Environmental Monitoring System Research Articles

Microservice architecture for a remote management platform for pastured poultry farming using Amazon Web Services and wireless mesh sensor networks

Introduction: A variety of innovative solutions known as Precision Livestock Farming (PLF) technologies have been developed for the management of animal production industries, including Wireless Sensor Networks (WSN) for poultry farming.
 Problem: Current WSN-based systems for poultry farming lack the design of robust but flexible software architectures that ensure the integrity and proper delivery of data.
 Objective: Designing a microservice-based software architecture (MSA) for a multiplatform remote environmental management system based on Wireless Mesh Sensor Networks (WMSN) to be deployed in pastured poultry farming spaces.
 Methodology: A review about MSAs designed for animal farming was conducted, to synthesize key factors considered for the design process of the system data flow, microservice definition and the environmental monitoring system technology selection.
 Results: A cloud MSA with a multi layered scheme using the Amazon Web Services (AWS) platform was developed, validating the persistence of environmental data transmitted from WMSN prototype nodes to be deployed in mobile chicken coops.
 Conclusion: Defining an End-to-End data flow facilitates the organization of tasks by domains, allowing efficient event communication between components and network reliability both at the hardware and software levels.
 Originality: This study presents a novel design for a remote environmental monitoring system based on WMSN for mobile coops used in pastured poultry and a multi layered MSA cloud management platform for this specific type of food production industry.
 Limitations: Software architecture technology selection was based only on services offered, to the date of the study, in the free tier of the Amazon Web Service platform.

Development of boron doped diamond electrodes material for heavy metal ion sensor with high sensitivity and durability

We report on the optimized substrate pretreatment and deposition process conditions for boron-doped diamond (BDD) electrodes fabricated by hot-filament chemical vapor deposition (HFCVD). The optimized BDD electrode with a doping concentration of 8000 ppm showed high accuracy and precision in detecting Cd(II), Pb(II), and Cu(II) ions. In addition, this demonstrates excellent selectivity against external metal ions under the optimized stripping voltammetry measurement conditions. The detection limits of the target ions of Cd(II), Pb(II), and Cu(II) were 0.55 (±0.05), 0.43 (±0.04), and 0.74 (±0.06) μg/L (S/N = 3), respectively. In real samples spiked with 100 μg/L Cd(II), Pb(II), and Cu(II), both the accuracy and precision of the BDD electrode were within 5%; the interference with organic matter was also negligible. The excellent selectivity and long-term stability indicate that the BDD electrode developed in this study are potentially useful for online water environment monitoring systems.

Mechanical intelligent wave energy harvesting and self-powered marine environment monitoring

Harvesting wave energy to power wireless sensors can realize marine environment monitoring. However, the low frequency and irregularity of waves, as well as the harsh marine environment, have become bottlenecks limiting the development of wave energy harvesting. Herein, we propose a novel concept of mechanical intelligent energy harvesting, i.e., adaptive external excitation and regulation of energy harvesting system by mechanical structure or mechanism rather than electrical components, and illustrate this concept by designing an irregular wave energy harvesting system. The proposed gravity-driven roller and seesaw-inspired structure are sensitive to low-frequency and irregular excitation, thereby wave energy can be more easily harvested into the system. Further, the bidirectional swinging of the seesaw is converted into a high-speed unidirectional rotation of the permanent magnet disc in one direction, resulting in a significant increase in the efficiency of electromechanical conversion. Moreover, symmetrical discs on both sides can rotate synchronously via magnetic coupling mechanism, which increases the magnetic flux in the coils for higher output power and the consistent phase of the voltage is conducive to the use of electricity. Besides, the rolling behavior is used by the triboelectric nanogenerator (TENG) to convert more mechanical energy into electricity without affecting the electromagnetic energy harvester. The experimental results show that the harvester can work effectively at the ultra-low frequency (0.1 Hz) and it can charge a 0.47 F capacitor to 5 V within 6 min to achieve a self-powered wireless marine environment monitoring system under simulated wave excitation. This work demonstrates that mechanical intelligent energy harvesting is a potential solution for irregular energy harvesting and self-powered Internet of Things (IoT).

