Temperature Sensor Network Research Articles

Evaluation of nature-based climate solutions for agricultural landscapes in the Galápagos Islands

The Galápagos Islands are highly vulnerable to climate and environmental change, and nature-based solutions can help local communities adapt local agricultural systems. Through a comparative analysis, we evaluated the effects of three land management strategies on soil temperature, soil water availability and storage, and carbon stocks in Santa Cruz Island (Galápagos Archipelago). We installed six monitoring sites that consisted of two replicates per pathway: (i) the avoided loss of tropical forest, (ii) the conservation of scattered trees and living fences in at-risk agroforestry system, and (iii) the increase in biomass after a reduction of the grazing intensity. The monitoring sites were equipped with a dense network of rain gauges, air temperature and relative humidity sensors, and capacitance/frequency probes that registered volumetric water content and soil temperature. After pedological characterization of the soil profiles, the soil physico-chemical and hydrophysical properties were determined in laboratory. Over a period of 30 months (July 2019 to December 2021), hydrometeorological and soil environmental data were collected.We assessed differences in soil temperature, moisture availability and soil organic carbon content between native forests, sites under traditional agroforestry and under passive restoration. Forest soils were 12 % cooler;, and soil moisture under forest was 20 % higher than in parcels with silvopastural management. Forest soils had a lower dry bulk density, lower saturated hydraulic conductivity and higher water retention capacity in comparison with the other two management types. In silvopastural systems, a decrease of grazing intensity had a positive effect on soil carbon stocks, that were about 50 % higher than in soils under traditional management. This study shows that avoided loss of tropical forest within an agricultural landscape is a promising strategy to mitigate increasing soil temperatures, agricultural drought, and decline in soil organic carbon content. Continued monitoring of the experimental sites is necessary to corroborate the findings of this investigation at longer temporal scales.

Insights on seasonal solifluction processes in warm permafrost Arctic landscape using a dense monitoring approach across adjacent hillslopes

Solifluction processes in the Arctic are highly complex, introducing uncertainties in estimating current and future soil carbon storage and fluxes, and assessment of hillslope and infrastructure stability. This study aims to enhance our understanding of triggers and drivers of soil movement of permafrost-affected hillslopes in the Arctic. To achieve this, we established an extensive soil deformation and temperature sensor network, covering 48 locations across multiple hillslopes within a 1 km2 watershed on the Seward Peninsula, AK. We report depth-resolved measurements down to 1.8 m depth for May to September 2022, a period conducive to soil movement due to deepening thaw layers and frequent rain events. Over this period, surface movements of up to 334 mm were recorded. In general, these movements occur close to the thawing front, and are initiated as thawing reaches depths of 0.4–0.75 m. The largest movements were observed at the top of the south-east facing slope, where soil temperatures are cold (mean annual soil temperatures averaging −1.13 °C) and slopes are steeper than 15°. Our analysis highlights three primary factors influencing movements: slope angle, soil thermal conditions, and thaw depth. The latter two significantly impact the generation of pore water pressures at the thaw–freeze interface. Specifically, soil thermal conditions govern the liquid water content, while thaw depth influences both the height of the water column and, consequently, the pressure at the thawing front. These factors affect soil properties, such as cohesion and internal friction angle, which are crucial determinants of slope stability. This underscores the significance of a precise understanding of subsurface thermal conditions, including spatial and temporal variability in soil temperature and thaw depth, when assessing and predicting slope instabilities. Based on our observations, we developed a factor of safety proxy that consistently falls below the triggering threshold for all probes exhibiting displacements exceeding 50 mm. This study offers novel insights into patterns and triggers of hillslope movements in the Arctic and provides a venue to evaluate their impact on soil redistribution.

