UAVs as Data Collectors in the WSNs: Investigating the Effects of Back-and-Forth and Spiral Coverage Paths in the Network Lifetime

Antunes, Jean Roberto; Brisolara, Lisane; Ferreira, Paulo R.

doi:10.1109/sbesc51047.2020.9277860

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Network lifetime is a key design consideration for battery-powered U-WSNs. There exist number of research focus on the lifetime maximization issues [49,50] or on the prediction of the remaining lifetime [51,52]. Other research focus on the energy-efficiency issues so as to extend the network lifetime [53][54][55][56], and the trajectory planning of the UAV is one of the most consideration among these schemes [30,57,58].…”

Section: Network Lifetimementioning

confidence: 99%

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Kacimi

Liu³

et al. 2022

Drones

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Although Unmanned Aerial Vehicles (UAVs)-aided wireless sensor networks (WSNs) have gained many applications, it is not for long that research works have been produced to define effective algorithms and protocols. In this article, we address the UAV-enabled WSN (U-WSN), explore the performance and the capability of the UAV, define the UAV functionalities as a communication node, and describe the architectures and the relevant typical technologies that emerge from this new paradigm. Furthermore, this article also identifies the main factors which influence the U-WSN design and analyzes the open issues and challenges in U-WSN. These insights may serve as motivations and guidelines for future designs of UAV-enabled WSNs.

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Section: Network Lifetimementioning

confidence: 99%

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Kacimi

Liu³

et al. 2022

Drones

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“…Coverage refers to the ability to reliably monitor areas or targets of interest using sensors, while connectivity involves transmitting sensor data from sensors to a central processing station [11]. In many applications, it is essential to ensure both coverage and connectivity because radio networks are responsible for continuously monitoring and analyzing targets or areas [12,13]. Moreover, mobile networks are dynamic, and a non-coverage area can cause network links to change, leading to collisions during network planning and packet transmission.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Nguyen,

Chien

et al. 2023

IJEETC

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Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.

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“…Los nodos sumideros se encargan de recopilar la información de los nodos sensores y luego enviarla hacia una estación base (Base Station, BS) o un centro de datos para su visualización y tratamiento [8]. Generalmente, las WSN se despliegan con base a una topología tipo malla, donde la información debe ser retransmitida por diferentes nodos antes de llegar al nodo sumidero [9]. Por lo tanto, se deben realizar estudios que permitan garantizar la conectividad entre los nodos sensores de la WSN para que la información logre llegar al nodo sumidero.…”

Section: Introductionunclassified

Red inalámbrica de sensores con topología clúster con nodo sumidero en vehículo aéreo no tripulado para la recolección de datos de las variables edafoclimaticas de un cultivo de banano

Camargo-Ariza,

Gasca-Mantilla,

Castrillo-Fernandez

2021

Respuestas

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En este trabajo se establecen los parámetros para la conectividad de una red inalámbrica de sensores con topología clúster con nodo sumidero en vehículo aéreo no tripulado para la recolección de datos de las variables edafoclimaticas de un cultivo de banano variedad Willams. Para esto se modelan las pérdidas del canal inalámbrico en la plantación a una frecuencia de 900 MHz, con el propósito de estimar la distancia entre los nodos de la red. Además, se estima la altura de sobrevuelo y la distancia de acercamiento del dron al nodo cabeza de clúster, empleando transmisores a 2400 MHz y antenas λ/4 con línea de vista. Se obtiene un modelo de pérdidas con una atenuación promedio del canal de 68.99 dB, un exponente de pérdidas de camino de 4.88 y una desviación estándar de 4.2 dB. Se determina una distancia entre nodos de 71 metros usando una sombra de 4.3 dB para una probabilidad de éxito del 85%. Se establece una altura de sobrevuelo de 40 metros con distancia de acercamiento al nodo cabeza de clúster de 20 metros. Se concluye que es posible implementar este tipo de red en esta plantación usando tecnología XBee para la comunicación entre los nodos de clúster y WiFi para conectar el nodo sumidero y los nodos cabeza de clúster.

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UAVs as Data Collectors in the WSNs: Investigating the Effects of Back-and-Forth and Spiral Coverage Paths in the Network Lifetime

Cited by 5 publications

References 13 publications

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Red inalámbrica de sensores con topología clúster con nodo sumidero en vehículo aéreo no tripulado para la recolección de datos de las variables edafoclimaticas de un cultivo de banano

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