The TraVel framework enhances secure IoT data management through blockchain and transfer learning techniques.
TraVel is introduced as a blockchain and transfer learning-based framework aimed at secure IoT data management. It utilizes decentralized storage through IPFS and integrates with a private Ethereum blockchain to improve data integrity.
The research is conducted by multiple authors and affiliated institutions focusing on blockchain technology and IoT security.
Researchers outline their findings and introduce TraVel as part of the study published recently, but no specific date is provided.
The research discusses smart home (SH_m) applications and is evaluated on blockchain and IPFS systems within controlled simulations.
Given the increasing threats to IoT data from centralized systems, TraVel aims to offer enhanced security and integrity for IoT data, particularly as the estimate suggests 41.6 billion devices will be producing vast quantities of data by 2025.
The framework employs Ethereum's smart contracts to facilitate access controls and utilizes transfer learning models for detecting and filtering malicious data before storage.
TraVel shows effective performance evaluation through simulations conducted on REMIX IDE and InterPlanetary File System, addressing challenges of data integrity and system reliability.
“The proposed TraVel scheme ensures the delivery of the data stream in a secure, decentralized, and reliable way.”
“Smart home systems are vulnerable to various security breaches.”
“The efficacy of the TL-based learning model is evaluated on parameters, for example, accuracy, loss, recall, precision, F1-score, and AUC score.”
1. Introduction: Introduce the significance of secure IoT data management and present the TraVel framework, emphasizing its aims to address security issues through decentralized systems.
2. Background: Discuss the current vulnerabilities faced by smart home IoT systems and the inadequacies of conventional security methods, framing the necessity for solutions like blockchain and transfer learning.
3. Methodology and Discovery: Elaborate on how TraVel utilizes blockchain and transfer learning models to secure data, describe the implementation of smart contracts, and explain the use of IPFS for data storage.
4. Findings and Implications: Present the core results derived from the evaluation of TraVel, emphasizing the framework’s performance metrics, reliability, and scalability benefits compared to traditional methods.
5. Conclusion: Summarize the key takeaways from the research, its significance for the future of IoT security, and propose directions for future research and practical applications of TraVel.