Transaction Security Research Articles

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

Enhancing dealership management software with AI integration for improved customer service and future innovations

This review paper explores the integration of Artificial Intelligence (AI) into dealership management software to enhance customer service and foster future innovations. The primary objective is to synthesize existing research and industry practices to understand how AI technologies can streamline operations, personalize customer interactions, and drive business growth within automotive dealerships. The paper reviews key AI applications, including AI-driven chatbots and virtual assistants that provide automated support, predictive analytics for customer behavior and sales forecasting, and personalized marketing strategies. The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction. Predictive maintenance and inventory management powered by AI algorithms ensure optimal resource allocation, minimizing downtime and operational costs. Additionally, the paper identifies potential future innovations facilitated by AI advancements, such as augmented reality (AR) for virtual vehicle tours and blockchain for secure transaction management. These innovations promise to elevate the customer experience and streamline dealership processes, creating a competitive edge in the market. The integration of AI into dealership management software presents substantial benefits in improving customer service and operational efficiency. The paper emphasizes the need for continuous investment in AI technologies and employee training to fully harness these advantages. Future research should focus on exploring emerging AI applications and their long-term impact on the automotive industry, ensuring dealerships remain adaptable and competitive in a rapidly evolving technological landscape. Keywords: Artificial Intelligence, Dealership Management Software, Customer Service, Predictive Analytics, Augmented Reality, Blockchain, Automotive Industry.

Smart Contracts Management: The Interplay of Data Privacy and Blockchain for Secure and Efficient Real Estate Transactions

The digital transformation of the real estate industry is being significantly influenced by blockchain technology and smart contracts, which promise enhanced efficiency, transparency, and security in transactions. This study aims to develop a secure and efficient smart contract management protocol that balances the benefits of blockchain with robust data privacy practices. The methodology involves descriptive analytics of transaction data from the Ethereum blockchain, feasibility studies using synthetic transaction data, and a regulatory compliance analysis to map the impact of different regions' regulations on blockchain adoption in real estate. The findings reveal that while smart contracts can automate various processes and reduce reliance on intermediaries, challenges related to data privacy and regulatory compliance persist. Higher privacy features in smart contracts are associated with increased execution costs, indicating a trade-off between privacy and cost efficiency. Smart contracts with privacy level 3 had an execution cost of 0.025 ETH, compared to those with privacy level 1 at 0.02 ETH. Integrating permissioned blockchains and zero-knowledge proofs offers a promising solution, though their complexity limits broader adoption. Zero-knowledge proofs maintained high privacy (achieving privacy levels of up to 0.76) at a reasonable computational cost (proof generation time of 1.9 seconds). Thus, the integration of permissioned blockchains and zero-knowledge proofs offers a promising pathway to address these challenges. However, the complexity of these techniques requires specialized knowledge, limiting broader adoption. The study concludes with recommendations to develop specialized training programs, collaborate on regulatory frameworks, invest in advanced cryptographic research, and implement targeted strategies to overcome adoption barriers. These efforts will contribute to the digital transformation of asset management, fostering innovation and enhancing the overall efficiency of real estate transactions.

Implementasi Teknologi Pengenalan Wajah Untuk Login Website Chic Glasses Berbasis Convolutional Neural Network(CNN)

The research discussed the development of a face recognition technology for the login system of an e-commerce website. The study's main focus was enhancing the security and efficiency of online transactions through the use of biometric identification systems. The researchers implemented a Face Recognition Login Website using a CNN, known for its ability to extract features and classify faces effectively. The research involved various stages, such as data collection through a web camera, data pre-processing, and the design of a CNN architecture with feature learning and classification layers. By deploying this CNN-based facial recognition technology on e-commerce websites, the researchers expect to add an extra layer of security, boost user trust, and offer a safer and more convenient online transaction experience.

