, particularly banks. Facebook, Amazon, Apple, and Google platforms have all been able to exploit the information they acquire about their customers to deliver increasingly sophisticated services. In fact, these platforms appear to be developing their own self-contained digital ecosystem, which has the potential to substitute banks. The major corporations are attempting to determine how to remain competitive. The governments are attempting to build a regulatory framework that is suitable for emerging market players. Smaller businesses are integrated into a broader ecosystem as a consequence of increased connection among all players engaged in the value proposition, which provides better coordination opportunities. Mobile wallets, such as Google Wallet or Apple Pay, are gaining strength among consumers, allowing them to make credit or debit card payments using their smartphones. Mobile payment solutions are rapidly being used in loyalty cards, airline passes, concert tickets, discounts, and a range of other applications. In the second quarter of 2018, Venmo, a payment network located in the United States, handled transactions totaling $14 billion dollars, up 78% year-on-year growth. Some of these peer-to-peer technologies are being integrated into social media networks as well. Ripe is a third-party payment provider for online marketplaces, ranging from huge retailers like Amazon to small businesses that wouldn’t be able to create their own infrastructure to sell their products online and reach such a wide audience worldwide. The business benefits as well because most businesses that establish their own checkouts commit mistakes or do so in inefficient ways. Payment systems have become faster and more efficient as a result of the introduction of virtual currencies. Stripe makes performing a payment from a mobile device as simple as clicking a button. The faster you can trade, the less you have to pay in transaction costs. For example, if a transaction is time-stamped with information that is shared or distributed through a ledger, the transaction is irreversible, and there is no dispute about who received how much. Virtual currencies let funds be transferred directly, safely, and economically to anyone, anywhere in the world. Jorge investigates the potential benefits of Blockchain in general and the Ripple network in particular, whose transaction currency, Ripple (XRP), has gained the interest and engagement of significant worldwide banks, in a new insight article [46] published by Institute de Estudios Superiores de la Empresa, IESE. Many companies are trying to create new ways to give people more affordable and efficient insurance. The insurance company can use telematics (or the Internet of Things) to determine the customer’s activity and make adjustments in their rates accordingly. Artificial intelligence enables detailed data analysis and fine-tuning of

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premiums to meet each consumer, so countering the problem of homogeneity. The tremendous volume and availability of real-time data also enhance the accuracy of risk assessments, allowing for more timely and personalized coverage. Companies can lower their fixed expenses and control the variables by being able to establish pricing in a precise, individualized manner. Telematics or the Internet of Things is used to communicate that activity to the insurer. An insurance company uses telematics or the Internet of Things to track the activity of the car to insure the driver. The vast expansion and accessibility of realtime data also increase the accuracy of risk assessments, allowing for more timely and personalized coverage. Robot process automation (RPA) can help companies make decisions more efficiently with less human intervention. There are plenty of platforms that are using algorithms and big data to generate far more cost-effective and precise financial suggestions. Its capacity to monitor data from social media networks and analyze natural language has also aided insurers in detecting fraud. RPA can improve the accuracy of accounting records by decreasing manual errors and gathering data from several registers automatically. Employees can dedicate more energy to more creative, innovative tasks when repetitive, systematic operations are automated. Smart contracts are particularly useful for supply chain management since they can automatically monitor product location, measure standards, record movements, and check quality. Because verification and compliance occur automatically, fraud or unauthorized alterations were becoming increasingly difficult or obvious. Because these contracts may be programmed as a sequence of if-then stages, no action will be taken if your preset requirements aren’t satisfied. Smart contracts take the place of traditional contracts, allowing for more efficient and less expensive execution. Blockchain can enhance data security through the decentralization of data storage and also helps weed out cyber threats before they cause harm. There are some drawbacks, but Blockchain’s decentralized data storage is one of the many benefits. The amount of data available on the Internet is immense, and even Internet heavyweights like Facebook and Google frequently fail to separate it. The acquisition of a suitable dataset for training a neural network is currently the most difficult task in data science. Blockchain is a data management system that allows data to be structured and qualified, as well as provides transparency and traceability. It also allows direct control of data access via Identity Document (ID) authentication and a public/private key architecture. A paper from the European Union Blockchain Observatory discusses how private blockchains could help comply with the General Data Protection Regulation in Europe, with the intention of protecting individuals’ personal data. AI can create a blockchain for data and decision-making, resulting in a more efficient and transparent governance model for automated and quick verification of data/value/asset transfers among various stakeholders. These firms may collect user data and utilize it to develop and improve AI algorithms. Insufficient transparency contributes to customers’ increased worry and lack of confidence in new technology. This results in significant inequalities and market distortions when it comes to utilizing AI’s expansion. Using blockchain technology, AI can be made more trustworthy by distributing ownership of data and breaking the oligopoly of large platforms. It can be a platform

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to support individual rights while benefiting from the aggregation of vast amounts of data from the Internet of Things. Supply chains’ industry is an industry that will bring great opportunities to the world, due to the convergence of artificial intelligence, Blockchain and IoT. Because of the complexity of the industry, the technology is straining the operation. IoT is the critical basis for connecting the physical and digital worlds through data gathering and transfer. Blockchain technology can offer the trusted registry required to ensure AI learning is based on reliable and “clean” data. Each transfer point in the supply chain monitors the state of the IoT devices and records the data on the Blockchain. Blockchain technology has the potential to speed the development of IoT and AI data exchanges by offering a more efficient, secure, and transparent data storage and management alternative, as well as enabling a platform for value and asset exchange. Enterprise blockchains can grow across sectors, overcoming silos and simplifying administrative procedures for the supply chain’s financial and physical layers. Most of the companies that use blockchain-enabled supply chain platforms are not open to the public but require each participant’s permission before any information is shared. In the development of enterprise or consortia distributed ledger technology solutions, there is a concern that market domination by a few big players could lead to oligopolistic behavior. The Internet of Things is the basis for connecting the physical and digital worlds through data gathering and transfer. Blockchain may serve as a trusted registry, ensuring that AI learning is based on reliable and “clean” data. The state of IoT devices is tracked at each transfer point in the supply chain, and the data is stored on the Blockchain. Blockchain technology has the potential to speed up the maturation of IoT and AI data exchanges by offering a more efficient, secure, and transparent data storage and management alternative, as well as a platform for the exchange of value and assets. Enterprise blockchains can rise across industries and the supply chain, breaking down barriers and simplifying administrative procedures for the supply chain’s financial and physical layers. Additionally, private blockchains have a trade-off between their ability to have “privacy by design” and their reduced security. The data that goes on the chain is controlled by people who decide what gets added to it. It is assumed that these decisions may be more susceptible to explicit or implicit biases regarding the information entered into the algorithms. When making people more likely to open bank accounts, banks may be making it harder for people who are new to get into the banking system. In the design of collecting and processing information that goes into AI decision-making, bias should be avoided. The need for improved trust across global value chains has been well documented, and Blockchain, IoT, and AI can enable an automated trust mechanism to confirm user identity and the source of items, assuring data transparency and utilization. There is 80% less data entry needed for transport documents, streamlining required checks of cargo and customs, and industry players are forming consortia to test solutions and develop common standards for the food, pharmaceuticals, logistics, and finance industries. It is critical to utilize a single electronic document rather than a traditional paper document to bring all parties engaged in a commercial transaction together. A single electronic document must have all of the properties of a typical

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paper document [47]. Process integration is achieved by combining all trade documents into a single universal international trade document. The blockchain idea is well-suited to online documents that allow for multi-party usage. Transactions may be completed without the involvement of a trusted third party, thanks to blockchain technology. The use of blockchain technology to combine many documents will change the integration of multilateral business operations. The use of blockchain technology in supply chain business operations is expected to result in considerable cost reductions and the elimination of wasted time [2]. Therefore, there is increasing research on the use of blockchain technology in the supply chain. New projects typically focus on payment methods such as blockchain-based electronic bills and similar documents and letters of credit. However, the electronic processing processes of a foreign trade business do not require the issuance of a separate document for each transaction. You can also combine the functionality of all documents into one document [47]. This claim is also supported by the decentralized multilateral nature of blockchain technology. Blockchain technology is referred to as the Second Internet Revolution [48] because of its devasting potential [2]. The seamless integration of corporate processes in international trade is made easier by blockchain technology. The absence of trusted third parties is another benefit of blockchain technology. Before blockchain technology, a centralized authority was required to bring together all the parties involved in a certain international commerce transaction. Some transactions can be handled automatically by utilizing artificial intelligence technology in this one single tamper-proof integrated document [47]. Blockchain and artificial intelligence are working together to provide a way for total integration. Paper documents and complex company procedures may be abolished due to these disruptive technologies. Blockchain technology enables the creation of safer and more straightforward payment mechanisms. With the aid of these new payment methods, you can replace traditional paper documents used in international trade (such as bills of lading, invoices, certificates of origin, analysis certificates, and packing lists) with a single multipurpose electronic foreign trade document. Finally, the promotion of trade comes from the deletion of several foreign trade documents. A single platform connects all parties engaged in a trade transaction, enabling them to execute the operations in a single integrated electronic document. Artificial intelligence can also execute transactions on the connected platform without human interaction. One may refer to this platform as an intelligent trading matrix. Integration and tracking in transportation are made possible by the merging of intelligent trade matrix systems with autonomous logistics and warehousing applications. All physical components of the supply chain are connected to an intelligent trading matrix system using the Internet of Things. Therefore, all of these procedures may be optimally carried out by artificial intelligence without human involvement. Figure 4 shows the processes involved in cross-border trade transactions, such as first, the commercial transaction between exporter and importer that includes an offer, purchase order, invoice, etc., second, the trade financing banks, which include a letter of credit, bill of exchange, etc., third, the transport logistics providers, which include bills of landing, insurance policy, etc., fourth, the official control measures between ministries, customs, and so on, which includes sanitary and phytosanitary

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Fig. 4 Processes involved in cross-border trade transactions

certificates, conformity certificates, certificate of origin, and import/export licenses. Figure 5 shows the traditional trade transaction between an exporter and an importer. The exporter deals with an exporter’s bank to enable financial transactions; then, he goes to the export’s customs for the exportation of goods; after that, the goods are transported by truck transporter to the export port. Through the export port, goods are transported either by trucks, vessels, or airplanes to reach the import port; then, these goods are transported through a truck transporter from the import port to the import’s customs to reach the importer. Also, the importer is dealing with an importer’s bank for financial transactions.

Fig. 5 A traditional trade transaction

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Fig. 6 Difference between traditional payment and blockchain-enabled AI payment

Figure 6 illustrates the difference between traditional payment and Blockchainenabled AI payment. The traditional payment includes documents, such as sales contracts and shipment between importer and exporter, request for letter of credit and claim of payment between importer and importer’s bank, letter of credit issued from the importer bank, document control, release of payment at maturity and present documents between importer’s bank and exporter’s bank, and letter of credit and present documents between exporter’s bank and exporter. At the same time, the Blockchain-enabled AI payment depends on a permissioned distributed ledger, including encrypted smart contracts for payments. It is obvious from the preceding explanation that fusing blockchain technology with IoT devices and AI can create new business models for generating revenue from IoT devices. There has been a paradigm shift in business strategy as a result of the introduction of next generation technologies and its focus on data provision, driving insights toward competitive advantage. There has been very little research on the operational use cases, integrated applications, challenges, and business consequences of significant next generation technologies that firms have utilized in isolation for business transformation [49]. An illustration of how the healthcare sector has benefited from digital transformation is the provision of high-quality patient care, electronic health records (EHRs), digital imaging, and prescriptions, as well as increased access to historical and real-time data to deliver better and more secure services [50]. Next generation technology-based digital business transformation has already had an influence on a variety of industries. Several examples from real life about adopting digital transformation, including disruptive technologies, such as AI And Blockchain, are going to be discussed. For example, the automotive sector is striving to compete with disruptive automakers such as Tesla and Faraday Future. To keep ahead of

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their competitors, any long-established manufacturer today knows the importance of combining digital technology with conventional processes. Audi launched “Audi City,” in which the implementation of digital transformation in sales, marketing, and operations is done to better match local demand. Disney launched a magic smart wristband that provides smart wristbands for individualized client experiences in Disney World resorts. The UK-based company Argos remodeled five of its storefronts to make them digital stores, making it easier for consumers to shop. For analysis, the Kensho company initiated an analytic software to employ big data and machine learning. Datum is launching a blockchain-based marketplace in Hong Kong where users may share and sell data on their own terms. Deep Brain, a Singapore-based organization, is developing a distributed AI computing platform that is low-cost and privacy-protecting to train AI utilizing AI and blockchain technologies. Cortex, a Chinese company, offers a low-cost, off-chain option for AI research. A decentralized cloud storage provider allows people to store a copy of their data from a number of different devices for safekeeping. McKinsey Solutions is a software and technology-based analytics company that offers businesses software and analytic solutions to improve benchmarking, pricing, and promotional efforts. IBM’s Watson computing platform is being used by KPMG to improve professional services. Cyware Labs is a company that leverages blockchain and AI to assist users in staying safe online. Verisart certifies and verifies works of art using blockchain technology and artificial intelligence. AI is being used by US-based Vytalyx and BotChain to give medical professionals blockchain-based access to healthcare intelligence and insights to improve data security. Lemonade’s “social good” component is one of the things that appeals to millennials in particular, and it sets the tone for the business relationship. Users go to the Lemonade app and submit a claim via a chatbot, which compares the claim to the policy and runs it through numerous anti-fraud algorithms before determining whether to approve it or not. Claimants must take an oath of honesty and speak on camera, which makes people less likely to lie or perpetrate insurance fraud. To infuse positive psychology into the system, the founders drew on insights from well-known behavioral economist Dan Ariely. The above example demonstrates the creative possibilities for developing more socially responsible alternatives to typical financial exchanges that reduce adversarial tendencies. If no one in the group files a claim, everyone in the group receives monetary cash back. The group’s reserves decrease as claims are paid out, but no one ever pays more than their premium. Artificial intelligence allows for granular data analysis and fine-tuning of premiums to meet each consumer, addressing the issue of homogenization. This is already happening in the drone insurance industry, with Flock insuring drones on a pay-as-you-fly basis to enhance process efficiency and customer service. Audi, for example, acquired significant advantages by implementing digital transformation in sales, marketing, and operations, allowing them to satisfy local demand [51] better. Metal factories harness the potential of digitalization to boost production rates by visualizing performance, optimizing processes, and gaining insights into

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failure reasons [52]. Pharmaceutical producers are now consuming less production space, and quality control has improved due to the ease of detecting counterfeit medications and chemicals. The consumer packaged products industry has also benefited from digital transformation by getting more connected to its customers and building longer lasting connections, which reflect in repeated business and increased satisfaction [49]. Consumers benefit from speedier reaction times as a result of improved distribution systems with a huge decrease in cost. For example, instead of 40 people, ten employees may now complete the same activity in half the time, resulting in much cheaper operational expenses. The defense sector has also benefited greatly from the development of digital tools that facilitate information exchange and cooperation among suppliers in their complicated supply chain networks [52]. GrainChain, for example, is a software solution that combines Internet of Things data, market data, and farmers’ data into a blockchain platform for agricultural commodities market transactions. The system promises to bring together banks, insurers, vendors, cooperatives, exporters, and farmers on a single platform through the use of smart contracts [53]. With the help of a digital wallet that enables distant and unbanked farmers to gain access to a financial exchange system with numerous trading partners for the first time, its flagship pilot project in Honduras focused on the coffee supply chain tracking and contract brokering between farmers and coffee buyers. Another company that is disrupting the industry is Trade in Space, which uses smart contracts with satellite data to enable peer-to-peer commodity trading. Through the utilization of Sentinel 2 images for productivity tracking, the company’s TradeWinds platform, built on Hyperledger, is now providing services for Brazilian coffee producers and commodity traders. The future target market for these next generation and similar blockchain-based applications (so-called farm-to-fork) is the supply system management for agricultural commodities. For commodities like corn, wheat, soy, sorghum, coffee, cotton, and livestock, there are already a variety of solutions being developed and tested by both innovative start-up companies like GrainChain, Bext360, AgriLedger, AgriDigital, and Trade in Space, and major corporations like Starbucks, which intended to put the entire supply chain on the Blockchain. Industry heavyweights like Unilever and Nestle have committed to complete supply chain transparency via supply chain food provenance on Blockchain in line with this trend. Blockchain can improve supply chain dynamics and close the knowledge gap between trade players. The suggested approach makes use of the OpenSC blockchain platform from the World Wildlife Foundation (WWF), which was developed by the WWF and Boston Consulting Group Digital Ventures to offer monitoring and verification services for sectors industries ranging from palm oil to fisheries [54]. Digitally advanced companies like Google, Netflix, Uber, and Airbnb have successfully established and used their digitized, open, and participatory business models, which are embedded into a networked ecosystem of suppliers and customers. NetObjex [55] is a company that is developing technology for smart cities. They make the technology by combining AI, Blockchain, and IoT. Devices are connected to cloud-based services using this technology, including weather services.

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Smart Cities, Transportation/Automotive, Supply Chain and Logistics, and Manufacturing/Industry 4.0 are the four vertical markets that NetObjex focuses on. The platform makes it possible to track and trace assets, enhance asset performance, maximize asset utilization, and lower shrinkage, waste, and inefficiencies associated with assets. The platform supports all of the main communication protocols, including features like a rules engine, alerts and messaging, business integration, blockchain integration, and more. Smart electricity meters, dynamic wireless electric vehicle charging, smart parking, smart media, fleet management of kiosks, powering smart medical equipment, and more solutions have been developed utilizing NetObjex. By utilizing these cutting-edge skills in the area of digital asset management, NetObjex has a force multiplier. Traditional IoT systems’ present restrictions are removed by this solution by offering • • • •

A Blockchain-based Universal ID A decentralized ledger for device discovery and authentication Support for short-range protocols to enable connectivity between devices Devices with optional digital wallets for transactions (your automobile, for example).

