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UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 22984

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Prof. Dr. Maurizio Magarini

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Guest Editor

Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
Interests: advanced coding and modulation schemes for wireless communication; channel equalization and estimation; Interference management; alignment; and cancellation in wireless networks; massive MIMO for mmWaves;UAV-based communications and networks; molecular communications

Dr. Sudheesh Puthenveettil Gopi

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Guest Editor

Department of Electronics & Communication Engineering, Manipal Institute of Technology, Manipal 576104, India
Interests: machine learning in wireless communication; joint optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The integration of airborne and terrestrial wireless communications is a new emerging paradigm that is gaining interest from academia and industry due to the need for potential increase of coverage, capacity, delay, and so on. Unmanned aerial vehicles (UAVs), acting as aerial base stations (ABS), are considered as the key enabling technology for such an integration scenario. Depending on their altitude, UAVs can be classified into high-altitude platforms (HAPs) and low-altitude platforms (LAPs). Networked UAVs are used in various fronts of our day to day life. By fitting them in a cellular connected UAV or UAV assisted communication-based architecture, UAVs can be used in wide variety of applications. Apart from being used as am ABS and express cargo delivery platform, which is revolutionizing present-day technology, various other applications include surveillance, remote sensing, search and rescue, aerial photography, crop surveys, on-demand communications, etc. In these scenarios, the main role of UAVs is to act as data collectors for wireless sensor networks (WSNs). The aim of this Special Issue is to attract original and unpublished research contributions advancing the frontiers on the use of UAVs for the development of vertical heterogeneous WSNs and related research, technologies, and applications.

Dr. Maurizio Magarini
Dr. Sudheesh Puthenveettil Gopi
Guest Editors

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Keywords

The scope of the Special Issue includes (but is not limited to) the following broad-scope research areas:

Vertical heterogeneous WSN
UAV-based IoT
UAV-assisted SWIPT in IoT
UAV-WSN systems for large area monitoring, exploration, and surveillance
UAV-based mission critical WSN
UAV-aided information dissemination and data collection
UAV-aided relaying in WSN
Dynamic routing and energy efficiency for UAVs supporting WSN
UAV Communications for 5G and beyond
Flying ubiquitous sensor networks
FANET (flying ad-hoc network)
FANET testbed results
FANET for emergency applications

Published Papers (5 papers)

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Research

27 pages, 9143 KiB

Open AccessArticle

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

by Mehran Behjati, Aishah Binti Mohd Noh, Haider A. H. Alobaidy, Muhammad Aidiel Zulkifley, Rosdiadee Nordin and Nor Fadzilah Abdullah

Sensors 2021, 21(15), 5044; https://doi.org/10.3390/s21155044 - 26 Jul 2021

Cited by 55 | Viewed by 7851

Abstract

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN^®) technology, a multi-channel LoRaWAN^® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN^®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN^® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case). Full article

(This article belongs to the Special Issue UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications)

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22 pages, 880 KiB

Open AccessArticle

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

by Hassan Ishtiaq Minhas, Rizwan Ahmad, Waqas Ahmed, Maham Waheed, Muhammad Mahtab Alam and Sufi Tabassum Gul

Sensors 2021, 21(12), 4121; https://doi.org/10.3390/s21124121 - 15 Jun 2021

Cited by 15 | Viewed by 2666

Abstract

In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle- (UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately

42 %

in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by

27 %

using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different. Full article

(This article belongs to the Special Issue UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications)

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14 pages, 1009 KiB

Open AccessArticle

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

by Azade Fotouhi, Ming Ding and Mahbub Hassan

Sensors 2021, 21(6), 1960; https://doi.org/10.3390/s21061960 - 11 Mar 2021

Cited by 10 | Viewed by 2292

Abstract

In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments. Full article

(This article belongs to the Special Issue UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications)

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27 pages, 8098 KiB

Open AccessArticle

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

by Jie Hu, Tuan Wang, Jiacheng Yang, Yubin Lan, Shilei Lv and Yali Zhang

Sensors 2020, 20(19), 5473; https://doi.org/10.3390/s20195473 - 24 Sep 2020

Cited by 13 | Viewed by 2895

Abstract

Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV. Full article

(This article belongs to the Special Issue UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications)

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16 pages, 5806 KiB

Open AccessArticle

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

by Yahui Guo, Hanxi Wang, Zhaofei Wu, Shuxin Wang, Hongyong Sun, J. Senthilnath, Jingzhe Wang, Christopher Robin Bryant and Yongshuo Fu

Sensors 2020, 20(18), 5055; https://doi.org/10.3390/s20185055 - 5 Sep 2020

Cited by 56 | Viewed by 5534

Abstract

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R²s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions. Full article

(This article belongs to the Special Issue UAV-Based Wireless Sensor Networks Systems: Research, Technologies, and Applications)

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