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Suspended foreign objects on transmission lines will shorten the discharge distance, easily leading to phase-to-ground or phase-to-phase short circuits, which induces outage accidents. Foreign objects are small and difficult to identify, resulting in low detection accuracy. An improved foreign object detection method based on Swin Transformer V2 and YOLOX (ST2Rep–YOLOX) is proposed. First, the feature extraction layer ST2CSP constructed by Swin Transformer V2 is used in the original backbone network to extract global and local features. Secondly, hybrid spatial pyramid pooling (HSPP) is designed to enlarge the receptive field and retain more feature information. Then, Re-param VGG block (RepVGGBlock) is introduced to replace all 3 × 3 convolutions in the network to deepen the network and improve feature extraction capabilities. Finally, experiments are carried out on the transmission lines foreign object image dataset, which was obtained using unmanned aerial vehicles (UAVs). The experimental results show that the average accuracy of the ST2Rep–YOLOX method can reach 96.7%, which is 4.4% higher than that of YOLOX. The accuracy of the nest, kite, and balloon increased by 9.3%, 15.4%, and 9.6%, and the recall increased by 6.5%, 9.4%, and 2.5%, respectively. This method has high detection accuracy, which provides an important reference for foreign object detection in transmission lines. [ABSTRACT FROM AUTHOR]

Wearable glucose biosensors enable noninvasive glucose monitoring, thereby enhancing blood glucose management. In this work, we present a wearable biosensor based on carbon black nanoparticles (CBNPs) for glucose detection in human sweat. The biosensor consists of CBNPs, Prussian blue (PB), glucose oxidase, chitosan, and Nafion. The fabricated biosensor has a linear range of 5 µM to 1250 µM, sensitivity of 14.64 µA mM−1 cm−2, and a low detection potential (−0.05 V, vs. Ag/AgCl). The detection limit for glucose was calculated as 4.83 µM. This reusable biosensor has good selectivity and stability and exhibits a good response to glucose in real sweat. These results demonstrate the potential of our CBNP-based biosensor for monitoring blood glucose in human sweat. [ABSTRACT FROM AUTHOR]

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. [ABSTRACT FROM AUTHOR]

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a "W" distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2. [ABSTRACT FROM AUTHOR]

Ozone is a very important trace gas in the atmosphere, it is like a "double-edged sword". Because the ozone in the stratosphere can effectively help the earth's organisms to avoid the sun's ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA's Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a "-" distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit. [ABSTRACT FROM AUTHOR]

Given the increasing impact of extreme rainfall and flooding on human life, studying and predicting changes in atmospheric water vapor (AWV) becomes particularly important. This paper analyzes the moderate-resolution imaging spectroradiometer (MODIS) data of the East Asian region from January 2003 to February 2023. The AWV data are examined in the time and frequency domain using methods such as empirical orthogonal function (EOF), Mann–Kendall (MK) analysis, and others. Additionally, four prediction models are applied to forecast the monthly average AWV data for the next year. The accuracy of these models is evaluated using metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings reveal several key insights: (1) The East Asian region exhibits highly variable seasonal variability in AWV, with identified mutation points after the MK test. (2) Spatial analysis shows high AWV data in the southern coastal areas of China, Thailand, Myanmar, Nansha Islands, and other regions during winter, while the Qinghai-Tibet Plateau region experiences low AWV during summer. (3) The first mode obtained through EOF decomposition contributes over 60% of the variance. Analysis of this mode reveals an increasing trend in AWV data for regions such as the Indian peninsula, Mongolia, and central and northeastern China over the past nine years. Conversely, the Bay of Bengal, Spratly Islands, eastern coast, and certain areas display a decreasing trend. (4) Employing the ensemble empirical mode decomposition (EEMD), the study identifies AWV data as a non-stationary series with an overall decreasing trend from 2003 to 2022. The filtered AWV series undergoes fast Fourier transform (FFT), uncovering periodicities of 2.6 years, 5 years, and 19 years. (5) Among the four forecasting models compared, the seasonal autoregressive integrated moving average model (SARIMA) demonstrates superior performance with the smallest MSE of 0.00782, MAE of 0.06977, RMSE of 0.08843, and the largest R2 value of 0.98454. These results clearly indicate that the SARIMA model provides the best fit. Therefore, the SARIMA forecasting model can be effectively utilized for forecasting AWV data, offering valuable insights for studying weather variability. [ABSTRACT FROM AUTHOR]

Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 years, from September 2002 to February 2022, and conducts in-depth research using the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods. We found that the spatial distribution of OLR is zonal symmetry and gradually decreases with the increase of latitude. Using EOF analysis, it is found that the total variance contribution of the first four decomposed spatial features exceeds 70%, and the overall change trend of the four-time coefficients in the past 19 years all show a downward trend. OLR is positively correlated with total column water vapor (TCWV), air temperature (AT), and cloud top temperature (CTT), but negatively correlated with cloud top pressure (CTP). OLR has a similar spatial correlation distribution with TCWV and AT, while the spatial correlation between OLR and CTP is opposite to the first two parameters. In most parts of East Asia, the spatial correlation with CTT exceeds 0.8. The change in OLR value is affected by various meteorological parameters. In East Asia, the positive correlation between 30° N and 60° N is significantly affected by TCWV, AT, and CTT; and the negative correlation is more significantly affected by CTP. At 0–25° N, the positive correlation is significantly affected by CTP and CTT, while the negative correlation is significantly affected by TCWV and AT. [ABSTRACT FROM AUTHOR]

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities. [ABSTRACT FROM AUTHOR]

Outgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the characteristic distribution of OLR value over China and predict its results in time series using the seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) methods based on the OLR data by the Atmospheric Infrared Sounder (AIRS). The Mann–Kendall (MK) mutation test was used to analyze the annual average of OLR values in China and the mutation points in the four seasons. The empirical orthogonal function (EOF) is used to decompose the spatial characteristics and temporal variation of OLR values in China. The MK mutation test is used to obtain the mutation points in the three seasons of spring, summer and autumn. The cumulative variance contribution of the four modes obtained by EOF decomposition exceeds 70%, and the variance contribution of the first mode exceeds 50%. The prediction accuracy with SARIMA model is 99% and LSTM algorithm is 97%. The results of spatiotemporal analysis show that the OLR value near the equator is significantly higher than that of the north and south poles and decreases with the increase of latitude; the OLR value in spring, summer and autumn is higher than that in winter. The results of the MK test show that there are many mutation points in autumn, and the location of the mutation points cannot be determined. The mutation points in spring and summer meet the confidence interval; the first mode of EOF decomposition has a meridional structure, and the OLR value is dropped within 18 years as a whole. The spatial characteristics of modes 1 and 3 have obvious changes in the Qinghai-Tibet Plateau and Northeast China. The prediction results show that the prediction accuracy of SARIMA is higher than that of LSTM. Therefore, the results predicted by SARIMA may provide a reference for the study of the radiation balance of the earth–atmosphere system in China. [ABSTRACT FROM AUTHOR]

The long-term global distribution study of Aerosol Optical Depth (AOD) has great significance for air pollution control, forest fire prevention and climate studies. The global spatial-temporal distribution and variation of the AOD at 550 nm are analyzed using AOD 550 product of Modis/Aqua during 2003–2021. The AOD 550 is also studied by Empirical Orthogonal Function (EOF) analysis method. The results show that the distribution of AOD 550 has obvious regional and seasonal characteristics, and the interannual variation is distinct. The AOD 550 distribution with latitude/longitude is closely related to the distribution of population and aerosol natural source. The high-AOD 550 areas are mainly distributed in eastern Asia, southeast Asia, south Asia, the Middle East, northwestern Africa, central and western Africa, western waters of Africa and the Amazon region of South America; The AOD 550 seasonal distribution results are JJA > MAM > DJF > SON over globe, SON > DJF > (MAM ≈ JJA) over the southern hemisphere, and MAM > JJA > DJF > SON over the northern hemisphere, respectively. Moreover, the seasonal distribution characteristics varies in different regions especially over ocean and land. The results of interannual variation show an upward trend during 2005–2011, and fluctuates during 2012–2020. The annual mean AOD 550 over the northern hemisphere (0.187–0.207) is significantly higher than that of southern hemisphere (0.110–0.135), while the latter shows an increasing trend (0.0009/year). The EOF analysis results demonstrate the complexity of the AOD 550 global distribution. The annual mean results show the change trends of aerosols in different regions and find some special aerosol events. While the monthly mean results show that aerosols over ocean are mostly dominated by land sources (dust and biomass burning sources), and the ocean transport pattern presents an annual cycle. • The global distribution of Aerosol Optical Depth is studied using MODIS level 3 data from 2003 to 2021. • The seasonal and interannual variations of Aerosol Optical Depth are reported with the global perspective. • The Empirical Orthogonal Function analysis of Aerosol Optical Depth is performed. [ABSTRACT FROM AUTHOR]

