Blockchain-based Smart Contracts Research Articles

Analysis of Implementation of Anti-Money Laundering Mechanisms in Blockchain-Based Smart Contracts under Indonesian Regulation

One of the most widely adopted forms of information technology application today is electronic trade through the internet network to make transactions. This transaction system was initially centralized, but with the risk of system failure and hacking, distributed systems such as blockchain were developed to improve security and efficiency. Blockchain also gave birth to derivative technologies such as smart contracts, automated blockchain-based digital agreements. Despite offering many advantages, the anonymity and decentralization of smart contracts raise concerns of misuse for illicit activities such as money laundering. Therefore, regulatory updates in Indonesia are needed to regulate the validity of smart contracts and money laundering actions through this technology. This article analyses the validity and regulation of smart contracts and efforts to implement the AML system in blockchain-based transactions in Indonesia.

Decentralised autonomous organisations: targeting the potential beyond the hype

ABSTRACT Decentralised Autonomous Organisations (DAOs) aim at innovating the organisation forms for business activities. They are complex blockchain-based smart contracts, which allow token holders to participate directly in decision-making processes and decentralised entrepreneurial activities as much as possible. The advocates of this new kind of digital organisation argue that DAOs enjoy significant operational efficiencies and can effectively work outside of any legal recognition. This paper analyses DAOs through the lenses of the economic and legal theories on the firm and on business organisation. The analysis makes three contributions: first, it contributes to the literature on the theory of the firm, looking at the role of digital technology in innovating the organisation of business activities. Second, it enriches the literature on the legally recognised forms of business organisation, analysing the tension between the essential role of the law and the limitations of tamper-resistant technologies, such as the blockchain. Third, it overcomes the largely ideological and dichotomic debate on the promises of DAOs, providing analytical guidelines as to why current forms of sector-specific regulation fail to leverage the potential of DAOs.

A Study on the Legal Status of Blockchain-Based Smart Contracts

A Study on the Legal Status of Blockchain-Based Smart Contracts

MDAPW3: MDA-based development of blockchain-enabled decentralized applications

Web3.0 Decentralized Application (DApp) is a class of decentralized software in which at least the business logic of the software is implemented using blockchain-based smart contracts. Features such as transparency, decentralized execution environment, no need for a central authority, immutability of data from manipulation, as well as a native transaction-based payment system based on cryptographic tokens are the main advantages of Web3.0 DApps over conventional Web2.0 software in which the business logic and user data are centrally controlled by companies with no transparency. However, the development lifecycle of Web3.0 DApps involves many challenges due to the complexity of blockchain technology and smart contracts as well as the difficulties concerning with the integration of DApp on-chain and off-chain components. To alleviate these challenges, a Model Driven Architecture (MDA) approach for the development of Web3.0 DApps is proposed in this paper that streamlines the development of complex multi-lateral DApps and results in a product that is verifiable, traceable, low-cost, maintainable, less error-prone and in conformance with blockchain platform concepts. Opposed to previous studies in this area that applied MDA only for the development of smart contracts, our proposed MDA-based approach covers the full architecture of Web3.0 DApps: on-chain, off-chain and on-chain/off-chain communication patterns. The method application was demonstrated by implementing a land leasing Dapp where the requirement model (a BPMN choreography model) was transformed into CIM, PIM, and PSM instances successively, and finally, the code-base was generated based on the Ethereum platform technology stack. Epsilon Validation Language (EVL), Epsilon Object Language (EOL), and Epsilon Comparison Language (ECL) were used for the verification/validation of the model instances at each step. Furthermore, by evaluating the quality metrics of the proposed meta-models, we show that they have a better ontology coverage and are more reusable and understandable compared to previous meta-models.

