## 1 Introduction  
Recently, obtaining environmental monitoring data efficiently in remote areas without public networks has become an important goal, as more and more researchers study global environmental changes. For environmental monitoring in remote areas, especially in areas without infrastructure, many studies use UAV and WSN (Wireless Sensor Networks) for data collection [1; 2; 3]. Among them, Zigbee technology is typically used for WSN [4] but suffers from a range of several hundred meters. It can expand the network coverage by multi-hop communication. However, with the increase in hops, the real time and reliability of the network degrade. In addition, multi-hop routing protocols are complex in low power WSN. LPWAN (Low Power Wide Area Network) [5] is a promising research direction, among existing LPWAN technologies, LoRa (Long-Range) [6] technology receives great attention from researchers, due to its advantages such as long-range communication, low power consumption, low cost, large network capacity, etc., and can be deployed in remote areas without infrastructure.  
In the paper, we consider the large area environmental monitoring scenarios without infrastructure, using UAV collection data in LoRa networks, as shown in Figure 1. Sensor nodes are deployed in each monitoring area with the clustering structure, in which cluster heads are selected for collecting data of local clusters. Each sensor node is equipped with a solar cell module and various environmental sensors. The UAV collects data from each cluster head using LoRa technology in the whole network, according to the planned path.  
In such systems, how to reduce the UAV flight distance and time is a focus problem, by selecting the proper cluster heads and optimizing the flight route. Moreover, the low data rate of LoRa is a disadvantage for data transmission between UAV and cluster heads, especially for the cluster heads with large amounts of data. At present, many studies based on UAV and LoRa systems mainly use LoRa technology to send control commands to UAVs [7; 8; 9; 10; 11], regardless of data rate. However, when cluster heads store a large amount of data, the UAV often needs to hover for data transmission [12]. This will increase the energy consumption of the UAV and reduce the efficiency of the UAV data collection task [13; 14].  
Motivated by the above challenges, in this paper we propose optimization schemes for UAV data collection with LoRa 2.4 GHz technology in the above scenarios. Firstly, an improved clustering algorithm for the LoRa network is proposed, which considers the influence of distance between the cluster heads and the UAV take-off point. Secondly, we present an improved GA (Genetic Algorithm) for path planning to reduce the UAV flight distance, which introduces the Teaching-Learning-based Optimization (TLBO) [15] and local search optimization algorithms to improve convergence rate and the path solution. Then, an LoRa 2.4 GHz adaptive data rate strategy with a dual channel is designed based on distance and link quality, to reduce the data transmitting time between the UAV and the cluster head nodes, and their energy consumption.  
The subsequent sections of this paper are organized as follows. In Section 2 we introduce some related work. Then, Section 3 presents the model formulation and scheme design for UAV data collection in the LoRa 2.4 GHz network. Section 4 tests and analyzes LoRa 2.4 GHz communication performance for UAV and cluster heads. Section 5 presents the simulations and experimental results for the proposed schemes. Finally, Section 6 concludes our work and mentions future work.