FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed

Analysis of Soil Moisture Change Characteristics and Influencing Factors of Grassland on the Tibetan Plateau

Soil moisture is an important component of the soil–vegetation–atmosphere terrestrial hydrological cycle and is an important factor affecting terrestrial ecosystems. In the context of extensive vegetation greening on the Tibetan Plateau (TP), it is important to investigate the effect of vegetation greening on soil moisture to maintain ecosystem stability and protect the sustainability of ecological restoration projects. To evaluate the effect of vegetation greening on soil moisture on the TP, the spatial distribution and trends of soil moisture and vegetation on the TP were analyzed using GIMMS NDVI data and ERA5 soil moisture data from 1982 to 2015. The effects of grassland NDVI, precipitation, and temperature on SM were also explored using multiple regression apparent and SEM. The main results are as follows: from 1982 to 2015, both grassland NDVI and SM showed a stable increasing trend. Precipitation was the most important factor influencing SM changes on the TP. In the context that vegetation greening is mainly influenced by temperature increase, vegetation plays a dominant role in SM changes in soil drying and soil wetting zones. In this paper, the climate–vegetation–soil moisture coupling mechanism of grasslands on the TP is investigated, and the related results can provide some theoretical references and suggestions for global ecosystem conservation and the sustainable development of ecological restoration projects

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed

Agents Affecting the Plant Functional Traits in National Soil and Water Conservation Demonstration Park (China)

Plant functional traits (PFTs) can reflect the response of plants to environment, objectively expressing the adaptability of plants to the external environment. In previous studies, various relationships between various abiotic factors and PFTs have been reported. However, how these factors work together to influence PFTs is not clear. This study attempted to quantify the effects of topographic conditions, soil factors and vegetation structure on PFTs. Four categories of variables were represented using 29 variables collected from 171 herb plots of 57 sites (from different topographic and various herb types) in Xindian SWDP. The partial least squares structural equation modeling showed that the topographic conditions and soil properties also have a direct effect on plant functional traits. Among the topographic conditions, slope (SLO) has the biggest weight of 0.629, indicating that SLO contributed the most to plant functional traits and vegetation structure. Among soil properties, maximum water capacity (MWC) contributes the most and is followed by soil water content (SWC), weighted at 0.588 and 0.416, respectively. In a word, the research provides new points into the quantification of the correlation between different drivers that may be important for understanding the mechanisms of resource utilization, competition and adaptation to the environment during plant recovery

Stability and Spatial Structure of Chinese Pine (<i>Pinus tabuliformis</i> Carr.) Plantations in Loess Hilly Region: A Case Study from Huanglong Mountain

In contrast to intensive management practices focused on wood production, plantations designed to safeguard fragile environments prioritize the sustainable fulfillment of ecological functions. To assess the potential for Chinese pine (Pinus tabuliformis Carr.) plantations in the Loess Hilly Region to effectively serve their ecological protection role over the long term, we selected nine indices representing biological stability, resistance stability, and functional stability. Employing a novel unit circle method, we evaluated the total stability (sum of the three stability components) of 44 plantation plots in Huanglong Mountain. We also explored the connections between total stability and standing spatial structure parameters to offer insights for promptly enhancing stability through thinning. The findings revealed that 79.5% of Chinese pine plantations exhibited moderate total stability, with 20.5% demonstrating good stability. Most plots displayed a random distribution pattern, moderate size differentiation, low species spatial mixing, and high stand crowding. Among the correlations analyzed, mingling exhibited the highest coefficient, followed by differentiation, while the uniform angle index showed the weakest correlation, and crowding displayed an insignificant correlation. While the presence of good functional stability contributed to the moderate total stability, addressing inadequate biological and resistance stability necessitates thinning measures. This study identifies spatial structure types negatively linked to total stability, offering targeted management insights for enhancing the stability of Chinese pine plantations. The stability assessment methodology and indicators presented in this study can serve as a valuable reference for similar plantations with comparable functions and planting conditions