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Machine learning has been used to mine the massive data collected by automatic environmental monitoring systems and predict the changes in the environmental factors in lakes. However, further study is needed to assess the feasibility of the development of a universal machine-learning-based turbidity model for a large shallow lake with considerable spatial heterogeneity in environmental factors. In this study, we collected and examined sediment and water quality data from Lake Taihu, China. Three monitoring stations were established in three lake zones to obtain continuous time series data of the water quality and meteorological variables. We used these data to develop three turbidity models based on long short-term memory (LSTM). The three zones differed in terms of environmental factors related to turbidity: in West Taihu, the Lake Center, and the mouth of Gonghu Bay, the critical shear stress of bed sediments was 0.029, 0.055, and 0.032 N m−2, and the chlorophyll-a concentration was 23.27, 14.62, 30.80 μg L−1, respectively. The LSTM-based turbidity model developed for any zone could predict the turbidity in the other two zones. For the model developed for West Taihu, its performance to predict the turbidity in the local zone (i.e., West Taihu) was inferior to that for the other zones; the reverse applied to the models developed for the Lake Center and Gonghu Bay. This can be attributed to the complex hydrodynamics in West Taihu, which weakens the learning of LSTM from the time series data. This study explores the feasibility of the development of a universal LSTM-based turbidity model for Lake Taihu and promotes the application of machine learning algorithms to large shallow lakes.

Construction of electrochemiluminescence aptasensor for acetamiprid detection using flower-liked SnO2 nanocrystals encapsulated Ag3PO4 composite as luminophore

Acetamiprid is a kind of broad-spectrum pesticides that has been largely applied in agriculture and household pest control. In this paper, a simple, novel and highly sensitive electrochemiluminescence aptamer sensor (ECL aptasensor) based on Ag3PO4-NH2-SnO2 was proposed. The ECL signal was greatly amplified with the synergistic effect of Ag3PO4 and NH2-SnO2 (Ag3PO4-NH2-SnO2) which was dropped on a glass carbon electrode (GCE) and used as working electrode. We designed an “on–off” ECL sensing strategy for the sensitive and specific detection of acetamiprid. Under the optimal conditions, the linear range of the sensor for acetamiprid detection was 1.0 × 10−11 ∼ 1.0 × 10−5 M, with a detection limit of 2.5 × 10−12 M (S/N = 3). The method showed simple and fast operation, high sensitivity and selectivity, strong anti-interference ability and good stability. More importantly, the developed aptasensor exhibited excellent recognition ability towards residual acetamiprid in actual water samples, which might open promising avenues to develop new ECL systems for biological analysis and environmental water monitoring.

Internet of Things (IoT)-Based Environmental Monitoring and Control System for Home-Based Mushroom Cultivation

The control and monitoring of the environmental conditions in mushroom cultivation has been a challenge in the mushroom industry. Currently, research has been conducted to implement successful remote environmental monitoring, or, in some cases, remote environmental control, yet there is not yet a combination of both these systems providing live stream images or video. As a result, this research aimed to design and develop an Internet of things (IoT)-based environmental control and monitoring system for mushroom cultivation, whereby the growth conditions of the mushrooms, such as temperature, humidity, light intensity, and soil moisture level, are remotely monitored and controlled through a mobile and web application. Users would be able to visualize the growth of the mushroom remotely by video and images through the Internet. The respective sensors are implemented into the mushroom cultivation process and connected to the NodeMCU microcontroller, which collects and transfers the data to the cloud server, enabling remote access at any time through the end device with internet connection. The control algorithm regulates the equipment within the cultivational chamber autonomously, based on feedback from the sensors, in order to retain the optimum environment for the cultivation of mushrooms. The sensors were tested and compared with manual readings to ensure their accuracy. The implementation of IoT toward mushroom cultivation would greatly contribute to the advancement of the current mushroom industry which still applies the traditional cultivation approach.

NO2 Sensor Based on Faraday Rotation Spectroscopy Using Ring Array Permanent Magnets.

Faraday rotation spectroscopy (FRS) exploits the magneto-optical effect to achieve highly selective and sensitive detection of paramagnetic molecules. Usually, a solenoid coil is used to provide a longitudinal magnetic field to produce the magneto-optical effect. However, such a method has the disadvantages of excessive power consumption and susceptibility to electromagnetic interference. In the present work, a novel FRS approach based on a combination of a neodymium iron boron permanent magnet ring array and a Herriott multipass absorption cell is proposed. A longitudinal magnetic field was generated by using 14 identical neodymium iron boron permanent magnet rings combined in a non-equidistant form according to their magnetic field's spatial distribution characteristics. The average magnetic field strength within a length of 380 mm was 346 gauss. A quantum cascade laser was used to target the optimum 441 ← 440 Q-branch nitrogen dioxide transition at 1613.25 cm-1 (6.2 μm) with an optical power of 40 mW. Coupling to a Herriott multipass absorption cell, a minimum detection limit of 0.4 ppb was achieved with an integration time of 70 s. The low-power FRS nitrogen dioxide sensor proposed in this work is expected to be developed into a robust field-deployable environment monitoring system.