Personalization and Customer Experience in E-Commerce

Innovation is crucial for maintaining a competitive edge and delivering exceptional shopping experiences in the ever-evolving realm of e-commerce. Businesses continuously seek creative approaches to enhance the e-commerce landscape, incorporating technologies like immersive experiences, seamless payment solutions, Artificial Intelligence (AI), and personalized services. This study investigates the emerging technologies and innovations within e- commerce, particularly focusing on the application and impact of AI. Utilizing conceptual research methodology and secondary data from various scholarly articles, journals, and online sources, the study concludes that recent innovations such as AI-driven personalization, Augmented Reality (AR) and Virtual Reality (VR), chatbots, blockchain for transaction security and supply chain management, social commerce, drone delivery, and e-wallet solutions have significantly advanced the e- commerce sector. Noteworthy AI applications include platforms like Google AI Cloud Platform, IBM Watson, BigML, and Infosys Nia, which are also leveraged for product pricing and distribution strategies. Additionally, the adoption of diverse payment methods such as e- wallets and UPI contributes to enhancing user satisfaction in e-commerce transactions.

Comparative secured transactions law: how and to what extent do global standards affect the reform of Japanese inventory financing law

Abstract Since March 2021 Japan has been undertaking official legislative discussions at the Committee of Secured Transactions Law Reform under the Legislative Council of the Ministry of Justice Japan to draft a statute of secured transactions of personal property. These discussions are now in their final stages. Enhancing the reliability and feasibility of security devices for inventory financing is the core purpose of the proposed legislation. As a result of policy-making decisions, the conventional Japanese security device of ownership transfer for security purposes will not be replaced by a new regime which reflects the modern principles originating from the UCC9 and illustrated in the UNCITRAL Model Law on Secured Transactions. However, the significance of such global standards in the context of Japanese law has been two-fold. First, they illustrate what conventional Japanese law achieves through its own alternative rules. Second, the Council Committee is likely to propose several changes to refresh our conventional security device by adding new principles which have traditionally been of less concern to Japanese practitioners. In this article, the author substantiates the influence of these global standards on Japanese legislation. This article focuses on the functional approach taken by the ownership transfer type security interest, the rules relevant to publicity of security interests as modified by the first-to-register-or-possession rule as suggested by global standards, the concept of aggregate of inventory as collateral as modified by registration to cover perfection in security interests in goods joining the aggregate after a debtor's dishonest disposition, and the assignment of accounts receivables accompanied with ownership transfer of inventory which may be viewed as functionally close to security interests in proceeds of inventory.

Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security

Financial fraud poses a significant threat to the global economy, necessitating advanced measures for detection and prevention. This paper explores the application of machine learning techniques to enhance transaction security and combat financial fraud. It provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, neural networks, and anomaly detection. Each technique's application in identifying and preventing fraudulent activities is discussed, along with their advantages and limitations. Challenges in implementing machine learning for fraud detection, such as data quality, scalability, real-time processing, and model interpretability, are examined. Ethical and privacy concerns associated with using machine learning in financial transactions are also addressed. By highlighting these aspects, the paper aims to contribute to developing more effective and ethical machine learning-based fraud detection systems, ensuring robust transaction security and fostering trust in financial institutions.

Blockchain technology in financial services: enhancing security, transparency, and efficiency in transactions and services

Blockchain technology revolutionizes financial services by enhancing security, transparency, and efficiency in transactions and services. This paper examines the fundamental aspects of blockchain, including its core concepts and components, and explores its application in financial services. Blockchain's cryptographic hashing, decentralization, and immutability significantly improve security by preventing tampering and reducing fraud risks. Increased transparency through immutable audit trails, real-time updates, and open ledgers fosters trust among customers, regulators, and institutions. Furthermore, blockchain streamlines operations by automating processes, reducing intermediaries, and expediting transactions, resulting in substantial cost reductions and improved efficiency. Despite these advantages, challenges such as scalability, interoperability, regulatory uncertainty, and environmental impact must be addressed for widespread adoption. The prospects of blockchain in financial services are promising, with potential applications in decentralized finance, digital identity verification, and central bank digital currencies. This paper underscores the transformative potential of blockchain technology in creating a more secure, transparent, and efficient financial ecosystem.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

Analisis Perlindungan Hukum bagi Pelaku Usaha Penyedia Sistem Pembayaran Qris (Quick Response Indonesian Standard) berdasarkan Undang-Undang Nomor 8 Tahun 1999