Due to the vast array of features, NetObjex is better able to assist its clients in enhancing asset performance, usage, efficiency, tracking, and traceability. IoT and artificial intelligence (AI) work well together because the IoT is an excellent data acquisition mechanism for AI. AI is a technology that requires a lot of data. The NetObjex team is enthusiastic about telecommunication advancements like Fifth Generation (5G) since they create new communication avenues. The sheer amount of data that needs to be processed is the issue in AI, and the NetObjex team adopts new, more effective methods of storing, retrieving, and analyzing data. In addition, NetObjex creates solutions for smart edge gadgets that offer localized processing power and intelligence at the edge of IoT networks. Table 2 depicts digital transformation as it has been applied in several industry areas. Table 2 Digital transformation in various industries Industry sector

Company name and its product/service

Digital technologies

Smart cities

NetObjex—decentralized cloud storage provider

Developing technology for smart cities by combining AI, blockchain, and IoT

Cybersecurity

Cyware Labs—online security

A company that uses AI and blockchain to help people stay safe online

Artistic works verification

Verisart—checking artistic works

Uses artificial intelligence and blockchain technology to certify and verify works of art (continued)

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Table 2 (continued) Industry sector

Company name and its product/service

Digital technologies

Health care

US-based Vytalyx and BotChain—security data in health

Are using AI to provide healthcare professionals with blockchain-based access to healthcare intelligence and insights

Automobiles

Audi—Audi City

Implements digital transformation in sales, marketing, and operations to better match local demand

Management consulting

McKinsey Solutions—software and technology-based analytics

Offers businesses with software and analytic solutions to improve benchmarking, pricing, and promotional efforts

Retail

Argos—digital stores

Remodeled five of its storefronts, making it easier for consumers to shop

Business consulting

KPMG—Watson Cognitive computing platform

IBM’s Watson computing platform is being used by KPMG to improve professional services

Technology

Kensho—analytic software

For analysis, the company employs big data and machine learning

Entertainment

Disney’s magic bands—smart wristband

Providing smart wristbands for individualized client experiences in Disney World resorts

Agriculture commodity markets

GrainChain—transactions in the agriculture market

Combines Internet of Things data, market data, and farmer data into a blockchain platform for agricultural commodities market transactions

Agriculture surveillance

TradeWinds—a Hyperledger-based platform for services to Brazilian coffee producers

Showcases services for Brazilian coffee producers and commodity traders that use Sentinel 2 images for production monitoring

Blockchain-based marketplace Datum—blockchain marketplace

Uses blockchain technology in market transactions

A distributed AI computing platform

Developing a distributed AI computing platform that is low-cost and privacy-protecting utilizing AI and blockchain technologies

Deep Brain—computing platform

(continued)

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Table 2 (continued) Industry sector

Company name and its product/service

Digital technologies

Decentralized cloud storage provider

Cortex—decentralized cloud storage provider

A decentralized cloud storage provider that allows people to store a copy of their data from a number of different devices

Social business-oriented application

Lemonade app—social Runs numerous anti-fraud business-oriented application algorithms

The drone insurance industry

Flock insuring drones company—insurance

Uses drones with predictive risk analytics to enhance process efficiency and customer service

Supply chain transactions

Innovative start-up companies like GrainChain, Bext360, AgriLedger, AgriDigital, Trade in Space

Uses blockchain to track the whole supply chain

Food

Large corporations such as Starbucks

Chose to use blockchain solutions to track the whole supply chain

Food

The industry giants such as Unilever and Nestle

Have committed to complete supply chain transparency using blockchain

Agriculture and animal monitoring

The WWF and the Boston Consulting Group Digital Ventures—founded the WWF’s OpenSC blockchain platform

Uses a mix of satellite images, live video surveillance, and worker biometric data

Entertainment, transportation, and lodging

The most digitally advanced firms, such as Google, Netflix, Uber, and Airbnb

Established participatory business models, which are embedded in a networked ecosystem of producers and consumers

5 AI with Blockchain for Sustainable Circular Economy Circular Economy as a Sustainable Economic model concept was initiated to tackle sustainability challenges. Building wealth and at the same time harming the environment leading to the need for alternative economic models [56], circular economy has become of increasing interest in recent years. The circular economy is a concept and practice still being developed in research and implementation [57]. There is currently no agreement on the notion and its definition [58, 59]. In the revised literature, Homrich et al. [59] discovered over 20 definitions of the circular economy; Kirchherr et al. [58] discovered up to 114 definitions. Despite differing perspectives and definitions, everyone agrees that resource loops should be closed and expanded rather than degraded or wasted [60]. The following definition serves as the basis for the following study, and the term circular economy refers to a resilient industrial

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economy in terms of intent and design. In the circular economy, products are designed to be easily reused, dismantled, readjusted, or recycled, so reusing large amounts of material from used products rather than extracting resources can be considered the basis for economic growth. There exist specific strategies and actions in discourse. The three R-strategies of reducing, reusing, and recycling have received a lot of attention [58]; occasionally, a fourth strategy called recovering has been added. Reducing involves actions like the material redesign, rejection, and review that occur during the early development and consumption phases. Reuse activities are those that close the material cycle and repair the product. Reusing waste and other materials is included in recycling. When materials are burned, recovery also includes energy recovery. Some research [58, 60] went one step further and summarized the “Resource Life Extension Strategies” framework, which included the R-strategies, servicing and product longevity techniques, material cascades, and waste-to-energy principles. Blomsma and Tennant [61], who suggested the resource states and frameworks centered on products, parts, and particles, provided a different perspective. Since 2013 [57], the Ellen MacArthur Foundation has held practical conversations on the circular economy, covering both broad issues and specific opportunities in certain industries or geographical areas. Business growth is positively impacted by the circular economy. Consultants, governments, and non-governmental organizations (such as PwC, the European Commission, and the World Economic Forum) define a circular economy, offer guidance on its principles, discuss potential economic and social consequences, and gather case studies of success. They are all intrigued by the promise of the circular economy [62]. Numerous industries, including plastics, building, food, important raw materials, and biomass, are also seen as crucial in a circular economy. There is ongoing discussion over whether the current economic structure can be improved incrementally rather than adopting fundamental changes to support a circular economy [58]. According to Sihvonen and Ritola [63] and Van Buren et al. [64], it is cited that the circular economy adds to the debate around alternate economic growth models and that another R-framework comprises three to nine R-strategies. Some aspects of traditional economic growth and property rights’ assumptions are threatened by the circular economy. The concept of a circular economy can be seen as a means to design an economic model that increases the efficiency of production and consumption, through the use, recycle, and exchange of resources appropriately, and do more with less. As such, a circular economy can be an activity that aims to make more efficient use of resources, regardless of whether it is achieving limited growth, decline, or a fixed economy. The circular economy’s major areas of intervention are the economic and environmental dimensions of sustainability, whereas the social dimensions and future generations are hardly addressed [58]. Others think that the notion of a circular economy lacks social and ethical components. Social and institutional factors, according to Murray et al. [65], are often ignored. This prompts calls to address the connections between a stronger circular economy and issues of sustainability [58, 60].

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The circular economy and blockchain technology, along with other disruptive technologies, have emerged as new and separate phenomena in the real world and new and distinct bodies. The potential of blockchain technology in circular supply chains when paired with digital technologies has been the subject of several publications. The ability to reverse cycles to gather resources for reuse or recovery, new business models, and underlying system conditions must all be satisfied in order to integrate the circular economy. The infrastructure for information exchange and collaboration platforms is one of the system’s criteria. Because transparent and shareable information provides the foundation for developing multiple flows of resources and materials, this infrastructure is critical for a circular economy. Blockchain technology may be used to power such an Information Technology (IT) infrastructure. By developing a shared information infrastructure on a blockchain, the technology can enable the alternative provision of renewable inputs and improve resource efficiency. By tracking the flow of resources and materials across supply chains and consumption stages, the technology can also assist manufacturers and consumers with material recovery, particularly remanufacturing and recycling. Blockchain does not handle other aspects of the circular economy, such as sustainable design and optimizing product usage [62]. Scenarios of how blockchain technology can be applied for a CE depend on sharing and updating information using databases linked in decentralized, open-access, peerto-peer networks. Blockchain was developed to make a guarantee that data is saved and updated securely, permanently, and in a tamper-proof way. Since blockchain technology is still in its early stages and the field’s research has grown rapidly, it is crucial to evaluate the ethical and long-term effects of blockchain growth and use. The CE gives a high priority to enhancing sustainability and social responsibility in addition to economic progress. By lowering transaction costs, strengthening supply chain performance and communication, protecting human rights, improving patient privacy and well-being, and reducing carbon emissions, blockchain technology has the potential to contribute to CE. The CE promotes a positive human future by utilizing technology and smart design to maximize resource efficiency and reduce waste. Focusing on innovative ways, such as sustainable resource development and distribution, product replication, and recycling, is one way to make the CE more successful. Another option is to ensure that recycled items purchased by companies and customers are not created from raw materials. It is likely to revert to the present linear economy in the absence of transparency and reliability. This trust is likely to grow as a result of blockchain. Blockchain design helps with two main uses of CE, that is, verifying the origin of a product and promoting constructive behavior regulation. The CE supports the sustainable management of resources, waste reduction, and resource recycling. Transparency is required to link these applications and behaviors in order to manage trust in the bought items and the people from whom items are bought. More transparency about product origins would encourage constructive feedback and force companies to change how they bring in sources. Blockchain is viewed as

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a basic technology for creating a clean digital supply chain that gives a complete record of goods transactions. Rewarding cyclical purchase, use, or disposal is one way to promote CE. Natural resources may be tokenized using blockchain technology, which gives them a distinct digital identity that can be used in financial transactions. This approach makes the value of resources more apparent, allowing a new mechanism for valuing and trading natural resources. It also directs individuals to adopt circular behaviors. Data from IoT devices or Radio Frequency Identification (RFID) tags can be stored on the blockchain. This connection to the real world poses difficulties, particularly in terms of data quality. IBM and Maersk have developed a blockchain-based global supply chain system that aims to sell 10 million supply chain events each week. By delivering product assurance and dynamic enhancement, blockchain has the potential to turn CE into a sophisticated ecosystem. The current phase is most likely the testing phase, but as soon as large-scale pilots show the importance of blockchain, massive adoption will follow. Today’s societies will have the opportunity to explore and test the technology through proofs of ideas and experimentation. Considering the capacity of blockchain, as well as the rapid rate of advancement of technology, it is the right time now to start experimenting with it. Much research was done on incorporating disruptive technologies in manufacturing processes to achieve a circular economy. As manufacturing is under increasing pressure to cut costs and lessen its environmental impact, it gets increasingly complex and integrated. Smart manufacturing offers strategies for making production more environmentally friendly. Bag et al. [66] proposed a research framework that shows how I4.0, sustainable manufacturing, and the circular economy are linked. The survey was carried out on two levels: level one involved reviewing existing literature to identify obstacles, possibilities, and problems, and level two involved developing a research framework. The study’s findings helped in the development of seven prepositions by offering a proposal for integration and a detailed understanding of the research framework developed. To facilitate a seamless transition to CE and enhance CE functionalities, the authors recommended that manufacturers embrace sustainable manufacturing practices. Bag et al. [67] investigated how institutional resources and pressures, such as internal acceptance of Big Data Analytics for automakers to improve technical aspects of the workforce, enhance sustainable manufacturing methods, and develop CE capabilities, affect the industry. A total of 219 South Africa-based automakers provided information for the study on the role of institutional resources and pressures in the adoption of big data analytics. The authors suggested emphasizing tangible resources, staff skills, big data analytics, and artificial intelligence to improve sustainable manufacturing processes and CE capabilities. A study framework that supports CE principles and is based on comprehensive literature research on smart manufacturing was developed by Kerin and Pham [68]. The authors discovered 329 papers that discussed the triple bottom-line method to achieve sustainability. According to the authors, variations in product ownership models would have an effect on the remanufacturing industry. Remanufacturing can also be accomplished inside the I4.0 scenario by strengthening relationships

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between consumers, product manufacturers, and remanufacturers. According to the authors, organizations can use I4.0 technology for recycling. In order to develop a smart CE framework to assist manufacturers in transforming CE strategies into Sustainable Development Goal (SDG) 12 and resulting in sustainable consumption and production, Kristoffersen et al. [69] conducted theoretical reviews. The framework has helped practitioners identify the strategic actions needed to bridge gaps between the desired and current business analysis requirements. The framework also provides insight into the relationship between digital technologies and CE, which is necessary to fulfill SDG 12 and sustainable consumption and production. Productoriented businesses are gradually shifting to service-oriented businesses in in-house companies. Companies deliver individualized products and services as an integrated solution customized to the demands of clients, which is also known as providing a service. This business model can also be called Product Service Systems (PSS). Bressanelli et al. [70] investigated the effect of digital technology in overcoming the CE hurdles of product service system business models in this regard. The findings suggest that these technologies aid in the avoidance of CE hurdles such as operational risk, user willingness to pay, asset loss, return flow uncertainty, and technological advancements in PSS business models. Zheng et al. [71] conducted a survey of smart product-service systems in order to examine technological features, obstacles, and future prospects. The research yielded significant findings in three areas: technology, economy, and environment. They concentrated on the intelligence of both humans and machines from a technical standpoint. On the business side, it was suggested that digital services be used. The environmental aspect promotes the deployment of disruptive technologies in an eco-centric environment. Tunn et al. [72] investigated the impact of digitization on user perceptions of the product service system. The results of consumer surveys and interviews were presented in the study, demonstrating that short-term PSS has a major impact on digital media. The authors add to consumer awareness of product service system digitization. Wang et al. [73] proposed a PSS-dependent predictive maintenance model. The primary goal of this research was to better integrate the PSS delivery process with other maintenance activities. To reduce resource consumption and enhance device use, it is recommended that a platform for network devices be developed. Waste reduction and improved remanufacturing activities receive substantial support from the integration of I4.0 disruptive technologies and CE procedures. In order to develop key stakeholders’ roles in attaining a sustainable society, CE’s ReSOLVE model, and conceptual and practical trends related to comprehensive data management, Jabbour et al. [12] merged CE and big data with new technologies. The research has resulted in the presentation of a new integrated framework. Practitioners, researchers, policymakers, and managers were all given socio-technical insights by the framework that had been built. After performing a systematic review of CE, and I4.0, Okorie et al. [13] created an internal framework that combines CE practices with digital technology. This research shows a rising trend in I4.0 and business research efforts, and advises exploring the concept of technological nutrients for a successful CE strategy. Bressanelli et al. [70] through the incorporation

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of I4.0 technologies, such as IoT and others, to support the adoption of CE in the enterprise’s supply chain, presented a conceptual framework. With the application of I4.0 technologies that increases three CE’s drivers; improved resource efficiency, prolonged product lifecycle, and closing the circular loop, the research identified eight supporting aspects. The I4.0 barriers to achieving sustainability were investigated by Rajput and Singh [74]. This study used the Industry Smart Manufacturing (ISM) method to investigate the connections between I4.0 obstacles, I4.0 technology, and CE practices. The findings show that digitalization and semantic interoperability are very motivating. Some I4.0 obstacles promote the development of other I4.0 obstacles, and removing these obstacles would result in the effective adoption of I4.0 technology and CE practices. As a result, this study advises that more attention be paid to these barriers. Pham et al. [75] analyzed the background of I4.0 and considered the elements that influence I4.0 to promote the sharing economy in the context of CE, using an electric scooter case study in Taiwan as an example. When CE was introduced, the results revealed that I4.0 had a significant impact on the sharing economy. The authors also discussed how workers who use autonomous machines can achieve high-efficiency workflows. Using an interpretive structural modeling approach, the influential factors were identified. To improve I4.0 readiness, the authors recommended putting in place a big data infrastructure. Nascimento et al. [9] studied combining I4.0 technologies and CE to create a waste reuse business model. In this study, a three-stage research model is developed; the first stage includes a review of factors and barriers related to the transition to CE and I4.0 technologies such as Cyber-Physical Systems (CPS), AI, and Big Data; the second stage is the development of a conceptual framework that merges CE and I4.0; and the third stage is the development of a conceptual framework that merges CE and I4.0. To validate the suggested framework, the third stage gathers data through expert interviews. I4.0 technologies are used by Chauhan et al. [76] to address challenges linked to CE practices. The SAP-LAB Linkage Framework is used in this study to evaluate I4.0 applications in CE business models. The survey’s findings revealed that senior managers are key players in the usage of I4.0 technologies to achieve sustainability. Articles about I4.0 technologies and the circular economy were reviewed by Piscitelli et al. [77]. In this study, 72 articles were examined over the course of a decade (2010–2020). According to the survey, there is an increasing trend toward research in the I4.0 and CE domains. The combined effects of I4.0 and CE on economic growth were studied by Zhou et al. [78]. Environmentally and energyefficient technologies constitute the technological engine of economic progress, according to this study. Dantas et al. [79] also outline an essay on using I4.0 technologies in the CE concept in order to achieve the 17 Sustainable Development Goals. From a corporate ethical standpoint, Shayganmehr et al. [80] investigated CE and I4.0 enablers for clean production. The authors have developed a framework for evaluating the relevance of I4.0 enablers in implementing clean manufacturing processes in the context of CE. According to the findings, important I4.0 enablers include

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“Technical Performance,” “Politics and Regulation,” “Safety and Security,” “Education and Participation,” “System Flexibility,” and “Support and Maintenance.” The I4.0 enabler’s readiness score was evaluated using the fuzzy assessment method. To promote CE and cleaner manufacturing, this study recommends focusing on the development of technology infrastructure. Demestichas and Daskalakis [81] conducted a literature review on ICT-based CE solutions. They divided the answers into two sections, focusing on CE’s core principles and other concepts related to the technical aspects of the solutions. As a result, ICT solutions have been developed for IoT, blockchain, artificial intelligence algorithms, data collection, and analysis. The findings also recommended focusing on the “reduction” aspect of CE in addition to ICT solutions. Yadav et al. [5] established a framework for integrating I4.0 technologies and CE principles to address the challenges of long-term sustainable supply chain management (SSCM). There are 28 SSCM challenges and 22 solutions in this study. Furthermore, the study revealed that management, economic, and organizational challenges are essential to SSCM. The study recommended that effective solutions be used to solve the significant concerns identified. Ranta et al. [8] conducted a survey of four northern European enterprises that are implementing CE models and digital technologies. This survey makes two contributions: first, it provides empirical research to demonstrate resource flow and value creation, and second, it develops a business model that combines CE and digital technologies. Focusing on Singapore has always endeavored to strike a balance between economic development and ecosystem sustainability. In order for the economy to grow, resources such as water and energy are needed, but on the other hand, waste and solid waste are generated and must be disposed of. To overcome these obstacles, Singapore must adopt a circular economy strategy, which will result in a long-term shift from mental to physical usage and recycling. Singapore is working on rules to assist sustainable manufacturing and consumption, beginning with e-waste (ewaste), covering the extended producer responsibility (EPR). Singapore is a potential example of the creation of a circulating city. Singapore’s circular economy practices differ from those of other developing nations, and these practices offer insight into how circular economy policies are developed and implemented in cities with similar attributes. Despite its uniqueness as a small island nation, Singapore has different types of services. But several of Singapore’s sustainable transportation technologies have already been implemented in other cities, including Shanghai, London, and Stockholm [82]. Given the country’s economic ranking in terms of the Gross Domestic Product (GDP) ranking of 37 [83], this has a significant impact. In terms of architectural and commercial development, the country is also regarded as a tropical island in Southeast Asia inhabited by four major communities [84]. Some enterprises are aligned with the “Garden City” vision, which dates back to 1963, or the “City in a Garden” vision, which was recently given by the Singapore Center for Livable Cities, a governmental organization under the Ministry of National Development [85]. The country’s full history explains how it has dealt with the difficulties of transforming from a colonial hinterland backwater to a prosperous metropolis. For example, in a worldwide study concentrating on the innovation rate, Singapore has consistently ranked first among the most creative economies [86].

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Embracing the circular city process is a struggle, even though everything is being rebuilt and planned. Singapore has named 2019 as the Year of Zero Waste [87] as the country works to become a waste-free country by lowering material input and raising reprocessing and recycling rates. The country’s Zero Waste Master Plan was unveiled in 2019. The strategy focuses on three waste streams: e-waste, packaging waste, and food, as well as new technologies and ideas that the government is evaluating in order to totally eliminate or close waste loops. Understanding these trends is currently dependent on the recycling rate and conducted research that concentrates on recycling. The Resource Sustainability Bill was introduced around the same time in 2019. Being approved, it imposes obligations for the collection and treatment of electrical, electronic, and food waste, as well as the reporting of packages imported or used in Singapore, to allow the establishment of regulations for persons operating under producer responsibility schemes, and to achieve the goal of promoting resource sustainability. Yet, the country has taken some notable steps in recent years toward a circular economy using well-known disruptive technologies. Some of these efforts are considered as follows: known stakeholders, called JTC, which is an industrial land agency investigating how the industrial area of “Jurong Island” becomes more circular and has begun researching the CE model [88]. By converting raw materials such as waste animal fat, plant oils, and processed plastic waste to renewable energy, Singapore will be able to advance in the production of renewable diesel and aviation fuel by 2022, with recent cooperation with Neste, the world’s largest provider of renewable diesel. Singapore responds by emphasizing ideas and becoming an outstanding force with the help of CE and disruptive technologies. Most of the country’s zero waste agenda is driven by recent government efforts (for example, a 30% decrease in waste generation and a 70% growth in household recycling by 2030). The private sector will be encouraged to use a circular economy through offered financial incentives. A report [89] was done on how disruptive technologies can drive CE worldwide, with an emphasis on Singapore. Figure 7 shows Singapore’s zero waste framework. Singapore’s Zero Waste Framework includes Legislation and Policies, such as Zero Waste Master Plan, Sustainable Singapore Blueprint, Resource Sustainability Bill, as well as Circular Economy with Incentives, including Towards Zero Waste Grant and Governmental Research Grants.

