Demystifying Emerging Trends in Green Technology E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Emerging Trends in Computation Intelligence and Disruptive Technologies
- Sprache: Englisch
Demystifying Emerging Trends in Green Technology explores the transformative intersection of computational intelligence, disruptive technologies, and green innovations. This volume offers insights into diverse fields such as blockchain, IoT, artificial intelligence, machine learning, and sustainable development. Each chapter presents cutting-edge research and practical solutions addressing environmental sustainability, energy efficiency, and eco-friendly technologies.
With contributions from leading researchers, this book discusses advancements like blockchain-based security, green marketing, smart waste management, sustainable agriculture, and innovative healthcare solutions. It emphasizes the role of interdisciplinary approaches in driving a greener and smarter future.
Key Features:
- Integration of AI, IoT, and blockchain in sustainable systems
- Applications in healthcare, agriculture, energy, and environmental science
- Practical and innovative solutions for real-world challenges
- Insights into future trends in green technology and disruptive innovation
Readership:
Ideal for academics, researchers, professionals, and enthusiasts seeking to advance green technology practices.
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Seitenzahl: 782
Veröffentlichungsjahr: 2025
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PREFACE
This book titled, Emerging Trends in Computational Intelligence and Disruptive Technologies, is revolutionizing the way we approach the ecological footprint of computer networks. Communication systems, and other IT infrastructures are growing due to high energy consumption and greenhouse gas emissions. Addressing these issues and creating a sustainable environment require new energy models, algorithms, methods, platforms, tools, and systems to support next-generation computing and communication infrastructure.
The chapters within this volume serve as portals to diverse domains where these trends intersect, offering insights, analyses, and projections to innovative ideas. Through these contributions, readers will embark on a journey through cutting-edge developments, envisioning a future where computational intelligence and disruptive technologies intertwine to revolutionize the way we live, work, and interact.
In this book, we explore the role of Disruptive Technologies and Computational Intelligence which aims to bring together leading academic scientists, researchers, and research scientists to exchange and share their experiences and research results in various aspects of green technology and energy science. It also provides a major interdisciplinary platform for researchers, practitioners, and educators to present and discuss the latest innovations, trends, and issues in computational intelligence and disruptive technologies, as well as practical challenges and adopted solutions.
This book is a comprehensive guide for anyone interested in learning about the role of emerging trends in computational intelligence and disruptive technologies in various sectors.
List of Contributors
Trust-Based Neighbor Selection Protocol to Elect Leader in Blockchain using zk-SNARKs Algorithms
Satpal Singh1,*,Subhash Chander2Abstract
Blockchain stores and writes all the transactions because of the unlimited storage capacity. Leader election is the process of electing a node as an overall in-charge of the distributed network. Leader election is a complicated task as we have to choose a leader by giving equal opportunity to all the nodes. We implement all the algorithms of the DONS protocol in order to elect a leader but in our TBNS (Trust Based Neighbor Selection) protocol, we add zk-SNARKs proof to enhance the security of Blockchain. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a type of proof used in cryptography to prove the authenticity of information without revealing any additional information. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself. In the end, the results of our proposed model are compared with RTT-NS and DONS.
REFERENCES
[1]Tan T.G., Sharma V., Zhou J.. 2020Right-of-Stake: Deterministic and Fair Blockchain Leader Election with Hidden Leader, International Conference on Blockchain and Cryptocurrency (ICBC). : 1-9. [CrossRef][2]Esgin M.F., Ersoy O., Kuchta V., Loss J., Sakzad A., Steinfeld R., Yang W., Zhao R.K.. , . (2022), A New Look at Blockchain Leader Election : Simple, Efficient, Sustainable and Post-Quantum. Cryptology ePrint Archive, Paper 2022/993. https://eprint.iacr.org/2022/993[3]Jingjing, Zhou; Tongyu, Yang; Jilin, Zhang; Guohao, Zhang; Xuefeng, Li; Xiang, Pan. Intrusion Detection Model for Wireless Sensor Networks Based on MC-GRU., In the preceding of Wireless Communications and Mobile Computing.2022: 1-11. [CrossRef][4]Saraswat D., Verma A., Bhattacharya P., Tanwar S., Sharma G., Bokoro P.N., Sharma R.. Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions., IEEE Access.2022; 10: 33154-33182. [CrossRef][5]Baniata H., Anaqreh A., Kertesz A.. DONS: Dynamic Optimized Neighbor Selection for smart blockchain networks., Future Gener. Comput. Syst..2022; 130: 75-90. [CrossRef][6]Abraham I., Dolev D., Halpern J.Y.. Distributed protocols for leader election: A game-theoretic perspective, International Symposium on Distributed Computing.2013: 61-75. [CrossRef][7]Al Refai M.. A new leader election algorithm in hypercube networks, Symposium Proceedings. 