Technological developments have changed the paradigm of payment systems from cash to non-cash, including in the form of electronic money (e-money) and QR-Code. QR-Code has become a popular payment method, especially with the implementation of Quick Response Indonesian Standard (QRIS). However, with technological advances also come new challenges related to legal protection for businesses that provide QRIS payment systems. This paper discusses the urgency of legal protection for business actors providing QRIS payment systems based on Law Number 8 Year 1999 concerning Consumer Protection. In this context, the concepts of freedom of contract, consent, sale and purchase, and consumer protection are elaborated to understand the rights and obligations of the parties in electronic payment transactions. The importance of the application of law as a preventive and repressive measure to prevent and overcome violations is also discussed. In addition, the relationship with Law Number 19 of 2016 concerning Electronic Information and Transactions and the implementation of Bank Indonesia regulations related to QRIS are highlighted in enforcing the rules and ensuring security and legal certainty in electronic payment transactions.

Barriers and prospects for the adoption of blockchain technology in property valuation

PurposeBlockchain is an emerging digital technology proposed and trialled among different built environment professions. The technology has been proposed to introduce transparency, security and trust in property transactions. Despite this proposition, few studies have analysed the barriers and prospects in property valuation, especially in markets plagued by low transparency and a lack of stakeholder trust. Using Nigeria as a case study, this study assesses the barriers and prospects for adopting blockchain technology in property valuation.Design/methodology/approachData was collected from 180 valuers practising in Nigeria through an online survey, and the data was analysed using mean score ranking and the chi-square (χ2) test of independence.FindingsFirstly, there was a low awareness of the application of blockchain technology and an association between the number of valuation jobs executed annually and awareness of the application of blockchain technology. The most important barriers revolved around the knowledge, technical know-how of blockchain and the cost of implementing such technology. The prospects for blockchain are very high as all identified prospects were considered important, with transparency being the most crucial factor for its adoption, followed by the monitoring activities in real time and the permanence in storing records.Research limitations/implicationsThis study's implications lie in the potential benefit of transparency identified for blockchain, which could act as a tool to introduce transparency into valuation industries that battle key issues surrounding transparency and trust. Furthermore, this study can be utilised by policymakers and property industry players in mapping strategies to adopt the beneficial use of blockchain as one among the suite of proptech tools disrupting the property valuation scene, in their practice. This also presents an opportunity to draw upon insights from this study to better prepare for using blockchain in property valuation.Originality/valueThis study appears to be the first to empirically assess barriers and prospects for blockchain in property valuation practice. It contributes to the literature by identifying key factors that will deter and/or promote the application of blockchain, an emerging and disruptive digital technology.

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

Leveraging advanced technologies in Supply Chain Risk Management (SCRM) to mitigate healthcare disruptions: A comprehensive review

The Healthcare sector is undeniably one of the most critical sectors in any given economy and disruptions in this industry have dire consequences that affect patient care, the efficiency of healthcare systems and by far public health. This publication seeks to analyze and discuss the vital role of supply chain risk management in addressing disruptions, becoming resilient and ensuring that healthcare services function effectively. The pandemic (COVID-19) brought to fore just how critical it is to have a robust and agile system for supply chain risk management in healthcare, the sudden and unprecedented surge in demand for essential medical equipment like personal protective equipment (PPE) left millions of people at risk thereby jeopardizing their safety and health. This crisis echoed the urgent need for efficient and effective supply chain risk management strategies to mitigate such risks and ensure the availability of vital medical supplies. This review paper will be sharing different strategies and actionable tips to develop contingency plans, identify, reduce potential disruptions and recommended practices for risk management in the healthcare supply chain. This will highlight key areas such as inventory management and stockpiling, which focuses on maintaining safety stock levels to buffer against sudden disruptions, using advanced analytics to forecast demand and manage inventory levels. It will also examine risk assessment and identification by pinpointing potential attacks, threats, and vulnerabilities within the healthcare supply chain through thorough risk assessments. Additionally, it will cover supplier base diversification, emphasizing the importance of engaging multiple suppliers to reduce dependency on a single source and enhance supply chain resilience by balancing global and local suppliers to mitigate geographic disruption risks. Furthermore, it will explore the integration of technology, leveraging innovative solutions like blockchain for enhanced transparency, security, and traceability of supply chain transactions.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Pengaruh Faktor Kepercayaan, Keamanan Transaksi dan Kualitas Pelayanan Terhadap Keputusan Pembelian Online pada Aplikasi Shopee Bagi Pengguna di Kota Depok