6 AI with Blockchain for Alternative Money System Money has three primary functions: it serves as a store of value, a measure of value, and a medium of exchange. Automation and artificial intelligence technologies boost productivity in the post-digital ecosystem. As a result of the rise in productivity, there is also an increase in unemployment. Also, consumers’ purchasing power shrinks as a result. This cycle will continue until the existing economic system collapses. Excessive fiat money has been advocated as a remedy, and consumer debt ratios have increased. In the previous 30 years, the ratio of personal debt to disposable income

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Fig. 7 Singapore’s zero waste framework

has doubled [90]. However, this is really a monetary bubble. Then the bubble will burst, and all that fiat money will be irrelevant. The shift is due to the digitization of money. Traditional money may be utilized for years to come, but people’s perceptions of money will change, and alternative ways to replace money will certainly emerge after the end of the global economic crisis. The decentralized collective structure of manufacturing facilities will emerge in the future sharing economy [91]. Capital as a factor of production will become useless once the capitalist economy collapses. Knowledge is becoming increasingly significant as a production factor in these changes in the digital economy. As capital becomes less important, individual and group productivity increases. The capital owner will not be able to profit from the production. The government must then step in and intervene in economic activities. Because governments have a greater effect on the economy, ownership of industrial facilities shifts from capital owners to collective units. In addition, the company’s capital structure will be altered through the use of the token economy and crowdfunding. Unmanned manufacturing, aided by automation and artificial intelligence, has enhanced productivity in recent years. Renewable energy, ultra-cheap and efficient transportation, and 3D printers all help to lower production costs. The unemployment rate, on the other hand, will rise. With the post-digital era’s high unemployment and ruthless inequality, the ecosystem will exert indirect pressure on firms’ profitability. Government intervention will be necessary to disrupt this economy and manufacturing cycle. The world’s capitalist system is being phased out in favor of a mixed economy governed by the government. The new economic structure will rely heavily on collectivism and government involvement. Today, as production processes evolve, social and economic systems will undergo abrupt structural shifts. The unprecedented global economic crisis will be the catalyst for this crustal shift. Unemployment will increase in the post-digital ecosystem. From an optimistic standpoint, it will no longer be necessary to work in the flourishing economy of the future. On the other hand, being unnecessary might be regarded as a disease that affects humanity. The sharing economy is viewed as a viable alternative to capitalism. The new alternative system has been dubbed “crowdbased capitalism” by some researchers [92]. The surplus fiat money can be revoked or withdrawn from the economic system, regardless of the term chosen while modifying

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Table 3 The difference between the digital economy and the future sharing economy Parameter used

The digital economy

The future sharing economy

The most important factor of production

Knowledge

Knowledge as a factor of production is becoming more important

The company capital structure

Ownership of production facilities refers to capital owners

Decentralized collective structure production facilities will appear, token economy and crowdfunding may be used

Money

Traditional money may continue to be used

The excess fiat money can be revoked or extracted from the economic system. Classic money will not be needed

the economic system. There will be no need for traditional money. In the application areas of blockchain technology, such as decentralized alternatives to banking systems, there have recently been signals of a new economic system. Also, artificial intelligence can play an important role in enhancing the security of financial transactions along with blockchain technology in a decentralized environment in the future sharing economy. Table 3 shows the difference between the digital economy and the future sharing economy.

7 AI with Blockchain for Token Economy Trustworthy third parties, such as banks and public officials, are frequently used to transfer ownership of assets (fiat money, company stocks, and usage rights, for example) between agents in order to increase the transfer process’ legitimacy (here, persons or organizations). Third Trusted Party (TTP) mediation has a number of drawbacks, including greater costs, longer processing times, and the presence of a single point of failure. TTPs are rapidly automating and decentralizing portions of their services as a result of these drawbacks. Without the need for TTPs, technological advancements have made it possible to digitally represent and manage asset ownerships using tokens on decentralized digital platforms. The ability to express assets in the form of digital tokens on a decentralized digital platform and define ownership of these assets to agents in a fraud-resistant manner may reduce the drawbacks associated with TTPs (such as the existence of a single point of failure) and allow for the emergence of a new type of economy: the token economy. The token economy has significant transformative potential in overcoming the drawbacks associated with TTPs, which have an impact on businesses (e.g., by permitting new business models and boosting transparency in business processes) and daily lives (e.g., by allowing monetization of personal data instead of just leaving it away). Technical protocols are used in the token economy to execute a variety of

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activities, such as operating distributed digital platforms that can validate individual agents’ legal ownership of assets and keep a tamper-resistant record of their transfer of ownership. Furthermore, decentralized digital platforms enable agents to implement and utilize tokens as identifiers for other sorts of assets (e.g., use rights, land ownership, money, and so on), providing them with more flexibility in their token usage. The Token economy can facilitate collaboration and cooperation between agents in terms of trust by decreasing the requirement for traditional TTPs and boosting flexibility. Furthermore, because ownership of real or digital assets may be transferred using tokens, the token economy will eventually lead to new types of business models (e.g., decentralized crowdsourcing) and enhance business partnerships (e.g., increased transparency of corporate processes). Token transfers, like traditional asset transfers, require strong security guarantees from decentralized digital platforms. For example, in the token economy, decentralized digital platforms need to prevent users from using the same tokens multiple times at the same time (i.e., use it twice or double spending) while reaching high availability and tamper resistance at the same time. Many of the security guarantees needed for a business ecosystem are covered by distributed ledger technology (DLT) security features, such as fraud prevention, high availability, and tamper resistance. DLT allows the highly available and connected distributed ledgers with computing devices (i.e., nodes) to operate in unreliable environments (i.e., double payment) [93]. From a microeconomic point of view, there exist multiple instances of the token economy. For developing a Token Economy instance based on DLT, there are two primary options: one is to employ custom tokens in the distributed ledger. The second option is to produce tokens for a distributed ledger that already exists [94]. In the first option, only authorized agents can join the distributed ledger, read transactions, and contribute new transactions to the distributed ledger [93]. The first option is often handled by a consortium of agents operating on a private distributed ledger (e.g., using the Private Ethereum blockchain). The second option obligates the agent to select an existing distributed ledger for token economy instantiation. Agents use smart contracts to create custom tokens. Each option has its strengths and weaknesses. Tokenization is the process of using a distributed ledger to digitally securitize rights and commodities. This will allow current financial instruments to be transferred and new digital assets to be created. This facilitates a transformational process across the financial market value chain at all levels. Tokens may be used to digitally securitize a wide range of rights and products. Three sorts of tokens are distinguished by Boerse Stuttgart. Payment tokens are digital payment mechanisms with little or no extra features that do not require a central instance. Cryptocurrencies like Bitcoin and Ripple are examples of payment tokens. Ultimately, utility tokens such as vouchers grant access to the services or goods of their respective issuers. Token marketplaces are becoming more global and immediately accessible (e.g., tZERO, OpenFinance Network, and Boerse Stuttgart Digital Exchange). These marketplaces can operate as secondary markets for tokens that have already been issued. The DLT takes over the functions of brokers and ensures transaction security and transparency. Through customer-specific interfaces, institutional and private investors from all around the

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world may connect directly to the token marketplace, bypassing any brokers such as TTPs or related expenses. Depending on the legal framework, the market can also present procedures to prove the identity of the customer and a means to counter money laundering. The exchange of tokens between buyers and sellers can be done in near real-time nowadays, so there is no need to pre-check the available financial resources. These aspects, as known today, can make brokers obsolete in the process chain. Consumer protection, money laundering, and funding for illicit activities are just a few of the major issues that have prompted regulators to look at cryptocurrencies. Concerns regarding pricing and financial stability, the influence on monetary policy, and the general integrity of traditional payment systems were the key concerns and obstacles cited by regulators. The possibility of cryptocurrencies impacting fiat currency demand and upsetting the regulation of the money supply through open market operations was one of the first issues examined. There was concern that a potential challenge to central bank balance sheets would be to replace privately issued cryptocurrencies with central banks’ money. Central banks can basically lose control and influence over the evolution of money and credit if cryptocurrency dominates the financial sector. Cryptocurrencies’ intrinsic lack of stability and extreme volatility can lead to broader financial instability, particularly if they are traded in huge volumes and broadly accepted in the economy. Without regulation and government oversight, cryptocurrency users and crypto blockchain platform participants are exposed to a variety of risks, including credit, liquidity, operational and legal risks. The introduction of Stablecoins, which have many of the same properties as traditional cryptocurrencies, sparked the next wave of regulatory worries regarding cryptocurrencies. In addition, although it is a blockchain token that adopts an encryption verification method, it aims to stabilize the price by connecting the value of the coin to the asset or the pool of assets. The most famous Stablecoin project is Libra, which has resulted in astonishment among regulators and authorities around the world. Stablecoins present new challenges to regulators. A comprehensive list of concerns that Stablecoins of different sizes could cause was developed by the G7 Working Group on Stablecoin after it examined the effects of global Stablecoin [95]. There are risks associated with governance, market integrity, payment system efficiency and integration, illegal funding, legal certainty, governance, security, and investment regulations for stability measures. Stablecoin is regarded as a challenge in terms of data privacy and protection, investor and consumer protection, and tax compliance. The following concerns about global Stablecoins are frequently expressed: monetary policy, currency sovereignty, financial stability, and fair competition. All of these concerns could have an impact on the entire global monetary system. Initial Coin Offerings are receiving a lot of interest from regulators. Particularly for retail investors, these are high-risk investments. The dangers associated with investing in tokens produced through an ICO are much higher than those associated with investing in regulated financial instruments. First, promoters are largely unaffected by investors. They frequently make investments at the beginning of the investment life cycle, relying solely on projects or ideas and a scale of knowledge asymmetry that is heavily in their favor. Transparency was hampered by

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the absence of disclosure rules for the majority of early ICOs. ICOs that operate outside of the corporate governance and regulatory framework result in legal and regulatory issues that expose investors to significant risk and volatility. Additionally, investors have no rights to legal or regulatory protection or compensation, particularly in the case of bankruptcy or project termination. For legislators, regulators, and supervisors, the murky legal structure governing blockchain tokens has proven to be a significant obstacle. The blockchain network’s limitless, unmediated, decentralized structure also thwarts any efforts to pinpoint relevant legal frameworks, participant locations, and prospective regulatory hubs. When creating regulatory frameworks and requirements, regulators must examine a number of factors. The public interest, system stability, market integrity, and business behavior monitoring are among these factors. They can take a functional regulatory approach and concentrate on token products and services. Alternatively, they can choose an institutional approach where regulations target product and service providers. One of the basic regulatory issues is whether to integrate crypto assets into the existing legal framework (which can be adjusted as needed), provide personalized and customized regulatory treatment, or remain unregulated. Regulators will continue to assess the “novelty” of technology in terms of the nature and function of financial markets and whether blockchain-based crypto assets will introduce new market solutions that go beyond innovative technology parameters. Similarly, providing regulatory legitimacy for new and rapidly evolving technology can prematurely provide a complete validation of that technology whose time, quality, and resilience have not yet passed all tests. On the other hand, if the potential of technology is not recognized and innovation is not addressed by separating crypto assets from existing regulatory systems, technology development may be curtailed, and regulatory arbitrage may happen. Some regulatory issues for crypto assets have been noted by the Cambridge Center for Alternative Finance [96]. Understanding the key ideas, the underlying technological infrastructure, and the accompanying possible damages and risks is one of the initial steps in such a process. To do so, regulators must have a thorough understanding of token issuance, distribution, transfer methods, intermediary activities, and risks connected with tokens. The economic and legal implications of large-scale tokenization for financial markets and their participants are numerous. These difficulties span from regulatory and legal concerns to technological ones, including scalability, interoperability, and cyber risk. Artificial Intelligence can play an important role in securing blockchain transactions based on tokens by finding weak points in transactions’ processes and eliminating these weak points, and so, lowering the risks of cyber crimes. The key task for regulators is to develop a regulatory approach that better provides a transition from an existing regulatory system built on bilateral relations to the increasingly decentralized blockchain-based tokenization financial world [97]. Among the numerous regulatory initiatives, declarations, and governmental efforts, only a few big approaches have emerged. Current legislation can apply to blockchain tokens, sometimes with adjustments including prohibited changes and certain extensions. Also, custom-made legal frameworks can be initiated. Figure 8 illustrates multiple entities in layers in a decentralized digital economy based on tokens. In this scheme, a smart contract is invoked by the user.

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Fig. 8 Multiple entities in layers in a decentralized digital economy

Every time a user needs to call a smart contract, he signs a transaction to interact with the blockchain. The data inside the transaction are encrypted with the user’s public/private key before being stored in the smart contract, to go to the presentation layer, consisting of a web server, then the smart contract goes to the business logic layer, representing an environment based on using AI and Tokens, to be verified and executed automatically, then, verified and executed data contained inside the smart contract are stored in a data storage and management layer, represented by a peer-to-peer network.

8 Conclusions and Future Works Blockchain technology is still in its infancy and difficult to predict how it will change the world. The final outcome is unknown, and predicted advancements in AI, distributed computing, and quantum computing will undoubtedly speed blockchain’s technological progress. The integration of AI and blockchain in the business field speeds up businesses toward digitization and enhances several processes, like crossborder trade and transactions’ payments. Also, the effect of their integration on the circular economy helps in reaching a zero waste ecosystem, and on the alternative money system leads to the transformation of the current financial system. And the effect of their integration on the token economy provides secured decentralized digital platforms. Also, including an AI arbitration mechanism in a smart contract dematerializes and simplifies the settlement of disputes. The industry is becoming more digitalized than it has ever been previously, thanks to artificial intelligence. The bank is like a platform with modular components that improve financial literacy, with no actual products or services. Also, the primary question for executives of core systems and infrastructure is to create goals for a system architecture that allows IT to connect anything to anything. The notion of service-oriented architecture can be used for a wider range of applications. The majority of future jobs can be automated by adopting artificial intelligence and blockchain. The future of financial institutions will require a significant reorientation.

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A good example can be thought of as “peer-to-peer” or more “community-led forms of corporate organization and governance.” In this model, organizational management systems are developed, and technology facilitates the adoption of authoritative decisions in the absence of a centrally designated body that makes and enforces these decisions in the community. The centralized reality, with its hierarchical organizations, rules, regulations, and institutions, still prevails. It seems that there will be a goodbye to the usual centralized reality to move to a new emerging decentralized reality. However, if a clever way can be figured out to get them to cooperate, the overall positive externalities can increase.

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Blockchain in Big Data for Agriculture Supply Chain Jenita Thinakaran, Sujni Paul, Beulah Christalin Latha Christudas, and Grasha Jacob

Abstract Agricultural food supply chain is a complex system starting from the production of food on a farm to the table of the consumer involving multiple stakeholders and a variety of processes. In recent years, the food supply chain has grown rapidly across nations, with customers demanding fresh, exotic foods all year round. The global shutdown due to the COVID pandemic has further complicated the food supply chain which has become prone to various contaminations and adulterations. Adulterated food is highly toxic to human health leading to several health issues, nutritional deficiencies, kidney disorders, and failure of vital organs. The existing systems used in the food supply chain do not provide enough transparency, traceability, food safety, or consumer trust. With today’s Big Data integrated supply chains, such technologies are highly ineffective. In order to ensure food safety and consumer satisfaction, this chapter proposes using blockchain as an efficient technology to provide transparency, traceability, and trust in food supply chains. The chapter discusses the significance of smart agriculture and how blockchain might help agricultural supply chains that have Big Data incorporated overcome their difficulties. A thorough description of exclusive applications of blockchain in Big Data integrated food supply chains is provided. The chapter also describes how blockchain is integrated into each stage of the food supply chain management process and explores the challenges in implementing blockchain in Big Data integrated food supply chain systems.

J. Thinakaran School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India e-mail: [email protected] S. Paul Faculty of Computer Information Science, Higher Colleges of Technology, Dubai, UAE e-mail: [email protected] Beulah Christalin Latha Christudas (B) Department of Digital Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India e-mail: [email protected] G. Jacob Govt. Arts and Science College, Nagalapuram, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_9

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Keywords Agriculture · Food supply chain · Blockchain · Big data · Food safety

1 Introduction India is a global agricultural powerhouse. The agricultural industry is considered to be the backbone of the Indian economy as it is estimated to be the chief source of livelihood for about 58% of the nation’s population and the largest contributor to GDP [1]. The Economic Survey of India projected an increase of 11.44 million tons in the financial year 2020 when compared to 2019. The present population of India is 1,399,124,343 as of November 2021, as per the Worldometer elaboration of the latest United Nations data. Catering to the food security needs of a huge population that is 17.7% of the global population is a great challenge for agriculturalists and food supply chain systems in India [2]. The introduction of information communication technology (ICT) in agriculture supply chains has enabled the automation of the processes as a decision support system for farmers. The ICT tools help in the reduction of cost, improved logistics, increased traceability, and better food safety [3]. They also update information about weather, new crop varieties, and good agricultural practices. Such technologies have succeeded not only in making supply chains effective but also in generating huge volumes of data. Real challenge is being faced by experts in managing such Big Data supply chains. This chapter highlights the technological innovations that facilitate the use of data-driven agriculture to create trustworthy food supply chains using blockchain technology. Agriculture, which began thousands of years ago has transformed into industrial agriculture based on large-scale production of various crops across the globe. Agriculture 4.0 is expected to revolutionize farming methods through precise and accurate analysis of data and information using advanced tools and technology. Today’s population prefers healthy and nutritious food and snacks such as protein bars, salads, oatmeal cookies, nuts, dehydrated fruits, and veggies, in place of sweet, fatty, and carb-rich foods. The global crisis introduced by the pandemic has made our generation rethink the quality of food we eat, the source/origin of the food, and the processing methods used, on account of numerous reports of contamination and counterfeiting throughout the agricultural supply chain, which is one of the biggest challenges of the food industry. These adulterants not only reduce the nutritional value of the food but also contaminate the food rendering it harmful and could lead to acute or chronic health problems for humans. During the pandemic, people have experimented with various recipes from across the globe for utilizing the unexploited and underexploited grain crops such as minor millets, quinoa, and amaranth. Food Safety and Standards Authority of India (FSSAI) is the regulatory agency set up under the Food Safety and Standards (FSS) Act of 2006 which is responsible for monitoring and regulating the manufacturing, distribution, storage, and sale by food businesses in India. Food industry has begun to play a significant role in the Indian economy, and it is important to monitor and detect food adulterations and admixtures (additives). There are instances in which spoiled food products cause poisoning and

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even death [4]. The agriculture supply chain generates huge volumes of data being stored in various parts of the supply chain by multiple stakeholders which are neither transparent nor traceable using the existing technologies. Various types of sensors used in supply chains in the recent past, the mobile apps used in supply chains, the latest technologies such as RFID and QR codes, etc. generate huge volumes of data referred to as Big Data in supply chains. The modifications made in the supply chains, either in databases or in transmissions, cannot be traced out as well [3]. Achieving customer satisfaction and high performance are other challenges faced by agriculture supply chains. Some of the existing technologies are successful in tackling these challenges to a certain extent in small supply chains. However, these issues continue to be difficult in massive supply chains that generate enormous data. To summarize, the biggest problems faced by agriculture supply chains are people’s concern about getting healthy food, lack of mechanisms for detecting food adulteration, lack of traceability, lack of transparency, managing Big Data generated in supply chains, and ensuring food safety and fair price for farmers. A mechanism that ensures trust in agricultural supply chain management is the need of the hour. This chapter on blockchain technology is focused on transforming the agricultural industry, ensuring product safety and traceability, eliminating counterfeits, and securing fair prices for farmers by connecting farmers, traders, and Farmer Producer Organizations. ICT-based technologies and procedures involve the use of ID codes or barcodes for item identification in agriculture supply chains to enhance food safety, quality, and traceability. Creating barcode labels is expensive, hence implementation in a system will increase the cost of packaging. For low-budget products, it is a real challenge to have barcoded labels. A customized modification has to be done to the labeling machine to fit the special needs of the barcode. The labor cost involved for people who manually scan and record barcodes and IR staff is also high. Barcodes alone do not provide the transparency and traceability that customers need in agriculture supply chains. QR codes are used in customer products instead of barcodes. The customer can get details about the source of the product and ingredients by scanning the QR codes. This is one of the mechanisms through which the entire product life cycle can be traced from the farm to the store shelf. QR codes are common and have an ease of use because of their inclusion in mobile phones. Europe, North America, and Germany use QR codes in many of their agriculture supply chains. Though QR codes are easy to be used and provide a means to detect food fraud, they have limitations. QR codes are inherent to be duplicated, and prone to counterfeiting. They are cheap and easy to make, allowing imposters to create counterfeit codes [5]. Moreover, QR codes are also used by cybercriminals for phishing. Another technology used for the improvement of food supply chains is the Internet of Things (IoT). The primary task in the agriculture supply chain is the collection and acquisition of data. The sensors used in IoT systems make the data acquisition process simple. Digital tags and sensors along with data integration tools help in tracking food products using artificial intelligence. Image analysis, hyperspectral imaging, and food sensing technologies like biosensing help in analyzing the ingredients of food products and detecting the potential risks of contamination [6]. Care must be taken in using the

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sensors for monitoring temperature, and humidity and suitable environments must be provided. These technologies are also expensive and have difficulty in adaptability. Blockchain has been proposed as a solution to the problems and challenges presently observed in the existing agriculture food supply systems. It enables the detection of counterfeit food products and contaminated substances in food products and ensures zero tampering in any part of the supply chain. If implemented properly, blockchain can create a trust mark that provides the manufacturer and producer with an assured quality product. Blockchain assures consumers by significantly securing each process and control involved. Consumers can be confident that their food is healthy and safe. Smart contract-based blockchain technology is a decentralized ledger that maintains transaction records on distributed computers simultaneously. The key features of blockchain are decentralization, security, transparency, unforgeability, and verifiability to enhance trust and security among consumers. These technical characteristics allow for broad application prospects in agricultural food traceability, authenticity, and integrity of transactions thereby ensuring food quality, safety, and sustainability. The proposed scheme of this chapter is to use blockchain technology in the agriculture supply chain to address the key issues of maintaining food safety, tracing agricultural products, and managing transaction costs with effective marketing. The authors have analyzed the existing technologies that are used in securing food supply chains and identified the shortcomings of such technologies. The authors have contributed significantly to the conception and layout of the chapter. A detailed analysis of integrating Big Data in an agricultural food supply chain has been carried out and the challenges in managing such supply chains are discussed. Blockchain has been proposed as a solution for the existing issues and the effectiveness of its implementation is explained in detail. The rest of this chapter is organized as follows: the “Fundamentals of Smart Agriculture” provides the basics of smart farming, its types, advantages, and disadvantages. The section on “Applications of Blockchain in Big Data for Agriculture Supply Chain” delivers an insight into blockchain, Big Data, and its applications in various fields. “Food Supply Chain Management Based on Blockchain-Integrated Big Data” section provides details on supply chain management in the context of blockchain. The section on “Challenges to Implement Food Supply Chain Management Based on Blockchain-Integrated Big Data” analyzes the challenges faced while implementing blockchain for food supply chain management.

