Joint Power Allocation and Placement Scheme for UAV-assisted IoT with QoS Guarantee

TL;DR

This work tackles deploying multiple UAVs to collect IoT data in disaster or remote regions under diverse QoS demands $r_k$. It introduces QoS-demand based power allocation (QDPA) to balance transmit power across IDs and equalize a common coverage radius $d_{sv}$, a data-rate-maximizing placement (DRMP) strategy for a single UAV, and a joint power allocation and placement (JPAP) scheme that sequentially covers all IDs with multiple UAVs to maximize the average UAV data rate $rac{1}{N}\sum_{n\in\mathcal{N}} \widehat{R}_n$. JPAP leverages QDPA for power allocation, DRMP for placement, and a boundary-driven coverage order to minimize UAV count while ensuring QoS guarantees. Simulation results show that JPAP outperforms EPA-based JPAP, K-means, and spiral deployments in both average UAV data rate and required UAV numbers, demonstrating practical benefits for large-scale IoT coverage in challenging environments.

Abstract

In the disaster and remote regions, unmanned aerial vehicles (UAVs) can assist the data acquisition for Internet of Things (IoT). How to cover massive IoT devices (IDs), which require diverse quality-of-service (QoS), is a crucial challenge. For UAV-assisted IoT, this paper studies the deployment scheme with QoS guarantee to place multiple UAVs for covering all ground IDs and maximizing the average data rate of UAVs. First, for the ground ID, we propose the QoS demand based power allocation (QDPA) algorithm to solve the diversity of QoS with respect to data rate demand. Then, the data rate maximization placement (DRMP) algorithm is proposed to optimize the placement of single UAV. Finally, based on QDPA and DRMP algorithms, we propose the joint power allocation and placement (JPAP) scheme with QoS guarantee, which can cover massive IDs, to deploy multiple UAVs for maximizing the average UAV data rate. Simulation experiments are conducted to verify the superiority of our proposed JPAP scheme, which can minimize the UAV number and maximize the average data rate of UAVs.

Figures (3)

• Figure 1: Average UAV data rate versus ID number
• Figure 2: Demonstration of proposed JPAP scheme and EPA algorithm based JPAP scheme
• Figure 3: UAV number under various ID number