Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios
In the context of the rapid development of Internet of Things (IoT) technology and the extensive proliferation of the global Internet, the authenticity of data has become a focal point of societal demand. It plays a decisive role in enhancing the quality of decision-making and operational efficiency...
| Egile Nagusiak: | , , , , , , , |
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| Formatua: | Artikulua |
| Hizkuntza: | English |
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Elsevier
2024-07-01
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| Saila: | Journal of King Saud University: Computer and Information Sciences |
| Gaiak: | |
| Sarrera elektronikoa: | http://www.sciencedirect.com/science/article/pii/S1319157824002064 |
| _version_ | 1827163517131685888 |
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| author | Chufeng Liang Junlang Zhang Shansi Ma Yu Zhou Zhicheng Hong Jiawen Fang Yongzhang Zhou Hua Tang |
| author_facet | Chufeng Liang Junlang Zhang Shansi Ma Yu Zhou Zhicheng Hong Jiawen Fang Yongzhang Zhou Hua Tang |
| author_sort | Chufeng Liang |
| collection | DOAJ |
| description | In the context of the rapid development of Internet of Things (IoT) technology and the extensive proliferation of the global Internet, the authenticity of data has become a focal point of societal demand. It plays a decisive role in enhancing the quality of decision-making and operational efficiency. However, the storage and authenticity verification of large-scale IoT real-time data present unprecedented technical challenges. Faced with the inherent data security risks of traditional centralized cloud storage, blockchain technology reveals its unique potential for solutions with its inherent immutability and decentralization. Nevertheless, current blockchain-based data storage solutions are still restricted by high costs and inefficiency. To address these challenges, this paper innovatively proposes the BI-TSFID framework, which leverages the benefits of Ethereum and IPFS and optimizes the Merkle Tree structure and verification mechanisms. The BI-TSFID framework adopts a strategy of on-chain data summary storage and off-chain computation. This approach provides IoT with efficient and reliable data storage, reduces operational costs, and simplifies the verification process. This research has improved the data computation efficiency by refining the structure of the Merkle Tree and analyzed its optimal branch number. Additionally, the study introduces a sampling-based data integrity verification method that significantly reduces resource consumption during the verification process. Experimental results show that the solutions proposed in this paper effectively enhance the efficiency and security of IoT data management and provide valuable guidance for the theory and practice of real-time data storage and verification, further promoting the development and innovation in the related technological fields. |
| first_indexed | 2025-03-21T01:01:58Z |
| format | Article |
| id | doaj.art-b9d2557cea4a413086286b51d85dedda |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2025-03-21T01:01:58Z |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-b9d2557cea4a413086286b51d85dedda2024-08-03T04:24:17ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782024-07-01366102117Study on data storage and verification methods based on improved Merkle mountain range in IoT scenariosChufeng Liang0Junlang Zhang1Shansi Ma2Yu Zhou3Zhicheng Hong4Jiawen Fang5Yongzhang Zhou6Hua Tang7School of Computer Science, South China Normal University, Guangzhou 510000, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510000, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510000, ChinaSchool of Artificial Intelligence, South China Normal University, Foshan 528200, ChinaSchool of Artificial Intelligence, South China Normal University, Foshan 528200, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510000, ChinaCenter for Earth Environment & Resources, Sun Yat-sen University, Zhuhai 519000, China; Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Sun Yat-sen University, Zhuhai 519000, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510000, China; Corresponding author.In the context of the rapid development of Internet of Things (IoT) technology and the extensive proliferation of the global Internet, the authenticity of data has become a focal point of societal demand. It plays a decisive role in enhancing the quality of decision-making and operational efficiency. However, the storage and authenticity verification of large-scale IoT real-time data present unprecedented technical challenges. Faced with the inherent data security risks of traditional centralized cloud storage, blockchain technology reveals its unique potential for solutions with its inherent immutability and decentralization. Nevertheless, current blockchain-based data storage solutions are still restricted by high costs and inefficiency. To address these challenges, this paper innovatively proposes the BI-TSFID framework, which leverages the benefits of Ethereum and IPFS and optimizes the Merkle Tree structure and verification mechanisms. The BI-TSFID framework adopts a strategy of on-chain data summary storage and off-chain computation. This approach provides IoT with efficient and reliable data storage, reduces operational costs, and simplifies the verification process. This research has improved the data computation efficiency by refining the structure of the Merkle Tree and analyzed its optimal branch number. Additionally, the study introduces a sampling-based data integrity verification method that significantly reduces resource consumption during the verification process. Experimental results show that the solutions proposed in this paper effectively enhance the efficiency and security of IoT data management and provide valuable guidance for the theory and practice of real-time data storage and verification, further promoting the development and innovation in the related technological fields.http://www.sciencedirect.com/science/article/pii/S1319157824002064Trusted data storageData integrity verificationMerkle mountain rangeSampling verification methodEthereum and IPFSInternet of things(IoT) |
| spellingShingle | Chufeng Liang Junlang Zhang Shansi Ma Yu Zhou Zhicheng Hong Jiawen Fang Yongzhang Zhou Hua Tang Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios Journal of King Saud University: Computer and Information Sciences Trusted data storage Data integrity verification Merkle mountain range Sampling verification method Ethereum and IPFS Internet of things(IoT) |
| title | Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios |
| title_full | Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios |
| title_fullStr | Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios |
| title_full_unstemmed | Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios |
| title_short | Study on data storage and verification methods based on improved Merkle mountain range in IoT scenarios |
| title_sort | study on data storage and verification methods based on improved merkle mountain range in iot scenarios |
| topic | Trusted data storage Data integrity verification Merkle mountain range Sampling verification method Ethereum and IPFS Internet of things(IoT) |
| url | http://www.sciencedirect.com/science/article/pii/S1319157824002064 |
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