UAV-Enabled Software Defined Data Collection from an Adaptive WSN

Abstract

Unmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.