Research and Development of Cellar Pool Environmental Monitoring System Based on NB-IOT and Cloud Platform

NB-IOT and cloud platform technology are used to monitor the cellar pool environment, which is difficult to collect by hand. Under the zigbee self-organizing network, the system collects the parameters of the terminal nodes distributed in each cellar pool through a coordinator, and then sends them to the ali cloud internet of things platform for display and control through the nb-IOT communication module. Experiments show that the system can display the environment data of the cellar pool in real time and open the execution device outside the threshold range, which proves the practicability and convenience of the system.

Designing an Evaluation Framework for IoT Environmental Monitoring Systems

Environmental monitoring systems have been evolving dynamically to embrace modern Internet of Things (IoT) technology in the last decade. Despite this progress there are, however, continuing limitations and issues with some IoT designs. Thus, past research has identified areas of concern in areas such as communications, interoperability, reliability, and scalability. As enabling technologies evolve in an accelerated manner, there will no doubt be a plethora of environmental monitoring solutions under development. Such solutions need to be well evaluated so that potential users are knowledgeable in relation to the best solution for their specific applications. Along these lines, this paper puts forward a framework to evaluate proposed IT designs relevant to smart environmental monitoring systems. This framework is based on model standardized software engineering requirements found in ISO 25010, which it uses as an aid to develop business-driven ‘smart’ environmental monitoring systems.

Re-emergence of Rift Valley fever: an update and preventive measures.

Re-emergence of Rift Valley fever: an update and preventive measures.

Methodology for forecasting and modeling the migration of pollutants in the system of integrated environmental monitoring of chemically hazardous objects

Methodology for forecasting and modeling the migration of pollutants in the system of integrated environmental monitoring of chemically hazardous objects

Retracted: A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things

Retracted: A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things

Effective resource monitoring and management system for cloud environment

Effective resource monitoring and management system for cloud environment

Design of smart learning environment monitoring system based on multi-mode

The traditional smart campus pays attention to improving the management efficiency and neglects to improve the learning effect. This paper studies that the physical factors of smart learning environment affect students' learning effect, and designs a monitoring system of smart learning environment. The sensor is used to sense the temperature, humidity and air quality of the learning environment. The microcontroller system processes the sensing information of the sensor, and the server analyzes the data and generates the physical environment report and equipment control information to build a multi-mode multi-user smart learning environment monitoring system.

Effective resource monitoring and management system for cloud environment

Effective resource monitoring and management system for cloud environment

GREENBOX Technology III - Financial Feasibility for Crop Production in Urban Settings

Highlights We proposed to use GREENBOX technology for urban crop production in warehouse settings. We assessed the profitability of the application of GREENBOX technology using Benefit Cost Analysis (BCA) to evaluate the Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PP). We conducted sensitivity analyses on NPV, IRR, and PP over different scenarios. GREENBOX was found financially feasible for all the hypothetical scenarios in major cities in the USA. Abstract. Food security pressure, especially in urban areas, continues to rise due to surging demand for food resulting from a growing population and declining resources. It has been critical to improve crop production and make food readily available to consumers without traveling long distances in an economically sustainable manner. The novel GREENBOX technology uses Controlled Environment Agriculture (CEA) principles for leafy green crop production in urban structures. A GREENBOX is an individual thermally insulated chamber with an artificial lighting source and a soilless cultivation system (hydroponics) in an environment that is controlled at the grower's discretion. This study performed a financial feasibility study of GREENBOX technology for urban crop production in various scenarios to evaluate the system's profitability from an individual business's perspective and used market prices of the goods and services paid for or received by a project. The representative GREENBOX unit in the base case scenario had dimensions of a standard shipping pallet (1.0 x 1.2 x 0.9 m, or 40 x 48 x 36 in) and included thermally insulated walls, an LED artificial lighting source, a camera for monitoring growth, a Nutrient Film Technique (NFT) hydroponic growth platform, and an environmental monitoring and control system. A warehouse can host numerous GREENBOX units for mass production. We carried out a benefit-cost analysis by assessing the Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PP). These parameters were evaluated for a base case scenario from data collected or estimated for a representative GREENBOX unit. We also applied the base case scenario to investigate the financial performance of the GREENBOX setup in selected urban areas in the United States; New York City (New York), Miami (Florida), Los Angeles (California), Dallas (Texas), Atlanta (Georgia), Chicago (Illinois), Boston (Massachusetts), and Philadelphia (Pennsylvania). We then carried out a sensitivity analysis on NPV, IRR, and PP by keeping all the parameters in the base case scenario invariant except for one at a time. We obtained a summary equation to understand the variation of the financial parameters with changing lettuce sale price, electricity cost, rental cost, labor cost, and the number of GREENBOX units. A GREENBOX unit would require an initial investment of $398 to assemble and an annual outflow of $157 to cover operating expenses. GREENBOX cultivation was financially viable in the base case scenario and in all the cities studied, with varying degrees of financial performance. The sensitivity analysis revealed that GREENBOX cultivation was financially viable in all scenarios except when skilled labor costs were beyond $19/hr, and there were fewer than 300 GREENBOX units. A statistically significant regression equation was derived in which rising rental costs, labor costs, and electricity prices negatively impacted the NPV, while the rising lettuce sales price and the number of GREENBOX units positively impacted the NPV. GREENBOX farming may serve as a local source of fresh crops for urban customers, with various benefits including improved food security, greater freshness and nutrition of food, the potential to contribute to the local economy by the creation of jobs and revenues from sales, and educational opportunities through extension programs on food nutrition and production. Keywords: Agricultural business, Environmental control, GREENBOX, Lettuce, Urban agriculture.

