Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unified Clustering and Blockchain-Optimized Neural Networks

Wireless Sensor Network, through the Internet of Things (IoT), has contributed immensely by enhancing several sectors by linking numerous devices. These networks help collect and exchange important information which is crucial for various platforms. Nevertheless, accurate node localization of these networks remains pivotal for ensuring both the data and network integrity. The dynamic environment of IoT networks means variable network conditions, node density and susceptibility to malicious acts, which creates difficulties in achieving accurate and reliable localization. Existing methods used to perform node localization have some drawbacks such as poor accuracy, expensive computational complexity and no protection mechanisms from malicious attacks. To tackle these challenges, this research presents a novel methodology, Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unification of Clustering and Blockchain-Optimized Neural Networks. Our proposed method combines several state-of-art methods to improve the node localization. In more detail, the novel algorithms employed are Ski-Opt to promote clustering efficiency; ProofWrk to boost the security of the blockchains utilized C2ANet for enhancing the prediction ability significantly and OsOpt for accurate and safe node positioning. This integrated solution is highly effective as compared to the current methods because of these issues. Our approach improves the clustering accuracy by 95%, and has a 98.7% prediction accuracy rate, which is quite impressive, and a localization accuracy rate of 97.5%. This method not only outperforms the conventional methods but also raises the benchmark of safe and accurate node localization in IoT-based WSNs. Our methodology's success reliably addresses the difficulties of IoT networks, employing notable improvements in both rate and protection.

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Published
2024-12-12
DOI
https://doi.org/10.1109/icuis64676.2024.10866817
Primary Topic
Security in Wireless Sensor Networks
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article
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Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unified Clustering and Blockchain-Optimized Neural Networks

K. Suresh, Rania M. Hathout
Security in Wireless Sensor Networks
article

Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unified Clustering and Blockchain-Optimized Neural Networks

K. Suresh, Rania M. Hathout
article en

Abstract

Wireless Sensor Network, through the Internet of Things (IoT), has contributed immensely by enhancing several sectors by linking numerous devices. These networks help collect and exchange important information which is crucial for various platforms. Nevertheless, accurate node localization of these networks remains pivotal for ensuring both the data and network integrity. The dynamic environment of IoT networks means variable network conditions, node density and susceptibility to malicious acts, which creates difficulties in achieving accurate and reliable localization. Existing methods used to perform node localization have some drawbacks such as poor accuracy, expensive computational complexity and no protection mechanisms from malicious attacks. To tackle these challenges, this research presents a novel methodology, Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unification of Clustering and Blockchain-Optimized Neural Networks. Our proposed method combines several state-of-art methods to improve the node localization. In more detail, the novel algorithms employed are Ski-Opt to promote clustering efficiency; ProofWrk to boost the security of the blockchains utilized C2ANet for enhancing the prediction ability significantly and OsOpt for accurate and safe node positioning. This integrated solution is highly effective as compared to the current methods because of these issues. Our approach improves the clustering accuracy by 95%, and has a 98.7% prediction accuracy rate, which is quite impressive, and a localization accuracy rate of 97.5%. This method not only outperforms the conventional methods but also raises the benchmark of safe and accurate node localization in IoT-based WSNs. Our methodology's success reliably addresses the difficulties of IoT networks, employing notable improvements in both rate and protection.

SRM Institute of Science and Technology (IN)
Affordable and clean energy
Openalex Percentile: Top 68%
Security in Wireless Sensor Networks
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Secure Node Localization in IoT-Based Wireless Sensor Networks Using SkiProofAdaptiveNet-OsOpt: Unified Clustering and Blockchain-Optimized Neural Networks — K. Suresh, Rania M. Hathout · (2024) | TGRS Research Map | TGRS