ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Enhancing Blockchain Transaction Validation in Wireless Sensor Networks Using Random Forests


Affiliations
1 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, India
2 Department of Computer Science and Engineering, Kamla Nehru Institute of Physical and Social Sciences, India
3 Department of Computer Engineering, Vasantdada Patil Pratishthan College of Engineering and Visual Arts, India
4 Department of Electronics and Communication Engineering, KSK college of Engineering and Technology, India
     

   Subscribe/Renew Journal


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.

Keywords

Blockchain, Wireless Sensor Networks, Transaction Validation, Random Forests, Ensemble Learning, Resource-Constrained Nodes, Security, Integrity, Efficiency, Decentralized Ledger, Ensemble Learning.
Subscription Login to verify subscription
User
Notifications
Font Size

  • I.A. Omar, R. Jayaraman, K. Salah and S. Ellahham, “Applications of Blockchain Technology in Clinical Trials: Review and Open Challenges”, Arabian Journal for Science and Engineering, Vol. 46, No. 4, pp. 3001-3015, 2021.

  • T.K. Agrawal, V. Kumar and Y. Chen, “Blockchain-Based Framework for Supply Chain Traceability: A Case Example of Textile and Clothing Industry”, Computers and Industrial Engineering, Vol. 154, pp. 1-12, 2021.

  • I. Karamitsos, M. Papadaki and N.B. Al Barghuthi, “Design of the Blockchain Smart Contract: A Use Case for Real Estate”, Journal of Information Security, Vol. 9, No. 3, pp. 177-187, 2018.

  • H. Rathore, A. Mohamed and M. Guizani, “A Survey of Blockchain Enabled Cyber-Physical Systems”, Sensors, Vol. 20, No. 1, pp. 282-291, 2020.

  • J. Li, “Data Transmission Scheme Considering Block Failure for Blockchain”, Wireless Personal Communications, Vol. 103, No. 1, pp. 179-194, 2018.

  • S.R. Maskey, S. Badsha, S. Sengupta and I. Khalil, “ALICIA: Applied Intelligence in Blockchain based VANET: Accident Validation as a Case Study”, Information Processing and Management, Vol. 58, No. 3, pp. 1-12, 2021.

  • K. Praghash, G. Peter and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Springer, 2023.

  • K. Praghash, G. Peter and A.A. Stonier, “Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning”, Springer, 2023.

  • R. Indhumathi, K. Amuthabala, G. Kiruthiga and A. Pandey, “Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System”, Wireless Personal Communications, Vol. 128, No. 4, pp. 2811-2829, 2023.

  • K. Suresh Kumar, V.A. Athavale and V. Saravanan, “A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis”, Journal of Food Quality, Vol. 2022, pp. 1-13, 2022.

  • M. Jagdish and V. Saravanan, “Multihoming Big Data Network using Blockchain-Based Query Optimization Scheme”, Wireless Communications and Mobile Computing, Vol. 2022, pp. 1-15, 2022.

  • B. Gobinathan, M.A. Mukunthan, S. Surendran, and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-7, 2021.

  • J. Singh, J. Jegathesh Amalraj and S. Sakthivel, “Energy-Efficient Clustering and Routing Algorithm using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-16, 2022.

  • A. Bhandari and F. Kamalov, “Machine Learning and Blockchain Integration for Security Applications”, Proceedings of International Conference on Big Data Analytics and Intelligent Systems for Cyber Threat Intelligence, pp. 129-173, 2023.

  • K.T. Selvi and R. Thamilselvan, “Privacy-Preserving Healthcare Informatics using Federated Learning and Blockchain”, Proceedings of International Conference on Healthcare 4.0, pp. 1-26, 2022.

  • R. Chaganti and V. Ravi, “A Survey on Blockchain Solutions in DDoS Attacks Mitigation: Techniques, Open Challenges and Future Directions”, Computer Communications, Vol. 78, pp. 1-13, 2022.


Abstract Views: 205

PDF Views: 0




  • Enhancing Blockchain Transaction Validation in Wireless Sensor Networks Using Random Forests

Abstract Views: 205  |  PDF Views: 0

Authors

T. Gobinath
Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, India
Sanjay Kumar Sonkar
Department of Computer Science and Engineering, Kamla Nehru Institute of Physical and Social Sciences, India
Vinod N. Alone
Department of Computer Engineering, Vasantdada Patil Pratishthan College of Engineering and Visual Arts, India
C. Thiripurasundari
Department of Electronics and Communication Engineering, KSK college of Engineering and Technology, India

Abstract


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.

Keywords


Blockchain, Wireless Sensor Networks, Transaction Validation, Random Forests, Ensemble Learning, Resource-Constrained Nodes, Security, Integrity, Efficiency, Decentralized Ledger, Ensemble Learning.

References