Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

TL;DR

This paper presents an energy-efficient UAV-based data collection method for IoT sensor networks with time constraints, optimizing cluster head placement and UAV trajectories to minimize energy use.

Contribution

It introduces a two-step approach for optimal cluster head placement and UAV routing, improving energy efficiency in deadline-constrained IoT data collection.

Findings

CH placement near dockstations reduces energy consumption

Tabu search outperforms other algorithms in UAV trajectory planning

Battery capacity and deadlines significantly affect energy use and UAV deployment

Abstract

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.