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Journal articles on the topic 'Environmental sensor'
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MATSUMOTO, Yoshinori. "Environmental Sensor." Journal of The Institute of Electrical Engineers of Japan 134, no. 3 (2014): 140–43. http://dx.doi.org/10.1541/ieejjournal.134.140.
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Supriya Rai,, Dr, Tabassum Nadeem,, Piyush Bhansali,, Bhavin Sanghvi,, Harshith Bafna,, and Mehul Lunkkadd. "Biodegradable Sensors in Environmental Monitoring." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43525.
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The increasing need for sustainable and eco-friendly environmental monitoring solutions has driven advancements in biodegradable sensor technology. Traditional sensors, while effective in collecting data on soil health, pollution levels, and climate patterns, contribute to electronic waste (e-waste) and environmental degradation due to their reliance on non-renewable materials and energy sources. This research introduces the concept of Biodegradable Bioelectric Soil Sensors (BBS-Sensors)—a novel sensor technology designed to eliminate battery dependency by utilizing plant-root bioelectricity as a self-sustaining power source. Unlike conventional sensors, BBS-Sensors are fully biodegradable, ensuring they decompose into nutrient-rich compounds that enhance soil fertility rather than contribute to waste accumulation. Through an extensive review of existing biodegradable sensor technologies, this study explores the current limitations in power sustainability, scalability, environmental adaptability, and industrial adoption. The BBS-Sensor is proposed as an innovative alternative, leveraging bioelectricity from plant roots to provide a self-sustaining, zero-waste solution for precision agriculture and environmental monitoring. Findings suggest that BBS-Sensors can play a significant role in advancing climate-smart agriculture, soil health restoration, and sustainable environmental conservation. However, barriers such as scalability, energy efficiency, regulatory frameworks, and industrial viability must be addressed for widespread implementation. Future research should focus on enhancing bioelectric energy harvesting, integrating AI-powered analytics, and optimizing cost-effective production techniques. This study contributes to the growing body of research in biodegradable electronics, offering a transformative approach to environmental monitoring and sustainability. KEYWORDS: biodegradable sensors, environmental monitoring, sustainability, water quality, soil health, climate change and natural ecosystems
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Long, Keyu. "Advancements in Environmental Sensor Technology: Analysis, Applications, and Future Prospects." Highlights in Science, Engineering and Technology 97 (May 28, 2024): 21–28. http://dx.doi.org/10.54097/h4cq8647.
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With the rapid growth of science and technology and the quest to liberate labor and achieve a better quality of life, sensors have been invented to assist humans in detecting the environment and in human production and life. This technology has been updated at a high rate and has had a significant impact in different fields. This paper focuses on environmental sensors and provides an in-depth study of how sensors work, how data is processed, the results of the applications, and the related technologies. This paper analyzes the sensor technology by studying the sensors, analyzing some of the existing sensor systems, and researching the application results. Through the research, this paper investigates the specific role of sensors in human society in a wide range of important ways and describes the important role of sensor technology in protecting the environment and protecting human life and health. This paper concludes by summarizing the sensor technology, draws out the current shortcomings of the sensor technology, provides possible solutions to these problems, and predicts the future developments in sensor technology.
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Azizi, Shoaib, Ramtin Rabiee, Gireesh Nair, and Thomas Olofsson. "Effects of Positioning of Multi-Sensor Devices on Occupancy and Indoor Environmental Monitoring in Single-Occupant Offices." Energies 14, no. 19 (2021): 6296. http://dx.doi.org/10.3390/en14196296.
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The advancements in sensor and communication technologies drive the rapid developments in the applications of occupancy and indoor environmental monitoring in buildings. Currently, the installation standards for sensors are scarce and the recommendations for sensor positionings are very general. However, inadequate sensor positioning might diminish the reliability of sensor data, which could have serious impacts on the intended applications such as the performance of demand-controlled HVAC systems and their energy use. Thus, there is a need to understand how sensor positioning may affect the sensor data, specifically when using multi-sensor devices in which several sensors are being bundled together. This study is based on the data collected from 18 multi-sensor devices installed in three single-occupant offices (six sensors in each office). Each multi-sensor device included sensors to measure passive infrared (PIR) radiation, temperature, CO2, humidity, and illuminance. The results show that the positions of PIR and CO2 sensors significantly affect the reliability of occupancy detection. The typical approach of positioning the sensors on the ceiling, in the middle of offices, may lead to relatively unreliable data. In this case, the PIR sensor in that position has only 60% accuracy of presence detection. Installing the sensors under office desks could increase the accuracy of presence detection to 84%. These two sensor positions are highlighted in sensor fusion analysis as they could reach the highest accuracy compared to other pairs of PIR sensors. Moreover, sensor positioning can affect various indoor environmental parameters, especially temperature and illuminance measurements.
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Gao, Rui, Wenjun Zhang, Junmin Jing, et al. "Design, Fabrication, and Dynamic Environmental Test of a Piezoresistive Pressure Sensor." Micromachines 13, no. 7 (2022): 1142. http://dx.doi.org/10.3390/mi13071142.