Variations in Bedrock and Vegetation Cover Modulate Subsurface Water Flow Dynamics of a Mountainous Hillslope

AbstractPredicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below‐ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream‐driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

Categorization of crowd-sensing streaming data for contextual characteristic detection

The growing reliance on large wireless sensor networks, potentially consisting of hundreds of nodes, to monitor real-world phenomena inevitably results in large, complex datasets that become increasingly difficult to process using traditional methods. The inadvertent inclusion of anomalies in the dataset, resulting from the inherent characteristics of these networks, makes it difficult to isolate interesting events from erroneous measurements. Simultaneously, improvements in data science methods, as well as increased accessibility to powerful computers, lead to these techniques becoming more applicable to everyday data mining problems. In addition to being able to process large amounts of complex streaming data, a wide array of specialized data science methods enables complex analysis not possible using traditional techniques. Using real-world streaming data gathered by a temperature sensor network consisting of approximately 600 nodes, various data science methods were analyzed for their ability to exploit implicit dependencies embedded in unlabelled data to solve the complex task to identify contextual characteristics. The methods identified during this analysis were included in the construction of a software pipeline. The constructed pipeline reduced the identification of characteristics in the dataset to a trivial task, the application of which led to the detection of various characteristics describing the context in which sensors are deployed.

Application of FWM-Based OFC for DWDM Optical Communication System with Embedded FBG Sensor Network

Abstract Four-wave mixing optical frequency comb fibre-based setups (FWM-OFCs) have the potential to improve the combined dense wavelength division multiplexed passive optical network (DWDM-PON) and fibre Bragg grating (FBG) temperature sensors network providing easier application, broader technological opportunities for network development, and energy efficiency by substituting a power-demanding laser array. In this research, OFCs are generated for the purpose of combined network application of DWDM-PON and FBG optical sensors. The paper also investigates compatibility scenarios with OFCs in such systems. The mathematical simulation model has been developed and the performance of FWM-OFC based 8-channel 50 GHz spaced non-return-to-zero on-off keying (NRZ-OOK) modulated DWDM-PON transmission system, operating at 50 km single-mode fibre (SMF) with a bit rate of at least 10 Gbps embedded with 7 FBG optical temperature sensors, has been studied. As it is shown, FWM application results in OFC source that has fluctuations of the individual comb tones of less than 3 dB in power, and with an extinction ratio of about 33 dB for operation range of 192.9–193.25 THz, acting as a unified light source for all the data transmission channels. Embedded FBG optical sensors network causes negligible 0.3 dB power penalty.

Measurement and Control of Corrugated Board Production Parameters Taking into Account Individual Operator Preferences.

The article presents a proposal for optimizing the production process of corrugated cardboard based on measurements of process variables as well as the knowledge and skills of the operator conducting production. This technique involves continuous recording and analysis of process quantities that affect the quality of the produced cardboard. For this purpose, a network of temperature sensors with a system of continuous registration and monitoring of the process variables was designed and installed in the industrial environment of the corrugator. The recorded data is analyzed to estimate the desired values of the measured process variables, giving clues to how to control the production line. Unlike existing systems, the proposed algorithm for controlling production parameters allows each operator to use their preferred values for process variables independently of others. The proposed system allows for improving the quality of the produced cardboard and increasing the efficiency of its production by taking into account the individual experience and habits of the operator conducting production.

The silent impact of underground climate change on civil infrastructure

Urban areas increasingly suffer from subsurface heat islands: an underground climate change responsible for environmental, public health, and transportation issues. Soils, rocks, and construction materials deform under the influence of temperature variations and excessive deformations can affect the performance of civil infrastructure. Here I explore if ground deformations caused by subsurface heat islands might affect civil infrastructure. The Chicago Loop district is used as a case study. A 3-D computer model informed by data collected via a network of temperature sensors is used to characterize the ground temperature variations, deformations, and displacements caused by underground climate change. These deformations and displacements are significant and, on a case-by-case basis, may be incompatible with the operational requirements of civil structures. Therefore, the impact of underground climate change on civil infrastructure should be considered in future urban planning strategies to avoid possible structural damage and malfunction. Overall, this work suggests that underground climate change can represent a silent hazard for civil infrastructure in the Chicago Loop and other urban areas worldwide, but also an opportunity to reutilize or minimize waste heat in the ground.

