Abstract
Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.
Highlights
Farmland is a basic resource required for ensuring human survival
High-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data obtained by wireless sensor networks (WSNs), like soil moisture data obtained from three different depths, meteorological data and so on, are two key monitoring methods with substantial potential for conducting farmland quality monitoring [4,5,6,7]
The performance of the improved UAV-WSN system and the data transmission optimization routing protocol (DTORP) proposed in this study was assessed for a WSN composed of a single cluster using Monte Carlo simulations for different spatial resolution images captured by a UAV based on comparisons with the performance obtained for a conventional UAV-WSN system employing the low-energy adaptive clustering hierarchy (LEACH) protocol
Summary
Farmland is a basic resource required for ensuring human survival. the quality of farmland dictates the quantity and quality of the extracted crops, which, in turn, affect the quality and health of human life [1,2]. The common disadvantage of the data transmission protocols discussed above for use in UAV-WSN system applications is that they do not provide sufficient flexibility to UAVs for the collection of remote sensing data due to the constraints associated with the collection of ground sensor data [47,48,49,50] This necessitates that the UAV must fly over a study area twice, where one flight path is planned for visiting each communication node in the WSN (i.e., the sensor nodes, route nodes, or head nodes, depending on the protocol in use) and another is planned for collecting high-resolution remote sensing data. The designed protocol must accommodate the flexible routing of UAVs and adhere to the principles of aerial photogrammetry while enabling the efficient collection of ground sensor data These issues are addressed in this work by developing an integrated UAV-WSN system based on a newly proposed data transmission optimization routing protocol (DTORP).
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