Article: Blockchain-empowered secure localisation scheme in WSN using trust assessment and deep adaptive extreme learning Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2025 Vol.29 No.3 pp.213 - 231 Abstract: Wireless Sensor Networks (WSN) are one of the intrinsic factors of the current prevalent and universal computing. Moreover, the localising of the nodes in WSN becomes a challenging issue and also it needs more concentration on the trust of beacon nodes. Thus, an innovative deep learning-based localisation in WSN using blockchain technology is suggested. Here, the beacon node's trust value is calculated by Deep Adaptive Extreme Learning Network (DAELNet). The attributes in DEL are optimised by the Enhanced Migration Algorithm (EMA). The trusted nodes utilise the blockchain to secure information. Further, the localisation process in WSN is optimally performed by EMA. The location of target nodes is determined by an optimisation algorithm concerning beacon nodes. Finally, the validation process is performed. The numerical findings of the developed model achieve 93% and 94% in terms of accuracy and sensitivity measures. From the validation, the developed model shows enriched performance over existing algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: Blockchain-empowered secure localisation scheme in WSN using trust assessment and deep adaptive extreme learning

Authors: Moorthy Agoramoorthy; S. Maheswari; A. Hemlathadhevi; Hari Kumar Palani

Addresses: Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India ' Computer Science and Engineering, Easwari Engineering College (Ramapuram), Chennai, Tamil Nadu, India ' Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, MIT School of Computing, MIT Art, Design and Technology University, Pune, Maharashtra, India

Abstract: Wireless Sensor Networks (WSN) are one of the intrinsic factors of the current prevalent and universal computing. Moreover, the localising of the nodes in WSN becomes a challenging issue and also it needs more concentration on the trust of beacon nodes. Thus, an innovative deep learning-based localisation in WSN using blockchain technology is suggested. Here, the beacon node's trust value is calculated by Deep Adaptive Extreme Learning Network (DAELNet). The attributes in DEL are optimised by the Enhanced Migration Algorithm (EMA). The trusted nodes utilise the blockchain to secure information. Further, the localisation process in WSN is optimally performed by EMA. The location of target nodes is determined by an optimisation algorithm concerning beacon nodes. Finally, the validation process is performed. The numerical findings of the developed model achieve 93% and 94% in terms of accuracy and sensitivity measures. From the validation, the developed model shows enriched performance over existing algorithms.

Keywords: decision-making; big data; enhanced honey badger algorithm; adaptive cascaded long-short term memory and auto-encoder; map-reduce framework.

DOI: 10.1504/IJWMC.2025.148585

International Journal of Wireless and Mobile Computing, 2025 Vol.29 No.3, pp.213 - 231

Received: 22 Nov 2023
Accepted: 31 Aug 2024
Published online: 14 Sep 2025 *

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Title: Blockchain-empowered secure localisation scheme in WSN using trust assessment and deep adaptive extreme learning

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