Finding Representative Sampling Subsets in Sensor Graphs Using Time-series Similarities

With the increasing use of Internet-of-Things–enabled sensors, it is important to have effective methods to query the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) only a subset of the sensors at any given time, since the values of the non-sampled sensors can be estimated from the sampled values. If we can divide the set of sensors into disjoint so-called representative sampling subsets , in which each represents all the other sensors sufficiently well, then we can alternate between the sampling subsets and, thus, increase the battery life significantly of the sensor network. In this article, we formulate the problem of finding representative sampling subsets as a graph problem on a so-called sensor graph with the sensors as nodes. Our proposed solution, SubGraphSample , consists of two phases. In Phase-I, we create edges in the similarity graph based on the similarities between the time-series of sensor values, analyzing six different techniques based on proven time-series similarity metrics. In Phase-II, we propose six different sampling techniques to find the maximum number of representative sampling subsets . Finally, we propose AutoSubGraphSample , which auto-selects the best technique for Phase-I and Phase-II for a given dataset. Our extensive experimental evaluation shows that AutoSubGraphSample can yield significant battery-life improvements within realistic error bounds.

Study and Development of IOT based Smart Irrigation System using Soil Moisture and Weather Prediction

Abstract: The smart irrigation system was developed to optimize water use of crops. The system has a distributed wireless network of soil-moisture temperature sensors placed in the root zone of the plants. Also, a gateway unit handles sensor information, triggers, actuators, and transmits data to the web application. The intelligence of The proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web- services and a web-based information visualization and decision support system provides real-time information insights based on the analysis of sensor dataand weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging

Improved uncertainty evaluation for a long distance measurement by means of a temperature sensor network

The aim of the research described in this paper was to more accurately determine the measurement uncertainty of an interferometric long distance measurement. The chosen method was to install a temperature sensor network in the laboratory to measure in more detail the ambient temperature profile to more accurately determine the local values of the refractive index on the path travelled by the laser light. The experimental measurements were supplemented by a theoretical analysis of the mathematical model being used. The outcome of the performed work was that the claimed measurement uncertainty of the distance measurement, which is based on using only 5 temperature sensors, was justified. However, if in the future a lower uncertainty would be needed, a sensor network like the one that was temporarily installed would be needed. During the measurement campaign an offset in the mean temperature of 0.2 °C was found, which was equal to the maximum allowed bias in view of the claimed uncertainty for the long distance measurement. At a more general level, it was concluded that such sensor networks provide a useful new tool to increase the understanding of other measurements, to validate assumptions and optimize existing measurements.

The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback

Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.

Contrasting Journal on Agriculture Systems Using the Internet of Things

Agriculture is being rebuilt by the Internet of Everything (IoT) Temperature, humidity, and soil fertility are monitored using remote sensors. A field interruption, exact location, and wild plant species can all be found through an IOT-based investigation. The same wireless sensors may be used to monitor the soil's pH rate, temperature, and water level. A distributed wireless network of soil moisture and temperature sensors placed in plant root zones make up the implementation, of an automated watering system powered by solar panels. The Arduino board, which serves as an IoT gateway, receives data from these sensors, which continually monitor the settings, and processes it. A smartphone app gives customers access to live sensor data and enables farmers to take appropriate steps to meet the needs of the land. Farmers can use robotic systems for pesticide spraying, cultivating, and other such activities. Shows a block diagram of proposed concepts for an Internet of Things system.

Stand-alone Low Power Consumption FEE and DAQ for the Readout of Silicon Photomultipliers.

We developed a front end electronics (FEE) and data acquisition (DAQ) system with a low power consumption, especially intended for stand-alone applications in unattended environments without standard electricity supply. The system works autonomously thanks to dedicated algorithms that are embedded. The FEE is based on the EASIROC chip, designed for the readout of Silicon photomultipliers (SiPMs). It digitizes the amplitude of the signals and provides time information with time of flight capability. The trigger logic is programmable and physical and accidental coincidences rates can be measured. The SiPMs temperature is controlled by thermoelectric cells. Thanks to a network of temperature and humidity sensors, a real-time software sets the optimal operating point of the SiPMs depending on external conditions and if necessary halts the system to avoid damage to the electronics. The system has been used in several muon radiography experiments.