Cooperation Among Strangers: Algorithmic Enforcement of Reciprocal Exchange with Blockchain-Based Smart Contracts

Cooperation Among Strangers: Algorithmic Enforcement of Reciprocal Exchange with Blockchain-Based Smart Contracts

APPLICATION OF SMART CONTRACTS IN ELECTRONIC SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGIES

In the era of digitization, where information technology and business processes are closely intertwined, the development and implementation of blockchain-based smart contracts become key to achieving a new level of automation, security, and efficiency. This article deeply analyzed how blockchain smart contracts can enhance the execution of contractual obligations, making processes more transparent and efficient. The main aspect of study is the technical details of smart contracts and exploration of their practical application for optimizing business procedures, significantly reducing risks associated with fraud and the need for intermediaries. A practical demonstration of deploying a smart contract, executed in the Python programming language, is proposed as a method used in the article, highlighting the possibilities and challenges related to scalability and regulation. Results underscore a notable boost in operational efficiency and security, while also identifying barriers to broader technological adoption. Concluding, the significant role of smart contracts in evolving information systems is underlined, advocating for novel approaches to secure, autonomous contract fulfillment and emphasizing the importance of ongoing research to exploit their full capabilities in fortifying information security and operational efficacy.

Kaizen: A Street Smart Low Latency-Aware Resource Choreography Scheme for Decentralized Autonomous Organization (DAO) and Non-DAO Based Application Execution Over Blockchain and MEC Empowered 6 G Network

AbstractDecentralized autonomous organization (DAO)-based applications can revolutionize traditional centralized decision-making procedures and services by enabling scalable, decentralized, autonomous, and democratized decision-making processes. Unlike traditional applications, DAO-based decentralized applications use Ethereum blockchain-based smart contract programs to execute policies or make automated decisions. To that end, 6 G technologies can improve the latency and reliability of many existing blockchain-based DAOs. Previous research did not investigate the execution of DAO and non-DAO-based applications, as well as an adaptive resource choreography scheme, while accounting for various 6 G technologies, blockchain, and mobile-edge cloud (MEC). To prevail over the previous shortcomings, this article supplies a street smart multi-platform coordination, application scheduling, and low-latency-aware resource choreography scheme for both DAO and non-DAO-based application execution over blockchain and MEC-enabled 6 G networks by taking heterogenous DAO and non-DAO application count, application requirements, physical worker, virtual worker, and communication resource status into account. The results verified that the proposed kaizen scheme delivers at least 11.26% app work completion delay gain, 7.2% user energy overhead gain, 6.55% user economic charge gain, and 21% service provider profit than the compared schemes.

Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare

An automatic medical diagnosis service based on deep learning has been introduced in e-healthcare, bringing great convenience to human life. However, due to privacy regulations, insufficient data sharing among medical centers has led to many severe challenges for automated medical diagnostic services, including diagnostic accuracy. To solve such problems, swarm learning (SL), a blockchain-based federated learning (BCFL), has been proposed. Although SL avoids single-point-of-failure attacks and offers an incentive mechanism, it still faces privacy breaches and poisoning attacks. In this paper, we propose a new privacy-preserving Byzantine-resilient swarm learning (PBSL) that is resistant to poisoning attacks while protecting data privacy. Specifically, we adopt threshold fully homomorphic encryption (TFHE) to protect data privacy and provide secure aggregation. And the cosine similarity is used to judge the malicious gradient uploaded by malicious medical centers. Through security analysis, PBSL is able to defend against a variety of known security attacks. Finally, PBSL is implemented by uniting deep learning with blockchain-based smart contract platforms. Experiments based on different datasets show that the PBSL algorithm is practical and efficient.

Synergizing AI and Blockchain: Innovations in Decentralized Carbon Markets for Emission Reduction through Intelligent Carbon Credit Trading

This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.

Transaction Security and Management of Blockchain-Based Smart Contracts in E-Banking-Employing Microsegmentation and Yellow Saddle Goatfish