2 Fundamentals of Smart Agriculture Smart agriculture refers to the management of farming using ICT and tools. It refers to precision farming using the Internet of Things, Robots, and Artificial Intelligence. Smart agriculture or precision farming manages spatial and temporal variability related to various aspects of farming in improving crop production and environmental sustainability [7]. It is a technology-based approach to farming management

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that monitors and meets the needs of crops based on analytics. Big Data analytics and the Internet of Things are the two main trends that shape precision agriculture to a greater extent. As per the definition of the European Parliament, precision agriculture is a modern farming concept that uses digital technology for monitoring and optimizing agricultural production. Smart or precision agriculture aims to develop a decision support system (DSS) for farm management optimizing returns on inputs with optimal resources. The other names synonymously used are. • • • • • •

Site-specific crop management (SSCM) GPS Agriculture Variable Rate Farming Satellite Farming Smart farming Digital Farming.

2.1 History of Smart Agriculture The concept of Smart Farming or Precision Agriculture (PA) emerged in the late 1980s with variable-rate fertilizer application equipment based on soil fertility maps created by grid-based soil sampling techniques. With the introduction and use of the Global Positioning System (GPS) in 1990 for rapid and exact vehicle location and navigation, electronic controllers were available commercially to handle the new positioning information. The first workshop on Smart Farming was organized in 1992 in Minneapolis which led to the dissemination of this concept worldwide. By this time, GPS became fully operational which allowed for monitoring and mapping of the yield variations within fields. For the first time, the yield variability data was mapped with differences in soil fertility which marked the beginning of precision agriculture. GPS enables farmers to visualize farm maps with precise locations highlighting points of interest. GPS also helps farmers to sample the exact locations year after year for soil sampling and yield monitoring. Of late, proximal (ground-based) and remote (aerial- and satellite-based) platforms allow for continuous measurements of the soil and crop growth parameters. The success of these operations has the potential for further advancements in agriculture and is attracting the focus of researchers, practitioners, and corporations to venture into precision farming. The objectives of precision farming are depicted in Fig. 1. It aims at increased production at optimum quality by minimizing environmental impact and risk.

2.2 Significance of Smart Agriculture Feeding a global population of 9 billion by 2050 will require a 70% increase in food production [8]. Availability of arable lands, increasing impacts of climate change,

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Fig. 1 Objectives of precision farming

rising demands for freshwater, decreasing labor supply for agriculture due to urbanization, and so on pose serious constraints to achieving this demand. Increasing food prices is a serious concern since 2010 that has pushed millions of people into poverty. This demonstrates a definite need for a new revolution to increase production and reduce food prices through smart agricultural interventions. Agriculture has already started moving from a traditional occupation mode to an industry mode driven by data and modern technologies. Massive amounts of information collected from soil sampling, crop fields, and weather data coupled with specialized sensors for farm management enable precise decision-making by overcoming the challenges of the agriculture system in a smart way. This offers a lot of scope for radical technological improvements in the agricultural domain. Several smart farming startups have recently been initiated in Silicon Valley. According to the statistics from Investopedia, there are more startups in the field related to food and farming. The population of India is predicted to touch 1.6 billion in the next thirty years and therefore, its annual food production should rise to 333 million tons from the existing level of 252 million tons. India accounts for 16% of the world’s population with a smaller land area of just 2% of the global land area [9]. There is an urgent need to overcome several significant obstacles and historical challenges such as underinvestment in agriculture, resource scarcity, fragmented farm holdings, non-availability of inputs (seeds, fertilizers, and pesticides), lack of farm mechanization, poor market access for selling of farm produce, and inadequate transport and storage facilities in order to enhance food production in India. The growing Indian population is posing concerns about major resources of land and water, needless to mention the associated issues such as the degradation of cultivable lands, a fall in the per capita arable land, and water scarcity. All the above challenges

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are therefore alarming the country to come up with realistic and sustainable solutions that will bring about a technological transformation in the agricultural sector. Creating awareness and focus on precision farming using smart agricultural technologies among researchers and administrators is needed to bring about a technological revolution in agriculture. For a complex system like agriculture to remain viable and sustainable, it is essential that all inputs must be carefully managed to enhance agricultural productivity. Seeds, soil, water, fertilizers, and pesticides interact with each other and with climatic factors in a complex manner. Therefore, a thorough consideration of the ecological impacts of input resources is needed to avoid environmental degradation. There is an increasing awareness among the general public about environmental consciousness and the need to transform farming practices in a sustainable yet economically viable manner of conserving natural resources such as air, soil, and water. It is also expected that there will be an increased demand for agricultural labor availability, and it is time to unite ICT with agriculture to deploy environmentally sustainable and economic systems for crop production. The inception of the concept of Precision Agriculture in the United States dates back to the early 1980s when a team of researchers at the University of Minnesota attempted technology-based systems for varying inputs for crops. Grid sampling and Global Positioning Systems were also introduced to facilitate the application of variable rates of agricultural inputs. A sample precision farming system is shown in Fig. 2. Technology Precision Farming Combinations of various technologies under one unit constitute Precision farming, which consists of mutually interconnected components. For example, farmers can use GPS and GPS-aided tractors, harvesters, and

Fig. 2 Smart agriculture or precision farming

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other farm machinery to perform operations precisely. Other geo-referenced and site-specific practices that are followed in the Western world include. • • • • • •

Acquisition of soil samples. Collection of crop yield data. Aerial imagery. Mapping various characteristics and categories of soil. Mapping drainage levels. Estimation of potential yields.

The above-mentioned geo-referenced data layers enable subdividing a bigger farm into smaller manageable zones called grids. Exclusive agronomic interventions for each grid help in minimizing inputs, eliminating waste, and increasing production.

2.3 Advantages of Smart Agriculture Smart agriculture offers a lot of advantages. Some of its benefits are listed below. Increased Production: Smart agriculture provides the crops with an optimum amount of water, pesticide, and manure because of accurate planning. This increases the production rates. Farmers can adjust the quantity of inputs to increase the quality of production. Farmers can increase productivity despite deteriorating soil, weather fluctuation, and water scarcity. Prevention of Soil Degradation: As per the VRT reports, precision farming can reduce soil erosion of agricultural land to a significant extent. The draining of water and mud can be reduced by up to 95%. The usage of special plows prevents mud streams from making streets dirty and provides a worthwhile solution for soil erosion. Water Conservation: Usage of soil moisture sensors and temperature sensors allows water to be used only as and when it is needed and at optimal levels. This leads to the conservation of water, especially in areas where there is water scarcity. Prediction of Production using Data Analytics: Smart farming enables farmers to visualize and control various criteria related to the crops such as production levels, soil moisture, and sunlight intensity. This enables them to make accurate decisions regarding crop management. Reduced Operation Costs: Automated planting, irrigation, and harvesting processes can optimize resource consumption, and minimize human error and overall cost. IoT-based agriculture enables demand-based irrigation, automatic fertilizing, and robot harvesting at a reduced cost. Smart farming enables optimal usage of resources such as water, energy, and land. Precise Insights in Crop Yield: Tracking of various processes in the fields enables precise prediction of crop yield and farm value. Reduced Environmental Footprint: Water conservation and increased production per land unit directly impact the environmental footprint providing a greener farming environment. Smart agriculture makes farming greener and reduces the use of pesticides and fertilizers.

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Minimized Application of Chemical Fertilizers: Precision farming helps farmers to use chemical fertilizers only in the appropriate quantity as required by the crops. Farmers can apply fertilizers using automated tools in exact quantities as computed by the system. Increased Agility of Processes: ICT tools and techniques enable farmers to monitor numerous fields in diverse locations around the globe easily. This enables monitoring the fields even in the middle of some other work. It facilitates ubiquitous decision-making also. The choice of farming machinery and equipment can also be based on production rates, labor effectiveness, and failure prediction. Real-time monitoring and prediction systems enable quick response to significant fluctuations in weather, humidity, air quality, soil status, and the health of crops. Smart farming helps farmers to save crops from extreme changes in weather. Facilitation of Urban Agriculture: The introduction of IoT-based greenhouses and hydroponic systems made feeding a larger population using shorter food supply chains a reality. Smart closed-cycle agricultural systems allow growing food basically everywhere—in supermarkets, on skyscrapers’ walls and rooftops, in shipping containers, and, of course, at the comfort of everyone’s home. Minimized Production Cost: Precision farming enables farmers to choose appropriate fertilizers, and chemicals using geographical information and mapping systems and manages to improve production at a reduced cost. Farmers can receive precision field information through the technologies that provide them control over the application of fertilizers, water, and similar inputs. Planting an adequate number of seeds, precise application of fertilizers and pesticides, swath control, and minimized wastage not only help farmers to save costs but also make farming less stressful. Improved Socio-Economic Status of Farmers: Precision farming plays a major role in water conservation, fuel savings, pesticides/herbicides use, and energy conservation. Farmers also are trained to be techno-savvy and use modern technology for improved productivity. They also contribute significantly toward greener environment sustainability [10].

2.4 Disadvantages of Smart Agriculture Requirement of Ubiquitous Internet Connectivity: Smart agriculture requires an Internet connection at any place and at any time. This may be challenging, especially, when the farms are in rural areas. Techno-Savvy Users: Smart farming requires the users to be techno-savvy, who would understand and learn the technology. It may be difficult to educate users in remote areas. Expensive Deployment: The initial investment required for deploying smart farming systems in fields may be expensive as the farmers have to procure various tools and systems and install them in their fields. Small landowners may find it too expensive to install such systems. However, the benefits of smart farming outweigh the cost incurred.

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Heterogeneity of cropping systems: The heterogeneity of crop and cropping systems adopted by farmers not only varies in space (from farm to farm) but is also highly temporal (differs from year to year) and strongly dynamic in nature. The heterogeneity of agricultural landscapes with varying species in agroecosystems has challenges and concerns of pest management and implementation of precision agriculture methods in already fragmented farmlands. Market imperfections: Farming has always been a tricky business that is difficult to deal with and in order to cope with adverse weather, pests, and highly volatile markets, farmers are uncertain about what crops to raise, when and where to sell, and other farm management decisions. The above risk factors will undoubtedly influence decisions about adopting precision farming methods.

2.5 Intelligent Sensors and Machinery Used in Agriculture Industry 1.

Global Positioning System (GPS) GPS is a satellite-based system that is used to decide exact geographical locations on earth. GPS uses 24 satellites in the earth’s orbit that identifies the position on earth using radio signals processed by a ground receiver. The actual position is shown with 95% accuracy in the range of 10–15 m from its actual position [11]. GPS provides data for precisely mapping the farms and with additional software, allows the growers to determine the water and fertilizer requirements at various locations of the farm. This helps the growers to monitor the status of crop growth, pest, and disease incidence and receive updates on any other deficiencies or abnormalities at any location of the farm in a precise manner. The Global Positioning System offers precision in space and time and forms the basis for the concept of precision farming. All inputs namely water, fertilizers, pesticides, etc. are precisely controlled and applied in the right amounts as needed by crops for increased productivity and maximized profits. Application of inputs at the right time, in the right quantities, and at the right location is typically referred to as “Site-specific Management” [12].

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Uses of GPS in precision farming Precision plowing: Modern technology devices such as automatic steering systems along with GPS help in preparing furrows in the field with accuracy. Planting and manuring: GPS guides in precisely placing seeds and seedlings in prepared furrows following planting patterns and helps to save time and minimize seed loss. Drip irrigation systems help in the fertigation of the field. GPS in combination with the smart management system helps to identify nutrient-deficient zones. Field mapping: GPS technology helps in precisely determining the boundaries of a field rather than eye judgment or manual measurement using measuring tapes. Yield monitoring: GPS along with mass flow sensors help in determining the yield of crops in fields. Efficiency and accuracy: GPS offers cost-effective alternate solutions to map pest, disease, and weed infestations on farms. They help in identifying problem areas for appropriate management interventions and input recommendations. The data is utilized in aircraft spraying for precise swathing of fields eliminating the need for human “flaggers” for guidance. Crop dusters equipped with GPS also fly accurate swaths, applying chemicals in apt quantities, and minimizing chemical drift and environmental pollution. Geographic Information System (GIS): GIS is a software that analyzes and maps all types of data. GIS connects any type of data to a map and integrates location data with meaningful information. It analyzes spatial location and organizes layers of information into maps. GIS provides deeper insights into data and reveals patterns and relationships enabling users to make guided decisions. GIS stores layers of information such as yield, soil survey maps, remotely sensed data, crop scouting reports, and soil nutrient maps which are used in producing visual maps for interpretations and further decision-making. The commonly available maps are soil survey maps, historical maps of crops, region-specific cropping patterns, and aerial and satellite photographs. The global need for food will double by 2050 because of a growing population and dietary changes [13]. So, there is a need for more food production and agricultural activity. GIS is an integrated system of components of information about the real world that has been abstracted and simplified into a digital database of spatial and non-spatial features, in conjunction with specialized software and computer hardware. The components of GIS include the database, which is the information, the data behind the maps that are used for analysis, and the software that runs the database and understands the inputs and outputs. Aerial imagery maps, obtained in layers, derived using Normalized Difference Vegetative Index (NDVI) is a graphical indicator to visualize the remotely sensed data/images taken from satellites or aircraft, or drones. A satellite image reveals everything between the satellite and the ground on a single flat plane and in layers. One layer may reveal differences between vegetation, wet soil, and bare soil using different colors. Another layer may reveal the reflectance

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and temperature measured by aerial sensors. The third layer may reveal high water-stress areas in one color and low water-stress areas in a different color to differentiate one from the other. The layers are superimposed one on top of the other using GIS mapping technology, and the same field is analyzed for conditions of vegetation, water distribution, and soil conditions in a combined manner revealing insights allowing the grower to make informed decisions of micromanaging the farm through better irrigation practices to address different soil conditions and crop growth. They provide useful spatial and temporal information from agricultural land in a low-cost, quick, and easy way. Remote sensing techniques help in increasing efficiency by geo-referencing into a GIS database. A majority of precision agriculture relies on image-based remote sensing data. The remote sensing data serves as a source of point data. The recorded trends and frequencies are used to convert the dataset into spatial data that can be used to reflect management zones within each farm. With the help of spatial data, zone-wise agronomic interventions are specifically designed. This helps the growers to make informed decisions and address the prevailing problematic situations. Spatial data, more commonly called geospatial data, describe a specific location on the surface of the earth. It is also possible to corroborate the spatial data with the field notes or scouting reports to confirm the accuracy of data and to micromanage farms through better management practices to monitor crop production under varying soil conditions and vegetation growth. In the case of large farms, these comparisons are affected using GIS software. This enables the farmer to maximize production under various conditions. This also necessitates the use of automated farm machinery. Grid sampling The entire field is broken down into smaller manageable plots known as grids for the purpose of determining the appropriate rate of fertilizers to be applied in each zone based on the nutrient test report from the laboratory for each of the grid samples. The fertilizer application map is plotted with reference to the soil samples in the grid. After plotting, the application map is loaded into a computerized variable rate fertilizer spreader. The GPS receiver directs the “fertilizer delivery controller” in the variable rate fertilizer spreader machine, using the application map, for effective fertilizer delivery according to varying fertility requirements of the field. Variable Rate Technology (VRT) Based on the data collected in each sampling grid, the field will be sprayed on a “need-based” approach as opposed to completely spraying the entire field. The varied rate of the requirement of inputs is fulfilled by combining the existing field machinery with an “Electronic Control Unit” (ECU) and onboard GPS. Spray booms, spinning disk applicators with ECU, and Global Positioning System have been used effectively for spraying in patches. In the process of preparing the nutrient requirement map for VRT, a general word of caution is to use profit-maximizing fertilizer rates rather than yield-maximizing fertilizer rates.

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Yield Maps Yield mapping refers to the process of acquisition of geo-referenced data on crop yield and various other factors such as moisture content and grain weight, during the harvesting process in the combined harvester. A range of sensors developed and used in harvest data collection include grain flow sensor, grain moisture sensor, clean grain elevator speed sensor, GPS antenna, header position sensor, and so on. The data from an adapted combine harvester that is equipped with GPS is used for developing Yield maps. Yield maps record the grain flow simultaneously while the actual location is recorded in the field. Remote Sensors Remote sensing refers to data collected by sensors from a remote field. Remote data sensors can be of different categories such as hand-held, mounted on aircraft, and mounted on a satellite. The data acquired using remote sensors provide a tool for the evaluation of crop health, and plant stress on account of nutrients, moisture, diseases, and other biotic and abiotic factors that can often be easily detected through the images. Electronic cameras capture near-infrared images that are highly correlated with healthy plant tissues. High-quality information is received from satellites using modern image sensors with high spectral resolution. Remote sensing data help to reveal in-season variability affecting crop yields (such as pest, disease, nutrient deficiencies, and stress due to lack of water) and paves the way for taking agronomic interventions that improve the yield well in advance by spot treatment ways. Proximal Sensors Proximal sensors obtain signals from the sensor’s detector when it is in contact with or close to the soil (within 2 m). Soil parameters such as N status, soil pH, soil moisture, soil temperature, and growth parameters such as plant height, plant growth, plant biomass, and so on are measured. The sensor is attached to the tractor and the measurements are recorded during the passage of the tractor through the field. The advent of wireless sensors has opened a lot of avenues to collect data from crop fields through the Internet. This area of research or operation is technically categorized as the Internet of Things (IoT). Big Data Analytics Huge volumes of data are generated in agricultural production systems which are made available for researchers and private Institutions for data analysis and to make it available in the form of maps, graphs, charts, or reports. Some of the popular analytics software that is being used are Python, R, and so on. For visualization of data, Tableau is commonly preferred by most. Precision irrigation systems The application of technology-based filters and emitters with high clogging resistance to provide an optimal irrigation system is the objective of precision irrigation systems. Commercial use of sprinkler irrigation is very common these days for irrigating vast stretches of farmlands. GPS-based controllers are being recently developed for controlling irrigation motion. Wireless communication

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and sensor-based technologies are being deployed for monitoring soil conditions, and operating irrigation machines using automated varying parameters in order to achieve water conservation. 10. Controlled Traffic Farming (CTF) CTF is a farming system with a holistic view of farming that aims to minimize the machinery loads, avoid crop damage and soil compaction by heavy machinery, and reduce the cost of operating heavy farm machinery. Controlled traffic methods minimize permanent traffic lanes by confining field vehicles by using decision support systems. The advantages of CTF are. • Minimal soil disturbance. • Cover crops maximize biomass production and intensive cropping frequency and provide greater residue return to the system. • Precise management such as inter-cropping and accurate application of chemicals and fertilizers. • Spatial monitoring, mapping, and management. Automated farm machinery is driven with the help of Navigation Geographic Information Systems (NGIS). NGIS is a combined technology that connects GPS with GIS systems to enable farm machinery to function in the desired way, very precisely. It is an automated system that can operate without human effort and significantly increases the productivity of farms. 11. Unmanned Aerial Vehicle (UAV) An unmanned aerial vehicle is an aircraft devoid of a human pilot onboard and a type of unmanned vehicle that operates by itself. It is also called an uncrewed aerial vehicle/remotely piloted aircraft (RPA)/drone. UAVs have wide applications in surveillance, weather forecasting, visual arts/media, military, marine, logistics, agriculture, paramedics, traffic management, etc. They are variously classified based on the following aspects: range (very close range, close range, short range, mid-range), motor type (brushed/brushless), size (nano, micro, small, medium, large), cost (cheap-[$20–$100 toy purpose for fun and recreation], medium cost [$150–$500], expensive [$600–$200], very expensive [>$3000]), number of arms (single, two or more, fixed), and application (starter drones, racing drones, camera drones, professional drones, rescue drones, and so on). UAVs have been extensively used in many areas of agriculture, which include crop monitoring, insecticide, and fertilizer prospecting (estimation of chemical pesticide and fertilizer requirement), spraying of pesticides and weedicides, planting, soil fertility assessment, mapping, search and rescue in mountain areas, and transport of food to remote and inaccessible areas. Some of the major classification types are listed in Table 1. The huge demand for agricultural UAVs has resulted in the emergence of several venture companies. Agricultural UAVs are expected to cover a market size of about $32.4 billion by 2025, which will account for nearly 25% of the global UAV market. Some of the leading manufacturers of UAVs are DJI, Parrot, Precision hawk, AGEagle, and Trimble Navigation [14]. Although there are matured UAV technologies that have been

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Table 1 Classification of UAVs for aerial platforms Classification types

Name

Components

Based on size (relative comparison may vary depending on the application)

Nano

Designed to fit in very small spaces; could be upon an insect—only a few centimeters in length and breadth

Small

Easily lifted in arms, thrown into the air, and left to glide away on their own. Size < 2 m and wings are fixed

Medium

Weigh up to 200 kgs which need support to be lifted into the air to glide on their own

Large

Size of a small aircraft and used for military purposes

Single Rotor Drone (SRD)

Consists of only one rotor with a small tail to control its direction. Similar to helicopters with less load

Based on the number of wings/propellers

Based on range (relative comparison may vary depending on application)

Tricopter

Three propellers

Quadcopter

Four propellers

Hexacopter

Six propellers

Octocopter

Eight propellers

Fixed Wing Drone (FWD)-

Uses a propeller to lift off and wings for gliding

Very close range

Flies up to a range of 5-km radius

Close range

Can sustain flight up to 6 h. Flown up to 50 km away from the controller—used in quick surveillance

Short range

Can be flown up to 150 km away from the user. Can sustain flight for up to 12 h in the air

Mid-range

Fitted with powerful equipment and can sustain flight for days and up to 650 kms from the user

commercialized, challenges still remain to be addressed for advanced agricultural solutions. Leading technologies for UAVs that still need improvements include precision positioning, navigation, controls, imaging, communications, sensors, materials, batteries, circuits, and motors. Based on the use of drones in the agricultural sector, various technologies (e.g., equipment development,

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nozzle controls, and so on) need to be improvised. Major disadvantages are due to Government regulations (evolving laws), safety, and being easily hacked.