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Microelectromechanical system (MEMS) pressure sensors have a wide range of applications based on the advantages of mature technology and easy integration. Among them, piezoresistive sensors have attracted great attention with the advantage of simple back-end processing circuits. However, less research has been reported on the performance of piezoresistive pressure sensors in dynamic environments, especially considering the vibrations and shocks frequently encountered during the application of the sensors. To address these issues, this paper proposes a design method for a MEMS piezoresistive pressure sensor, and the fabricated sensor is evaluated in a series of systematic dynamic environmental adaptability tests. After testing, the output sensitivity of the sensor chip was 9.21 mV∙bar−1, while the nonlinearity was 0.069% FSS. The sensor overreacts to rapidly changing pressure environments and can withstand acceleration shocks of up to 20× g. In addition, the sensor is capable of providing normal output over the vibration frequency range of 0–5000 Hz with a temperature coefficient sensitivity of −0.30% FSS °C−1 over the temperature range of 0–80 °C. Our proposed sensor can play a key role in applications with wide pressure ranges, high-frequency vibrations, and high acceleration shocks, as well as guide MEMS-based pressure sensors in high pressure ranges and complex environmental adaptability in their design.
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Nistor, P., and I. Orha. "Environmental Parameters Monitoring System." Carpathian Journal of Electronic and Computer Engineering 14, no. 2 (2021): 6–10. http://dx.doi.org/10.2478/cjece-2021-0007.
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Abstract The project presents the development of a system for monitoring environmental parameters. At the base of this system is the ESP-32S board that collects, processes and transmits data from the three sensors to the two web interfaces. The role of these web interfaces is to display the data collected from the sensors. The local web interface consists of two windows, the first window contains the table of sensors that displays the data measured by the sensors at that time. In the second window you can see the data measured by the sensors through graphs. They store the sensor data, giving the user the ability to view previously measured data. The local web interface provides sensor data only in the Wi-Fi network coverage area, and its data is deleted when the server is closed. The global web interface displays data using graphs. At the base of this web interface is the ThingSpeak platform that allows the system to transmit data anywhere in the world, store data in the Cloud space and the possibility of using special analysis functions.
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Singh, Ravinder, and Kuldeep Singh Nagla. "Comparative analysis of range sensors for the robust autonomous navigation – a review." Sensor Review 40, no. 1 (2019): 17–41. http://dx.doi.org/10.1108/sr-01-2019-0029.
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Purpose The purpose of this research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment. Design/methodology/approach Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot. Findings This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot. Originality/value This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.
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Anuj Singh Solanki, Akshay Kumar Asopa, Charu Shree, Sanjiv Kumar,. "The Role of Sensor in Environmental Monitoring." Tuijin Jishu/Journal of Propulsion Technology 43, no. 4 (2023): 259–62. http://dx.doi.org/10.52783/tjjpt.v43.i4.2348.
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Environmental pollution has become the major problem for us in recent times. Harmful gases are discharged by industries and many others cause pollution. Hence, there is a need to monitor and control these harmful contaminants, especially in air and water. So the sensors in environmental monitoring, highlighting their diverse applications, such as measuring air quality, water quality, soil conditions, and more. Sensor devices also offer high sensitivity, and fast response as per the user requirements. The role of sensors in environmental monitoring is enabling data collection and give information for decision-making. These devices detect physical, chemical, and biological parameters, such as temperature, humidity, pollutants, and biodiversity. They provide accurate, real time data. Sensor also give early warning systems for natural disasters and pollution incidents.As technology advances, sensor networks enhance our ability to monitor and safeguard the earth supporting global efforts to protect ecosystems and human well-being.
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Antunes, Alex. "Cheap Deployable Networked Sensors for Environmental Use." Journal of Telecommunications and the Digital Economy 2, no. 4 (2020): 15. http://dx.doi.org/10.18080/jtde.v2n4.271.
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We profile the utility and limits in deployment of inexpensive disposable sensor networks by amateurs to achieve environmental monitoring goals. Four current technology movements — PC-on-a-chip processors such as Arduino, prebuilt sensors, 3-D printing, and Wi-Fi and mesh networking — enable rapid sensor platform creation and make it easy for non-specialists to create general sensor-bearing platforms. Deployment of a cluster of multiple sensor-bearing platforms is, however, communications-limited in terms of both range and number of devices supported, and generally requires a base station with internet access. We examine how inexpensive technology supports scenarios for short-term environmental modelling by average citizens.
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Saputri, Fahmy Rinanda, and Vanessa Lee. "Web-based Environment Monitoring System." G-Tech: Jurnal Teknologi Terapan 7, no. 3 (2023): 807–15. http://dx.doi.org/10.33379/gtech.v7i3.2498.
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Green building is the concept of designing, building, operating, and maintaining buildings that pay attention to the use of natural resources, indoor air quality, and the health of the occupants. This green building is the choice in responding to the issue of limited non-renewable energy. To monitor the environmental conditions of green buildings, a sensor system is needed to maintain environmental conditions according to standards. This study aims to design a sensor system to monitor environmental conditions in Universitas Multimedia Nusantara. A real-time monitoring system is needed to maintain the green building environment's condition. The results of the study succeeded in designing a real-time monitoring system for green building environmental sensors, especially web-based classrooms using the NodeMCU ESP8266, DHT22 Sensor, and LDR Sensor. The difference in readings between the monitoring sensor system and the environment meter for lighting levels, temperature, and humidity respectively is 0.67 lux, 0.9°C, and 5.8%RH.
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Hart, Jane K., and Kirk Martinez. "Environmental Sensor Networks*." Bulletin of the Ecological Society of America 86, no. 3 (2005): 155–57. http://dx.doi.org/10.1890/0012-9623(2005)86[155:esn]2.0.co;2.
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Hart, Jane K., and Kirk Martinez. "Environmental Sensor Networks." Eos, Transactions American Geophysical Union 86, no. 16 (2005): 162. http://dx.doi.org/10.1029/2005eo160007.