The variation trends of the cold-point tropopause (CPT) are presented using radiosonde observations from 77 stations over China during 1979–2014. The latitude regions over China from 18°N to 53°N are divided into 7 latitude zones with every 5° intervals, and the spatial areas of 18°N–53°N, 75°E–135°E are divided into 27 lattices with 5° × 10° grids. The annual-mean values of height-of-CPT (H-CPT) and temperature-of-CPT (T-CPT) are then calculated by all the available samples within each latitude and longitude bin. By using the least squares regression method, it is found that the H-CPT increases with the rate of 273 m/decade, and overall the significant cooling rate of − 0.70 K/decade for the T-CPT over the whole of China. Then, the trends and latitude distribution of H-CPT and T-CPT for each latitude zone are analyzed. The difference of H-CPT among latitude distribution characteristic is reducing year by year, and the difference of T-CPT is enlarging. The H-CPT displays a rising trend between 28°N–53°N latitude region with the positive change rates, and it has decline trend between 18°N–28°N latitude region with the negative change rates. The change rates of T-CPT are negative values for all latitude zones. At last, the nonuniform latitudinal and longitudinal distribution of long-term trends of H-CPT and T-CPT are first presented for each spatial cell. [ABSTRACT FROM AUTHOR]

Cirrus clouds are crucially important to weather, climate and earth energy balance studies. The distribution of cirrus reflectance with latitude and season is an interesting topic in atmospheric sciences. The monthly mean Level-3 MODIS cirrus reflectance is used to analyze the global distribution of cirrus clouds, which covers a period from 1 March 2000 to 28 February 2018. The latitude, from 90° S to 90° N, is divided into 36 latitude zones with 5° interval. Data in each latitude zone are analyzed. The research results show that the slopes of cirrus reflectance variation in the Northern and Southern Hemisphere are −1.253 × 10−4/year and –1.297 × 10−4/year, respectively. The yearly-average cirrus reflectance reveals strong negative correlation with time in the Northern Hemisphere, i.e., the correlation coefficient is −0.761. Then the statistical analysis of cirrus reflectance is performed in different seasons, the results show that cirrus reflectance varies obviously with seasonal change. Additionally, for the [30°, 90°] latitude regions, cirrus reflectance reaches the minimum in summer and the maximum in winter in the Southern and Northern Hemisphere. [ABSTRACT FROM AUTHOR]

Spatiotemporal modal analysis and prediction of tropospheric atmospheric CO concentration in the world and East Asia from 2002 to 2021 using the inversion data of airs sounder. The results show that: The CO concentration in the northern hemisphere is higher than that in the southern hemisphere; from the upper troposphere to the lower troposphere, the CO concentration changes from " ∧ " to "√"; the fluctuation range of near surface CO concentration in the northern hemisphere is relatively intense, and the fluctuation range in the southern hemisphere is relatively small. Using MK, Sen slope estimation, and EOF analysis, it is found that CO concentration in the convective middle layer tends to decline in more than 90% of the global area, and the decline rate in the northern hemisphere is significantly higher than that in the southern hemisphere. In East Asia, the CO concentration in the lower tropospheric marine area is significantly lower than that in the land area. The average concentration and decline rate of CO in East Asia is always higher than that in the world; the CO concentration in East Asia is the highest in spring and winter in the lower troposphere; and the CO concentration in East Asia is lower in the northeast and higher in the southeast in the upper troposphere in spring, autumn, and winter, and higher in the northeast and Central Plains in summer. Compared with the three-exponential smoothing model, the prediction error of the VMD-LSTM hybrid model for atmospheric CO concentration is significantly reduced, which indicates that the improved neural network prediction model has higher prediction accuracy. The factors affecting the change of tropospheric CO concentration are not only affected by the ground factors, but also related to indirect factors such as water vapor, methane, and atmospheric temperature in the atmosphere. [ABSTRACT FROM AUTHOR]

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed 'bell-shaped' curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region. [ABSTRACT FROM AUTHOR]