3 Applications of Blockchain in Big Data for Agriculture Supply Chain Blockchain and Big Data are currently emerging and booming technologies that are introduced and adopted by most companies. They considerably transform industries and firms in a more secure way to get exact information. Blockchain is a robust technology that is used in a variety of domains where security is of utmost significance. In this section, the applications of blockchain in the agriculture domain are discussed. The possible effective applications of blockchain in food supply chains include securing data generated from agricultural fields, food supply chain management, smart farming, and Big Data management [15–18]. All the above domains generate huge volumes of data commonly referred to as Big Data in which security is highly significant.

3.1 Introduction to Blockchain Blockchain is an autonomous decentralized architecture model introduced by Satoshi Nakamoto and has been used for cryptocurrency transfers for quite some time. Blockchain is viewed as a platform that could make secure transactions possible; users have added more computational elements to its infrastructure and begun using it in diverse applications. Nowadays, blockchain has permeated a broad spectrum of applications including finance, health care, manufacturing, supply chain, travel, tourism, and so on [19–21]. Blockchains are composed of transactions. A transaction is the fundamental element of a blockchain. A block is formed by combining multiple transactions. These blocks are linked together through a digital data link that forms a chain, termed a blockchain. For a new block to be linked to an existing chain, the block should succeed in the consensus process in which every peer agrees to add a block. The block is then verified and linked to the blockchain. The transactions are validated and then linked to the existing blockchain after consensus from special peer node computers known as miners. The structure of a blockchain is shown in Fig. 3. Blockchains are also classified as private, permissioned, and public blockchains based on access permissions. Bitcoin is an excellent example of public blockchain. The bitcoin blockchain allows anyone to join and leave the blockchain without any restraint. Private blockchain access, on the other hand, is restricted only to a group of users, for instance, the employees of an organization. The third classification, namely permissioned blockchain is also known as consortium blockchain. Permissioned blockchains allow controlled access to only users with permission to collaborate and transact. In permissioned blockchains, users belonging to a consortium, for example,

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Fig. 3 Structure of a blockchain

a consortium of health organizations can collaborate and transact. Supply chains usually use permissioned blockchains [22]. Blockchain was initially introduced as an open-source framework for transactions using cryptocurrencies, especially with bitcoins. Later, the Ethereum blockchain extended it as a security framework called a smart contract. Smart contract enabled embedding business logic into the blockchain. A smart contract is a piece of computer code that enables transactions to carry out sophisticated operations. The operations may involve a conditional transfer, some evaluation, processing of signatures for transfer of capital, or waiting for an event to occur. It consists of data, functions or methods, and getter and setter functions. Ethereum provides the computational infrastructure to execute any arbitrary code. Figure 4 shows the deployment of a smart contract. Transactions enable the Externally Owned Accounts EOA to interact with the blockchain. Figure 5 shows a sample smart contract transaction. Blockchains are secured using Hashing and cryptographic techniques. Figure 6 depicts the process of asymmetric key cryptography. Rivest Shamir Adleman (RSA) algorithm is the most commonly used algorithm for asymmetric key encryption.

Fig. 4 Deployment of a smart contract

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Fig. 5 Smart contract transactions

Ethereum uses the Elliptic Curve Cryptography (ECC) algorithms for generating the pair of keys. ECC is more secure than RSA and works better for a given number of bits. Blockchain uses a technique, namely hashing, for preserving the integrity of transactions and the confidentiality of data. A hash function or hashing is used to transform the input data into a different format. Figure 7 shows a digitally signed encrypted transaction. All the items in the transaction such as timestamp, nonce, account balance, and fees are verified. A secure chain is a single main chain with a consistent state. Every valid block added to this chain adds to the trust level of the chain. Blocks will be created by multiple miners and every miner will compete to add their respective blocks to the chain. The unique Timestamp on the nonce is verified to prevent any double use of

Fig. 6 Asymmetric key cryptography

Fig. 7 A digitally signed encrypted transaction

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Fig. 8 Trust and integrity of blockchain

digital assets. Blockchain includes such proven and mature practices for handling exceptions to establish trust. Figure 8 shows how trust and integrity are inclusive of a blockchain.

3.2 Introduction to Big Data Big Data refers to a conglomeration of massive and complex datasets. The process of examining huge volumes of data is referred to as Big Data analytics. Big Data contains data generated by humans, nature, and by machines. The datasets are so huge that specialized tools and technologies are needed for processing and analyzing them. Traditional tools like Relational Database Management Systems (RDBMS) and Structured Query Language (SQL) are not powerful enough to store and process Big Data. Big Data uses scalable NoSQL databases like MongoDB for storage and distributed and parallel data processing frameworks like Hadoop for processing the data. The type of data may be structured, semi-structured, or unstructured and drawn from various sources. Big Data is characterized by 5Vs, namely Volume, Velocity, Variety, Veracity, and Value. Volume refers to the size of data that is used in Big Data. In general, datasets categorized under Big Data are measured in terabytes or petabytes. Velocity refers to the high speed at which data is generated, processed, stored, and analyzed. Variety refers to the categories of data used: structured, semistructured, and unstructured, as opposed to the structured data that is normally stored in traditional relational database management systems. Veracity refers to the quality of data; as data is generated from various sources, it may contain inappropriate contents which may affect the accuracy of results during processing. Value refers to the usefulness of the data to a business [23, 24]. Big Data techniques and applications are associated with various challenges such as data security and privacy, energy

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management, scalability of infrastructure, data processing, and Big Data Intelligence [25]. Every year, 2.5 quintillion bytes of data are produced in the agriculture supply chain which amounts to extremely large complex and unstructured datasets. These large datasets are analyzed computationally to reveal patterns and trends related to human behavior and interactions. There are three different categories of data, namely structured, semi-structured, and unstructured. Structured data is organized and labeled into a formatted repository, typically a database, so that its elements can be addressed for analysis that is more effective. The best example is an excel database. Unstructured data is an unknown form that is difficult to organize and very hard to classify. A typical example of unstructured data is data from heterogeneous data sources such as words in a text, emails, pictures, and videos. The output produced by a Google search is also an example of unstructured data. Natural language processing algorithms are used for processing unstructured data. Semistructured is neither structured data nor unstructured data but a combination of both. Sometimes, it is seen as a structured form but it is actually not defined. Table definition in Relational Database Management Systems (RDBMS) and Extended Markup Language (XML) data are examples of semi-structured data. For example, on Twitter, the number of followers and number of tweets are structured, whereas the content or images shared are unstructured [26]. In general, Big Data is generated from traditional data sources; it however also includes social data and other macro segments of demand that are heavily associated with the supply chain systems. Telemetric devices can be attached to mobile devices or physically attached to the employees’ warehouses in supply chains for precise monitoring of operations. Such telemetric data sources are widely used in supply chains nowadays and they significantly contribute to Big Data. Modern supply chains are also customer-led rather than traditional manufacturer-led supply chains. In traditional supply chains, products are sold to the customers based on the manufacturers’ choice. But modern supply chains are customer-led and demand-driven. The products are sold based on the customers’ choice and preference. Such supply chains need micro models for managing multiple types of consumers in the supply chain. Hence, the future of the supply chain is not only about price but it is more about innovation and service.

3.3 Integrating Blockchain with Big Data The voluminous data generated in multiple phases of the agriculture supply chain through IoT sensors, humans, and by nature lack traceability, transparency, and food products are not fully trustable. Processing such data to ensure traceability and transparency is a challenge faced by industrialists. Blockchain integrated with Big Data is effective in managing traceability and information sharing more accurately in supply chains [27]. Blockchain is highly efficient in Big Data analytics of multifarious domains such as data management, security, privacy, quality management, data

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sharing, and transportation. Some of the processes that involve Big Data in agriculture supply chains are soil and crop management, waste management, and traceability management [28]. Big Data analytics help stakeholders make the right decisions at the right time [29] and are useful in the analysis of food quality, analysis of soil quality, prediction of weather conditions, and marketing and trade management in the agricultural supply chain. The application of Big Data in food supply chains helps to enhance the operational processes of food businesses, thereby increasing their profit, and improving economic gains while optimizing resource allocation. Big Data can also support precision agriculture by improving water conservation in dry areas, soil preservation, limited carbon emissions, and improving productivity. Big Data can also improve food supply chains and manage daily operations by managing relationships among stakeholders in the food supply chain [16]. The major challenges in the processing of Big Data are discussed below. 1. Scalability of Blockchains Blockchain scalability is the ability of the network to increase the throughput as the network grows. Not all blockchains are scalable and a scalable blockchain has the ability to process more transactions per second as the network grows. One of the major issues faced in integrating blockchain with Big Data is scalability. Scaling blockchains to inherently massive data depends on the size and nature of Big Data and is a serious concern in the processing of data. 2. Use of Specialized Algorithms, Tools, and Techniques As Big Data is generated from multiple data sources which could be of multiple data formats, traditional algorithms are not sufficient to process Big Data. Big Data often requires specialized algorithms, tools, and techniques for processing because of the nature and size of the data. Scalability of computer infrastructure is also considered to be a critical issue in blockchains which requires specialized computer resources for processing compute-intensive tasks in Big Data. 3. Data Privacy and Security Another critical issue in Big Data systems is data privacy and security. The type of information stored in agriculture supply chains and the fact that it is exposed to a series of threats necessitate the importance of data privacy and security in Big Data. Moreover, most of the IoT systems store data in third-party clouds. This also makes data privacy and security a major concern. Traditional security solutions are not very effective considering the volume of Big Data. Such security solutions cannot be implemented in most cases due to the massiveness of data which makes the use of cloud storage inevitable. 4. Reliability of Data Big Data agriculture systems also face the issue of data reliability as data is generated from multiple data sources. Blockchain which is inherently decentralized, immutable, transparent, and secure can be an effective solution in providing data privacy, security, and reliability in agriculture supply chains. The encryption algorithms used in blockchains make the data more secure and ensure that privacy is not compromised.

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Blockchains have been used in agriculture supply chains since 2018. Implementing blockchains in edge computing servers in IoT-based agriculture systems can make compute-intensive tasks to be processed with utmost security [30].

3.4 Applications Blockchain has been proposed as a solution for numerous challenges faced by agriculture supply chains. This section discusses some of the applications of blockchain in Big Data integrated agriculture supply chains.

3.4.1

Supply Chain Management

An agriculture supply chain incorporates various stakeholders and multiple transactions. Figure 9 shows the typical processes involved in a traditional food supply chain. The diagram depicts the multiple players and sequence of processes involved in a typical agriculture supply chain. The seeds received by growers through seed suppliers will be planted. The crops will be managed and harvested and passed on to processing units for producing final products. The finished products are then transported to the distributors. The distributors, on receipt of orders, dispatch the products to the retailers. From retailers, the product is finally delivered to the customers. To improve the resilience of the food supply chain, government regulations, approvals, and audits can be digitized using the blockchain, IoT, and Big Data. Additionally, traceability certifications recording all supply chain footprints can be generated via blockchain platforms. Because of the epidemic, fewer human contacts

Fig. 9 Sequence diagram of a traditional food supply chain

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Fig. 10 Future of food packaging industry

are needed in the food processing and packaging industries in this and the following decade. Cyber-physical systems and intelligent robotics in the food processing and packaging sector can enhance the quality of the Food Supply Chain. Technology based on Big Data and blockchain can help farmers practice ethical procurement to uphold sustainability requirements on an economic and environmental level. Figure 10 illustrates an entire food supply transformation-based operational paradigm in future.

3.4.2

Big Data Management

As discussed in Sect. 3.3, agriculture supply chains generate huge volumes of data in various phases from multiple data sources. There is a rapid increase in data generated by agriculture supply chains due to the integration of Internet of Things (IoT) into agricultural systems. Big Data requires huge data storage centers and computeintensive infrastructures for analysis. Therefore, most of the data in agriculture supply chains are stored in third-party clouds. Inclusion of third parties in the agriculture supply chains increases data breaches and cyber threats. The size of data makes traditional algorithms and techniques inefficient to secure such data. Blockchain provides strong encryption algorithms that encrypt the data during storage and transmission and ensures the prevention of data from any unauthorized access. Supply chain data is prone to tampering by many players involved in it leading to food adulteration and contamination. Blockchains are inherently immutable and prevent any tampering with data. This ensures food quality and reliability. Blockchains integrated with Big Data can also help in real-time data analytics that can be used for making timely decisions for the improvement of quality and profit. Blockchains can also prevent redundancy and leakage of data during transmission and storage. Blockchains also make supply chains transparent and traceable.

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Table 2 Applications of blockchain in big data management in agriculture supply chains Supply chain process

Challenges faced by big data systems

Probable solutions through blockchain

Data collection

Collection of data through Blockchains can provide secure diverse sources exposes the data data collection by filtering to cyberattacks and threats redundant data and identifying malicious data

Data transmission or data sharing

Data is prone to tampering during transmission to different parts of the blockchain and clouds. This may lead to food contamination

Data storage

Agriculture supply chains need Blockchains use efficient huge storage and therefore, cryptographic algorithms for most of them use cloud storage secure data storage in distributed locations. Securing the data in storage is a big challenge

Data analytics

Big Data systems require specialized tools for data analysis and processing. Traditional tools are not sufficient for processing due to the nature and size of the data

Blockchains integrated with Big Data can enable secure real-time data analytics that can be used for making timely decisions for the improvement of quality and profit

Data security and privacy

Sensitive data stored in supply chains may be misused

Blockchain provides facilities to control users’ private data. The immutable nature and cryptographic features can protect data from misuse

Blockchains are immutable and they do not allow tampering. The timestamping and hash values prevent the data from being altered

Agriculture supply chains collect data from multiple data sources. The data sources include humans who are the stakeholders involved, sensors from IoT systems, and nature. The huge volume of data collected and the diversity in data collection make such agriculture blockchains prone to cyberattacks and threats. Smart contract blockchains can be used for securing data collection. Data is exposed to threats during transmission from one part of the supply chain to another, as well as during transmission to the cloud. Blockchain-based transactions will help in the prevention of tampering of data during transmission due to their immutable nature. The application of blockchain in various phases of Big Data management is summarized in Table 2.

3.4.3

Detection and Prevention of Food Adulteration

Food adulteration refers to the addition of harmful substances to food items in order to earn better profit and is one of the biggest threats prevalent in society today. People live in constant fear of the dangers of food adulteration. Food adulteration is very common in developing countries like India. According to FSSAI, food adulteration

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in India has increased to 28% in 2018–19. Up to 90% of food adulteration has been reported in states such as Andhra Pradesh, Kerala, Maharashtra, Madhya Pradesh, and Uttar Pradesh [31]. Food fraud and adulteration can happen in any part of the food supply chain. Food adulterations not only deteriorate the quality of food by reducing its nutritional value, but they also cause illnesses including chronic ones. People always live in constant fear of being affected by such illnesses. In India, most of the chronic diseases such as diabetes, hypertension, cholesterol, and thyroid are attributed mainly to the consumption of unhealthy food. One of the main reasons could be attributed to the lack of mechanisms to track the food frauds that occur in the supply chain. Small quantities of non-nutritious or hazardous substances are added to the food intentionally to give a fresh appearance to vegetables and fruits, to improve the texture of food items, and to improve the shelf life of foodstuff. There are six common methods of food adulteration. 1. Mixing Foodstuffs are mixed with sand, clay, pebbles, and marble chips in order to increase their weight. For example, grains like rice are mixed with small pebbles. 2. Substitution Cheaper and inferior substances are replaced wholly or partially with good quality food and sold at a high price. This process deteriorates the quality as well as the nutritious value of food. 3. Decomposed Food Decomposed fruits and vegetables are mixed with fresh ones and sold. 4. Misbranding or False Labels Duplicate foodstuffs are sold with false brand labels; chemical fertilizer mixed items are sold with organic labels; expired goods are sold with false manufacturing and expiry dates. 5. Addition of Toxicants Harmful substances, hazardous chemicals, and non-edible substances are mixed with food items. Some of the common food adulterations in India include the following: • Waxing of apples in order to increase the storage time and give a glossy appearance. • Artificial ripening of fruits like mangoes, papayas, bananas, and so on, to achieve faster and uniform ripening appearance and to attract customers. Harmful chemicals such as carbide, acetylene, and ethylene are used for such purposes. • Green vegetables are dipped in colored dyes and laced with copper sulfate to give them a fresh look and bright artificial coloring. • Turmeric powder, chilli powder, and coriander powder are mixed with harmful substances like chalk powder, toxic synthetic dyes (metanil yellow), brick powder, and horse dung. • Starch, urea, and detergents are added to milk. Such adulterations are harmful to children.

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• Dairy and poultry products are mixed with harmful chemicals to increase shelf life. • Seafood is mixed with formalin to enhance the storage life. • Honey is adulterated with corn syrup, sugar syrup, and even with antibiotics. Though the above-mentioned substances are banned by the Food Adulteration Act of India, most of them go unnoticed due to negligence and corruption. Blockchain can provide an effective solution for detecting and preventing food adulteration. Blockchain demands every transaction be recorded and prevents tampering. The immutable and decentralized nature of the blockchain will be an effective mechanism for solving the problems of food adulteration. Recently, global players such as Walmart, Nestle, and Unilever have participated in pilot projects for improving food supply chain transparency and for the detection and prevention of food adulteration [31]. Carrefour has integrated blockchains to enable the traceability of chicken, honey, eggs, and milk using mobile phones [32]. Blockchain will be a boon to mankind, if implemented well and will be a lifesaver, protecting humans from harmful diseases.

