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Bluetooth (BT) mesh networks are used in several ad hoc network communication scenarios, including natural disasters, calamities, war zones, etc. However, there are situations where Bluetooth mesh networks are misused by anti-social elements during riots etc. As of now there no such research article that showcases detection of BT mesh networks using machine learning tools. Therefore, there is a need to build novel methods for detecting the very presence of Bluetooth mesh networks to aid the law enforcement agencies. This paper contributes to building a new dataset for the aid of machine learning tools to detect the presence of Bluetooth mesh networks. The dataset is extracted from a simulated Bluetooth mesh network with 52 BT network traffic attributes, using the Bridgefy application. Further, the dataset is optimized by identifying prime features, using label encoding, one-hot encoding, correlation analysis and evaluated by various Machine Learning (ML) tools. An accuracy of 99.6% is achieved after optimizing the feature set. [ABSTRACT FROM AUTHOR]

In this communication, a planar dual port multiple input multiple output antenna of size 1.2λ0 × 0.6λ0 × 0.008λ0 with LHCP/RHCP features is reported for the fifth-generation new radio n77/n78 sub-6 GHz wireless applications band. The single unit of the proposed design consists of a modified L-shape rectangular radiator with Z-shape slot loaded DGS. The defected ground structure is optimized through machine learning algorithms to achieve the maximum ARBW (output) by Right Shifting (RS) and left shifting (LS) the DGS and obtaining input features. The performance metric for ANN with ADAM optimizer was found to be optimal with MSE and R2 of 0.99 and 0.82, respectively. ANNs can leverage gradient information to guide the optimization process. This enables faster convergence towards optimal solutions compared to popular GAs and PSO, which are often gradient-free optimization methods. The MIMO configuration is achieved by creating a mirror image of the single unit about the x-axis. The salient features of the proposed design are (a) Impedance bandwidth (IBW) of 3.0–4.2 GHz covering the n77/n78 band, (b) 3-dB axial ratio bandwidth (ARBW) of the 2.6–3.9 GHz (c) Port-1 is generating RHCP while Port-2 is generating LHCP, results in polarization diversity. Different diversity performance parameters (ECC < 0.005, DG ~ 9.99 dB, and MEG < 3 dB) are in the optimum range confirming the proposed configuration as a suitable design for a MIMO radiator. [ABSTRACT FROM AUTHOR]