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Martinez, K., J. K. Hart, and R. Ong. "Environmental sensor networks." Computer 37, no. 8 (2004): 50–56. http://dx.doi.org/10.1109/mc.2004.91.
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Schober, Sebastian A., Cecilia Carbonelli, and Robert Wille. "Simulating Defects in Environmental Sensor Networks Using Stochastic Sensor Models." Engineering Proceedings 6, no. 1 (2021): 88. http://dx.doi.org/10.3390/i3s2021dresden-10094.
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Chemiresistive gas sensors are an important tool for monitoring air quality in cities and large areas due to their low cost and low power and, hence, the ability to densely distribute them. Unfortunately, such sensor systems are prone to defects and faults over time such as sensitivity loss of the sensing material, less effective heating of the surface due to battery loss, or random output errors in the sensor electronics, which can lead to signal jumps or sensor stopping. Although these defects usually can be compensated, either algorithmically or physically, this requires an accurate screening of the entire sensor system for such defects. In order to properly develop, test, and benchmark corresponding screening algorithms, however, methods for simulating gas sensor networks and their defects are essential. In this work, we propose such a simulation method based on a stochastic sensor model for chemiresistive sensor systems. The proposed method rests on the idea of simulating the defect-causing processes directly on the sensor surface as a stochastic process and is capable of simulating various defects which can occur in low-cost sensor technologies. The work aims to show the scope and principles of the proposed simulator as well as to demonstrate its applicability using exemplary use cases.
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Stuetz, R. M., and J. Nicolas. "Sensor arrays: an inspired idea or an objective measurement of environmental odours?" Water Science and Technology 44, no. 9 (2001): 53–58. http://dx.doi.org/10.2166/wst.2001.0507.
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The measure of annoyance odours from sewage tratment, landfill and agricultural practise has become highly significant in the control and prevention of dorous emissions from existing facilities and its crucial for new planning applications. Current methods (such as GC-MS analysis, H2S and NH3 measurements) provide an accurate description of chemical compositions or act as surrogates for odour strength, but tell us very little about the perceived effect, whereas olfactometry gives the right human response but is very subjective and expensive. The use of non-specific sensor arrays may offer an objective and on-line instrument for assessing olfactive annoyance. Results have shown that sensor array systems can discriminate between different odour sources (wastewater, livestock and landfill). The response patterns from these sources can be significantly different and that the intensity of sensor responses is proportional to the concentration of the volatiles. The correlation of the sensors responses against odour strengths have also shown that reasonable fits can be obtained for a range of odour concentrations (100-800,000 ou/m3). However, the influence of environmental fluctuations (humidity and temperature) on sensor baselines still remains an obstacle, as well as the need for periodic calibration of the sensory system and the choice of a suitable gas for different environmental odours.
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Shashank, Pasupuleti. "Advanced Sensor Technologies in Autonomous Robots: Improving Real-time Decision Making and Environmental Interaction." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 6 (2024): 1–10. https://doi.org/10.5281/zenodo.14759551.
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The integration of advanced sensor technologies has revolutionized the capabilities of autonomous robots, enabling them to make real-time decisions and interact dynamically with their environment. Key sensor technologies such as LiDAR, cameras, ultrasonic sensors, IMUs, and force sensors provide critical data that helps robots navigate, avoid obstacles, and perform tasks autonomously. Through three case studies—LiDAR for autonomous navigation, vision and ultrasonic sensors for path planning, and force-torque sensors for robotic manipulation—we investigate how sensor fusion improves the accuracy and efficiency of robots in real-world applications. Additionally, we provide sample datasets, code snippets, and integration strategies for each case study, illustrating the practical applications of these sensor systems. This paper investigates how these technologies improve decision-making algorithms, discusses the challenges associated with their integration, and presents practical case studies that demonstrate real-time applications. Additionally, the paper explores methods of sensor fusion and provides sample code for real-time decision-making scenarios.
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Honeycutt, Wesley T., M. Tyler Ley, and Nicholas F. Materer. "Precision and Limits of Detection for Selected Commercially Available, Low-Cost Carbon Dioxide and Methane Gas Sensors." Sensors 19, no. 14 (2019): 3157. http://dx.doi.org/10.3390/s19143157.
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The performance of a sensor platform for environmental or industrial monitoring is sensitive to the cost and performance of the individual sensor elements. Thus, the detection limits, accuracy, and precision of commercially available, low-cost carbon dioxide and methane gas concentration sensors were evaluated by precise measurements at known gas concentrations. Sensors were selected based on market availability, cost, power consumption, detection range, and accuracy. A specially constructed gas mixing chamber, coupled to a precision bench-top analyzer, was used to characterize each sensor during a controlled exposure to known gas concentrations. For environmental monitoring, the selected carbon dioxide sensors were characterized around 400 ppm. For methane, the sensor response was first monitored at 0 ppm, close to the typical environmental background. The selected sensors were then evaluated at gas concentrations of several thousand ppm. The determined detection limits accuracy, and precision provides a set of matrices that can be used to evaluate and select sensors for integration into a sensor platform for specific applications.
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Tie, Yuhe, and Peiming Chen. "Environmental monitoring system design based on STM32 platform." Theoretical and Natural Science 53, no. 1 (2024): 1–9. http://dx.doi.org/10.54254/2753-8818/53/20240656.