3.5 Benefits of Blockchain in Agriculture The following is a summary of the several advantages that can be realized when blockchain technology is applied to agriculture: • It helps to maintain mutual trust and confidence between the customers and suppliers by tracing the source of origin of various foods. • It eliminates intermediary or middlemen in the process. • Blockchain enables businesses to enhance the value of their product, thereby increasing competitiveness in the marketplace. • It prevents entry of suppliers of poor-quality or fraudulent products with continued malpractices. • Fully transparent peer-to-peer transactions. • It improves the quality of the food supply chain. • It provides users with reliable and authentic information. • It facilitates various data-driven innovations. • Coupled with smart contracts, it smoothens the nature of transactions between various stakeholders. • Increased trust and integrity that is inherently established through a decentralized, distributed transaction infrastructure. • Decentralization allows for ease of data distribution to multiple stakeholders thereby minimizing data loss and distortion. • It addresses concerns about quality and food safety. • Freedom for consumers to interact with producers. • Provision of digital payment options at zero-transactional fees.

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• It enables purchase of agricultural products by consumers with improved confidence. • It helps farmers market and sell products online covering a greater audience and getting more profit. • Reliable to conduct transactions with anonymous members. • It detects and reports fraud by use of smart contracts in real time.

4 Food Supply Chain Management Based on Blockchain-Integrated Big Data Food Supply Chain Management (FSCM) is indispensable for both the food producer industries and consumers. Basically, they are designed to ensure transparency in food handling and process, leading to high-quality food production. This is a lengthy process that contains supplier, manufacturer, distributor, retailer, and consumer. Any product goes through innumerable stages and in the end, every product carries unique data and the FSCM process. Blockchain technology complements Big Data where huge amounts of data are transacted over the Internet. The data growth rapidly increases by 50% per month, and the expected total amount could be 800% in the next 5 years. It is expected by 2025, the total sum of data will be around 50 zettabytes [33]. Blockchain and Big Data technologies are so crucial for businesses and customers, and they will greatly enhance corporate operations.

4.1 Integrating FSCM with Big Data and Blockchain Nowadays, all Government and Private sectors are investing profoundly in Big Data and blockchain technologies because of their great importance in solving real-world problems. In today’s world, customers are prone to do any transaction online, and this increases the amount of data generated every day. This exponential rise creates more opportunities for industries to know customer needs and their purchasing patterns. Big Data uses many statistical and data mining techniques in analyzing huge datasets. Another important advantage of integrating blockchain with Big Data in agriculture is to tackle and reduce food wastage. Figure 11 shows a food supply chain integrated with Big Data and blockchain. There are many stages in the food supply chain starting from harvest, post-harvest, processing, distribution, and consumption throughout which the food traverses till it reaches the customer. There will be food loss, especially during the post-harvesting and processing stages. Food waste also includes loss due to pests and diseases. The combined wastage leads to huge losses incurred by the farmers which in turn results in a price hike for the consumers. Technology orientation with blockchain and Big Data solutions have been employed to

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Fig. 11 FSCM integrated with big data and blockchain

manage food waste [22]. Big Data with IoT sends real-time data that is utilized to optimize production and enhance quality to satisfy customer needs. Anticipating safe and nutritious food from markets has become a commonplace trend following the COVID-19 pandemic. Integrating blockchain characteristics with IoT will effectively bring agriculture and food supply chain processes to an integrated smart system that guarantees customers receive perfect and healthy food. Figure 12 shows major players and the phases in a food supply chain. Let us look into the role of the major players in different phases of the food supply chain and how blockchain can be used to address the challenges faced in each phase of the supply chain.

Fig. 12 Phases of food supply chain integrated with blockchain

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1. Seed suppliers/Vendors The seed sellers or vendors are obliged to supply an improved range of seeds to the market which is sold to the growers/farmers to achieve a maximum yield and a good quality crop. All information pertaining to the purchase of seed, germination, and growth information is stored in the blockchain by the seed seller. Blockchain can be used in the seed supply chain for tracking its entire supply movement: from seed aggregators to farmers through distributors and retailers. Recently, poor-quality seeds affected the production of cotton crops and vegetable crops in India by at least 10%. At present, genuine seed suppliers use QR codes for tracking the distribution of seeds. A startup MyCrop in Gujarat, India, has launched a pilot project for tracking the movement of seeds in an agriculture supply chain using blockchain. The blockchain stores details such as the quality of seeds, pesticides used, and photographs to identify spurious seeds from genuine ones. 2. Farmers/Growers A farmer/grower purchases quality seeds and plants them and also needs to record all the details regarding the fertilizers used and other relevant information about the growth of the plant. Smart farming technology enables farmers to interact with suppliers and buyers of the blockchain. Farmers can use blockchain for tracking information related to crops such as the quality of seeds, how crops grow, and the harvest’s journey from the farm to the processor. Farmers can also ensure the customers that they produce quality food free from poisonous pesticides or any contamination. 3. Crop buyers/Contractors/Middlemen Crop buyers purchase the crop or grains from farmers and sell them to processors. The crop buyers can verify the details of the products using blockchain and ensure the quality of food. Blockchains also prevent any type of tampering or adulteration of the product by middlemen. Blockchains record the metadata of the transactions such as the owner and the timestamp along with the transactions. This helps in the non-repudiation of the transactions. 4. Food Processors Processors convert the raw grain into a finished product by performing moisture exploration. This is the place where a lot of food adulteration is possible. Including transactions in the blockchain will enable us to stop such adulterations. The transactions by the processor will record the type of ingredients used in processing, the preservatives used for the purpose of shelf storage, and the quality and expiry details of the ingredients. This can help the consumers to calculate the nutritional value of the food products. 5. Crop storage/Go downs The harvested produce prior to final sales is stored in warehouses, the details of which are recorded in the blockchain, enabling stakeholders to view and exchange required information. Recording such transactions in blockchains will ensure that the food products are stored under suitable conditions. Blockchains also make sure that clean and non-toxic containers are used for storage.

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6. Distributors The distributor will be the producer’s contact responsible for supplying produce to various retailers. He connects with retailers or buyers/sellers of the products. Distributors also work closely with wholesale mediators who invest in bulk purchases and huge quantities of commodities. Blockchains help to detect or contain any type of adulteration by distributors as well. They also prevent the selling of expired foodstuff by distributors. 7. Retailers Retailers are mainly money-making companies that will trade the products to the consumer. They search for the particular product which suits the market objectives and find vendors to get a good profit. Since everything is recorded in blockchain, it is easily traceable and makes it easy for the consumer to validate all details of the product. 8. Customers The customer will be the last vendor who will be involved in purchasing food from the dealer or merchant. All the investors have to apply a digital signature in the smart contract of the blockchain. The customers are happy about getting the right product with the right value.

4.2 Advantages Integrating blockchains with Big Data-based food supply chain management offers the following advantages: 1. Enhanced Data audit Integration of blockchains in Big Data-based food supply chains enables the auditors to easily verify the transactions and avoid data redundancies. 2. Data Processing Cost Integrating blockchain can reduce organizational costs associated with any other third party. 3. Data Accuracy Storing data on a network in an automated way between the blockchain nodes leads to high-level data accuracy. These records are clearly traceable and tamperproof. 4. Data Interoperability and Global data sharing All data will be stored on the framework of blockchain. It provides global access and traceability features. 5. Data privacy and security Integration of Blockchains in Big Data-based systems leads to a high-level secure data flow and permissions in terms of smart contracts. This also stores data in a distributed way in multiple places to avoid single-point failure.

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4.3 Real-Time Examples Some real-time examples of different food industries where blockchain and Big Data are integrated and are used to bring transparency to the supply chain network and gain consumer trust. • The Sustainable Shrimp Partnership (SSP) has collaborated with IBM in order to use the food trust ecosystem to provide them with complete traceability of packaged shrimp for their consumers. Food Trust is a certified safe and secure platform where data can be uploaded and shared. In this case, the data related to shrimp production will be uploaded onto the blockchain which can be accessed by the retailers and consumers at each and every stage of the process. This platform will also ensure verification of the shrimp’s SSP qualification, e.g., it is adhering to the Aquaculture Stewardship Council (ASC) Standard and is antibiotic-free [23]. • Nestle in collaboration with OpenSc which is a blockchain platform is used to trace mild from farms. This is in the pilot stage but will be a big step that demonstrates the trust and commitment to their transparency. • Malaysian Palm Oil Council (MPOC) in collaboration with BloomBloc has implemented blockchain for enhancing traceability. They have a blockchain mobile app and web interface to trace the palm oil throughout the supply chain. This enhances the transparency, accuracy, and credibility of all customers as it automatically creates an end-to-end digital database.

5 Challenges to Implement Food Supply Chain Management Based on Blockchain-Integrated Big Data Agriculture is faced with innumerable challenges that could affect the sustainability of the entire industry. The supply chain manages the flow of goods and services and all the processes that help transform the raw material into the final product and strives to integrate all operations, logistics, procurement, and information technology. It draws heavily from all components of the supply chain process: strategic planning, demand planning, supply planning, procurement, and manufacturing. Food supply chains involve several participants in the process, and managing a good connection between all of them is a real challenge. Traceability, visibility, reliability, and affordability are significant challenges that a supply chain faces to maintain its quality and authentication at a reasonable cost. Blockchain is an effective mechanism that can provide traceability and visibility in a supply chain. International policies that regulate the fine usage of blockchain technologies are a requirement. Protocols providing compatibility between different types of blockchains are also required for smooth functioning. This section details the challenges faced in implementing blockchains in Big Data integrated food supply chains.

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1. Cyberattacks The robust mechanisms used in blockchain for securing transparency and traceability are still facing various security challenges. One of the attacks faced by blockchains known as the 51% attack is caused by miners who add transactions to the blockchain ledger. A group of miners can control more than 50% of the network’s mining hash rate by the 51% attack. These attacks have the potential of interrupting the recording of new blocks by preventing other miners from completing the blocks. Successful 51% attacks can block transactions and make the users pay again for their transactions. This vulnerability is known as doublespending. Though bigger blockchain networks like bitcoin and Ethereum are not likely to suffer from such attacks, smaller networks are often targeted. Moreover, 51% attacks are prohibitively expensive. 2. Unauthorized Access Permissionless blockchains allow unauthorized users to join the blockchain. Such problems are solved by hyperledgers used in Ethereum blockchains. Hyperledgers use permissioned blockchain where only authorized users are allowed to make transactions. Another possible attack on blockchains is the Sybil attack, which enables a node in a blockchain to add malicious users. Malicious nodes can exploit more computer resources for accomplishing the attack. Blockchain follows a proof-of-work (PoW) consensus algorithm which evaluates the computational power and the miner’s right to add the new block to the blockchain for reaching consensus. PoW consensus algorithms can mitigate such types of attacks. Managing complex blockchains is challenging in resourceconstrained IoT environments which aggregate data from diverse sources. Longer blockchains become inherently complex since they need tremendous computational power. Blockchain consumes more computational load for key exchange, cryptography, digital signatures, and for the miners. 3. Reliability of Data Reliability of data is another challenge faced by agriculture supply chains. The information getting recorded in the blockchain could be fake and has to be tackled with high importance. Sometimes, the data saved through blockchain is not open to customers, so this becomes like a self-analysis tool. If any outbursts occur, the suppliers can use this data to give to the supervisors. For huge organizations with good regulations, it is easier but small-scale organizations may not be sharing all information with the consumers thinking that the system can be hacked easily. Still, much research is going on in this area. 4. Traceability Various products deal with different properties; the consumer’s viewpoint may vary for different products. For instance, in fresh fruits and vegetables, consumers pay more attention to the pesticides used instead of thinking about the country of origin. For frozen foods, consumers care about the authenticity if it is highquality meat. Based on this, there must be more tracking factors depending on the type of products. Therefore, choosing the type of data to be recorded may

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also be challenging. Tracking these parameters will be a critical challenge for the data uploader and also the entire blockchain system. 5. Affordability While it is expected that blockchains will help to maintain affordability by managing the predictable dangers, extensive use of sensors, the packaging and barcoding systems, etc. are predicted to enhance the costs initially. The ID code or barcode built for item identification will also increase the packaging cost. For low-budget products, it is a real challenge to have barcode labels. There should be a customized modification that can be done to the labeling machine to fit the special needs of the barcode. The labor cost involved for people who manually scan and record barcodes and IR staff to support this is also high. The use of sensors for monitoring temperature and humidity also increases the cost. Blockchains should be decentralized to include small and marginal farmers and rural households to empower fair food and fair price systems. 6. Integration with Legacy Systems Dealing with the blockchain system in order to combine it with pre-existing data is another challenge. At present, manufacturers use their own data updating system with repository volumes of information about the products. The recent challenge is to bridge the gap between the local agricultural farms and load this pre-existing data to the cloud or to the blockchain to create a huge repository. Also, this can be transferred easily to any government regulatory body to track, monitor, and audit the entire food supply chain. The blockchains should be decentralized.

6 Conclusion Blockchain has enormous potential to remarkably influence the way agricultural business is carried out. This technology helps to increase trust between parties and facilitates information sharing throughout the supply chain and helps to significantly reduce agricultural transaction costs. Within the agricultural domain, blockchain finds its niche in enhancing farm output through smart (or) precision farming, minimizing cost, and improving the quality of produce in the food supply chain, in agricultural finance, and insurance with the help of weather variables it improves the reliability of the entire process. Prices of agricultural produce can be lowered without compromising farmers’ selling prices and enable faster payment to all Agricommerce participants. Smart contracts are effective ways to ensure payment immediately on effecting the purchase or transfer of goods. Food companies conduct food recalls and investigations very quickly and seamlessly due to the traceability system of blockchain technology. Consumers are able to access information about the quality of food at every stage quite instantaneously. The food supply chain is a process that involves multiple transactions from multiple stakeholders. Big Data analytics can help autonomous decision-making in the food supply chain based on huge volumes of agricultural data. Blockchain has proved itself to be very promising, yet there are legal hurdles that could hinder this technology from realizing its full potential. As

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data is permanently added to the database, this causes some issues when it involves storing the personal and banking information of its users. Such privacy safeguarding challenges should be addressed and tested before gaining widespread acceptance. Although smart contracts and electronic transactions are very handy and the norm today, the establishment of contracts without formal agreements may cause confusion about what is real and what is virtual. It is also unclear how such contracts effected through blockchain technology would be interpreted by court of law. While blockchain transactions do not have any governance procedures in place and participants are free to establish their own ad hoc rules in a relatively small-scale business, large commercial houses may have reservations to exchange value over blockchain technology.

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Novel Smart Homecare IoT System with Edge-AI and Blockchain Tri Nguyen and Tuan Nguyen Gia

Abstract Healthcare data is sensitive information that demands security and confidentiality. Currently, remote monitoring is widely applied as it provides many advantages, including improving the quality of healthcare services. However, data must be transmitted and exchanged over a network or Internet where authorized parties can leverage invulnerability in remote health monitoring systems. Although many previous studies mention possible solutions for an intelligent healthcare system, healthcare challenges are still open, mainly in the centralized system. Thus, attention to a decentralized environment has recently risen in healthcare system construction. However, a decentralized formation requires a specific technology to store and reliably communicate among network participants immutably. As the candidate for this consideration, blockchain technology is a prominent solution to forming decentralized systems. Further, due to the demand for swift reaction, alerts require the minimization of latency in communication, which brings to the examination of edge computing. This work introduces a novel intelligent homecare system based on blockchain technology placed at the network’s edge. The proposed system overcomes the existing system’s security limitations and provides advanced services that help in improving the quality of healthcare service. Keywords Blockchain · Edge-AI · IoT · Smart homecare · Hyperledger Fabric

These authors contributed equally. T. Nguyen (B) University of Oulu, Erkki Koiso-Kanttilan katu 3, Oulu, Finland e-mail: [email protected] T. Nguyen Gia (B) University of Turku, Vesilinantie 5, 20500 Turku, Finland e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Namasudra and K. Akkaya (eds.), Blockchain and its Applications in Industry 4.0, Studies in Big Data 119, https://doi.org/10.1007/978-981-19-8730-4_10

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1 Introduction In nursing or individual homes for the elderly, healthcare systems are often applied to monitor the health status of elderly persons (e.g., sleeping status) and detect abnormalities such as a human fall. In addition, applying the healthcare systems helps overcome the issue of lacking nurses and medical doctors. Notably, a nurse or doctor can use the system to monitor several elderly persons simultaneously. However, many current healthcare systems cannot offer remote and real-time monitoring capabilities. One solution for overcoming this is applying Internet of Things (IoT) which consists of different technologies such as wearables, sensing, wireless communication, and cloud computing. The IoT systems collect bio-signals (e.g., temperature, pulse rate, respiration rate, blood glucose, and blood pressure) from the human body via wearable devices [1]. The collected data are transmitted to cloud servers where cloud services such as global storage and big data analysis are provided. End-users such as nurses or medical doctors can use a mobile application or a web browser to access the data anywhere at any time. The doctor then analyzes the data for detecting and diagnosing cardiovascular diseases, diabetes, or lung-related diseases. Depending on the system and diseases, the particular bio-signals can be more focused. For example, Electrocardiogram (ECG) and vital signs are often targeted when dealing with cardiovascular diseases. Many state-of-the-art home-based IoT systems for health monitoring have been proposed. For example, the authors in [2] introduce an ECG monitoring system based on IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), while the authors in [3] present fall detection using Bluetooth. However, the existing home-care IoT systems still have some limitations, which are discussed as follows: (i) when the Internet connection or wireless communication has issues, the system latency increases significantly. (ii) These systems do not offer advanced services to improve the quality of healthcare service. (iii) Many intelligent healthcare systems rely on cloud servers for providing services and Artificial Intelligence (AI). When the cloud servers have some issues or the Internet connection is unstable, many services, including AI, cannot be adequately maintained. (iv) These systems often underestimate security aspects. (v) Most health monitoring systems rely on a centralized system with drawbacks such as less control over data, mismanagement of data, non-transparency, and vulnerability to security threats. It is required to develop more advanced systems to overcome the limitations and improve the quality of healthcare services. One of the possible solutions is to utilize several advanced technologies together, including edge computing, cryptography algorithms, and blockchain. Mainly, edge computing brings cloud paradigms to the edge of a network and supports features unsupported by cloud computing. In addition, edge computing enables edge services to improve the quality of service and provide AI at the edge (Edge-AI). For instance, edge computing facilities distributed storage, local data analysis, situation awareness, and push notification. The system service is still properly maintained when the Internet connection is not stable or interrupted.

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Blockchain technology is the next generation for trust communication among participants [4–6]. Concretely, blockchain succeeds as a chain of data blocks, each of which manages the integrity of the confirmed data and current data in the block via a set of metadata. Therefore, without the permission of data change or tamper resistance, communities evaluate blockchain as the next generation of communication that forms trust among communication in a decentralized architecture. Besides, decentralization requires a consensus mechanism for securely gaining the data agreement in different participants. Consequently, blockchain supports the success of case studies established on a decentralized architecture with trust formation as securitybased technologies. In addition, the next step for blockchain growth is the shape of autonomous systems, where blockchain-based systems supply a platform for integrating tons of decentralized services. In other words, blockchain is a technology that involves techniques, for example, cryptographic schemes, consensus, and communication, to form trust in an open network or decentralization; the development of blockchain is also an autonomous system known as a blockchain-based smart contract. Furthermore, the combination of blockchain and cryptography algorithms can address the security issues such as authentication, authority, trust, integrity, and transparency. Nonetheless, it is challenging to harmoniously apply all mentioned technologies to a system as some have trade-off relationships. Particularly, blockchain node requires powerful hardware for running heavy computation. Moreover, this process often takes a significant latency, infringing the strict time requirements of real-time IoT monitoring. Therefore, this chapter aims to develop an advanced health monitoring system harmoniously utilizing edge computing and blockchain to overcome the existing limitations and enhance the quality of services. The main contributions of this chapter are presented as follows: • Edge-based architecture enabling advanced edge services. • Integration of blockchain and Edge-AI for overcoming the limitations of centralized healthcare systems. • A case study of human fall detection and counting the number of visiting times to the toilet per night. The remaining of this paper is organized as Sect. 2 is about the cutting-edge healthcare system, primarily based on edge computing and blockchain technology. Then, Sect. 3 is a brief description of blockchain technology with fundamentals and philosophy prior to detail in Hyperledger Fabric. Next, the perspective related to edge servers and blockchain technology in an IoT architecture is in Sect. 4. Meanwhile, the experimental setup and result with Hyperledger Fabric are mentioned in Sect. 5. Finally, Sect. 6 is to conclude this book chapter.

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2 Literature Reviews This section presents state-of-the-art approaches using blockchain for healthcare systems and health monitoring approaches utilizing edge computing to enhance the quality of healthcare services. As a result, a summary of related work is displayed in Tables 1 and 2.