. One of the most popular types of e-commerce today is the marketplace which is widely used by people to run businesses and helps them shop online to meet their needs. Shopee is one of them because it is often visited via the website. This research uses a quantitative approach method by utilizing primary data as a data source obtained from the results of a questionnaire in the form of a Google form. The analysis techniques used are data quality tests (validity and reliability tests), classical assumption tests (normality test, heteroscedasticity, multicollinearity and multiple linear regression), hypothesis testing (partial t test). ) and f test (simultaneous) and coefficient of determination test (R2). Data processing in this research uses SPSS version 25. Based on research, it shows that trust has a partial effect on purchasing decisions with a sig. 0.008. Transaction security does not partially influence purchasing decisions with a sig value. 0.296. Service quality partially influences purchasing decisions with a sig value. 0,000. So trust, transaction security and service quality influence decisions with a sig value of 0.015.

A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities.

Fort-Trust: Safeguarding online transaction by machine learning

In the digital age, the safety of online transactions has become an important situation for both customers and groups. The increasing sophistication of cyber-attacks necessitates the development of robust and sensible protection mechanisms. "Citadel-believe: Safeguarding online Transactions through device gaining knowledge of" proposes a complete solution leveraging machine studying techniques to decorate the safety of online financial transactions. This method aims to stumble on and mitigate fraudulent activities in real time, presenting a further layer of safety past conventional techniques. e-commerce has completely changed the way people shop, it has also made people more susceptible to online transaction fraud. This problem is addressed by the innovative framework Fort-Trust, which uses the XGBoost algorithm for fraud detection. Fort-Trust incorporates feature correlation analysis to solve a prevalent problem in this field: imbalanced datasets. This strategy aims to maximize detection accuracy while minimizing false positives. The high precision rate that XGBoost delivers, according to the results, is essential for lowering financial losses and increasing user confidence. All things considered, Fort-Trust strengthens the security and dependability of online transactions by providing a strong and useful solution for real-world fraud detection

Application of Blockchain Technology in Real Estate Transactions Enhancing Security and Efficiency

Blockchain technology, an innovative distributed ledger technology, has gained widespread application across various industries in recent years due to its decentralization, transparency, immutability, and traceability. This paper explores the application of blockchain technology in real estate transactions by analyzing four representative cases: Propy, PropertyClub, Ubitquity, and RealBlocks. The analysis demonstrates the significant advantages of blockchain in enhancing transaction security, improving efficiency, and reducing costs. The study reveals that Propy's platform, leveraging smart contracts, has automated global real estate transactions, reducing average transaction time by 32.4% and costs by 27.8%. PropertyClub utilizes blockchain to record and verify transaction information, ensuring transparency and security while achieving a 21.3% cost reduction. Ubitquity's blockchain-based record management system reduces record management time by 45.6% and increases data accuracy to 98.9%. RealBlocks has digitized and securitized real estate investments, enhancing liquidity by 52.1% and reducing investment costs by 20.5%. Future trends indicate a deep integration of blockchain with smart contracts and IoT, as well as the digitization and securitization of real estate assets. However, the widespread application of blockchain technology faces multiple challenges, including technical bottlenecks, legal and regulatory issues, market acceptance, and data privacy concerns. This paper further elaborates on these trends and challenges, proposing potential solutions. In conclusion, while blockchain technology has a promising future in real estate transactions, its widespread adoption requires concerted efforts from all stakeholders.

Ethereum blockchain-based hierarchical cooperative power sharing in a group of microgrids

ABSTRACT Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.