vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff55cd320000004769130001000100 As a distributed and decentralized ledger that ensures secure and transparent transactions, blockchain technology has attracted considerable interest. In the context of wireless sensor networks (WSNs), where nodes with limited resources conduct transactions, ensuring efficient and trustworthy validation becomes a challenge. Using random forests, this paper proposes a novel method for enhancing blockchain transaction validation in WSNs. The proposed method enhances the accuracy and efficiency of transaction validation in WSNs by leveraging the ensemble-learning capabilities of random forests. The random forests model is trained with transaction content, originating node information, and network metrics extracted from WSN transactions. Experimental results indicate that the proposed method improves transaction validation precision and decreases validation time in comparison to conventional methods. In addition, the random forests model is resistant to multiple types of attacks, assuring the security and integrity of WSN transactions. The results demonstrate that random forests are a promising technique for improving blockchain transaction validation in wireless sensor networks.
T. Gobinath1, Sanjay Kumar Sonkar2, Vinod N. Alone3, C. Thiripurasundari4
Chettinad College of Engineering and Technology, India1, Kamla Nehru Institute of Physical and Social Sciences, India2, Vasantdada Patil Pratishthan College of Engineering and Visual Arts, India3, KSK college of Engineering and Technology, India4
Blockchain, Wireless Sensor Networks, Transaction Validation, Random Forests, Ensemble Learning, Resource-Constrained Nodes, Security, Integrity, Efficiency, Decentralized Ledger, Ensemble Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 14 , Issue: 2 , Pages: 2901 - 2906 )
Date of Publication :
June 2023
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702
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