Table 1 Summary of blockchain-based healthcare systems Studies

Blockchain

Application

Weakness

[7]

Ethereum

Medical data access in decentralized record management

Lack of implementation and experiments with medical services

[8]

Hyperledger

A framework for the management of access control

Lack of implementation and experiments related to the framework

[9]

–

Auditing and controlling access to sharing medical data

Experiments are shallow and lack information related to blockchain

[10]

Ethereum

A secure decentralized framework for EHR sharing

Ethereum experiments are detailed; however, the weakness comes from the authorization

[11]

Hyperledger Fabric

User-centric health data sharing

The experiments are not clear in the setup and the conclusion

[12]

Ethereum

Access control and security for a medical record framework

Lack of experiments and detail in the setup

[13]

Ethereum

Authorization management for clinical data sharing

Lack of experiments and detail of the setup

[14]

Private

Data sharing in a peer-to-peer architecture

Lack of detail in smart contracts in the system

[15]

Private Ethereum

Connectivity between patients and clinicians

A private blockchain but lack experiments and detail of the setup

[16]

Ethereum

A privacy-preserving technique to maintain medical certificates

An Ethereum-based system with IPFS but lack of information related to deployment

[17]

Ethereum

Reduction of cost in the pharmaceutical supply chain

Lack of deployment and experiments details

[18]

Permissioned

Authenticity and privacy in the traceability of data

Lack of information related to deployment and experiments

[19]

Hyperledger Composer

A product recall problem with transparent and autonomous pharmaceutical supply chain management

Interesting experiments and comparison, but lack of detail in explanation

[20]

Private and consortium Ethereum

Secure analysis and management of medical sensor

System based on private and consortium, but deployment via Ethereum, and lack of experiments

[21]

Bitcoin

End-to-end architecture for patient monitoring

The experiments based on Bitcoin are not clear

[22]

–

Secure analysis and management of healthcare with big data via a novel architecture

Lack of information related to experiments and general architecture

[23]

Hyperledger Fabric

A novel platform for secure monitoring of signals from patients

The experiments do not show single failure avoidance of order services with Hyperledger Fabric

Novel Smart Homecare IoT System with Edge-AI and Blockchain Table 2 Summary of edge/fog-based healthcare systems Studies Application Advantages [29]

Fog-based glucose monitoring system

[30]

Fog-based health monitoring system using smart fog gateways

[31]

Fog-based ECG monitoring

[32]

[33]

[34]

[35]

Mobile used as fog gateway for offering edge services such as local data storage, push notification Support many fog services, including interoperability, ECG feature extraction, push notification, distributed storage ECG feature extraction; efficiency of bandwidth utilization

297

Weaknesses Need for extra hardware for communication

Non-support and implementation of Edge-AI

Non-support and implementation of Edge-AI, lack of many advanced edge/fog services Edge-based system for Support long-range Not high accuracy and fall detection and Edge-AI underestimation of security aspects Edge-based Support many edge Non-support and autonomous elderly services, the energy implementation of patient home efficiency of wearable Edge-AI, monitoring system devices underestimation of security aspects Edge-based ECG Many edge services Centralized system monitoring system such as ECG feature and need for extra extraction, push hardware for notification, the communication, energy efficiency of wearable devices ECG Arrhythmia Edge-AI with Centralized data Analysis Edge–Fog– one-dimensional CNN storage, Cloud-based for real-time ECG underestimation of system analysis security aspects

2.1 Usage of Blockchain in Healthcare Systems The most recent surveys [24–26] mention the interest usage of blockchain in the healthcare system in different fields. Due to communication support, blockchain expects to enhance information sharing (healthcare records, images, and log management), IoT-based remote care, claim management, and patient access. Along with those usages of blockchain in fields, these surveys indicate the characteristics of blockchain to contribute a set of features to a healthcare system, such as data

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privacy, real-time update and access, data authentication, data interoperability, data sharing, data integrity, and patient data management. Also, the novel generation of communication supports the growth of health information exchange and the success of Electronic Health Records (EHRs) [27]. Thus, [27, 28] mention the interests of blockchain from which healthcare systems earn, particularly support to improve the realism in healthcare intelligence via trust in collaboration among participants as service providers. Mayer et al. [27] explore the main contribution of blockchain to a healthcare system based on EHR storage and access management. A blockchain-based EHR system is exploited through five main aspects: security, scalability, privacy, interoperability, and governance. In addition, Gordon et al. [28] mention the contribution of blockchain to the success of patientdriven interoperability, such as digital access rules, data aggregation, data liquidity, patient identity, and data immutability. With the first utilization of blockchain for decentralization in a healthcare system, MedRec [7] is an EHR system to reduce latency and enhance the interoperability and the quality of data. Furthermore, based on smart contracts, MedRec also provides a solution for tracking the healthcare history of patients and health decisions. Therefore, MedRec is the first and the most exciting idea for utilizing blockchain to manage healthcare data and enable healthcare integration in patients, hospitals, and physicians. Another consideration for blockchain usage is from [8] to form an architecture based on the cloud and blockchain. This combination enhances access control management in systems, particularly doctors and patients, via smart contracts as pre-defined access policies. With the usage of blockchain for auditing, Xia et al. [9] propose an interesting integration in a cloud-based healthcare system called MeDShare, and then an extension with privacy preservation from Wang et al. [10] as MedShare. Via smart contracts, authors in [9] introduce a solution for auditing and controlling access to information with four layers: user layer, data query layer, database infrastructure, data structuring, and provenance layer for different specific tasks. For details, the user layer is about data for accessing via a graphical interface, while the data query provides a set of structures for answering requests. Another layer is database infrastructure for access by specialists. In recognizing the weakness of previous studies, Liang et al. [11] propose using blockchain to construct a decentralized environment for privacy protection and identity management. Interestingly, this consideration is based on a mobile technology environment where the data is continuously collected and shared with healthcare providers. However, due to the lack of privacy from blockchain technology, this work uses a cloud-based database to control the proof of identity and validation. Similar to previous studies in the 4 years, a study [12] mentions an exciting use of blockchain to enable data sharing with a decentralized architecture. In detail, Dagher et al. ponder the use of blockchain as a solution to form a connection between privacy and accessibility in EHRs. A blockchain-based framework called Ancile uses Ethereum-based smart contracts for access control and data obfuscation. In detail, Ancile mentions a set of six smart contracts, including consensus, classification, ser-

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vice history, ownership, permission, and re-encryption. For blockchain maintenance, the consensus smart contract handles this task using Ethereum. Also, FHIRChain [13] is about authorization management that supports clinical data sharing, authentication, and clinical decision-making interoperability via blockchain technology. Recognizing the lack of a national coordinator for health information technology, FHIRChain expects to solve current issues via encapsulating the HL7 Fast Healthcare Interoperability Resource (FHIR). In detail, the national coordinator requires a privacy identity ecosystem for authentication, data exchange for storage, access control, data consistency, and maintenance. Medical professionals collaborate and share data with the concept of on-chain and off-chain blockchain technologies. MedChain [14] is a recent blockchain-based data sharing in the healthcare domain to benefit patients and healthcare providers. Shen et al. leverage blockchain with digest chain in a peer-to-peer architecture to address the efficiency of the previous approaches. Concretely, the blockchain is about the fingerprint of data for integrity, whereas peer-to-peer storage contains data descriptions. Recently, another study [15] mentioned using blockchain as a technology to against hack troubles in security and privacy in patient-centric health information exchange. With the main idea from the smart contracts, the study discusses two main modules: linkage module as blockchain-based EHR databases at healthcare facilities and request module by which the patients allow clinicians to access their data. In another view related to data management for medical certificates, Namasudra et al. [16] propose architecture with the usage of blockchain to gain privacy-preserving techniques for IoT-based healthcare systems. Particularly, the authors in [16] introduce an Ethereum-based application for generating and maintaining medical certificates on IoT devices along with InterPlanetary File System (IPFS) as a decentralized storage system. In detail, the IPFS stores certificates for access control; meanwhile, the blockchain contains the healthcare center’s details. Bocek et al. [17] mention the use of blockchain in medicine traceability, known as the pharmaceutical supply chain, via the immutability of blockchain technology. Based on the work at that time for supply chain use cases, this work emphasizes the benefits of using blockchain, including identity, verification, and immutability for data related to logistics and preservation. Another interesting study in this field, [18], proposes blockchain-based drug traceability called Drugledger. By leveraging blockchain for authenticity and privacy in data traceability, the Drugledger idea is to introduce flexibility in architecture with peer-to-peer, expand unspent transaction output, and prune blockchain storage. Due to the similarity of this study with the supply chain, the first consideration is drug transaction logic from a supply chain perspective, then the transparency of stakeholders requires features such as authenticity and privacy. Due to the similarity between supply chain and drug traceability, authors in [19] propose a supply chain management with Hyperledger Composer. The primary usage of blockchain is to enable features related to forward and backward supply chains. In particular, the study utilizes blockchain technology to form a public ledger that maintains hash information related to the product supplier, manufacturers, distribu-

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tors, and pharmacy to reduce the amount of storage. Also, those parties manage the blockchain together for auditing and optimizing the time and cost of the process. The exciting use of blockchain in healthcare is the usage in patient monitoring [20]. From the view of the study, a blockchain-based smart contract is a bridge that supports communication between sensors and smart devices via capturing records as blockchain events. By this consideration, the security and real-time reactions are the main aims for leveraging the blockchain in a framework with three layers. The first layer is about raw sensor data, as sensor devices collect personal data related to healthcare data before sending it to the master device, like a smartphone. Then, after receiving information from sensors, the smartphone forwards information to blockchain participants. Similar consideration is a blockchain-based remote patient monitoring from [21] with privacy preservation via a patient-centric agent concept. Particularly, the authors in [21] indicate the reduction of remote patient monitoring’s challenges by using the idea of the patient-centric agent with an end-to-end data flow. From this view, the system mentions that patient-centric agent includes patient sensors, blockchain participants, and healthcare service providers’ devices. Also, the patient-centric agent decides the data storage as the stream and storage of data along with use cases, such as audit and key management. A novel blockchain-based framework for IoT devices is proposed by [22]. Due to the privacy issue in blockchain use cases, the proposed system uses a lightweight privacy-preserving ring signature scheme. The platform mentions cloud storage for patient data storage in detail. The overlay network is a peer-to-peer network with distributed architecture known as the blockchain layer. Each device needs to create an account after obtaining valid certificates for joining and storing information in the system. Also, a solution for improving the scalability is about the cluster of participants in the overlay network. In the system, the healthcare providers are insurance companies or patients; meanwhile, the healthcare service providers handle treatments from patients via the notification of the network. Another study related to secure monitoring of patient signals is by Jamil et al. [23]. This consideration mentions an environment such as smart hospitals where devices gather patients’ signals before storing them in blockchain to authorized parties. In detail, the proposed framework consists of four layers: application layer, IoT blockchain layer, connectivity, and healthcare IoT physical layer. The healthcare IoT physical layer is about healthcare devices with resources. The connectivity layer indicates routing management with security, broker, and network management, while the IoT blockchain service layer mentions a blockchain solution, including consensus, identity management, smart contract, storage, and communication. Finally, the application layer is about healthcare devices’ user interface and data visualization.

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2.2 Health Monitoring Systems with Edge/Fog Computing Gia et al. [29] propose an IoT system for glucose monitoring. The system consists of a wearable sensor device, a smart gateway based on a smartphone, a cloud server, and an end-user terminal. The wearable device collects blood glucose and transmits the data to a gateway via nRF protocol. The gateways based on smartphone acts as smart edge device for providing edge services. For instance, the gateway detects abnormalities and classifies a human health status by comparing data (e.g., blood glucose and bio-signals) from a wearable with some pre-defined standards. In addition, the gateway also offers distributed data storage and push notifications for informing the abnormalities in real time. Furthermore, the processed data is forwarded to a cloud server for further processing. Finally, an end-user, such as a caregiver or a patient, can use a mobile app or a web browser to access the data in real time. Rahmani et al. [30] present an advanced remote and real-time health monitoring system that utilizes fog computing, defined as the intermediate layer between sensor devices and cloud servers. The system offers advanced edge/fog services such as data compression, encryption, fusion, ECG feature extraction, and mobility support. In addition, with their authentication rights, end-users such as medical doctors or nurses can access the data in both graphical and textual forms in real time. Gia et al. [31] present a fog-based IoT system for ECG monitoring and feature extraction. The proposed system architecture has a sensor, fog, and cloud layer with the end-user terminal. The ECG data collected from a sensor device is transmitted to a smart gateway where the data is processed with algorithms to extract ECG features such as P wave, T wave, QRS wave, and heart rate. The extracted information can be fed to algorithms to detect abnormalities such as low/high heart rates. The processed data is then transmitted to a cloud server where an end-user can access the data via a mobile app or a web browser. Queralta et al. [32] propose an edge-based health monitoring system using LoRa and IoT for fall detection applications. The system utilizes edge, fog computing, and machine learning techniques to detect a human fall in real time at the edge of a network. The results show that the edge-based intelligence approach running LSTM Recurrent Neural Networks can achieve over 90% precision and 95% recall in fall detection. Ali et al. [33] introduce an intelligent autonomous home monitoring system for elderly patients. Sensor devices of the system collect different types of data, including bio-signals (e.g., blood glucose, body temperature, 3D accelerometer, and gyroscope) and contextual data (e.g., room temperature and humidity). The collected data is sent to a smart gateway via an nRF protocol. At the smart gateway, data can be processed for fast data analysis and decision-making. When abnormal cases are detected, responsible persons are informed in real time. Gia et al. [34] present a low-cost fog-assisted health monitoring system with energy-efficient sensor nodes. The system consists of three main layers: sensor, fog, and cloud layers, with an end-user terminal. The sensor layer consists of energyefficient sensor devices which collect bio-signals (e.g., ECG data, respiration rate, and

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body temperature) and forward the data to a smart gateway via an nRF protocol. The smart gateway in a fog layer provides many edge and fog services, such as distributed data storage, data analysis, security, push notification, and categorization service. The system can notify medical doctors for real-time actions when emergencies or abnormalities are detected. In addition, medical doctors can access the data stored at cloud servers anytime and anywhere. The sensor device of the system costs less than 26 Euros, and it can operate for up to 155 h. Cheikhrouhou et al. [35] propose a hybrid fog–cloud system for health monitoring. The system architecture consists of three main layers: sensor, fog, and cloud. The sensor layer includes health devices for collecting and streaming ECG data to fog nodes. The fog layer includes fog nodes and a connected distributed database for offering fog services such as local data storage, data pre-processing, and AI interference. The fog layer is deployed with an algorithm based on One-Dimensional Convolution Neural Networks (1D-CNNs) for analyzing and classifying ECG arrhythmia. The proposed algorithm can detect arrhythmia and cardiovascular diseases with an accuracy of 99% on the MIT-BIH database.1

3 Background Studies 3.1 Blockchain Issues and Challenges Due to the many benefits of blockchain technology, leveraging blockchain requires careful consideration. In the most recent works of Nguyen et al. [6, 36] about blockchain’s drawbacks in the 6G era, the usage of blockchain obtain features, including security, decentralization, fault tolerance, trust, and open access. With the decentralization where data is distributed entirely over the network, privacy is an open question for current blockchain use cases. As a solution, the zero-knowledge argument is a favorable consideration; however, current studies in this field exploit many resources [37]. Another weakness of blockchain technology is the performance. A difference from traditional systems is that blockchain technology provides a decentralized environment; however, this idea raises a need for numerous communications for synchronization from system states in the network participants. Besides, a question related to consensus mechanisms is a solution for leader selection at every consensus round. As aforementioned, consensus formation requires many resources that can be powerful computations like Proof-of-Work as Bitcoin protocol [38] or a voting mechanism with many communications like Raft [39]. Apart from the performance of the system, scalability is another concern. Since a blockchain-based system allows many participants to join and contribute to the network’s success, the system faces significant connectivity with communication 1

https://ecg.mit.edu/.

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and storage. Therefore, it can be a question related to scalability in using blockchain technology to satisfy an amount of throughput in the case of many participants. Along with issues of blockchain technology, a smart contract is a software via a programming language, as mentioned above. Smart contracts affect the security issues of typical applications, for example, secure bugs from compilers and developers. Besides, leveraging cryptographic schemes is another crucial task in blockchain technology. With the mistakes of cryptographic schemes, the attackers exploit and disclose privacy and terminate the whole system.

3.2 Hyperledger Fabric: Permissioned Blockchain The experiment is based on the Hyperledger Fabric setup as the fundamental for the system. However, instead of using a traditional permissionless blockchain like Ethereum, the experiment is based on a permissioned blockchain as Hyperledger Fabric. The main reason for the permissioned blockchain is the authorization with the identity of entities in the network. Meanwhile, the permissionless blockchain does not consider a particular access control management for each participant or request. Unlike previous research on Hyperledger Fabric, this consideration with Hyperledger Fabric is about the Raft consensus. Remarkably, participants in Raft involve in three main phases for a consensus round, and the main idea is to finalize a block to confirm data inside the consensus round. First, at the beginning phase, participants running the candidate role are waiting to wake up and send request vote messages to others. Then, once receiving the first vote request, participants send a vote acceptance to the person sending the vote request. Finally, after collecting numerous vote acceptance, the vote request sender recognizes as the leader for the current consensus round. At this phase, the leader listens to requests to update the system state before forming a final candidate and broadcasting to the entire network. Hyperledger is an execute–order–validate workflow, while Ethereum, a traditional blockchain platform, is based on an order–execute procedure. The Fabric process encompasses three phases: (1) execute, (2) order, and (3) validate. At the execute phase, the system allows receiving different requests from clients for execution before answering endorsed transactions. The endorsed transactions are then resent by clients to the system for ordering a list of endorsed transactions at the order phase. At this phase, a leader representing the current consensus round is elected to form a block of ordered endorsed transactions and then broadcast it to whole participants for validating at the validate phase. Finally, after confirming the validation of the block candidate, the client receives the final answer from the system for acceptance or rejection. Via the Hyperledger perspective, the experiment is set up as Fig. 1. The sensor devices as the data generators first gather information, such as patients’ heart rate, BMP, and EDA. This information is then sent to smart homecare for processing (step 3) before submitting changes to the edge server (steps 4 and 5). The edge servers

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3. Sensor data 4. Transaction proposal

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5. Response 4. Transaction proposal 5. Response 4. Transaction proposal 5. Response

6. Endorsed transaction proposal 7. Endorsed transaction

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9. Block candidate 9. Block candidate 9. Block candidate 10. Block event

Fig. 1 Sequence diagram of Hyperledger Fabric workflow in the proposed system. The figure is a sample for a smart home connecting to a set of three peers and three orderers

maintain a unique blockchain containing processed homecare information via steps 6–10. In detail, steps 1 and 2 are about Raft’s consensus to find the leader, while step 5 responds to the possibility of the requests. Therefore, confidentiality ensures with a cryptographic scheme, for example, an asymmetric key; also, sensitive information can be hidden from unauthorized parties via extra processes at the smart homecare. Once reaching the edge server, the information is broadcast to the entire edge servers in the system and waited for agreement as to the final confirmation among edge servers. After the confirmation, the information is ensured with integrity; thus, any changes are unsuitable. Through immutable information in the system, a healthcare system confidently maintains many services, for example, recommendations and advice from specialists, which enhances the quality of services. To maintain an array of services, smart contract design for smart homecare is the next step. The interest of a smart contract platform is an autonomous system where services as smart contracts are pre-defined by parties in the system. Also, these services can connect for cross-connectivity. For example, once detecting a fever and cough from a patient from IoT devices, the system can estimate the probability of diseases that the patient can be. Therefore, a set of tests can be proposed to the patient for a correct evaluation through specialists’ recommendations. In this case, a COVID-19 examination can be one of the possible suggestions for the patient. Along with patients, evaluating specialists can be another concern since highly qualified specialists propose potential suggestions. The evaluation is based on the information,

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history of support, and feedback; thus, the system supplies the patient with a rank list of recommendations.