Diagnosis of abnormal body temperature based on deep neural network

INTRODUCTION: A method for diagnosing abnormal body temperature based on deep neural network is proposed. OBJECTIVES: To improve the diagnostic accuracy, reduce the false alarm rate, and improve the diagnostic level of abnormal body temperature. METHODS: According to the weight of the temperature sensor node itself and its neighbor nodes, the network trust relationship is established, and the node trust value is output through the combination of decision-making. Use trust value and double threshold to identify and remove malicious nodes, and optimize the network structure. The optimized temperature sensor network is used to collect human body temperature data. RESULTS: A deep neural network is used to construct a diagnosis model of abnormal body temperature, so as to realize the diagnosis of abnormal body temperature. CONCLUSION: The experimental results show that the method in this paper has high diagnostic accuracy, low false positive rate and high diagnostic efficiency, and can improve the diagnostic level of abnormal body temperature.

Temperature Sensor Position Planning

One of the challenges in large volume metrology is that it is often difficult or impossible to control the ambient conditions in which the object is to be measured. Dimensional measurement results vary with those conditions and it becomes necessary to apply some form of compensation. Thermal compensation of dimensional measurement is primarily reliant on the ability to properly measure temperature across the volume, which can differ by several degrees, but conventionally a uniform scaling has been applied. This paper focuses upon temperature sensor network planning improvement to facilitate thermal compensation. Beyond assembly environments, data from sensor networks are increasingly used to make decisions, but appropriate design and testing of such networks can be limited. As the demand for production digital twins increase, appropriate methods to quantify and optimise uncertainty to improve confidence will be invaluable. A virtual test bed has been created for the design, test, and optimisation of temperature sensor networks supported by physical simulation. Sensor networks have been used to take virtual measurements from a known temperature distribution and used to reconstruct the temperature distribution. Random search optimisation on a subset of the sensor network was carried out to determine some initial rules for sensor network design. The positioning of the sensors within the measurement volume and the method of reconstructing the temperature field was found to be more important than the capability of the individual sensors. Two means of interpolating the ambient field have been investigated: polynomial fitting and kriging. Temperature sensor networks appeared more sensitive to changes at the spatial boundary and these positions seem to be most critical for accurate reconstruction. In the case of the barrel section assembly it was found that asymmetric sensor heights produced better results, for example. A polynomial interpolation model using a 16-sensor network with 0.1 °C (confidence interval, k = 2) uncertainty sensors could produce a consistent temperature reconstruction error of ~0.04 °C RMS, corresponding to a thermal expansion error of ~1.5 μm in aluminium over a 1.6 m-tall structure.

Real-time forecast of compartment fire and flashover based on deep learning

Forecasting building fire development and critical fire events in real-time is of great significance for firefighting and rescue operations. This work proposes an artificial intelligence (AI) system to fast forecast the compartment fire development and flashover in advance based on a temperature sensor network and a deep-learning algorithm. This fire-forecast system is demonstrated in a 1/5 scale compartment with various ventilation conditions and fuel loads. After training 21 reduced-scale compartment tests, the deep learning model can well identify the fire development inside the compartment and predict the temperature 30 s in advance with relative errors of less than 10%. The flashover can be predicted with a 20-s lead time, and the forecast capacity and accuracy can be further improved with additional test data for training. The AI-forecast model performs well for fires with different fuel types and ventilation conditions and has the potential to be applied to fire scenarios with wider conditions. This research demonstrates the real-time building fire forecast based on Internet of Things (IoT) sensors and AI systems that can help future smart firefighting applications.

Development of Self-Generating Wireless Temperature Sensor Network Utilizing Pb-Free Energy-Harvesting Technology to Prevent Fire Caused by Using Lithium-Ion Batteries in Power Plants

A recent battery fire accident in a power plant has drawn attention to fire safety management. The lithium-ion battery, the most widely used among secondary batteries, has a high energy density with a high risk of ignition, owing to shock, overheating, discharging, and overcurrent. Installing batteries in a hazardous area inside a power plant increases the risk of a fire. Therefore, there is a need to construct a facility capable of monitoring the state of the managed area without using a battery. Therefore, the aim of this study is to develop a self-powered wireless temperature sensor network using Pb-free piezoelectric energy-harvesting technology. The developed harvester consisted of a Pb-free piezoelectric element produced using Ba0.9Ca0.1Ti0.93Zr0.07O3 + 0 .3 m ol % CuO ( BCTZ0.3C) ceramic. When the Pb-free piezoelectric energy harvester was operated for 15 s, power to stably operate the wireless sensor network could be obtained, sufficient to transmit the temperature measured once every 1.2 s to the wireless transmitter.