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This study addresses the current societal demand for environmental monitoring by designing an environmental monitoring system based on the STM32 platform. This system assesses and monitors environmental conditions in real-time by tracking parameters such as CO, PM2.5, temperature, humidity, and light intensity. It holds significant value in preventing air pollution and improving indoor air quality. The system employs four types of sensors: the DHT11 digital temperature and humidity sensor, the BH1750FV light sensor, the GP2Y1010AUOF optical dust sensor, and the MQ-7 CO sensor to collect environmental data, which is then processed by the STM32F103C8T6 controller. This system is characterized by its real-time capabilities, high precision, and low power consumption, making it highly practical and valuable for widespread application. The paper provides a detailed discussion of sensor selection, measurement algorithms, and system design and implementation, offering valuable insights for research and applications in related fields.
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Tie, Yuhe, and Peiming Chen. "Environmental monitoring system design based on STM32 platform." Theoretical and Natural Science 41, no. 1 (2024): 16–24. http://dx.doi.org/10.54254/2753-8818/41/20240656.
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This study addresses the current societal demand for environmental monitoring by designing an environmental monitoring system based on the STM32 platform. This system assesses and monitors environmental conditions in real-time by tracking parameters such as CO, PM2.5, temperature, humidity, and light intensity. It holds significant value in preventing air pollution and improving indoor air quality. The system employs four types of sensors: the DHT11 digital temperature and humidity sensor, the BH1750FV light sensor, the GP2Y1010AUOF optical dust sensor, and the MQ-7 CO sensor to collect environmental data, which is then processed by the STM32F103C8T6 controller. This system is characterized by its real-time capabilities, high precision, and low power consumption, making it highly practical and valuable for widespread application. The paper provides a detailed discussion of sensor selection, measurement algorithms, and system design and implementation, offering valuable insights for research and applications in related fields.
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Hussein, Nadia Mahmoud, Yasmin Makki Mohialden, and Saba Abdulbaqi Salman. "Impact of IoT-Based Environmental Monitoring on Lab Safety and Sustainability." Babylonian Journal of Internet of Things 2024 (March 1, 2024): 16–26. http://dx.doi.org/10.58496/bjiot/2024/003.
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Several internal and external dangers might threaten a laboratory, the institution, and the public if not addressed properly. These hazards are addressed by our extensive laboratory monitoring system. This device detects elevated temperatures, gas leaks, fires, and solution pH. Elements of this system include an Esp32 microcontroller, LCD, Arduino Uno, mq2 gas sensor, DS18B20 temperature sensor, flame sensor, relay, water pump, and pH sensor for acidity measurement. The Arduino IDE controls sensors. Once the temperature reaches 50 degrees Celsius, an alarm, red LED, and mobile phone notification activate. Gas leaks activate a green LED, an alarm, and a message. Flame sensors notify and start water pumps when fires occur. Gas leaks and fires activate a buzzer. The IoT offers essential incident reporting and remote laboratory monitoring. The code is written in Arduino C++.
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Liu, Yan Ju, and Xin Hua Li. "Study on Application of Wireless Sensor Networking in Environmental Monitoring." Applied Mechanics and Materials 157-158 (February 2012): 1297–300. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1297.
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A novel wireless sensor networks is designed with integrating sensors, embedded operating systems and wireless networking technology. The temperature, humidity, light strength and pressure around the sensor could be measured accurately. The collected data by sensor networks are analysed and treated in PC computer via USB interface. LEACH communication protocol was introduced to ZigBee networks in this paper. The node programs were exploited based on IAR System platform to accomplish data collection.
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Han, Xiao, Hongwei Yan, Baojian Liu, and Wen Liu. "Emotional Feeling Evaluation Model in Underwater Environment Based on Wearable Sensor." Mathematical Problems in Engineering 2022 (March 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/2104465.
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Underwater sensor network technologies, as well as devices, are developing rapidly, and underwater IoT devices have been widely used in energy surveys, environmental indicator detection, military surveillance, and disaster event monitoring. The transmission of massive amounts of underwater data to the cloud for processing and analysis has become the dominant processing paradigm, and cloud computing has become a dominant computing paradigm. The preparation strategy of elastomer-coated hydrogel optical fibers for stable optical sensing proposed in this work opens up a new method and approach for developing low-cost and highly sensitive water flow sensors while analyzing the design of wearable smart devices to assess underwater environmental emotion perception evaluation schemes. In this paper, we propose a sensory data acquisition technique for event coverage detection of underwater environmental emotions, observing that an event may correspond to deviations from the normal sensory range of sensory data from multiple adjacent sensor nodes. Distributed edge computing is introduced to assume part of the cloud computing pressure, and an edge prediction-based data acquisition and sensing scheme for underwater sensor networks is proposed to realize the conversion of the acoustic communication transmission part of underwater data into data prediction transmission, thus reducing the energy consumption caused by acoustic communication. The model established in this paper effectively reduces sensor energy consumption while ensuring accurate data transmission and can respond to the underlying demand promptly, which is significantly better than the already existing schemes.
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Yadav, Sanchit, and Kamlesh Kumar Singh. "Smart Environmental Health Monitoring System." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 1 (2021): 1–5. http://dx.doi.org/10.54060/jieee/002.01.003.
