Blockchain Inspired Intruder UAV Localization Using Lightweight CNN for Internet of Battlefield Things

【Author】 Golam, Mohtasin; Akter, Rubina; Naufal, Revin; Van-Sang Doan; Lee, Jae-Min; Kim, Dong-Seong

【Source】2022 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM)

【影响因子】

【Abstract】On the Internet of Battlefield Things (IoBT), unmanned aerial vehicles (UAVs) provide significant operational advantages. However, the exploitation of the UAV by an untrustworthy entity might lead to security violations or possibly the destruction of crucial IoBT network functionality. The IoBT system has substantial issues related to data tampering and fabrication through illegal access. This paper proposes the use of an intelligent architecture called IoBT-Net, which is built on a convolution neural network (CNN) and connected with blockchain technology, to identify and trace illicit UAV in the IoBT system. Data storage on the blockchain ledger is protected from unauthorized access, data tampering, and invasions. Conveniently, this paper presents a low complexity and robustly performed CNN called LRCANet to estimate AOA for object localization. The proposed LRCANet is efficiently designed with two core modules, called GFPU and stacks, which are cleverly organized with regular and point convolution layers, a max pool layer, and a ReLU layer associated with residual connectivity. Furthermore, the effectiveness of LRCANET is evaluated by various network and array configurations, RMSE, and compared with the accuracy and complexity of the existing state-of-theart. Additionally, the implementation of tailored drone-based consensus is evaluated in terms of three major classes and compared with the other existing consensus.

【Keywords】Blockchain; convolution neural network (CNN); Internet of Battlefield Things (IoBT); mobile edge server (MES); Unmanned aerial vehicle (UAV) localization

【发表时间】2022

【收录时间】2023-05-31

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-军事领域