Secure remote sensing data with blockchain distributed ledger technology: A solution for smart cities
Particularly in the context of smart cities, remote sensing data (RSD) has emerged as one of the hottest study topics in information and communication technology (ICT) today. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to solve a number of issues, i...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute Of Electrical And Electronics Engineers Inc.
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27682/2/0248719072024135142.PDF http://eprints.utem.edu.my/id/eprint/27682/ https://ieeexplore.ieee.org/document/10530997 |
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| Summary: | Particularly in the context of smart cities, remote sensing data (RSD) has emerged as one of the hottest study topics in information and communication technology (ICT) today. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to solve a number of issues, including automation, control access, optimization, monitoring, and management. Simultaneously, there are significant issues with the design and development of the process hierarchy, including inadequate training records, centralized architecture, data privacy protection, and overall resource consumption restrictions. The development of Distributed Ledger Technology (DLT), on the other hand, provides a decentralized infrastructure that allows systems to eliminate centralized data-sharing procedures of smart cities while transferring from network node to network node, and third-party access control solves machine learning issues. To process an ideal data delivery mechanism for the smart cities analytical model, the paper employs Partial Swam Optimization (POS) in conjunction with a secure blockchain distributed consortium network. This work makes three contributions. Firstly, it offers a safe transmission method that combines blockchain and machine learning to optimize the path for reliable data delivery across secure channels. Second, neighborhood encryption sequences are carried out using NuCypher proxy re-encryption-enabled value encryption, a public key cryptographic approach that avoids cypher conversion. Third, Artificial Neural Networks (ANNs) can solve the data deliverance classification problem in smart cities by optimizing record management and preservation. |
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