A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial-Temporal Distribution of Users and Advanced Genetic Algorithms.

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP's trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.

Diverse responses of surface biogeophysical parameters to accelerated development and senescence of vegetation on the Mongolian Plateau.

Vegetation dynamics is essential for characterizing surface biogeophysical parameters. Speeds of vegetation development and senescence are well documented, however, the effects of vegetation growth rates on surface parameters during different growth stages remains unclear. By using such methods as trend analyses and correlation analyses, this study examines the variations and interactive relationships of leaf area index (LAI) and surface parameters including Albedo, evapotranspiration (ET), and land surface temperature (LST), derived from Moderate Resolution Imaging Spectroradiometer (MODIS), during the intra-growing season (April-October, GS) on the Mongolian Plateau (MP). Generally, LAI exhibited a significant upward trend across GS months. Significant changes in VLAI (the difference in LAI between 2 consecutive months) in April-May and September-October indicated that the vegetation change rates were accelerated in the early GS (April-June) and late GS (September-October). The effect of vegetation activity on surface parameters varies over time and space. The effects of VLAI on the speed of surface parameters were inconsistent during the intra-GS. As a result of the significant changes in LAI, VET (the difference in ET between 2 consecutive months) displayed a significant upward trend during the early GS but a significant downward trend during the late GS. With acceleration of vegetation activity, the effects of VET and VAlbedo (the difference in Albedo between 2 consecutive months) on LST could offset each other at different stages of the GS. In addition, the effect of VLAI on the speed of surface parameters varied significantly by vegetation types. Our findings imply that clarifying the impact of vegetation activity on surface parameters at different growth stages can advance our understanding of vegetation responses and feedbacks to climate change.

Diverse responses of gross primary production and leaf area index to drought on the Mongolian Plateau.

Drought is a crucial factor regulating vegetation growth on the Mongolian Plateau (MP). Previous studies of drought effects on the MP have mainly concentrated on drought characterization, while the response of vegetation to drought remains unclear. To close this knowledge gap, we examined the response of MP vegetation to drought in terms of gross primary production (GPP) and leaf area index (LAI) from 1982 to 2018. Our findings show that intra-seasonally the frequency of drought occurrence in autumn had a greater impact on GPP (relative importance over 70 %), while the intensity of drought was more influential for LAI (relative importance approximately 60 %). Inter-seasonally, summer droughts had the most pronounced effect on vegetation (with median standardized anomalies of -0.72 for GPP and -0.4 for LAI, respectively). Additionally, we found that meteorological drought was more consistent with atmospheric aridity (high vapor pressure deficit) than soil drought (low soil moisture). This study advances knowledge of vegetation's susceptibility to climate extremes and improves the precision of predicting ecosystem response to climate change.

Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth.

Vegetation productivity dynamics are closely related to climate change, and water availability determines vegetation growth in water-limited ecosystems. Nevertheless, how changes in the interactions between climatic factors and vegetation activity variation regulate the relationship between their trends remains unclear. The Normalized Difference Vegetation Index (NDVI) is an effective proxy of vegetation growth. First, we investigated the NDVI trends, and the results revealed a vegetation activity with weaker greening and greater spatial heterogeneity after an obvious land-cover breakpoint in 1999 compared with that before 1999 in northwest China. Notably, the Loess Plateau greatly led the greenness trends, but the Tibet Plateau showed mean browning after 1999, which implied that the coupling of climate change and vegetation trends varied with spatio-temporal changes. Subsequently, using the Geographical Detector Method (GDM), we quantified and compared the association between climate change and the interannual variability of NDVI in the two stages. Vegetation productivity variation is more closely related to changes in climatic factors after 1999 compared with that before 1999. Precipitation (PPT) and vapor pressure deficit (VPD) are the primary constraints to vegetation growth in both stages. Patterns in NDVI trend increases are consistent with those of increased PPT and decreased VPD and vice versa after 1999. However, the same patterns were not observed before 1999 because of the weak association between climate change and NDVI variation. This implicated a great significance of the association between climate change and changes in vegetation activity for the prediction of potential carbon sequestration due to the shift of dominant factors and their trends under future climate change.