The process security of money transactions is considered an important issue in e-banking. Additionally, it is an enormous problem if never controlled. particular, security should be a combination of fast and sturdy characteristics, which is the subject of previous studies suffered from. Our research attempts to improve the system in which banks deal with the security of financial transactions. This research leverages the idea of microsegmenting the entire system into designated zones to concentrate on security, where each zone has its own rules and limitations. These rules are managed by a smart contract, which decides whether they have been observed to verify the legitimacy of the customer. First, the two-phase commit algorithm (2PC) was used to specify the type of e-banking request. After this, the microsegmentation principle was applied to isolate each type of e-transaction process alone in a separate segment. Then, the yellow saddle goatfish algorithm (YSGA) was used to determine whether the smart contract conditions were optimized. Finally, if the customer is authorized, then the entire transaction process is saved in the blockchain's main ledger and secured by a unique hash. The blockchain application makes our system capable of dealing with large numbers of users in a decentralized manner. In addition, using a hash with each block prevents fraudulent transactions by adversaries. Our system has been examined against several recent well-known assaults/attacks, such as falsification, advanced persistent threat, bribery, spoofing, double spending, chosen text, race, and transaction replay attacks, and has proven to overcome them. In terms of the performance evaluation, we obtained an execution time of approximately 0.0056 nanoseconds, 3.75% complexity, and 1500 KB of memory and disk drive, which is considered low compared to that of state-of-the-art research. Thus, our proposed system is highly acceptable for banking sector applications.

Realtime polymorphic malicious behavior detection in blockchain-based smart contracts

Abstract This paper proposes an innovative approach to achieving real-time polymorphic behavior detection, and its direct application to blockchain-focused smart-contracts. We devise a method based on a non-deterministic finite state machine to perform approximate pattern-matching, using a look-ahead mechanism implemented through a concept similar to that of a sliding window, and using threshold-based similarity checking at every state in the automaton. We introduce and formalize our approach, discuss the challenges we faced and then test it in a real-world environment. The experimental results obtained showed a significant speed-up of our approach, as compared to the classic similarity measures used commonly in such scenarios.

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

Legality of Blockchain and Smart Contracts in India

India is presently experiencing a moment of profound transformation that encompasses revisions to its constitution and legal principles. In this era of remarkable transformation, it would be imprudent to overlook the technological innovations and digital advancements that have permeated the legal domain. One particularly vital aspect in this context is the field of Contract Law. Presently, electronic contracts in India are primarily governed by The Indian Contract Act, in conjunction with the Information Technology Act and the Indian Evidence Act, unless stated otherwise. These legal frameworks provide the foundation for legally enforceable electronic contracts in the country. This research paper emphasizes the urgent requirement for a legal adaptation in the field of Contract Law to align with the ever-evolving technological landscape. It also delves into the legal intricacies associated with blockchain-based Smart Contracts, elucidating how these contracts function within the current judicial landscape. Finally, it concludes by emphasizing the necessity for a pioneering effort to regulate this dynamic domain.

Collaboration of Heterogeneous Edge Computing Paradigms: How to Fill the Gap Between Theory and Practice

Edge computing paradigms such as cloudlet, mobile edge computing (MEC) and fog computing, are promising to reduce network transmission delay while meeting intensive computing requirements at the network edge. As always assumed in theoretical studies, to expand service capability, it is desirable to integrate edge resources into a unified resource pool and make different edge facilities collaborate. However, this collaboration is difficult in practice since the heterogeneity among paradigms leads to resource isolation. This article focuses on clarifying and shielding heterogeneity among edge computing paradigms for collaboration. First, based on cloudlet, mobile edge computing and fog computing, three main edge computing paradigms, i.e., small cloudy edge paradigm (SmallC), mobile integrated edge paradigm (MobInt) and front ad hoc edge paradigm (FrontAH), are defined. So that the heterogeneity among them can be analyzed, according to which, collaboration challenges in computing resource trading, information communication and resource sharing are identified. Promising techniques such as blockchain-based smart contract, media-independent handover (MIH), and software-defined network and network function virtualization (SDN/NFV) are then introduced and integrated into a complete collaboration solution. Finally, taking the unmanned vehicular navigation as an example, the performance of collaboration is evaluated, considering additional costs introduced by collaborations, including trading, network handoff and data transmission among edge facilities in different paradigms. Simulation results show that compared with existing schemes of the three paradigms, the collaboration can reduce the cost (i.e. weighted sum of energy consumption and delay) by at least 45.6%.