22006;[8]Al Refai, Mohammed. Leader election algorithm in hypercube netwok when the id number is not distinguished., Inf. Commun. Syst..2011: 229-237.[9]Biswas A., Maurya A.K., Tripathi A.K., Aknine S.. FRLLE: a failure rate and load-based leader election algorithm for a bidirectional ring in distributed systems., J. Supercomput..2021; 77(1): 751-779. [CrossRef][10]Alslaity A.N., Alwidian S.A.. A k-neighbor-based, energy aware leader election algorithm (kelea) for mobile ad hoc networks., Int. J. Comput. Appl..2012; 975: 8887.[11]Yakira D., Asayag A., Cohen G., Grayevsky I., Leshkowitz M., Rottenstreich O., Tamari R.. Helix: A Fair Blockchain Consensus Protocol Resistant to Ordering Manipulation., IEEE Trans. Netw. Serv. Manag..2021; 18(2): 1584-1597. [CrossRef][12]Yakira D., Asayag A., Cohen G., Grayevsky I., Leshkowitz M., Rottenstreich O., Tamari R.. Helix: A Fair Blockchain Consensus Protocol Resistant to Ordering Manipulation., IEEE Trans. Netw. Serv. Manag..2021; 18(2): 1584-1597. [CrossRef][13]Gebremariam, Gebrekiros Gebreyesus. Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning., In the preceding of Wireless Communications and Mobile Computing, Hindawi..2023: 1530-8669. [CrossRef]Electronic Healthcare Data Security Using Blockchain
Sharda Tiwari1,*,Namrata Dhanda1,Harsh Dev2Abstract
In this paper, a blockchain-based healthcare security system as a solution for preventing data forging is proposed. The proposed model for healthcare data security is based on blockchain and its smart contract execution in a secure way. We proposed a system where users can access the open surveys and participate in these surveys. Moreover, their answers cannot be changed by anonymous or 3rd party people with smart contract control. At the same time, to ensure the confidentiality of the patient’s data, we kept the hashed value of the information we collected from the patient’s survey data as evidence and the encrypted version so that data can be used for the evaluation, in the relevant storage units of the generated decentralized application.
INTRODUCTION
Technology is quickly advancing every day. Many tasks were previously completed physically in real life and then shifted to the digital workflow because of this advancement [1-5]. For instance, technology has an impact on finance, trade, multimedia, and advertising. At the same time, another area affected by these technological developments is the health field. Especially with the spread of the internet, the strengthening of computer technologies, and the learning of technology by people working in the field, most health-related data transactions began to be stored in digital areas. During these transitions, databases were used as the first solution for storing health data. As a result of this transition, the amount of data stored electronically has increased day by day. Due to both speed and memory requirements, distributed database structures were established. These
accomplishments and transitions have been very beneficial for healthcare and many other sectors [6-10]. However, it is an undeniable fact that there are parts of technology that cause difficulties in the field of health, as in every field. The most concerning subject among these challenges is the security of healthcare data. The medical operations and their results may contain sensitive or private data that is not desired to be captured or exposed by others. Therefore, ensuring the security of healthcare data is significantly necessary. Due to the necessity of providing security, many studies have been achieved about e-healthcare data protection, since the beginning of healthcare data processing in digital areas. The solution to the security concerns relies on the fundamentals of cryptography as confidentiality, integrity, and authentication. Many techniques have already been developed to provide these cryptographic fundamentals. These security problems can be eliminated with the combination of cryptographic techniques such as authorization control, encryption, masking, anonymization, etc. [11-15].
Research Motivation
The motivation behind the research is the need to revolutionize healthcare systems and improve patient outcomes.
One of the main motivations is the potential to detect diseases at an early stage. Early disease detection can lower healthcare expenditures and significantly improve treatment outcomes [16-20].Another motivation is the ability to remotely monitor and manage patients' health conditions. Remote monitoring allows healthcare providers to track patients' health status in real time, enabling early intervention and personalized care plans. Additionally, IoT Based Disease Prediction systems can contribute to the development of personalized medicine [21-23].A further driving force behind research has been the need to solve issues with existing healthcare systems, like excessive wait times, restricted access to care, and underutilization of available resources. IoT Based Disease Prediction systems have the potential to overcome these challenges by providing timely and accurate health monitoring, early detection of diseases, and efficient resource allocation.Furthermore, research improved population health management. By utilizing IoT devices and predictive models, healthcare providers can gain insights into population health trends, identify high-risk individuals or communities, and implement preventive measures to reduce the prevalence of diseases.
Research Gaps
While there have been significant advancements in the field of IoT-based Disease Prediction and management using Blockchain technology, there are still some research gaps that need to be addressed.
