Outdoor Environmental Monitoring Research Articles

In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered.

ELIXIR: An Expedient Connection Paradigm for Self-Powered IoT Devices

IoT devices usually work under power-constrained scenarios like outdoor environmental monitoring. Considering the cost and sustainability, in the long run, energy-harvesting technology is preferable for powering IoT devices. Since harvesting power is intrinsically weak and transient, the connection between IoT devices not only cannot be maintained constantly but is also extremely difficult to be established. In order to communicate, those devices should have a synchronized timeline so that both transmitter and receiver can start connection simultaneously. Yet due to the transient nature of ambient energy, volatile time data can be easily tampered with by the unstable power supply or corrupted completely by frequent power outages. As a result, unsynchronized IoT devices require significant efforts in time and energy to reconnect and communicate, which further escalates the performance degradation of the edge network. To adapt to ubiquitous self-powered scenarios on the IoT edge, we propose ELIXIR, an expedient connection paradigm, to synchronize swiftly and autonomously in a joint effort for self-powered IoT devices. The lightweight and highly efficient natures enable ELIXIR to be integrated into low-power IoT devices easily and efficiently. The experimental results show that the proposed ELIXIR can avoid a connection loop and help speed up the connection 2.83X and 1.84X faster than baseline methods.

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review.

Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.

Operation-Time Prolonging Method for IoTBased Outdoor Environment Monitoring System

It is important to analyze and comprehend outdoor environmental conditions to address air pollution problems. Accordingly, while remote sensing is suitable for analyzing global environment data, the Internet of Things (IoT) technique can help us analyze and comprehend local environment data. Moreover, considering how IoT-based outdoor environmental monitoring can operate for long periods in environments is necessary during difficulties in securing a power supply. Therefore, this study proposes an operation time-prolonging method for an IoT-based outdoor environment monitoring system by combining solar power, storage devices, and intermittent operation for stable operations in outdoor environments. With our proposed method, while solar panels provides power during the day, storage batteries provides power after sunset, with the sensor devices operating intermittently during this period. Furthermore, because the energy supplied depends on daylight hours, the proposed system has a sleep period that considers daylight hours when prolonging the system’s operation time. Consequently, the operation time of the sensor node during the adaptive sleep period control becomes 6.0 times longer than that without sleep period control, showing the effectiveness of the developed system. Hence, our results establish the effectiveness of the proposed adaptive sleep period control for prolonging the operation time of IoT network-based systems, indicating this finding’s usefulness for future research approaches.

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.

Safety Training Monitoring System of Ski Resort Based on Wireless Sensor Network

With the gradual rise in China’s ski industry, skiing is now the most common sport among winter outdoor sports, which is closely related to the origin of safety issues. Based on the ski resort, training is the key to safety detection, only to ensure the safety of training personnel and to maintain the momentum of high-speed development of skiing sports for a long time. The monitoring system plays an important role in the wireless sensor network monitoring system. Common monitoring systems are used for agriculture, smart home, and outdoor environment monitoring systems, most of which are professional fields. Based on the safety of ski resorts, this paper puts forward the research of ski training safety system based on the wireless sensor. In the research of ski training detection system based on the wireless sensor network, first, the safety system of domestic ski resort is analyzed and detailed suggestions are given. Then, this paper studies the routing protocol of wireless communication technology in detail and compares the service life and energy consumption of nodes under the use of two different routing protocols in wireless sensor networks. According to the data experiment in this paper, LEACH-MH protocol has great advantages over the LEACH protocol. The LEACH-MH protocol detects 185 more rounds than the LEACH protocol in the total detection lifetime. In the detection of energy consumption of 100 m ∗ 100 m, the LEACH protocol consumes 50J in 400 rounds, while the LEACH-MH protocol consumes only 50J in 760 rounds.