Existing wireless coal mine environmental monitoring system has large transmissions of redundancy data. Monitoring and treatment of multiple environmental parameters are mutually independent, with low instantaneity and reliability. To solve the above problems, this paper designs the coal mine environmental monitoring system based on multi-agent, taking advantages of the harmony and intelligence of the multi-agent technology. This system designs the environmental monitoring agent from both hardware and software aspects which can cooperate mutually and work harmoniously between each other. Thus, the quantity of transmission of data can be reduced, which optimizes the structure of the coal mine environmental monitoring system. Results of simulation experiments prove that the multi-agent-based coal mine environmental monitoring system can reduce the transmission delay and packet loss probability of data and enhance the instantaneity and reliability of data transmission in the system. Therefore, the new system is more applicable and advanced in the field of coal mine environmental monitoring. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a pivotal region for investigating climate change and the emission of greenhouse gases in terrestrial ecosystems. In this study, atmospheric methane columnar concentration (XCH 4) data obtained from GOSAT products were used from March 2010 to February 2022. The comparative study between GOSAT XCH 4 and Waliguan (WLG) ground-based methane measurements in annual terms demonstrated a high consistency, with a correlation coefficient of 0.996, indicating similar trends and amplitudes. Subsequently, a comprehensive investigation was carried out to explore the long-term trends and spatial differences of XCH 4 on the TP, along with its association with climate variables. The findings revealed an east-west gradient in the spatial distribution of XCH 4 , characterized by higher concentrations in the southeast and lower concentrations in the northwest. During the 12-year study period, a consistent upward trend of XCH 4 was exhibited across the whole plateau, ranging from 7.53 to 10.15 ppb/yr, and the areas of exceeding 9 ppb/yr accounted for 80.4% of the study area. The seasonal variation of XCH 4 displayed a typical unimodal distribution, with the highest XCH 4 occurring in late August to early September. The interannual growth rate of XCH 4 was the highest in summer, followed by autumn, winter, and the lowest in spring. To better understand the climate variables influencing XCH 4 on the TP, a Principal Component Analysis (PCA) was performed with a biplot. The results indicated that temperature (TEM), precipitation (PER), soil temperature (Soil_TEM), and soil humidity (Soil_HUM) were positively correlated with XCH 4. However, wind speed (WSPD) and wind direction (WDIR) demonstrated negative correlations with XCH 4. • XCH 4 exhibits a pronounced increasing trend in Tibetan Plateau during 2010–2022. • The spatial distribution of XCH 4 and its interannual growth rates indicate strong regional signals and seasonal cycle. • Principal Component Analysis (PCA) reveals different correlations between XCH 4 and climate factors. [ABSTRACT FROM AUTHOR]

With the introduction of the extended method in Hadamard transform ion mobility spectrometry (HT-IMS), the modulation defects and the false peaks inherently located in conventional HT techniques could be well suppressed. However, a few false peaks are still located in the demultiplexed data of the extended HT-IMS, which is hard to be explained and seriously degrades the performance of the extended HT techniques. In this research, in order to discover the origin of the false peaks in the extended HT-IMS, the measurements of double and multiple ion peaks with IMS have been implemented and the effects of the peak-peak repulsion on the multiplexed data of HT-IMS have been explored. Results show that in multiplexing IMS, the space charge effect existed between two adjacent ion packets would lead to the shifts (deviations) of the ion signal peaks. Simulation results prove that this tiny shifting in the multiplexed data is another critical reason for the false peaks in the demultiplexed data of extend HT-IMS. This is of positive significance for revealing those unknown false peaks in extended HT-IMS and other similar techniques. Simulation results of extended multiplexing HTIMS: (a) simulation spectrum of the signal averaging method; b) Multiplexed data of extended HT-IMS, S-matrix order is 255, number of the extended symbol is 4; c) The simulation demultiplexed data of EHT-IMS, in the inset of frame c, parts of the multiplexed data are demonstrated. Image 1 • The peak-peak repulsion effect on Hadamard multiplexing technique has been evaluated firstly. • A feasible explanation for the unknown false peaks in extended HT-IMS is given. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption in wireless sensor networks (WSNs), this paper proposes a distributed event-triggered communication for time synchronization. The clock feature of each node is modeled as the discrete-time second-order dynamics. Each node in the network determines whether and when to broadcast its states to its neighbors based on an event trigger mechanism. For each node in the network, the trigger function is designed based on the disagreement between its current states and those estimated from last transmitted, which thus is independent of the states of its in-neighbors. Then the condition about the updating period and the control gain is presented to guarantee the time synchronization of the network. It is shown that the proposed event-triggered communication can play a good tradeoff between the convergence rate and the communication rate of the time synchronization algorithm, and it also outperforms the corresponding time-triggered communication in the case that the same communication rate is used. Finally, simulation results are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]