Trajectory Optimization of UAV for Efficient Data Collection from Wireless Sensor Networks

Luo, Chuanwen; Wu, Lidong; Chen, Wenping; Wang, Yongcai; Li, Deying; Wu, Weili

doi:10.1007/978-3-030-27195-4_21

Cited by 2 publications

(1 citation statement)

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“…Their simulations show that a drone can collect the data produced by 80 sensors in a day (5760 bytes) in an area of 1.5km 2 without packet collisions. In [25], [26] the authors assume that sensors are deployed randomly on a field and the data are preliminary collected by a set of cluster heads, it is also assumed that the communication area of different cluster heads do not overlap. The problem of computing the optimal (minimum latency) trajectory for a drone through all cluster heads, in order to collect all data from them, has been studied.…”

Section: Introductionmentioning

confidence: 99%

Collection of Data With Drones in Precision Agriculture: Analytical Model and LoRa Case Study

Caruso

Chessa

Escolar

et al. 2021

IEEE Internet Things J.

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Unmanned Aerial Vehicles (UAVs) are autonomous devices employed as data collectors in precision agriculture to support a large number of applications. These UAVs gather data from on-the-ground wireless sensor networks, especially in scenarios that lack any kind of fixed communication infrastructure or where the available infrastructure does not fit the application requirements. Sensors on the ground can store sensing data, and in scenarios that do not require a real-time observation and analysis of the data, like in smart farming, a drone can be used maybe once or two times each day to collect and report the data to a command-and-control center that use them directly, without any other infrastructure (cloud or edges). In this paper, we study analytically how close the drone, that uses a LoRa radio, needs to fly over the sensors to collect data with a given quality of data collection. This can be used to properly spacing the sensor on the field at deployment time, to select among different type of drones, and to properly solve some tradeoff related to field size vs autonomy of the drones and the path used by the latter when collecting the data.

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Section: Introductionmentioning

confidence: 99%