A novel three-dimensional Ag nanoparticles/reduced graphene oxide microtubular field effect transistor sensor for NO2 detections

A novel three-dimensional (3D) microtubular NO2 field effect transistor (FET) sensor has been fabricated from 2D reduced graphene oxide (rGO) nanosheets decorated with Ag nanoparticles, by applying the self-roll-up technique. The electrical properties of 2D and 3D Ag NP/rGO FET sensors have been investigated and compared. Finally, the performance of the 3D sensors has been demonstrated, where the preliminary results show that our 3D Ag NP/rGO FET NO2 sensor exhibits a relatively fast response (response time of 116 s) to 20 parts per million NO2 with a response of 4.92% at room temperature at zero bias voltage and 2 V source–drain bias voltage. Moreover, characteristics of our 3D Ag NP/rGO FET sensors, e.g. response, response and recovery times, have been demonstrated to be tuned by adjusting the applied source–drain and gate biases. Compared to the 2D geometry, our 3D geometry occupies less device area, but with the same sensing area. This study provides a new way to optimize sensing device performance, and promotes its development for miniaturized and integrated gas-sensing applications for indoor health and safety detection, outdoor environmental monitoring, industrial pollution monitoring and beyond.

Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring

AbstractIn the current work, the artificial intelligence techniques multilayer perceptron neural network (MLP), radial basis function neural network (RBF), and group method of data handling (GMDH) ...

A mechanically bendable and conformally attachable polymer membrane microlaser array enabled by digital interference lithography.

The progressive miniaturization and thinning of photonic devices would enable the realization of multi-functional photonic integrated circuits and expand the application frontier to novel fields including wearable and disposable electronics. Herein, we have demonstrated a mechanically bendable and conformally attachable polymer membrane microcavity laser array using digital interference lithography. The developed lithography system could distribute a number of subwavelength grating pixels with both high efficiency (1k pixels per second) and excellent versatility (ease of control in the pixel size, spacing, and grating periodicity) as the microcavity laser array, in which a pair of subwavelength gratings constitutes a distributed Bragg resonator microcavity via coherent interference, furnishes a vertically emitting microcavity laser array for convenient light coupling and utilization. The microlaser array polymer membrane presented a total thickness of only 30 μm with excellent performance stability and reliability against long time operation and harsh environmental conditions, which could be further reversibly stretched, repeatedly bendable and conformally attached onto rounded or irregular surfaces or biological tissues with no degradation in single-mode or low-threshold characteristics, paving a way for on-chip optical functionalization toward wearable electronics and outdoor environmental monitoring applications.

Development of Open-Tubular-Type Micro Gas Chromatography Column with Bump Structures.

Gas chromatography (GC) is the chemical analysis technique most widely used to separate and identify gas components, and it has been extensively applied in various gas analysis fields such as non-invasive medical diagnoses, indoor air quality monitoring, and outdoor environmental monitoring. Micro-electro-mechanical systems (MEMS)-based GC columns are essential for miniaturizing an integrated gas analysis system (Micro GC system). This study reports an open-tubular-type micro GC (μ-GC) column with internal bump structures (bump structure μ-GC column) that substantially increase the interaction between the gas mixture and a stationary phase. The developed bump structure μ-GC column, which was fabricated on a 2 cm × 2 cm μ-GC chip and coated with a non-polar stationary phase, is 1.5 m-long, 150 μm-wide, and 400 μm-deep. It has an internal microfluidic channel in which the bumps, which are 150 μm diameter half-circles, are alternatingly disposed to face each other on the surface of the microchannel. The fabricated bump structure μ-GC column yielded a height-equivalent-to-a-theoretical-plate (HETP) of 0.009 cm (11,110 plates/m) at an optimal carrier gas velocity of 17 cm/s. The mechanically robust bump structure μ-GC column proposed in this study achieved higher separation efficiency than a commercially available GC column and a typical μ-GC column with internal post structures classified as a semi-packed-type column. The experimental results demonstrate that the developed bump structure μ-GC column can separate a gas mixture completely, with excellent separation resolution for formaldehyde, benzene, toluene, ethylbenzene, and xylene mixture, under programmed operating temperatures.

Ppb-level nitric oxide photoacoustic sensor based on a mid-IR quantum cascade laser operating at 52 °C

A ppb-level nitric oxide (NO) photoacoustic sensor was reported for outdoor environmental monitoring. The sensor employed a continuous wave (CW), distributed feedback (DFB), mid-infrared (mid-IR) quantum cascade laser (QCL) operating at a high temperature of 52 °C, which allow avoiding any water-cooling systems and thereby reduces its size, weight and cost of the photoacoustic NO sensor. The QCL emits at 5.26 μm, which corresponds to a strong NO absorption doublet R(6.5). A differential photoacoustic cell was designed to operate together with the QCL, resulting in a detection sensitivity of ˜7 ppb at atmospheric pressure with a 1-s averaging time. An Allan–Werle deviation analysis indicated that a ppt-level detection sensitivity can be reached with an integration time of >100 s. The performance of this sensor system was evaluated in terms of humidity, linearity and stability. Continuous measurements covering a haze period were performed to demonstrate the stability and robustness of the reported NO sensor system.

