Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J

Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited

Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China

This study aims to project future variability of reference evapotranspiration (ET0) using artificial intelligence methods, constructed with an extreme-learning machine (ELM) and support vector regression (SVR) in a mountainous inland watershed in north-west China. Eight global climate model (GCM) outputs retrieved from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) were employed to downscale monthly ET0 for the historical period 1960–2005 as a validation approach and for the future period 2010–2099 as a projection of ET0 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The following conclusions can be drawn: the ELM and SVR methods demonstrate a very good performance in estimating Food and Agriculture Organization (FAO)-56 Penman–Monteith ET0. Variation in future ET0 mainly occurs in the spring and autumn seasons, while the summer and winter ET0 changes are moderately small. Annually, the ET0 values were shown to increase at a rate of approximately 7.5 mm, 7.5 mm, 0.0 mm (8.2 mm, 15.0 mm, 15.0 mm) decade−1, respectively, for the near-term projection (2010–2039), mid-term projection (2040–2069), and long-term projection (2070–2099) under the RCP4.5 (RCP8.5) scenario. Compared to the historical period, the relative changes in ET0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%), during the near, mid- and long-term periods, respectively, under the RCP4.5 (RCP8.5) warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET0 with artificial intelligence is useful in practice for water-management policie

Separation of the Climatic and Land Cover Impacts on the Flow Regime Changes in Two Watersheds of Northeastern Tibetan Plateau

Assessment of the effects of climate change and land use/cover change (LUCC) on the flow regimes in watershed regions is a fundamental research need in terms of the sustainable water resources management and ecosocial developments. In this study, a statistical and modeling integrated method utilizing the Soil and Water Assessment Tool (SWAT) has been adopted in two watersheds of northeastern Tibetan Plateau to separate the individual impacts of climate and LUCC on the flow regime metrics. The integrated effects of both LUCC and climate change have led to an increase in the annual streamflow in the Yingluoxia catchment (YLC) region and a decline in the Minxian catchment (MXC) region by 3.2% and 4.3% of their total streamflow, respectively. Climate change has shown an increase in streamflow in YLC and a decline in MXC region, occupying 107.3% and 93.75% of the total streamflow changes, respectively, a reflection of climatic latitude effect on streamflow. It is thus construed that the climatic factors contribute to more significant influence than LUCC on the magnitude, variability, duration, and component of the flow regimes, implying that the climate certainly dominates the flow regime changes in northeastern Tibetan Plateau

The spatial and temporal contribution of glacier runoff to watershed discharge in the Yarkant River Basin, Northwest China

In this paper, a glacial module based on an enhanced temperature-index approach was successfully introduced into the Soil and Water Assessment Tool (SWAT) model to simulate the glacier runoff and water balance of a glacierized watershed, the mountainous region of the Yarkant River Basin (YRB) in Karakoram. Calibration and validation of the SWAT model were based on comparisons between the simulated and observed discharge with a monthly temporal resolution from 1961 to 2011 for the Kaqun hydrological station. The results reaffirmed the viability of the approach for simulating glacier runoff, as evidenced by a Nash–Sutcliff Efficiency (NSE) of 0.82–0.86 as well as a percentage bias (PBIAS) of −4.5% to 2.4%, for the calibration and validation periods, respectively. Over the last 50 years, the total discharge and glacier runoff both exhibited increasing trends with 0.031 × 109 m3·a−1 and 0.011 × 109 m3·a−1. The annual glacier runoff contribution to the streamflow was between 42.3% and 64.5%, with an average of 51.6%, although the glaciers accounted for only 12.6% of the watershed drainage area in the mountainous YRB. The monthly contribution of the glacier runoff ranged from 11.0% in April to 62.1% in August, and the glacier runoff from June to September accounted for about 86.3% of the annual glacier runoff. Runoff from the mountainous regions above 5000 m a.s.l. accounted for 70.5% of the total discharge, with glacier runoff contributions being approximately 46.4%

Community structure and plant diversity under different degrees of restored grassland in mining areas of the Qilian Mountains, Northwestern China

Background: Mining activities are known to exert significant effects on the structure and function of grassland ecosystems. However, the role of mining grasslands restoration in altering the plant community and soil quality remains poorly understood, especially in alpine regions. Here, we investigated species diversity in grasslands with dynamic changes and different restoration levels in the Tianzhu alpine mining area locating in the Qilian Mountains.Methods: The plant community structure and species composition of the grasslands with different restoration levels were analyzed by the sample method. We used five different restoration levels: very low recovered degree (VLRD), low recovered degree (LRD), medium recovered degree (MRD), and high recovered degree (HRD), and selected natural grassland (NGL, CK) as the control.Results: Plant community structure and species composition were significantly higher than those under the VLRD in the Tianzhu alpine mining area (p < 0.05), with HRD > MRD > LRD > VLRD. There were 11 families, 18 genera, and 17 species of plants, mainly in the families of Leguminosae, Asteraceae, Gramineae, Rosaceae, and Salicaceae; among them, Salicaceae and Gramineae played a decisive role in the stability of the community. The ecotype community showed that perennial herbaceous plants were the most dominant, with annual herbaceous plants being the least dominant, and no tree and shrub layers were observed; the dominance index was the highest in VLRD at 0.32, the richness index was the highest in HRD at 2.73, the diversity of HRD was higher at 1.93, soil pH and EC showed a decreasing trend, and SMC, SOC, TN, NO3-N, NH4-N, AN, TP, and AP content showed an increasing trend with the increase of grassland restoration.Conclusion: In summary, with the increase of restored grassland in the Tianzhu alpine mining area, plant diversity gradually increased and plant community structure gradually diversified, which was close to the plant diversity of NGL. The protection of partially VLRD and LRD grasslands in the mining area should be emphasized, and the mine grassland should be used rationally and scientifically restored

Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management

Load transference with the gain of excessive body mass: A two-year longitudinal study

Previous studies investigating the effect of excessive weight on the foot have commonly been cross-sectional; therefore, it is still unclear how the foot function gradually changes with the increased body mass that is physiologically gained over time. This study aimed to use a load transfer method to identify the mechanism of how the foot function changed with the increased excessive body mass over two years. Taking normal weight as the baseline, fifteen children became overweight or obese (group 1), and fifteen counterparts maintained normal weight (group 0) over the two years. Barefoot walking was assessed using a Footscan® plate system. A load transfer method was used based upon the relative force–time integral (FTI) to provide an insight into plantar load transference as children increased in weight. Significantly increased FTIs were found at the big toe (BT), medial metatarsal (MM), lateral metatarsal (LM), and lateral heel (HL) in group 1, while at BT, MM, medial heel (HM), and HL in group 0. Foot load showed a posterior to anterior transferal from midfoot (2.5%) and heel (7.0%) to metatarsal and big toe in group 1. The control group, however, shifted the loading within the metatarsal level from LM to HM (4.1%), and equally relieved weight from around the midfoot (MF) (3.0%) to BT, MM, HM and HL. Earlier weight loss intervention is required to prevent further adverse effects on foot functions caused by excessive weight-bearing. © 2021 by the authors. Licensee MDPI, Basel, Switzerland