Integration of IoT devices and sensors: Although IoT devices play a crucial role in collecting real-time health data; there is a need for further research on the integration of a wider range of IoT devices and sensors. This integration will allow for more comprehensive and accurate data collection, leading to better disease prediction and management.Standardization and interoperability: One of the challenges in implementing IoT Based Disease Prediction systems is the absence of common frameworks and protocols for data interchange and interoperability among various IoT platforms and devices. This research gap highlights the need for developing standardized protocols and frameworks that enable seamless interoperability between IoT devices, ensuring effective data sharing and analysis for disease predictionData security and privacy: Another research gap in the field of IoT Based Disease Prediction and management using Blockchain is the need for robust data security and privacy mechanisms. The security and privacy of patient data are becoming more and more important as healthcare data becomes more digital. Blockchain technology can help by enabling the decentralized and unchangeable storage of medical records.Integration with Electronic Health Records: EHR systems play a significant role in healthcare data management. However, there is a research gap in understanding how IoT Based Disease Prediction systems can effectively integrate with existing EHR systems. This integration would allow for seamless data sharing and analysis between IoT devices and EHR systems, leading to more accurate disease prediction and management.Scalability and performance: The scalability and performance of IoT Based Disease Prediction systems using Blockchain technology are important considerations. Research is needed to explore ways to optimize the performance of Blockchain-based IoT systems, addressing issues such as transaction processing speed and scalability to handle large amounts of data.Contribution
Data preservation and data immutability are the main requirements for electronic health record systems. In this paper, a system has been designed that aims to save the e-health record data and their results as evidence. Since proof immutability is the main requirement of these kinds of systems, an immutable ledger mechanism is an ideal solution for this requirement. Therefore, we have designed a system that can work in sync with the blockchain. The proposed model aims to provide a scalable, fast, and secure healthcare-proof system to users. While designing this model, we used cryptographic algorithms that comply with the standards. In addition, we chose to use parameters according to the specified standards for these algorithms. After this design, we propose an interface and works synchronously with the preferred blockchain structure.
Literature Review
Azaria et al. offered a blockchain-based user access control system named MedRec. They used the Ethereum blockchain and its smart contract mechanism. They used nodes with two roles as patient and provider nodes. In MedRec’s solution, there are three smart contract designs. The registrar contract ensures the relation between the Ethereum address and the system user. The summary contract provides a record history for each patient, and the patient-provider contract provides access control between patients and related providers. Rajput et al. proposed a blockchain-based control management system for the patient healthcare data. In Rajput’s system, Hyperledger Fabric was used for the blockchain mechanism. Business logic was implemented with the smart contract such as registering and retrieving data operations. In addition, they used an API connection between the application and the blockchain side. Moreover, they determined access control rules according to the roles of the users in the system for preventing unauthorized user activities on patients, doctors, or staff data. Shahnaz et al. developed a pure blockchain system with role-based authorization for ensuring the privacy of healthcare data. In this system, they used Ethereum and its smart contract mechanism Solidity. In their system, there are two types of smart contracts. The first one is the patient record contract, and the other one is for roles. The first one contains all create, read, update and delete (CRUD) functions such as patient record saving, viewing, grant or revoke access controls. The role contract is predefined via the Open Zeppelin library. Their user definitions are based on only Ethereum users. Therefore, the blockchain provides interaction between smart contracts and users. Xu et al. studied blockchain-based approach to IoT-based healthcare system named Health Chain. They constructed two related chains. The first one is a public blockchain named User chain and the second one is a consortium blockchain named Do chain. The user chain stores the user information data in Unblock. For the confidentiality of the user’s IoT data, they have used AES symmetric encryption. In addition, for storing the encryption key and encrypted user’s IoT data separately, they have used two different transactions. Doc-chain, on the other hand, stores the diagnostics of the related users in their block named D-block. As a consensus mechanism, the User chain has a PoW mechanism. However, they prefer the PoS-based Byzantine Fault Tolerance for the Doc chain. Furthermore, the encrypted data are stored on IPFS in storage nodes. Fan et al. suggested another blockchain-based electronic medical data-sharing solution named MedBlock. In this solution, they constructed a private blockchain. In parallel, they use a hybrid consensus mechanism for reducing resource wasting and improving the network speed. The data security is provided by a signature-based access control protocol. Accordingly, if the user signature is not among the signature collections in their system, user access is blocked. The studies we have mentioned so far are mostly blockchain studies based on providing access control. Li et al. suggested a blockchain-based medical data preservation system with Ethereum. They developed a three-layer application as many solutions have been proposed. These layers are the user layer, the application layer, and the blockchain layer. Briefly, they retrieve the data from the user layer, process, read, or update it at the application layer, and submit the encrypted and hashed data to the blockchain layer. In this process, they used algorithms such as AES and SHA-256 to preserve data confidentiality and integrity. Pavel et al. specified the problem as medical data transferring and proposed a blockchain-based solution to their PoS-based blockchain structure. They used signature-based authorization for the image transfer, retrieval, or viewing.