Multi-gene genetic programming for predicting the heat gain of flat naturally ventilated roof using data from outdoor environmental monitoring

In this work, a multi-gene genetic programming (MGGP) approach was implemented to predict the heat gain per square meter for flat naturally ventilated roof using experimental data set. Experiments were conducted using a test cell with an adjustable ventilated roof, designed and instrumented to measure the incoming heat flux under outdoor environmental conditions. An MGGP predictive model was trained and tested considering as input data: ambient air temperature, solar irradiation, wind speed, relative humidity, and different ventilated flat roof channel widths. The developed model was statistically compared with others multivariate analysis methods, achieving good statistical performance, high correlation fitness, and the best generalized performance capacity (RMSE = 3.74, R2 = 94.52% for training data and RMSE = 3.72, R2 = 94.30% for testing data). In addition, a sensitivity analysis was conducted to identify the relative importance of the input parameters in the predictive model. According to the results, the proposed methodology based on evolutionary programming is useful to model the complex nonlinear relationship between the ventilated roof heat gains and outdoor environment. Finally, the methodology based on MGGP can be applied to identify the adequate ventilated channel widths that ensure thermal comfort and energy saving.

Assessing overheating risk and thermal comfort in state-of-the-art prototype houses that combat exacerbated climate change in UK

There is growing evidence that terraced houses—thermally lightweight, well insulated, naturally ventilated with three exposed wall surfaces—are at risk of overheating, especially in south-eastern England. The aim of this study is to evaluate the building performance and develop a reliable building simulation, which will be employed in the second phase of the study: developing affordable and feasible passive design strategies to support the energy-efficient building systems of the construction industry. This paper reports on the results from the first phase of the study where a quantitative methodology, including indoor and outdoor environmental monitoring, in-situ measurements and building simulation modelling, was adopted. The performance of a case study was modelled and simulated via employing Integrated Environmental Solutions (IES) software suite.The results from the base-case were analysed according to the adaptive thermal comfort of Chartered Institution of Building Services Engineers (CIBSE) Technical Memorandum 52 guidelines: The Limits of Thermal Comfort—Avoiding Overheating in European Buildings. The spaces studied within the case study house were observed to exceed the acceptable limits of thermal comfort; particularly, the large bedroom within this zone exceeded the upper limit for overheating up to 11 h daily. Furthermore, the results from the monitoring study indicate a high risk of summertime overheating across all the case study settings, especially during short-term peaks in outdoor temperatures. The main reasons for the problematic thermal performance were identified as well-insulated and fully air-tight building fabric, the lack of sufficient ventilation through the living spaces and excessive heat gains through the composite cladding material.

Low-cost, Battery-Powered, Efficient Wireless Intelligent Sensor (LEWIS2): Outdoors and Remote Sensing Applications

Structural Health Monitoring (SHM) systems are used to monitor critical infrastructure, such as bridges, high-rise buildings, and stadiums, and it has been observed that SHM has the potential to both prolong the structure lifespan and to improve public safety. Within SHM studies and in the research on monitoring in general, the application of wireless sensors (WS) has recently gained considerable research interest due to the cost reduction, associated with the installation and maintenance of monitoring systems. However, despite the advances in the research on WS, there are several issues and challenges in the application of WS that require improvements, in particular for Outdoor Environment Monitoring (OEM) because of their harsh operational conditions, huge targeted areas and limited energy budgets. This paper presents a low-cost, battery-powered, efficient wireless intelligent sensor (LEWIS2) that stores acceleration and angular velocity on a memory card to accurately estimate the trajectory of different applications.

