IoT System Integrating Unmanned Aerial Vehicles and LoRa Technology: A Performance Evaluation Study

J.-M. Martinez-Caro,M.-D. Cano

doi:10.1155/2019/4307925

Abstract

Nowadays, the popularity of the unmanned aerial vehicles (UAVs) is high, and it is expected that, in the next years, the implementation of UAVs in day-to-day service will be even greater. These new implementations make use of novel technologies encompassed under the term Internet of Things (IoT). One example of these technologies is Long-Range (LoRa), classified as a Low-Power Wide-Area Network (LPWAN) with low-cost, low-power consumption, large coverage area, and the possibility of a high number of connected devices. One fundamental part of a proper UAV-based IoT service deployment is performance evaluation. However, there is no standardized methodology for assessing the performance in these scenarios. This article presents a case study of an integrated UAV-LoRa system employed for air-quality monitoring. Each UAV is equipped with a set of sensors to measure several indicators of air pollution. In addition, each UAV also incorporates an embedded LoRa node for communication purposes. Given that mobility is key when evaluating the performance of these types of systems, we study eight different mobility models, focusing on the effect that the number of UAVs and their flying speed have on system performance. Through extensive simulations, performance is evaluated via multiple quality dimensions, encompassing the whole process from data acquisition to user experience. Results show that our performance evaluation methodology allows a complete understanding of the operation, and for this specific case study, the mobility model with the best performance is Pathway because the LoRa nodes are distributed and move orderly throughout the coverage area.

Highlights

Introduction eInternet of ings (IoT) is gaining momentum
Mobility models used for unmanned aerial vehicles (UAVs) are usually classi ed according to its nature and are either created for other networks and adapted to this new environment or speci cally introduced as mobility models [5]
We show the results obtained after extensive simulations. e eight mobility models have been tested with a variable number of UAVs/LoRa nodes {5, 10, 15, and 20} and different speeds {10, 25, and 50 km/h}

Summary

Introduction

Introduction eInternet of ings (IoT) is gaining momentum. IoT represents a heterogeneous network scenario with virtually unlimited uses [1, 2]: Smart-Homes, Smart-Cities, Industry 4.0, Smart-Grids, etc. UAVs can be classi ed in terms of several features such as size, communication capacity, ight mode, and wing types. UAVs can work isolated or in groups, giving rise to a new type of communication network called Flying Ad hoc Networks (FANETs) [4]. FANET can be seen as an extension of Mobile Ad hoc Networks (MANETs) with singular features in terms of mobility, topology, wave propagation, and energy constraints. In contrast to other communication networks as MANET or Vehicular Ad hoc Networks (VANETs), the UAVs move freely in the air, including a third axis (z) to the mobility of the devices (x, y) considered so far. Mobility models used for UAVs are usually classi ed according to its nature and are either created for other networks and adapted to this new environment or speci cally introduced as mobility models [5]. Numerous factors have a notable e ect on the trajectory of UAVs such as energy constraints, collision

Objectives

Methods

Results