kumoh national institute of technology
Networked Systems Lab.

Rubina Akter, Mohtasin Golam, Van-Sang Doang, Jae-Min Lee and Dong-Seong Kim,"IoMT-Net: Blockchain Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things", IEEE Internet Things of Journal, (Early Access), May 2022, doi: 10.1109/JIOT.2022.3176310
By : Rubina
Date : 2022-05-16
Views : 42

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet-of-military-things (IoMT). However, the untrusted partys misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a
blockchain integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN network analyzed the radio frequency signal sent by the antenna array element to determine
the direction of arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to processes the received signal array through IoMT-Net. Moreover, the CNN network is designed with two different functional modules, such as the resource accuracy trade-off (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depth-wise and grouped convolution layers. These
sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the sub-units of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10 dB SNR) and outperforms
other state-of-the-art models.