ENZYME: An Energy-Efficient Transient Computing Paradigm for Ultralow Self-Powered IoT Edge Devices

Internet of Things (IoT) edge devices usually work under power-constrained scenarios like outdoor environmental monitoring. Considering the cost and sustainability in a long run, energy harvesting technology is preferable for edge devices. Nevertheless, the harvesting power is generally weak and unstable, making it difficult for edge devices to maintain the normal functionality. Hence, it is crucial to improve the energy efficiency of edge devices. This paper proposes a software paradigm, ENZYME, to improve the energy efficiency of edge devices for transient computing with ultralow energy harvesting power supplies. ENZYME consists of two lightweight yet highly efficient software modules including Routine Handler and frequency modulator (FM). Routine Handler assists power regulator to maximize power extraction from energy harvesters with proper operation routines. Further, FM maximizes the utility of the extracted energy for program execution via efficient runtime clock frequency modulation. The lightweight and highly efficient natures enable ENZYME to be integrated into low-power IoT edge devices easily and efficiently. Experimental results demonstrate that ENZYME achieves more than 8.8% energy efficiency over state-of-the-art techniques with Routine Handler, and 35.71% extra energy utility with FM upon applying Routine Handler.

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring.

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

High frequency surface acoustic wave resonator-based sensor for particulate matter detection

This paper describes the characterization of high frequency Surface Acoustic Wave Resonator based (SAWR) sensors, for the detection of micron and sub-micron sized particles. The sensor comprises two 262MHz ST-cut quartz based Rayleigh wave SAWRs where one is used for particle detection and the other as reference. Electro-acoustic detection of different sized particles shows a strong relationship between mass sensitivity (Δf/Δm) and particle diameter (Dp). This enables frequency-dependent SAWR sensitivity to be tailored to the size of particles, thus making these types of sensors good candidates for PM10, PM2.5 and ultrafine particle (UFP) sensing. Our initial characterisation demonstrated a typical SAWR frequency shift of 60Hz in response to a deposition of ca. 0.21ng of 0.75μm-sized gold particles (∼50 particles) on the sensor surface. Sensor responses to different sized particles, such as ∼30μm diameter silicon, gold (diameters of ∼0.75μm and ∼20μm), ∼8μm fine sugar, PTFE (∼1μm and ∼15μm), ∼4μm talcum powder, and ∼2μm molybdenum powder were evaluated, and an average mass sensitivity of 275Hz/ng was obtained. Based on the results obtained in this study we believe that acoustic wave technology has great potential for application in airborne particle detection. Moreover, acoustic resonator devices can be integrated with CMOS interface circuitry to obtain sensitive, robust, low-power and low-cost particle detectors for a variety of applications including outdoor environmental monitoring.

A Survey on Environmental Monitoring Systems using Wireless Sensor Networks

Air pollution is considered as crucial for people’s comfort, health, and safety. Monitoring the air pollution is crucial in both environments (indoors and outdoors), and can be accomplished using a small number of inexpensive sensing unities, distributed over an area of interest to measure the level of various critical gases including CO , H 2 S , and NO 2 . The deployment of Wireless Sensor Network offers an alternative solution through scattering a large number of disposable sensor nodes over an area of interest. Scientists may directly retrieve the sensed data from sensor field through a web server application. This paper provides a comprehensive review on the available solutions that support the wireless sensor network environmental monitoring. The existing systems are intensively studied and categorized into: indoor, outdoor, and greenhouse environmental monitoring systems

Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

Towards prolonged lifetime for deployed WSNs in outdoor environment monitoring

Recently, Wireless Sensor Networks (WSNs) emerged as a powerful and cost-efficient solution for unattended Outdoor Environment Monitoring (OEM) applications. These applications impose certain challenges on WSN deployment, including 3-Dimensional (3-D) settings, harsh operational conditions, and limited energy resources. To prolong lifetime of the deployed WSN, while mitigating the effects of these challenges, we propose the use of Relay Nodes (RNs) in addition to Sensor Nodes (SNs) in a distributed manner. While RNs facilitate reaching distant destinations, SNs can reserve their limited energy resources for sensing and data gathering. In addition, Mobile RNs (MRNs), which is a set of RNs capable of being reallocated (i.e. mobilized) at any point within the network lifetime, can be used to overcome possible link/node failure caused by the harsh conditions. It can also guarantee minimal energy consumption through imposing a balanced traffic distribution. This article proposes a 3-D grid-based deployment for heterogeneous WSNs (consisting of SNs, RNs, and MRNs). The problem is cast as a Mixed Integer Linear Program (MILP) optimization problem with the objective of maximizing the network lifetime while maintaining certain levels of fault-tolerance and cost-efficiency. Moreover, an Upper Bound (UB) on the deployed WSN lifetime, given that there are no unexpected node/link failures, has been driven. Based on practical/harsh experimental settings in OEM, intensive simulations show that the proposed grid-based deployment scheme can achieve an average of the expected UB.