Minimization of the Worst-Case Average Energy Consumption in UAV-Assisted IoT Networks

TL;DR

This paper proposes an optimization framework for UAV-assisted IoT networks to minimize the worst-case average energy consumption of devices by optimizing UAV placement, antenna beamwidth, and device transmit power, demonstrating improved energy efficiency.

Contribution

It introduces a novel optimization approach using geometric programming to minimize the worst-case energy consumption in UAV-assisted IoT networks, considering realistic QoS constraints.

Findings

Network planning reduces worst-case energy consumption compared to random deployment.

UAV hovering height significantly impacts energy efficiency for wider areas.

The proposed algorithm outperforms benchmarks in energy minimization and convergence speed.

Abstract

The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of Unmanned Aerial Vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-Signal-to-average-Interference-plus-Noise Ratio ($\bar{\text{S}}\overline{\text{IN}}\text{R}$) Quality of Service (QoS) constraints. We minimize the worst-case average energy consumption of the latter, thus, targeting the fairest allocation of the energy resources. The problem is non-convex and highly non-linear; therefore, we re-formulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst-case average energy consumption. Furthermore, we show that the target $\bar{\text{S}}\overline{\text{IN}}\text{R}$ is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.