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Pollution is a growing issue these days. It is necessary to analyze environment & keep it in check for a for best future as well as healthy living for all. Here we propose an Envi-ronment Monitoring System that permit us to watch & check live environment in espe-cially areas through Internet of Things (IOT). IoT supported a real time environmental monitoring system. It plays a crucial role in today’s world through a huge and pro-tract-ed system of sensor networks concerned to the environment & its parameters. This technique deals with monitoring important environmental conditions like temperature, humidity & CO level using the sensor & then this data is shipped to the web page. This information is often access from anyplace over the internet & then the sensor in-formation is presented as graphical statistics during mobile application. This paper explains & present the implementation & outcome of this environmental system uses the sensors for temperature, humidity, air quality & different environmental parameters of the surrounding space. This data is often used to take remote actions to regulate the conditions. Information is pushed to the distributed storage & android app get to the cloud & present the effect to the end users. The system employs a Node MCU, DHT-11 sensor, MQl35 sensor, which transmits data to WEBPAGE. An Android application is made which accesses the cloud data and displays results to the end users. The sensors interact with microcontroller which processes this information & transmit it over internet. This system is best method for any use in monitoring the environment and handling it because everything is controlled automatically through all the time of the process. The results say everything about the application of this system across different field where it was controlled precisely and effectively which further explains that this system easily makes our work easier because of this automatic monitoring system worries about other unexpected climate issues for world.
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Ding, Zhenke, Zhongliang Deng, Enwen Hu, Bingxun Liu, Zhichao Zhang, and Mingyang Ma. "A New Scene Sensing Model Based on Multi-Source Data from Smartphones." Sensors 24, no. 20 (2024): 6669. http://dx.doi.org/10.3390/s24206669.
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Smartphones with integrated sensors play an important role in people’s lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information—Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors—characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm.
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Huang, Qian, and Kane Rodriguez. "A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring." Applied Sciences 9, no. 5 (2019): 867. http://dx.doi.org/10.3390/app9050867.
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A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system.
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Khapalov, Alexander. "Source localization and sensor placement in environmental monitoring." International Journal of Applied Mathematics and Computer Science 20, no. 3 (2010): 445–58. http://dx.doi.org/10.2478/v10006-010-0033-3.
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Source localization and sensor placement in environmental monitoringIn this paper we discuss two closely related problems arising in environmental monitoring. The first is the source localization problem linked to the questionHow can one find an unknown "contamination source"?The second is an associated sensor placement problem:Where should we place sensors that are capable of providing the necessary "adequate data" for that?Our approach is based on some concepts and ideas developed in mathematical control theory of partial differential equations.
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Lu, Sheng Li, Qing Yun Gao, Yan Yan Tian, and Mei Ling Liu. "Atmospheric Environmental Monitoring System Based on Wireless Sensor Network." Advanced Materials Research 518-523 (May 2012): 1475–78. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.1475.
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The atmospheric environmental monitoring system based on wireless sensor network is made up of embedded gateway, sensor nodes and monitoring centre. Sensor node is composed of various sensors meeting the standards for environmental monitoring, ATmega16 microcontroller as a core and ZigBee wireless communication module used to send the acquisition data to the embedded gateway which runs Linux operating system in the S3C2440A embedded microprocessor. Atmospheric environmental monitoring center receives the environmental data uploaded by the embedded gateway and stores them in the database designed with Access 2007, and provides the function as data query based on VC.
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Zwick, Markus, Matthias Gerdts, and Peter Stütz. "Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results." Sensors 23, no. 2 (2023): 664. http://dx.doi.org/10.3390/s23020664.
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UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values.
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Vasiljevic, Dragana, Cedo Zlebic, Goran Stojanovic, Mitar Simic, Libu Manjakkal, and Zoran Stamenkovic. "Cost-effective sensors and sensor nodes for monitoring environmental parameters." Facta universitatis - series: Electronics and Energetics 31, no. 1 (2018): 11–23. http://dx.doi.org/10.2298/fuee1801011v.
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This paper reviews the design and characterization of humidity and pH sensors manufactured in the printed circuit board (PCB), ink-jet, and screen printing technologies. The first one (PCB technology) provides robust sensors with PET film which can be exposed to harsh environment. The second (ink-jet technology) can manufacture sensors on flexible substrates (foils and papers). The third (screen printing technology) has been used to implement a thick-film sensor. In addition to this, a multi-sensor cloud-based electronic system with autonomous power supply (solar panels) for air and water quality monitoring has been described. Finally, a flexible and modular hardware platform for remote and reliable sensing of environmental parameters has been presented.
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Blinova, Tatiana, Sanjay Singh Chauhan, Tara Singla, Shweta Bansal, Apeksha Mittal, and V. Sahithi Yellanki. "Performance Evaluation of IoT Sensors in Urban Air Quality Monitoring: Insights from the IoT Sensor Performance Test." BIO Web of Conferences 86 (2024): 01088. http://dx.doi.org/10.1051/bioconf/20248601088.
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In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment.
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Handayani, Ade Silvia, Nyayu Latifah Husni, and Rosmalinda Permatasari. "Environmental Application with Multi Sensor Network." Computer Engineering and Applications Journal 9, no. 1 (2020): 61–77. http://dx.doi.org/10.18495/comengapp.v9i1.322.
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This paper aimed to monitor temperature, humidity, and CO gas level using environmental application with multi sensor network (MSN). This system was applied in real life and real time, to be able to obtain data and information through mobile devices and other on internet network. In this research, environmental application is monitored remotely using displays on the web and sensors as device. This research obtained data in outdoor and indoor parking area also with obstacles and without obstacles, so it obtained the results from each of the different environmental conditions.
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Wang, Zhihan. "Evaluating the efficacy of machine learning in calibrating low-cost sensors." Applied and Computational Engineering 71, no. 1 (2024): 30–38. http://dx.doi.org/10.54254/2755-2721/71/20241635.