Creating Smart House via IoT and Intelligent Computation

This study mainly uses the concept of the Internet of Things (IoT) to establish a smart house with an indoor, comfortable, environmental, and real-time monitoring system. In the smart house, this investigation employed the temperature- and humidity-sensing module and the lightness module to monitor any condition for an intelligent-living house. The data of temperature, humidity, and lightness are transmitted wirelessly to the human-machine interface. The correlation of the weight of the extension theory is used to analyze the ideal and comfortable environment so that people in the indoor environment can feel better thermal comfort and lightness. In this study, improved particle swarm optimization (IPSO) is employed—an effective evolutionary method used to search the function extreme. It is simple and has a fast convergence. The convergence accuracy of this algorithm is not high at the beginning, and it can easily fall into the local extreme points. The effect of the inertia weight in mix extension theory and PSO becomes IPSO-Extension Neural Network (ENN), which was analyzed and found reliable. Motivated by the idea of power function, a new non-linear strategy for decreasing inertia weight (DIW) was proposed based on the existing linear DIW. Then, a novel hierarchical multi-sensor data fusion algorithm adopting this strategy was presented, and the weight factor of the data fusion was estimated. The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there is no available information about the reliability of the information sources, the degree of redundancy/complementarities of the information sources, and the structure of the hierarchy. It obtained effective information from the fusion data, successfully removed the noise disturbance, and achieved favorable results.

Calculating fire danger of cured grasslands in temperate climates – the elements of the Grassland Fire Index (GLFI)

Background Increasing extreme weather events due to climate change require updated environmental monitoring and prediction systems in Germany. Aim The Grassland Fire Index (GLFI), developed by the German Meteorological Service ~15 years ago for temperate climates, was revised to improve fire-danger predictions during the fire season. Our paper gives insight into the new model version. Methods The former fire-behaviour core, i.e. Fosberg’s Fire Weather Index (FWI), is replaced by the standardised fire-reaction intensity, a different fuel-moisture of extinction term, and a replica of the fire-spread rate of the Canadian FFBP-System. A standardised ease-of-ignition index is added as a measure of ignition success. The fire module is supplied with diurnal dead-grass fuel-moisture calculations based on the water-budget and energy-balance concept. Key results The GLFI output is compared with diurnal fuel-moisture measurements and results of Wotton’s Grass-Fuel-Moisture model, Fosberg’s FWI, and Cheney’s rate of spread equation. The GLFI computes periods with a high fuel moisture more realistically, whereas it exceeds Cheney’s rate-of-fire spread systematically at lower wind speeds, which leads to higher danger ratings during calm-air conditions (as requested by users). Conclusions and Implications The GLFI estimates dead-fuel moisture and fire danger on open, horizontal topography according to the current scientific level. Model extensions are necessary to run the model on complex topography under varying greenness and occasional frost conditions.

An Ultra-Low Energy Solution for Large-Scale Distributed Audio Monitoring

This paper presents a flexible, low-cost, and ultra-low-energy acoustic sensor network solution. This enables both highly distributed and real-time audio measurements to be employed in a wide variety of scenarios. These types of distributed measurements can be useful in environmental monitoring, healthcare settings, smart cities, and security surveillance systems. However, they are typically inflexible in their applications and require specialized deployment. The proposed solution is nonintrusive and uses long-range radio to enable low-power distributed audio monitoring. The system can recognize and be configured for different types of audio measurement and analysis tasks (such as sound pressure level, annoyance, direction of arrival, and roughness), and is shown to be as accurate r=0.998 as a high-cost measurement system. Remote control, time of day, and grid capture mechanisms can also be realized. The proposed solution provides the ability for an over-the-air configuration, leading to minimal physical access required once deployed. In addition, higher data rates are possible, offering real-time monitoring metrics.

Implementation of an IoT system for environment monitoring and remote web control using ARM Mbed cloud and GUI

Implementation of an IoT system for environment monitoring and remote web control using ARM Mbed cloud and GUI