Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV-HOP algorithm for DDoS mitigation and node localization

• Published in: EURASIP journal on wireless communications and networking Vol. 2025; no. 1; pp. 46 - 39
• Main Authors: Kaur, Bhupinder, Prashar, Deepak, Mrsic, Leo, Almogren, Ahmad, Rehman, Ateeq Ur, Altameem, Ayman, Hussen, Seada
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 19.06.2025; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Blockchain; Blockchain ledger; Communications Engineering; Cybersecurity; Data transmission; Deep learning; Denial of service attacks; DV-HOP algorithm; Energy consumption; Energy utilization; Engineering; Failure rates; Information Systems Applications (incl.Internet); Localization; Network security; Networks; Nodes; Reliable network devices; Signal,Image and Speech Processing; Wireless network devices; Wireless networks; Wireless sensor networks; Wireless network devices; Reliable network devices; Blockchain ledger; DV-HOP algorithm
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-025-02465-w

Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.