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Ambient air quality monitoring requires low-cost environmental sensor devices that are affordable and feasible for large-scale implementation. However, issues such as sensor drift, environmental sensitivity, and inter-sensor variability affect data accuracy and cannot be adequately addressed by traditional calibration methods. This paper summarizes the use of machine learning techniques for calibrating low-cost sensors. The literature review shows that machine learning models like Random Forest, Support Vector Regression, and Neural Networks significantly improve sensor accuracy and reliability. For instance, Random Forest models reduced the root mean squared error by 30% for PM2.5 measurements, while Neural Networks achieved an R value of 0.997 for methane sensors. Integrating machine learning with IoT and mobile technologies enhances real-time monitoring and spatial resolution. Identified gaps include the quality of training datasets, managing environmental variability, and improving model transferability across different contexts. Addressing these gaps through advanced models and real-time calibration methodologies will further enhance sensor performance, ensuring more precise and reliable environmental data.
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Buček, Pavel, Petr Maršolek, and Jiří Bílek. "Low-Cost Sensors for Air Quality Monitoring - the Current State of the Technology and a Use Overview." Chemistry-Didactics-Ecology-Metrology 26, no. 1-2 (2021): 41–54. http://dx.doi.org/10.2478/cdem-2021-0003.
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Abstract In recent years the monitoring of air quality using cheap sensors has become an interesting alternative to conventional analytical techniques. Apart from vast price differences conventional techniques need to be performed by the trained personnel of commercial or research laboratories. Sensors capable of measuring dust, ozone, nitrogen and sulphur oxides, or other air pollutants are relatively simple electronic devices, which are comparable in size to a mobile phone. They provide the general public with the possibility to monitor air quality which can contribute to various projects that differ in regional scale, commercial funding or community-base. In connection with the low price of sensors arises the question of the quality of measured data. This issue is addressed by a number of studies focused on comparing the sensor data with the data of reference measurements. Sensory measurement is influenced by the monitored analyte, type and design of the particular sensor, as well as by the measurement conditions. Currently sensor networks serve as an additional source of information to the network of air quality monitoring stations, where the density of the network provides concentration trends in the area that may exceed specific measured values of pollutant concentrations and low uncertainty of reference measurements. The constant development of all types of sensors is leading to improvements and the difference in data quality between sensors and conventional monitoring techniques may be reduced.
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Mayton, Brian, Gershon Dublon, Spencer Russell, et al. "The Networked Sensory Landscape: Capturing and Experiencing Ecological Change Across Scales." Presence: Teleoperators and Virtual Environments 26, no. 2 (2017): 182–209. http://dx.doi.org/10.1162/pres_a_00292.
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What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.
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Tanikawa, Tamio, Kenichi Ohara, and Kohtaro Ohba. "Structural environmental informatization using sensor network with heterogeneous sensors." Intelligent Service Robotics 3, no. 1 (2009): 21–28. http://dx.doi.org/10.1007/s11370-009-0055-6.
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Sathyamoorthy, Sruthi, Ugochukwu Okwudili Matthew, Temitope Samson Adekunle, and Nwamaka U. Okafor. "Advances and Challenges in IoT Sensors Data Handling and Processing in Environmental Monitoring Networks." HAFED POLY Journal of Science, Management and Technology 5, no. 2 (2024): 40–60. http://dx.doi.org/10.4314/hpjsmt.v5i2.3.
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Advances in IoT technologies provide new epoch in ecological sensing, leading to the deployment of millions of sensor devices to sense and monitor the environment. IoT sensors have the capacity to provide high spatial and temporal resolution data to supplement traditional data-gathering methods, thereby filling the gaps that exist within current environmental data-gathering methods. Applications of IoT sensors in environmental monitoring are broad ranging from monitoring air quality, to monitoring biodiverse regions including forests and peatlands to protecting endangered species. The use of IoT sensor devices in environmental monitoring, however, has raised several questions, especially pertaining to the quality of sensor data, reliability, accuracy, and in-field performance. IoT sensors are prone to failures and errors especially when deployed for medium to longer-term. A common question within the IoT research domain is how to handle IoT sensor data, especially in terms of processing, fusion with other data sources and analysis to glean useful insights from the data in support of effective decision-making. Several authors have proposed different data handling methods for IoT sensor data and proposed techniques have led to improvement in overall data quality and field performance. Methods for addressing IoT sensor data analysis integration with emerging technologies, such as cloud computing, fog computing, and edge computing along with methods to make Data storage choices have also been proposed. This paper surveys the various methods for handling and processing IoT sensor data in environmental monitoring networks, the prospects, challenges, and limitations of these methods are examined.
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Corke, Peter, Tim Wark, Raja Jurdak, Wen Hu, Philip Valencia, and Darren Moore. "Environmental Wireless Sensor Networks." Proceedings of the IEEE 98, no. 11 (2010): 1903–17. http://dx.doi.org/10.1109/jproc.2010.2068530.
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Linevych, Yaroslav Oleksiiovych, and Viktoriia Mykhailivna Koval. "Sensors Based on Nanoscale Silicon 1D Structures for Industrial, Environmental and Medical Monitoring." Microsystems, Electronics and Acoustics 27, no. 2 (2022): 264376–1. http://dx.doi.org/10.20535/2523-4455.mea.264376.
