Predictive Estimation of the Optimal Signal Strength from Unmanned Aerial Vehicle over Internet of Things Using ANN

TL;DR

This paper introduces an ANN-based method to predict signal strength and channel conditions for UAV-IoT networks, aiming to improve connectivity, QoS, and reduce power consumption in challenging environments.

Contribution

It presents a novel application of ANN for predictive estimation of signal strength in UAV-IoT systems, considering environmental effects on signal propagation.

Findings

ANN accurately predicts signal strength under various atmospheric conditions

Signal distortion can be significantly reduced using the proposed method

Enhanced QoS and energy efficiency in UAV-IoT communication networks

Abstract

This paper proposes an intelligent technique for maximizing the network connectivity and provisioning desired quality of service (QoS) of integration of internet of things (IoT) and unmanned aerial vehicle (UAV). Prediction of the signal strength and fading channel conditions enable adaptive data transmission which turn enhances the QoS for the end users/ devices with reducing the power consumption for data transmissions. UAV is data gathering robot from the difficult or impossible area for humans to reach. Hence, Atmospheric dynamics and environment influence the signal strength during traveling in space among UAV, IoT devices, and humankind. Therefore, Signal moving from the smart UAV is sensitive to the effects of attenuation, reflection, diffraction, scattering, and shadowing. We analysis the ability ANN to predictively estimate the signal strength and channel propagation from the drone and physical medium parameters, using ANN. Moreover, the results show that the distortion of the signal can be reduced and enhanced significantly.