Lack of vegetation exacerbates exposure to dangerous heat in dense settlements in a tropical African city

Both climate change and rapid urbanization accelerate exposure to heat in the city of Kampala, Uganda. From a network of low-cost temperature and humidity sensors, operational in 2018–2019, we derive the daily mean, minimum and maximum Humidex in order to quantify and explain intra-urban heat stress variation. This temperature-humidity index is shown to be heterogeneously distributed over the city, with a daily mean intra-urban Humidex Index deviation of 1.2 ∘C on average. The largest difference between the coolest and the warmest station occurs between 16:00 and 17:00 local time. Averaged over the whole observation period, this daily maximum difference is 6.4 ∘C between the warmest and coolest stations, and reaches 14.5 ∘C on the most extreme day. This heat stress heterogeneity also translates to the occurrence of extreme heat, shown in other parts of the world to put local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index of 40 ∘C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. Large intra-urban heat stress differences are explained by satellite earth observation products. Normalized Difference Vegetation Index has the highest (75%) power to predict the intra-urban variations in daily mean heat stress, but strong collinearity is found with other variables like impervious surface fraction and population density. Our results have implications for urban planning on the one hand, highlighting the importance of urban greening, and risk management on the other hand, recommending the use of a temperature-humidity index and accounting for large intra-urban heat stress variations and heat-prone districts in urban heat action plans for tropical humid cities.

A Machine Learning Approach to Monitoring the UHI From GNSS Data

The urban heat island (UHI) effect has been widely studied because of its impacts on the environment and human’s wellbeing. The surface UHI (SUHI) is typically monitored with remote sensing products, whereas the air UHI (AUHI) is typically monitored using temperature sensor networks. The first technique requires clear days to obtain good images, and the latter is limited by the number of weather stations in an urban and rural area. An alternative to monitor the AUHI is the usage of global navigation satellite system (GNSS) data which is available in all weather conditions and there are well-developed GNSS receiver networks around the world. In this article, we propose a machine learning (ML) approach to estimating the air temperature from GNSS data to take advantage of the quantity of data. By training a support vector regressor with three datasets, GNSS-derived zenith tropospheric delay (ZTD), temperature, and day-of-the year, we can estimate temperature from GNSS data without the need for other environmental variables. The regressor presented in this article is trained with data from 2016 to 2019 and tested with data from 2020 in five Japanese metropolitan areas. The results show that it is possible to predict temperature from only the ZTD of GNSS signals with high precision, as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> between 0.82 and 0.90 is found for the predictions. Predicted temperatures are then used to study the diurnal cycle of the AUHI in Sapporo, Tokyo, Nagoya, Osaka–Kyoto, and Fukuoka with an average precision of 0.8 °C.

Smart Irrigation System Using Intelligent Robotics

To optimize water use for agricultural crops while also verifying water scarcity in the field, an automated irrigation system was developed. Weed management and control are critical for high-yielding, high-quality crops, and developments in weed control technologies have had a significant impact on agricultural output. Any weed control method that is effective must be both durable and versatile. Despite the variety in field circumstances, robust weed control technologies will successfully manage weeds. Weed control technology that is adaptable can change its strategy in response to changing weed populations, genetics, and environmental conditions. The system includes a distributed wireless network of soil-moisture and temperature sensors, as well as conductive sensors in the plant's root zone. Agate way unit also manages sensor data, triggers actuators, and sends data to an Android mobile device. To control water quantity, an algorithm with temperature and soil moisture threshold values was developed and programmed into a microcontroller-based gateway. The added future of this research is that we are utilising a robot to monitor the condition of the crop to see if it is affected by insects or not. The robot will move around the field, and we will be able to track the crop's health on our device.