TY - JOUR
T1 - Smart methodology for defence asset management in blockchain environment
AU - Aljumah, Abdullah
AU - Ahanger, Tariq Ahamed
AU - Ullah, Imdad
AU - Bhatia, Munish
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
PY - 2025/10
Y1 - 2025/10
N2 - Information and Communication Technology (ICT) has significantly transformed the defense sector, enhancing asset longevity and reducing operational costs. This cutting-edge research emphasizes the potential of Digital Twins (DTW) technology to provide effective solutions for improving defense asset management. This paper introduces an innovative mechanism for securing defense asset data through the incorporation of the Internet of Things (IoT), DTW technology, and blockchain technology. Specifically, a comprehensive context-aware framework for monitoring the operational status of defense assets is proposed. Additionally, an Artificial Intelligence-inspired Convolutional Neural Network (CNN) technique is suggested for sequentially processing asset data to analyze real-time anomalies in military equipment. The framework ensures data security by utilizing advanced blockchain features, particularly through a Reputation-based Byzantine Fault Tolerance (RBFT) method within the consortium network. The proposed technique has been validated through extensive experimental simulations, showing enhanced performance with a latency rate of 1.55 s, data processing cost of (O((η-1)logη)), and high accuracy in model testing, achieving specificity (91.72%), sensitivity (93.47%), precision (92.40%), and F-Measure (92.44%).
AB - Information and Communication Technology (ICT) has significantly transformed the defense sector, enhancing asset longevity and reducing operational costs. This cutting-edge research emphasizes the potential of Digital Twins (DTW) technology to provide effective solutions for improving defense asset management. This paper introduces an innovative mechanism for securing defense asset data through the incorporation of the Internet of Things (IoT), DTW technology, and blockchain technology. Specifically, a comprehensive context-aware framework for monitoring the operational status of defense assets is proposed. Additionally, an Artificial Intelligence-inspired Convolutional Neural Network (CNN) technique is suggested for sequentially processing asset data to analyze real-time anomalies in military equipment. The framework ensures data security by utilizing advanced blockchain features, particularly through a Reputation-based Byzantine Fault Tolerance (RBFT) method within the consortium network. The proposed technique has been validated through extensive experimental simulations, showing enhanced performance with a latency rate of 1.55 s, data processing cost of (O((η-1)logη)), and high accuracy in model testing, achieving specificity (91.72%), sensitivity (93.47%), precision (92.40%), and F-Measure (92.44%).
KW - Blockchain
KW - Defence industry
KW - Digital twin
KW - Smart asset monitoring
UR - https://www.scopus.com/pages/publications/105015433330
U2 - 10.1007/s10586-025-05467-x
DO - 10.1007/s10586-025-05467-x
M3 - Article
AN - SCOPUS:105015433330
SN - 1386-7857
VL - 28
JO - Cluster Computing
JF - Cluster Computing
IS - 11
M1 - 690
ER -