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Article is devoted to the analysis of modern sensors based on silicon nanowires (SiNWs) to determine the influence of SiNWs synthesis parameters and their structural features on device characteristics. A modern trend in the development of electronic sensing devices is the use of various types of nanomaterials in order to increase sensor sensitivity and miniaturize of their size. 1D nanomaterials, namely SiNWs, have several advantages for sensor applications, such as a large surface-to-volume ratio and an increased rate of diffusion of the main charge carriers. Based on the literature analysis, an overview of modern SiNWs sensors was made. The advantages of silicon 1D structures were shown by comparison with other types of nanostructures. Also sensors were classified according to the methods of synthesis of SiNWs, sensor principle operation, kind of input value and types of applied modifiers. Silicon nanowires were most often synthesized by the method of metal-stimulated chemical etching, the advantages of which include the simplicity of implementation, low cost, and the ability to synthesize nanostructures with a high aspect ratio. The vapor-liquid-solid synthesis was also used, the advantages of which include the ability to be adapted to any technology of supplying a gas mixture with the target component and the possibility of obtaining nanowires with a diameter of 10 nm or less. According to the principle operation, the most of sensors developed on the basis of silicon nanowires are of electrical type (resistive, capacitive, electrochemical, diode or transistor type), optical sensors (fluorescent) are developed to a much lesser extent. Gas sensors (ethanol, oil vapor, formaldehyde, ammonia, nitrogen oxide, hydrogen, carbon dioxide,), liquid sensors (glucose, hydrogen peroxide, ethanol, heavy metal ions, pH), and physical values (humidity, temperature and illumination) have been developed on the basis of silicon 1D nanoscale structures. The following surface modifiers of nanowires were used to improve the performance characteristics: noble metal nanoparticles, metal-organic framework structures, carbon nanotubes, graphene, self-assembled monolayers, metal and metal oxide thin films. In particular, it was shown that the modification of the surface of the array of SiNWs with noble metals led to an increase in the sensitivity of the hydrogen sensor by 80%. Modification of formaldehyde sensor using reduced graphene oxide resulted in an improvement of sensor sensitivity by more than 2 times. The influence of SiNWs synthesis parameters on sensor performance characteristics was also determined. In particular, it was shown that increasing of SiNWs width from 20–30 nm to 500–600 nm led to an increase in the sensitivity of humidity sensor from 4.5 to 7.5%. Increasing the etching time caused the synthesis of longer nanowires, which improved the sensitivity of carbon dioxide sensors from 0.6 to 2.5%. Dependences established in this work will make it possible to develop the production technology of various types of sensors based on silicon nanowires with high sensitivity, selectivity, stability and operation speed.
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Caroleo, Fabrizio, Gabriele Magna, Mario Luigi Naitana, et al. "Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring." Sensors 22, no. 7 (2022): 2649. http://dx.doi.org/10.3390/s22072649.
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Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.
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Mori, Fumihide, Keishin Koh, and Takayuki Misu. "Development of a Sensor Module for Monitoring Indoor Environments." Applied Mechanics and Materials 475-476 (December 2013): 455–59. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.455.
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We developed a sensor module by using various commercial sensors and a wireless communicating module. The sensor module can be used to monitor an indoor environment via its temperature sensor, humidity sensor, light sensor, odor sensor and atmospheric pressure sensor. We also designed a user interface for a tablet PC. While communicating with the sensor module wirelessly, the data of an indoor environment were collected in real time. Using the environmental data, we calculated the Wet Bulb Globe Temperature (WBGT) and the discomfort index (DI) to determine the threshold values of an indoor environment for preventing heat illness and to obtain information on environmental quality.
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Veeramuthu, Loganathan, Manikandan Venkatesan, Fang-Cheng Liang, et al. "Conjugated Copolymers through Electrospinning Synthetic Strategies and Their Versatile Applications in Sensing Environmental Toxicants, pH, Temperature, and Humidity." Polymers 12, no. 3 (2020): 587. http://dx.doi.org/10.3390/polym12030587.
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Conjugated copolymers (CCPs) are a class of polymers with excellent optical luminescent and electrical conducting properties because of their extensive π conjugation. CCPs have several advantages such as facile synthesis, structural tailorability, processability, and ease of device fabrication by compatible solvents. Electrospinning (ES) is a versatile technique that produces continuous high throughput nanofibers or microfibers and its appropriate synchronization with CCPs can aid in harvesting an ideal sensory nanofiber. The ES-based nanofibrous membrane enables sensors to accomplish ultrahigh sensitivity and response time with the aid of a greater surface-to-volume ratio. This review covers the crucial aspects of designing highly responsive optical sensors that includes synthetic strategies, sensor fabrication, mechanistic aspects, sensing modes, and recent sensing trends in monitoring environmental toxicants, pH, temperature, and humidity. In particular, considerable attention is being paid on classifying the ES-based optical sensor fabrication to overcome remaining challenges such as sensitivity, selectivity, dye leaching, instability, and reversibility.
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L'Hommedieu, Michelle, Justin L'Hommedieu, Cynthia Begay, et al. "Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization." JMIR mHealth and uHealth 7, no. 12 (2019): e13305. http://dx.doi.org/10.2196/13305.
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Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.
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Zhang, Xuan, and Xiaomin Dang. "Study on setting method of temperature sensor in aircraft environmental control system." Journal of Physics: Conference Series 2764, no. 1 (2024): 012031. http://dx.doi.org/10.1088/1742-6596/2764/1/012031.
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Abstract This paper analyzes the working principle of each component of the aircraft environmental control system (ECS), takes the Boeing 737 aircraft as an example, and analyzes the distribution and role of temperature sensors in the aircraft ECS. Then, this paper presents a guideline for sensor setup suitable for each subsystem. The accuracy of the temperature sensor is measured, which verifies that the temperature sensor selection and set is reasonable and can ensure the good operation of the aircraft ECS.
