UAV-Enabled IoT Networks: A SWIPT Energy Harvesting Architecture with Relay Support for Disaster Response

TL;DR

This paper proposes a UAV-enabled IoT disaster response network architecture utilizing SWIPT for energy harvesting, improving communication rates and coverage, and optimizing UAV energy and flight time for maximum user service.

Contribution

It introduces a novel SWIPT-based energy harvesting model for UAV relays in disaster response IoT networks, enhancing efficiency and coverage over traditional methods.

Findings

SWIPT significantly increases UAV energy efficiency.

Optimized network parameters improve user coverage and communication rates.

Simulation confirms the effectiveness of the proposed architecture.

Abstract

Due to the wide application of unmanned aerial vehicles (UAVs) as relays to establish Disaster Response Networks (DRNs), an effective model of energy harvesting (EH) and energy consumption for the UAV-aided Disaster Response Network (DRN) is rising to be a challenging issue. This is mainly manifest in Internet of Things (IoT) scenarios where multiple users are looking to communicate with the UAV. In this paper, the possibility of connecting an UAV with several users is investigated where the UAV as a relay receives data from a DRN and delivers to another network considering two IoT scenarios. The first scenario represents a conventional method with limited UAV energy where low communication rates and inadequate service coverage for all users are challenges. But in the second scenario, a Simultaneous Wireless Information and Power Transmission (SWIPT) technique is used to serve users. Considering potential limitations in transmission energy of users within disaster networks, the SWIPT technique is applied to maximize energy acquisition by the UAV, leading to improve the efficiency of the investigated scenario. Finally, the required energy of the UAV to serve the largest number of users in the shortest possible time is clarified. Furthermore, by Considering the relationship between energy and UAV flight time and defining the UAV flight time optimization problem, optimal network parameters are obtained. Simulation results show the effectiveness of the proposed scenario.