A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things

Abstract

Owing to constant progress of wireless communications, the Unmanned Aerial Vehicles (UAVs) routing networks (UAVs-RN) under mobile Internet of Things (MIoT) have been prevalent tools to deal with natural emergencies. But the achievement of effective responses and proper utility, still remains a challenging task. It is required to analyze multi-source data of UAVs-RN, so that optimal planning schemes under MIoT can be found. To bridge such gap, this work mainly takes three aspects of factors into consideration: rapid response, finite budget and uncertain signal fading. Accordingly, a data-driven intelligent planning model for UAVs-RN under MIoT, is put forward in this paper. Data about wildfire happened in local areas of Australia is selected to build experimental scenarios. And two kinds of UAVs, Surveillance and Situational Awareness drones and Radio Repeater drones, are considered in this study. Firstly, the source data is visualized and the internal trend is analyzed to verify true validity. Then, a multi-objective planning model is accordingly established to aggregate multi-source data. At last, a case study is deeply investigated on real-world data to assess the proposed approach and suggest feasible planning schemes.