Proposed model
The proposed model consists of three main parts. The first part is the web application of the project. The web server provides the connection with the database and constructs all transaction data with the Algorand API in Python. In addition, the survey operations are creation, filling, and consent processed by the web application. During these operations, all requested body data are converted into designed back-end class objects. The other one is the blockchain side. On the blockchain, all types of transaction data are sent by the web server via Algorand API. Then the validity of the transactions is checked by the system. In this case, if the sent data is valid then the data is committed to the blockchain. Moreover, on the blockchain side, we use smart contracts and asset technology. The last part is the database module. For each generated survey, we create a new executable decentralized application with a unique ID. With this unique ID, we create a new register in the database and map this register with the smart contract ID. In addition, the survey data such as questions, options, descriptions, etc. are stored in the database. Most of the time, the database operations are quicker than backend data operations. However, due to the cryptographic library methods, database encryption is faster than back-end encryption. Hence, we use a database for sending survey data more quickly to the patient side instead of back-end operations. The overview of the general structure is given in Fig. (1) below.
Fig. (1)) Mechanism for smart contract execution.The smart contract initially verifies the kind of application transaction when it is performed by any application transaction. A new application will be produced if the application ID is equal to zero. When creating surveys, this control is utilized. There is already an application if it is not equal to zero. The smart contract then determines if the user has authorization after those controls. The user's MST balance is checked in order to implement this control. If the user's MST balance is more than zero, it is successful. If not, this user's smart contract automatically completes. Both the filling out of permission forms and survey requests use this control. The smart contract has now split into two branches. The permission form approval is handled by this one. The other is used to complete surveys. The smart contract is called when the consent form is filled out. The preceding stages are checked once more. If all requirements are met, the application first looks at the kind of smart contract that was invoked. If it is an opt-in kind, it offers a check to see if the user has already registered. In the event that one is given, the smart contract records the argument sent for the consent form as key-value pairs in the user's local storage. Otherwise, the agreement was broken by the smart contract. The cancellation causes the transaction to fail. When filling out a survey, the smart contract is contacted once again and the attributes are checked again. The smart contract checks two parameters if all requirements are satisfied. The first step is to determine whether or not the user is registered. The presence of previously recorded survey data is examined for the second. If the contract breaches the agreement and discovers any registration-related data that has been saved, the transaction will again fail. In the absence of this, the contract accepts the argument given for the encrypted survey responses and hashed survey data, and saves them as key-value pairs in the user's local smart contract storage as in the consent form procedure.
Survey Creation
Once a survey is created, the blockchain and application sides cooperate. The creation of smart contracts and transforming them into blockchain-compatible agreements are the most important aspects of this stage. Each survey generation activates the new decentralized application with a distinct ID in the blockchain. The doctor is the creator of the survey. Therefore, he prepares the survey and sends it to the application side with his mnemonic key. In this step, the system requires survey data in JSON format. Once the survey data reaches the application, it is adapted to the designed object class for processing. Then, the contract transaction creation process is performed. This transaction is named Application Call Transaction. After validation of the 31 blockchain, if there is not an invalid parameter or operation request, the transaction is committed to the system. And our smart contract is executed in the AVM. Otherwise, the transaction fails. In the successful case, we adapt the survey data to the data transfer object. After that, we save the survey information to our database. During registration in the database, we encrypt the survey data with the AES algorithm with the CBC mode (Fig. 2).
Fig. (2)) Mechanism of survey creation.Consent Form
Medical surveys are widely used in many areas of healthcare. These surveys can be done incrementally, once, continuously, or both before and after some medical operations. The answers or the results of the survey might contain sensitive data. Due to the principle of doctor-patient confidentiality, both doctors and patients avoid the disclosure of this data. Moreover, some tests might have unexpected consequences. Therefore, the consent form is filled out by the patients for the data confidentiality agreements and a disclaimer for unpredictable results. Because of these requirements in the medical surveys, we construct a consent form structure. Since the consent form is an agreement between the doctor and the patient, we store this consent form on the blockchain side for immutable proof of agreement. Fig. (3) explains the generation of transactions and the execution of the smart contract mechanism for the consent form. Once the participant requests to fill out the determined survey, the consent form first appears to the patients before the survey has been sent. The participant reads the terms and conditions. If the patient declines the terms and conditions, he redirects to the main page, and the survey is canceled until he accepts the consent form. Once he confirms the consent form, the consent data is constructed with the server-side functions and generates the designed object class. Then the hash value of the consent data H(C) is generated via the SHA-256 hash function. After this step, the contract transaction generation process begins. The flow chart of consent operation is given in Fig. (3) below.
Fig. (3)) Consent form mechanism.
