Editorial
A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks
@ARTICLE{10.4108/eetiot.7740, author={Mohd. Asif Gandhi and Atul D Narkhede and N. Noor Alleema and M.P. Indumathi and Deepak Sundrani and R. Sasikala }, title={A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks}, journal={EAI Endorsed Transactions on Internet of Things}, volume={11}, number={1}, publisher={EAI}, journal_a={IOT}, year={2025}, month={4}, keywords={Physics Informed Neural Networks, Smart Cities, SC, Block Chain, Neural Networks, Kernal Principal Component Analysis, KPCA, Feature Selection, Normalization, Privacy-Preserving and Secure Framework, PPSF}, doi={10.4108/eetiot.7740} }- Mohd. Asif Gandhi
Atul D Narkhede
N. Noor Alleema
M.P. Indumathi
Deepak Sundrani
R. Sasikala
Year: 2025
A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks
IOT
EAI
DOI: 10.4108/eetiot.7740
Abstract
The massive scale and extensive implementation of the Internet of Things (IoT) makes it difficult to provide secure and private communications over it. Privacy and decentralisation have been made easier using blockchain technology. Unfortunately, these solutions aren't practical for the majority of IoT uses because of how much time and computing power they require. Secure and private IoT that makes efficient use of available resources is proposed in this study. With the use of Physics Informed Neural Networks, the technique takes advantage of the computing power available in IoT settings like smart cities. This solution examines the reliability of the blockchain-based Smart Cities Architecture with respect to accessibility, privacy, and integrity. When weighed against the security and privacy advantages our system offers, our simulation findings reveal that the overheads (distribution, processing time, and energy usage) are negligible.
Copyright © 2025 Mohd Asif Gandhi et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
