The main aim of the proposed system is to facilitate secure and protected land registry in the domain of agriculture Industry 5.0. Considering the outlook of issues associated with it, we considered the blockchain and AI-based technology to fulfill the purpose of secure land registry. Establishing and confirming land ownership is essential for the land registry system in ensuring the protection of ownership rights, particularly crucial in the contexts of agriculture and Industry 5.0. In these sectors, land serves as a crucial resource for sustainable development and industrial innovation. Most of the existing works rely on legacy and centralized system to store land records; which result in high incidences of forgery and fraud. Therefore, maintaining a robust land registry system is essential to fostering economic investments, promoting green practices, and facilitating equitable access to land resources in agriculture and Industry 5.0 ecosystem. We proposed an AI and blockchain-enabled land registry system for agriculture and industry 5.0 that offers a more reliable, transparent, and efficient solution to the challenges of lack of transparency, data tampering, and inefficiency, which can result in disputes, fraudulent claims, and a lack of trust during the land registry. AI models, such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), are employed to classify the fraud and non-fraud land data. Only the non-fraud land data is forwarded into the blockchain network, thereby reducing the computational overhead of the proposed land registry system. In the blockchain network, we designed various smart contracts that validate the land data with unparalleled efficiency and security. Further, the slither solidity source analyzer tool is used for smart contract vulnerability assessment. After the assessment, the smart contract is deployed using the Sepolia test network. The non-fraudulent land data is redirected to the interplanetary file system (IPFS) that stores the original data and forwards the associated hash into the blockchain’s immutable ledger. The entire proposed system is evaluated with different performance parameters, such as AI statistical measures including accuracy, ROC, log-loss score, blockchain scalability comparison, gas cost utilization, and bandwidth utilization. Furthermore, the vulnerability assessment of the smart contract is analyzed using Slither to highlight the working of proposed system without any vulnerabilities. The proposed blockchain and AI-based land registry system ensure a secure and intelligent pipeline to combat against land forgery activities.
GreenLand: A Secure Land Registration Scheme for Blockchain and AI-Enabled Agriculture Industry 5.0
Pau, Giovanni
;
2024-01-01
Abstract
The main aim of the proposed system is to facilitate secure and protected land registry in the domain of agriculture Industry 5.0. Considering the outlook of issues associated with it, we considered the blockchain and AI-based technology to fulfill the purpose of secure land registry. Establishing and confirming land ownership is essential for the land registry system in ensuring the protection of ownership rights, particularly crucial in the contexts of agriculture and Industry 5.0. In these sectors, land serves as a crucial resource for sustainable development and industrial innovation. Most of the existing works rely on legacy and centralized system to store land records; which result in high incidences of forgery and fraud. Therefore, maintaining a robust land registry system is essential to fostering economic investments, promoting green practices, and facilitating equitable access to land resources in agriculture and Industry 5.0 ecosystem. We proposed an AI and blockchain-enabled land registry system for agriculture and industry 5.0 that offers a more reliable, transparent, and efficient solution to the challenges of lack of transparency, data tampering, and inefficiency, which can result in disputes, fraudulent claims, and a lack of trust during the land registry. AI models, such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), are employed to classify the fraud and non-fraud land data. Only the non-fraud land data is forwarded into the blockchain network, thereby reducing the computational overhead of the proposed land registry system. In the blockchain network, we designed various smart contracts that validate the land data with unparalleled efficiency and security. Further, the slither solidity source analyzer tool is used for smart contract vulnerability assessment. After the assessment, the smart contract is deployed using the Sepolia test network. The non-fraudulent land data is redirected to the interplanetary file system (IPFS) that stores the original data and forwards the associated hash into the blockchain’s immutable ledger. The entire proposed system is evaluated with different performance parameters, such as AI statistical measures including accuracy, ROC, log-loss score, blockchain scalability comparison, gas cost utilization, and bandwidth utilization. Furthermore, the vulnerability assessment of the smart contract is analyzed using Slither to highlight the working of proposed system without any vulnerabilities. The proposed blockchain and AI-based land registry system ensure a secure and intelligent pipeline to combat against land forgery activities.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