Blockchain-Based Smart Contract Model for Securing Healthcare Transactions by Using Consumer Electronics and Mobile-Edge Computing

Blockchain-Based Smart Contract Model for Securing Healthcare Transactions by Using Consumer Electronics and Mobile-Edge Computing

AI-assisted Blockchain-enabled Smart and Secure E-prescription Management Framework

Traditional medical prescriptions based on physical paper-based documents are prone to manipulation, errors, and unauthorized reproduction due to their format. Addressing the limitations of the traditional prescription system, e-prescription systems have been introduced in several countries. However, e-prescription systems lead to several concerns like the risk of privacy loss, the problem of double-spending prescriptions, lack of interoperability, and single point of failure, all of which need to be addressed immediately. We propose an AI-assisted blockchain-enabled smart and secure e-prescription management framework to address these issues. Our proposed system overcomes the problems of the centralized e-prescription systems and enables efficient consent management to access prescriptions by incorporating blockchain-based smart contracts. Our work incorporates the Umbral proxy re-encryption scheme in the proposed system, avoiding the need for repeated encryption and decryption of the prescriptions when transferred between different entities in the network. In our work, we employ two different machine learning models(Random Forest classifier and LightGBM classifier) to assist the doctor in prescribing medicines. One is a drug recommendation model, which is aimed at providing drug recommendations considering the medical history of the patients and the general prescription pattern for the particular ailment of the patient. We have fine-tuned the SciBERT model for adverse drug reaction detection. The extensive experimentation and results show that the proposed e-prescription framework is secure, scalable, and interoperable. Further, the proposed machine learning models produce results higher than 95%.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

THE EVOLUTION OF SMART CONTRACT PLATFORMS: A LOOK AT CURRENT TRENDS AND FUTURE DIRECTIONS

Blockchain-based smart contracts are self-running computer programs that can automate a variety of commercial activities. Currently, the majority of these decentralized applications are developed using smart contract platforms like Polkadot, Cardano, and Ethereum. In addition to analyzing current technology developments and prospective future applications, this article provides a historical review of smart contract platforms. The study emphasizes the significance of smart contract platforms for supporting blockchain-based applications and enabling decentralized finance (DeFi). It also looks at the emergence of layer-2 scaling solutions, the introduction of non-fungible tokens (NFTs), and the growing need of interoperability among different smart contract platforms. The article also looks at the potential for multi-chain smart contracts, the effects of quantum computing, the integration of AI and ML technologies with smart contract platforms, and the potential for smart contract platforms to support decentralized autonomous organizations (DAOs). The difficulties of expanding smart contract platforms, the requirement for uniformity in the creation of smart contracts, and the potential for smart contract platforms to revolutionize sectors like healthcare, real estate, and supply chain management are also covered. The paper emphasizes the significance of ongoing innovation and development in smart contract platforms for the expansion of the blockchain ecosystem as it draws to a close.

Smart Contracts for Mobile Network Operator Bandwidth Sharing

We propose a new way to share licensed spectrum bandwidth capacity in mobile networks between operators, service providers, and end users using blockchain-based smart contracts. We discuss the foundational building blocks in the contract as well as various extensions to support more advanced features such as bulk purchases and future reservations. Furthermore, we demonstrate how the system can be implemented with an open source, permissioned enterprise blockchain, Hyperledger Sawtooth. We show that our smart contract implementation can improve blockchain transaction performance, by approximately four orders of magnitude compared to serial transactions and one order of magnitude compared to parallel transactions, by using public key infrastructure driven bulk purchases of mobile access grants, paving the way for fully automated, efficient, and fine-grained roaming agreements. We conclude with a discussion of lessons learned from two end-to-end use cases we implemented to validate our distributed ledger design.

Internet of Things and Blockchain-Based Smart Contracts: Enabling Continuous Risk Monitoring and Assessment in Peer-to-Peer Lending

ABSTRACT Peer-to-peer (P2P) lending enables individuals and small companies to finance and invest without the intermediation of financial institutions. However, this business model is also associated with high delinquency risk and a lack of risk monitoring and control capabilities. This paper explores the potential of the Internet of Things (IoT), blockchain, smart contract technologies, and the Continuous Risk Monitoring and Assessment (CRMA) framework to re-engineer risk monitoring and control for P2P lending. We conducted a case study of a large Chinese P2P lending company to identify problems in its current risk monitoring and control processes and to design an IoT-smart contract CRMA system to continuously monitor and respond to delinquency risk via real-time data collection, automatic loan settlement, and in-time risk disclosure. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M49.