4 Proposed Scheme 4.1 The Proposed System Architecture The proposed system architecture shown in Fig. 2 has five layers, including the sensor layer, edge gateway layer, edge server layer, cloud layer, and application layer. Compared with sensor layers presented in the state-of-the-art remote health monitoring systems, the proposed sensor layer consists of different types of sensors, including wearable sensors, cameras, and ambient sensors. Particularly, wearable devices placed on a human body can collect different bio-signals such as heart rates, oxygen saturation (SpO2), and ECG. In contrast, ambient sensors collect contextual data such as room temperature, humidity, and air quality. Cameras are used to monitor human activities. The combination of these sensors helps collect different types of data that improve the quality of analysis [30]. For example, the heart rates of a human can vary significantly when the surrounding room temperature is different. When the contextual data is known, the disease diagnosis becomes more accurate. It is noted that wearable devices are often small, lightweight, and resourceconstrained (e.g., limited rechargeable battery capacity, small memory, storage, and computation resources). Therefore, it is required that the wearable device should be efficient in terms of energy and computation. To achieve a high level of energy efficiency, different sources that cause a large portion of the total energy consumption of wearable device need to be considered. Particularly, a wearable device’s wireless communication module and microcontroller, and sensors equipped in the wearable sensor are these main sources. It is important to decide particular tasks run at the

Sensor Layer

Edge Gateway Layer

Edger Server Layer

Fig. 2 The proposed five-layer system architecture

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wearable sensor and different tasks forwarded to an edge gateway. Contextual sensor nodes and cameras are often placed in a room or in a corridor and use the power supply from wall sockets. Therefore, energy efficiency of these devices is not the main concern. In a case study of fall detection, a wearable device consists of an ATmega328P microcontroller with a high-performance and low-power consumption 8-bit AVR microcontroller. The device uses a digital temperature sensor and an MPU6050 sensor having a three-dimensional (3D) accelerometer and 3D gyroscope. These sensors are low power and connected to the AVR microcontroller via Inter-Integrated Circuit (I2C). The wearable device uses nRF (i.e., an NRF24L01 module) for wireless communication with edge gateways. The nRF protocol is energy-efficient and flexible, e.g., supporting 255 kbps, 1 Mbps, and 2 Mbps. Since the sampling rate of these sensors is low, e.g., less than 60 samples/s, 255kbps is applied. Similarly, a contextual sensor node also uses ATmega328P and nRF for communicating with edge gateways. The contextual sensor uses different sensors, such as gas, CO, temperature, and dust. The selection of these sensors and components help achieve energy efficiency of a wearable device. In addition, tasks are carefully selected to be run on the wearable device to maintain a high level of energy efficiency and provide a high quality of service. For instance, cryptography primitives such as AES-256 can be run on the wearable device for providing some levels of security while running the AES-256 algorithm does not take much energy and latency. The edge gateway layer consists of interconnected gateways in which each edge gateway is responsible for a specific area. Gateways having overlapped areas can be set with the same Service Set Identifier (SSID) and password to maintain a low latency when mobility occurs. This setup enables different edge services such as mobility support, computation offloading, and distributed storage. The primary responsibility of the edge gateway is receiving data transmitted from sensor devices and forwarding the data to an edge server. In addition, the edge gateway can provide some essential edge services such as interoperability, data compression, cryptography primitives, and security. In a case study of fall detection, gateways can be built using Raspberry Pi, NRF24L01 modules, and Wi-Fi modules. The edge server layer of the proposed system consists of powerful edge servers able to perform heavy computation. Edge servers have two roles, including edge service provider and blockchain node. As an edge service provider, edge servers harmoniously cooperate with edge gateway to maintain a high quality of edge services. For example, edge servers can use deep learning models for real-time human activity recognition. The fourth layer is a cloud layer that consists of powerful cloud servers and cloud services. Cloud servers can store big data for extended periods, allowing multiple global and real-time access. In addition, cloud servers offer power resources to run complex algorithms and computation tasks such as big data analysis and training deep learning models. Commercial cloud services from Google, Microsoft, or Amazon can be used because they offer many benefits, such as large and backup data storage, security, big data analysis, pay-as-you-go, and affordable costs.

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Fig. 3 Detail proposed system: the blue arrows indicate the contribution to the blockchain, while black arrows pointing to the blockchain are blockchain readers

The final layer is the application layer consisting of mobile apps and terminals supporting end-users. Mainly, end-users such as laboratory technicians, doctors, specialists, and patients can use the mobile app and a web browser to access data (including raw and processed data) and provide inputs (e.g., recommendations) for the system. The access level depends on a user’s rights. Instead of forwarding the data directly from edge servers to cloud servers via conventional methods, the proposed system uses the blockchain shown in Fig. 3 to provide a decentralized environment. Edge servers and other organizations are part of a blockchain network. For example, after the patient data is stored in the blockchain, the specialists can advise the patients. With this consideration, the patient’s issues can have different views, and the specialists cross-check and directly discuss abnormal patient issues. Interestingly, the patient’s history or profile is stored in the blockchain for reference later. In detail, the proposed system considers possible organizations, including laboratory technicians, doctor specialists, and a set of hospitals, as a knowl-

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edge base from traditional systems. On the other hand, the system involves other novel considerations, such as state-of-the-art healthcare services, monitoring, and remote healthcare systems.

4.2 Edge Services Edge computing can enable many edge services, especially AI at the edge. Depending on the service, edge gateways, edge servers, or a combination of these can be chosen for implementing the service. For instance, data can be pre-processed at gateways before being sent to edge servers that perform heavy computation and run deeplearning models for human activity recognition or fall detection. Correspondingly, high quality of service can be provided. This section shows some of the most critical edge services implemented in the proposed system to improve the quality of service of the health monitoring system. Distributed Data Storage Edge devices can store data, including bio-signals, contextual data, and other information, such as secret keys for data encryption and parameters required for running algorithms. For instance, an edge gateway can be used to store temporary data as it has limited data storage (e.g., 64GB–256GB). When the storage is full, new-coming data will replace the oldest data. When a connection between an edge gateway and an edge server is temporarily disconnected, temporary data storage at an edge gateway can play an important role. That helps avoid losing data temporally. Edge servers can store a large volume of data (e.g., a few terabytes) for an extended period. For example, when nurses and medical doctors are currently staying at a nursing home, the data can be retrieved directly from an edge server. The distributed storage is encrypted to ensure some levels of security. Distributed data storage can be built by combining different types of databases or storage such as relational databases (e.g., SQL, MYSQL, SQLite, and Postgres), document-based databases (e.g., MongoDB, RavenDB, and CouchDB), hierarchical databases, and time-series databases. MongoDB is used together with a time-series database to build the distributed database in the proposed system. In this case, a 64GB SD card is equipped with a Raspberry Pi4 used for forming an edge gateway. Therefore, 30–35GB can be used for data storage. The storage volume can be more significant when a larger SD (e.g., 128GB) is equipped. The edge servers can store a large volume of data, e.g., up to a terabyte. Data Encryption It is required that data transmitted over a network needs to be protected. One of the widely used approaches for achieving the target is to apply cryptography algorithms. Particularly, data is encrypted before being sent over a network, and on the receiving side, data is decrypted. Two widely used cryptography are symmetric and asymmetric. In symmetric cryptography, the secret key is pre-shared between a sender and

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a receiver, while asymmetric cryptography uses a pair of public–private keys. Each type has its advantages and disadvantages. For example, symmetric cryptography is more suitable for resource-constrained devices and can be used for transmitting big data. However, symmetric cryptography is less secured and has a shorter key length (e.g., 128- or 256-bit key length) than asymmetric cryptography, which has RSA 2048-bit key length or longer. Advanced Encryption Standard (AES-256)—symmetric cryptography is applied at sensor devices, edge gateways, and edge servers in the proposed system. Notably, it takes around 200µs to encrypt 16B of data at a sensor node built from an 8-bit low-power micro-controller, while the edge gateway built by Raspberry Pi4 takes around 32µs and 36µs for encrypting and decrypting the data, respectively. A list of secret keys is used to increase a security level, in which the keys are pre-shared in advance among the participants (e.g., wearable sensor devices, gateways, and edge servers), and each key is used for a specific duration, such as 60 min. Human Detection and Counting An edge service for detecting a human and counting the times a person reaches a specific area (e.g., toilet) can be deployed at an edge gateway or an edge server. As mentioned, an edge server is much more powerful than an edge gateway. Therefore, the deployment decision is based on the complexity of the edge service. YOLO, a Convolutional Neural Network (CNN) for performing real-time object detection, can be used for implementing the edge service. Input images are fed into the YOLO model, which looks at the whole image and simultaneously predicts several class probabilities and bounding boxes. Compared with other object detection algorithms, YOLO can perform better to achieve high accuracy/precision in real-time detection. YOLO algorithms are based on three main concepts: residual blocks, bounding box regression, and intersection over the union. Detailed information on YOLO and these concepts can be seen in [40, 41]. There are several versions of YOLO, such as YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOR, and YOLOX. The authors of these models show that the newer version can provide faster-predicted results than older versions. In the implementation, YOLOv4-512 using Cloud Service Provider called CSPDarknet53 backbone has been trained at cloud servers (i.e., Google Colab). The trained model with weights is then deployed at an edge gateway and an edge server. The model uses images collected from cameras placed at a nursing home. Remarkably, the images are resized and pre-processed at an edge gateway or an edge server to suit the model’s requirements. The image pre-processing takes a minimum latency in terms of µs and milliseconds, depending on the hardware. The result shows that the YOLOv4-512 can achieve a high average precision (94%). When the model is deployed at an edge gateway, the predicted speed is around 0.23 Frame Per Second (FPS). When the model is deployed at an edge server, the predicted speed is 98FPS which is larger than the image acquisition speed of a camera (30/60FPS). Therefore, it is preferred to deploy the model at an edge server. The algorithm for counting the number of times an older adult visits the toilet per night is shown in Algorithm 1. The algorithm’s results help assess health status (e.g.,

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Algorithm 1 for counting the number of times which an older adult visits a toilet Input: (intersection_ar ea) 1: while N is true do 2: if intersection_ar ea is true then 3: if pr evious_state is intersection_ar ea then 4: count ← count + 1 5: else 6: count ← 0 7: count ← count + 1 8: pr evious_state ← intersection_ar ea 9: else 10: if pr evious_state is intersection_ar ea then 11: visiting_count ← visiting_count + 1 12: pr evious_state ← not_intersection_ar ea Output:(visiting_count) //the number of times which an elderly person visits a toilet

sleeping quality) more accurate. The algorithm can be extended to count a duration in which an elderly visits a toilet. If the visiting period is abnormal, the push notification can be triggered to inform a nurse in a nursing home for instant help. The processing speed of the algorithm running at an edge server depends on the processing speed of the YOLO model. In this case, the algorithm can provide results in ten milliseconds. Human Fall Detection A 3D accelerometer and gyroscope can be used to detect a human fall. When a fall occurs, there will be a significant variation in the sum vector magnitude of 3D accelerations and a sum vector magnitude of 3D angular velocities. The formula to calculate a sum vector magnitude is shown in the equation: SV Mi =



xi2 + yi2 + z i2 ,

where x, y, and z are three dimensions. Human fall detection service at an edge server is based on a recurrent neural network with long short-term memory layers (RNN-LSTM). The model can help reduce the vanishing gradient problem allowing long-term memory within the neural network [42]. The model is useful for time-series data such as 3D acceleration and 3D angular velocity. The model is trained using the MobiAct Dataset [43], which consists of acceleration, angular velocity, and orientation data. In detail, a subset consisting of four fall types (e.g., fall forward from standing with the first impact on knees, fall forward from standing with hands for dampening, fall aside from standing and fall back when trying to sit on a chair) and two activities, such as standing and lying. The model has been trained multiple times, and the results show that the model has seven layers consisting of an input layer, five hidden layers (i.e., four LSTM layers and one fully connected layer), and an output layer. 3D acceleration and gyroscope collected from wearable sensor devices are inputs for the trained model. Before feeding to the model, the collected data is processed to fulfill the model’s requirements. Keras and

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Tensorflow are used for implementing the LSTM-RNN model. The results show that the average precision for predicting a fall case is 92%. The edge server only takes ten milliseconds to provide a predicted result.

5 Performance Analysis This section is to evaluate the chapter’s consideration. Especially a simulation of a healthcare system with blockchain mentions an adaption of Hyperledger Fabric. After that, the throughput consideration for the simulation is evaluated with different message types and capacities.

5.1 Experimental Environment The experiments are about the throughput of the proposed system. The system’s setup consists of six virtual machines based on a physical machine with resources such as Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz, 3.19 GHz, 32 GB RAM, and 64-bit OS x64-based processor. Concretely, based on VirtualBox tool 5.2.22 r 126460 (Qt5.6.2), three virtual machines run peer roles, and the other three play an orderer role. The experiment deploys the system with three components for each different service to closely simulate a practical system that accepts single-point failure. The detail for resources of virtual machines with different services is in Table 3. Also, the deployment of Hyperledger services is in Table 4, with different sizes. In detail, each virtual machine that manages the peer service needs to deploy Hyperledger/fabricpeer, while virtual machines for orderer tasks manage Hyperledger/fabric-orderer. However, the system setup is based on permissioned blockchain; each group of virtual machines as peer and orderer groups requires a participant deploying a container Hyperledger/fabric-CA. Hyperledger/fabric-tools support communication connection from smart home to edge servers as Hyperledger Fabric blockchain interaction. Those Hyperledger Fabric containers are deployed on virtual machines via docker tool version 18.09.7.

Table 3 The resources and version of tools to form edge servers Components Information OS/Arch Docker Docker-compose Orderer Peer

Version: 18.09.7 Version: 1.17.1 Process: 2, 2GB Memory, fabric:2.2.1 Process: 4, 6GB Memory, fabric:2.2.1

Linux/x86_64 Linux/x86_64 Linux/x86_64 Linux/x86_64

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Table 4 The capacity for Hyperledger Fabric containers Docker image Size (MB) Hyperledger/fabric-peer Hyperledger/fabric-orderer Hyperledger/fabric-tools Hyperledger/fabric-ca

49.410 38.420 513.300 157.900

5.2 Results and Discussion For the experiment, three Hyperledger/fabric-tools containers parallelly send 300 requests to the system for each consideration. With the setup of Hyperledger, the work plans an experiment capturing the throughput and the amount of capacity per second. In detail, the experiment considers three types of messages, including update information (Update), query information (Query), and smart contract connectivity (Contract Calls). Along with those message types, the experiment attempts different capacities carried by each message, for example, 16 KiB, 32 KiB, 65 KiB, and 100 KiB. The result set is shown in Table 5. Observation from Table 5 shows the significant drop in the number of transactions per second the system can handle from 32 KiB to 65 KiB as 2.685 to 1.580, 7.349 to 7.039, and 2.382 to 1.541 related to Update, Query, and Contract Call, respectively. Another interesting point is the difference between the three request types. In detail, the Update’s results gain better performance than Contract Call requests since the Contract Call requests need to call a list of three contracts before updating information. Also, the comparison between Update and Query is interesting. For example, at the 16 KiB capacity of each message, the Update result achieves a better performance than the Contract Call’s result; nevertheless, the Query throughput is mostly double the Update result. This consideration is similar to the rest of the other capacities (32 KiB, 65 KiB, and 100 KiB). Interestingly, the result with 32 KiB is slightly more significant than the consideration with 16 KiB in the Query column. Therefore, with Query requests, the result of each request’s low capacity fluctuates. The difference between Update and Query results is from communication and data extraction mechanisms. In particular, updating information in the blockchain system requires validation, communication, and agreement among

Table 5 The number of transactions per second (Tx/s) that the system can handle in different capacities and request types in the Hyperledger platform Capacity (KiB) Update (Tx/s) Query (Tx/s) Contract Call (Tx/s) 16 32 65 100

3.277 2.685 1.580 1.528

7.325 7.349 7.039 7.003

3.061 2.382 1.541 1.218

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Table 6 The amount of data (KiB/s) that the system can handle in different capacities and request types in the Hyperledger platform Capacity (KiB) Update (KiB/s) Query (KiB/s) Contract Call (KiB/s) 16 32 65 100

52.432 85.920 102.700 152.800

117.200 235.168 457.534 700.300

48.976 76.224 100.164 121.800

participants; meanwhile, query information is to extract information handled by peers instead of communication and validation tasks. Besides, the slight difference between Update and Contract Call requests is about the higher operation via Contract Calls in this consideration being three contract calls; thus, along with different capacities, the results of Contract Calls are lower than Update’s results. Although the system’s throughput reduces with the larger capacities of requests sent by the client side, the total capacity increases with each request’s huger capacity. As a description in Table 6, with the increasing data size of requests, for example, 16KiB, 32KiB, 65KiB, and 100KiB, the amount of data the system can shallow more and more. The utilization of high capacity from each update request can expand the total capacity that the system can update despite the lower capacity of each request. Therefore, the system expects to manage a large capacity from each message suitably. Edge computing and AI at the edge have proved that they can help improve the quality of service of remote health monitoring systems. However, the target cannot be achieved if edge services do not carefully consider edge hardware’s specifications. A remote IoT health monitoring system can have different resources such as edge devices, edge gateway, edge servers, and fog servers where edge services can be deployed. The deployment of an edge service or an edge gateway can reduce system latency and improve the quality of service. For example, when running the YOLOv4 model using the CSPDarknet53 backbone, an edge gateway built from Raspberry Pi 4 requires 4.1–5s to predict a video frame of 512 × 512. Hence, real-time results cannot be achieved. Nonetheless, if the YOLOv4 model is at an edge server or a fog server, it only requires 15–300 ms to predict a frame having the same size. Therefore, choosing the proper location or resources is crucial to implementing an edge service. It is challenging to apply edge computing for allocating different tasks of an algorithm/software to different resources. Notably, some tasks of the algorithm/software can be run at different edge resources, while some tasks may be run at cloud servers. However, this decision relies not only on the available resources but also on network quality. Moreover, when the connection between the system and cloud servers is not stable, it cannot guarantee that the final result can be achieved appropriately in real time. Therefore, when applying edge computing for task allocation, it is necessary to consider different aspects and parameters such as network latency, network quality, edge resources with/without battery, interference, and available resources in run-time.

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Table 7 Comparison with related works Candidates Single Throughput Mining tolerance [10] [15]* [16]* [8] [23] [11] [19]

Yes Yes Yes No No No No

No No Yes No Yes No Yes

Yes – – No No No No

Proposed

Yes

Yes

No

Usage

Access

EHR sharing Expert connectivity Medical certificate Access control Monitoring signals Data sharing Pharmaceutical supply chain Multiservice platform

Permissionless Permissionless Permissionless Permissioned Permissioned Permissioned Permissioned Permissioned

* experiments based on simulation, the mining process does not detail

In a homecare ecosystem, leveraging blockchain technology enables security and trust in collaboration; as the most recent works in healthcare systems, utilizing blockchain proposes security features, for example, the integrity of data distribution. Therefore, breaking the traditional system by adopting blockchain technology is a move to decentralization, which expects an open world with security and trust. Also, this work is an architecture for a homecare system by which the quality of healthcare expects to enhance. However, despite the many benefits of blockchain technology, the usage of the technology requires carefulness with cryptographic schemes and architecture design to preserve the privacy of system information and participants. Despite existing similar works, the proposed system utilizes the advantages of blockchain technology, especially Hyperledger Fabric. Notably, in comparison with [8, 23] in Table 7, these works also deploy from Hyperledger Fabric; however, [23] setup does not require several orderers instead of one, and [8] does not provide an experiment detail related to Hyperledger Fabric. Hence, the proposed system setup provides the ability in the case of single-point failure due to the importance of the orderer. Also, other similar works decide to deploy the system via private Ethereum, but this consideration can lead to misunderstanding or ambiguity for the reader. The reason is that the utilization of Ethereum is about a public blockchain in which data is available and visible to any participant. Therefore, the private Ethereum is still a deployment of a public blockchain but does not connect to the Ethereum main net. More comparison is conducted in Table 7.

6 Conclusion and Future Works This chapter presented a novel smart homecare IoT system using Edge-AI and Blockchain to improve the quality of services. The proposed system architecture consisting of a blockchain network and five main layers, including the sensor layer,

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edge gateway layer, edge server layer, cloud layer, and application layer, enables a high level of security, trust, and smart services at the edge. Several critical edge services and Edge-AI were presented and implemented, such as data encryption, human fall detection, and counting the number of visiting times to toilets per night. The results showed that the proposed edge services and Edge-AI could provide realtime results that help improve the quality of healthcare. Particularly, when a fall is detected, a nurse is informed about the fall case in real time. Correspondingly, the nurse can take instant actions to aid the fallen person, which helps reduce adverse effects. In addition, a nurse can be informed when older adults cannot get a good sleep due to many times visiting a toilet per night, which the nurses can provide treatment for dealing with the case. With this consideration, the proposed system is expected as a state-of-the-art system that encompasses different technologies to boost the success of smart homecare systems. Interestingly, the appearance of blockchain recently provides the fittest solution and fills this gap in decentralization. Therefore, as the next step in this work, a set of services related to the homecare system is pondered via smart contracts. Acknowledgements Tri Nguyen is supported in a strategic research project TrustedMaaS under focus institute Infotech Oulu, University of Oulu, Nokia foundation, Tauno Tönning Foundation, and Academy of Finland, 6G Flagship program (grant 346208).

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