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Kirsanov, Dmitry, Subhankar Mukherjee, Souvik Pal, et al. "A Pencil-Drawn Electronic Tongue for Environmental Applications." Sensors 21, no. 13 (2021): 4471. http://dx.doi.org/10.3390/s21134471.
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We report on the development of a simple and cost-effective potentiometric sensor array that is based on manual “drawing” on the polymeric support with the pencils composed of graphite and different types of zeolites. The sensor array demonstrates distinct sensitivity towards a variety of inorganic ions in aqueous media. This multisensor system has been successfully applied to quantitative analysis of 100 real-life surface waters sampled in Mahananda and Hooghly rivers in the West Bengal state (India). Partial least squares regression has been utilized to relate responses of the sensors to the values of different water quality parameters. It has been found that the developed sensor array, or electronic tongue, is capable of quantifying total hardness, total alkalinity, and calcium content in the samples, with the mean relative errors below 18%.
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Bílek, Jiří, Ondřej Bílek, Petr Maršolek, and Pavel Buček. "Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation." Environments 8, no. 11 (2021): 114. http://dx.doi.org/10.3390/environments8110114.
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Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).
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Tereshkov, Mykhail, Tetiana Dontsova, Bilge Saruhan, and Svitlana Krüger. "Metal Oxide-Based Sensors for Ecological Monitoring: Progress and Perspectives." Chemosensors 12, no. 3 (2024): 42. http://dx.doi.org/10.3390/chemosensors12030042.
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This paper aims to provide a large coverage of recent developments regarding environmental monitoring using metal oxide-based sensors. Particular attention is given to the detection of gases such as H2, COx, SOx, NOx, and CH4. The developments and analyses of the design of sensors and types of metal oxide sensing materials are emphasized. The sensing mechanisms and peculiarities of metal oxides used in chemoresistive sensors are provided. The main parameters that affect the sensitivity and selectivity of metal oxide sensors are indicated and their significance to the sensor signal is analyzed. Modern data processing algorithms, employed to optimize the measurement process and processing of the sensor signal, are considered. The existing sensor arrays/e-nose systems for environmental monitoring are summarized, and future prospects and challenges encountered with metal oxide-based sensor arrays are highlighted.
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Pasupuleti, Murali Krishna. "Next-Gen Earth System Modeling: Real-Time Environmental Forecasting Using AI and Visual Sensor Fusion." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 200–211. https://doi.org/10.62311/nesx/rp1525.
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Abstract: The research paper titled "Next-Gen Earth System Modeling: Real-Time Environmental Forecasting Using AI and Visual Sensor Fusion" delves into the cutting-edge integration of Artificial Intelligence (AI) and Visual Sensor Fusion for advancing environmental forecasting in real-time scenarios. This pioneering approach combines the power of AI algorithms with visual data captured from sensors to enhance the accuracy and timeliness of environmental predictions. By leveraging the strengths of AI in processing vast amounts of data and the insights derived from visual sensor inputs, this research opens new avenues for improving Earth system modeling and forecasting precision. The synergistic blend of AI and Visual Sensor Fusion holds immense promise for revolutionizing how we monitor, analyze, and predict environmental changes with unprecedented speed and accuracy. Keywords: :Earth System Modeling, Real-Time Forecasting, Artificial Intelligence, Visual Sensor Fusion, Environmental Predictions
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Nalakurthi, Naga Venkata Sudha Rani, Ismaila Abimbola, Tasneem Ahmed, et al. "Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors." Sensors 24, no. 11 (2024): 3650. http://dx.doi.org/10.3390/s24113650.
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The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.
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Zhu, Bi Hua, Da Qing Zhu, and Ying Li Zhu. "Design of the Monitoring System for Valuable Chinese Herbal Medicine Growth Based on CC2430." Advanced Materials Research 945-949 (June 2014): 1948–51. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1948.
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Due to lots of the precious Chinese herbal medicines are on the edge of exhaustion, the paper presents a real-time monitoring system based wireless sensor network in order to protect Chinese herbal medicines and do artificial cultivation. Temperature, humidity and light condition is very important to the growth of Chinese herbal medicines growth. How to scientifically adjust the environment parameters is essential, it’s benefit for Chinese herbal medicines to improve the yield and quality. According to herbal medicines environmental characteristics, sensor networks were designed which the data can be accurately collected so as to track and monitor environmental factors of grow influence in real time or continuous. The system gives the hardware design of wireless sensor node and software implementations. Wireless sensor network nodes communication circuit was designed by CC2430, monitor environmental parameters with air temperature and humidity sensors and light intensity sensors. Experimental results show: the sensor network monitoring system can make the artificial cultivation of the medicines more modern, scientific and objective.
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Damodaran, Nedumaran, Sivamani Kalyansundaram, and Vinoth R. "Development And Testing of Arduino Based Autonomous Environmental Monitoring System." IJCRT Journal | UGC Approved Journal | Scopus Indexed Journal Norms 14, no. 4 (2024): 1–7. http://dx.doi.org/10.61359/2024050026.
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This paper presents the development of an Autonomous Environmental Monitoring System (AEMS) designed to track various environmental parameters. With the growing demand for autonomous monitoring systems in today’s modern era, we propose an Arduino-based real-time hardware solution to measure parameters such as flame, temperature, humidity, and distance. The system leverages advanced and cost-effective electronic sensors and hardware to monitor these environmental factors. We developed individual sensor modules for each parameter and integrated them into a unified multi-sensor system through sensor fusion. The collected data is then displayed on a 16 × 2 LCD module. The result is a fully functional autonomous monitoring system, tested under laboratory environmental conditions using a compact, handheld device.