Static Sensor Research Articles

Analysis of Water and Air Quality in and Around HIMSWM Treatment Plant- Hyderabad

In recent days the emission of air pollutants in to the environment has grown very quickly. The air pollution is caused due to the smoke emitted by vehicles, industries and other sources. Hyderabad is the largest and capital city of Telangana. It occupies 650km2 on the Deccan Plateau along the banks of the Musiriver, in the northern part of south India. According to the 2011 Census of India, Hyderabad is the fourth-most populous city in India with a population of 6.9 million residents within the city limits, and has a population of 9.7 million residents in the metropolitan region, making it the sixth-most populous metropolitan area in India. With an output of US$74 billion, Hyderabad has the fifth-largest urban economy in India. In India air pollution is monitored by Central Pollution Control Board(CPCB) along with State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with seven sensor stations 248 towns and cities have the air quality network of 591 air quality monitoring stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing the number to 883 to achieve the goal of 1500 by 2024. For our study because of some practical difficulties and analysis of the air quality. Air Pollutants has been collected from the CPCB. The dataset contains City, Date, Time, PM2.5,PM10, NO2, SO2, CO, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into six zones, air quality monitoring sensors are located at 14 places in and around six zones. For our study we selected one sensor that is Secunderabad, which covers the surrounding areas including Jawahar Nagar treatment plant. To analysis the maximum concentration of air pollutants such as PM2.5,PM10,SO2,NO2,CO,O3over the time period of three months (Jan to Mar 2023).Most physio-chemical properties, including as pH, EC, Alkalinity, Chlorides, Cu, Mn, Pb, and Cr, exceeded their acceptable limits at more than three sample sites, according to this study. The water sources were determined to be unsafe for drinking, agricultural, and irrigation uses due to elevated toxicity levels.It was observed that PM2.5,ranges 218 - 403µg/m3 is very poor causes respiratory illness, PM10 range 112 - 360µg/m3 is moderately polluted causes breathing discomfort ( Asthma, lungs), NO2 range 10 – 12ppm is satisfied causes minor breathing discomfort to sensitive people ,O3 range 20 – 35ppm is good causes minor effect, SO2 is 12 – 22ppm is satisfied causes minor breathing discomfort to sensitive people ,CO 109 – 119ppm is severe effects on healthy people and serious impact for those with existing diseases, so continuous air quality monitoring is necessary to protect environment and human health

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring.

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least. Note to Practitioners—In this paper, we investigate the problem of drift detection in sensor networks. This work was motivated by the fact that faulty nodes are rarely detected in existing in situ calibration algorithm prior to the correction of the instruments. Moreover, existing fault diagnosis algorithms for sensor networks do not specifically target drift and are often applicable to either (dense) static or mobile sensor networks but not both. We propose an algorithm designed for the detection of drift faults regardless of the type of sensor network and of the measurand. Specific attention is paid to the metrological quality of the measurement results used to carry out the diagnosis. The output of the algorithm provides information that can be exploited for the recalibration of faulty instruments. In future work, we will aim at providing tools and recommendations for the adjusment of the parameters of the diagnosis algorithm but also more elaborated approaches based on the results of our diagnosis algorithm to calibrate faulty nodes.

Time Series Models for Radiated Electromagnetic Energy of Lightning Strokes Detected by World Wide Lightning Location Network

Lightning is a significant atmospheric event and has a wide-ranging influence on the Earth’s climate. Recently a sensor station of World Wide Lightning Location Network (WWLLN) is hosted at Dhaka (23.81°N, 90.41°E) and one year (Jan 2021 to Dec 2021) lightning strokes energy data is investigated. In an effort to switch the analysis from descriptive to predictive, WWLLN lightning strokes energy data is modeled using time series analysis. The autoregressive integrated moving average (ARIMA) model has been applied to predict the data. According to the process of model selection, the auto-correlation function (ACF) and partial auto-correlation function (PACF) are examined to determine the auto-regressive (AR) and moving-average (MA) terms. Standard diagnostic tests are performed to ensure the goodness of fit of the chosen model. After necessary diagnostic tests, ARIMA (3,1,0)×(2,1,0)12 is selected as the best fitted model.

Formation of wireless sensor network protection system parameters for intrusion detection in the form of false event flows

Wireless sensor networks with stationary and mobile sensor nodes are studied. For mobile nodes, in addition to sensor nodes, the influence of node movement speed on the duration of the network life cycle for mobile AdHoc networks was also studied. When studying the impact of erroneous events on the sensor field, it was established that providing sensor nodes with mobility allows increasing the life cycle of the network. A model of intrusion into a wireless sensor network with the aim of shortening its life cycle has been developed, which differs from known models in that false event streams are used to achieve this goal. The model is developed based on typical geometric, quantitative and energy parameters of wireless sensor networks using a basic clustering algorithm for a homogeneous mobile sensor network under conditions of Poisson network intrusion and deterministic error event flows. It is established that the duration of the life cycle of a wireless sensor network can significantly depend on the type of the flow of erroneous events and, other things being equal, under the influence of a deterministic flow can be almost half as long as under the influence of a flow of erroneous events. the impact of the flow of false events. the Poisson flow effect. Detection of false events in a wireless sensor network can be considered as a target tracking task, and to detect false events with a given probability, taking into account the limited capabilities of sensor nodes, it is advisable to use the architectural characteristics of the network, the distribution of the density of nodes on the sensor field.

Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators.

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.

Regularity bounded sensor clustering

A non-empirical, unsupervised learning, and graph analytic techniques pipeline investigated the earthquake sensor station proximity (<30 km) and neighborhood density (2–5 sensor) constrained geometric properties of New Zealand’s GeoNet seismic network. They are specific to the Propagation of the Local Undamped Motion (PLUM) earthquake detection algorithm. The experimental design considered 14 cloud and graph clustering processes, with variable combinations of 10–50 km distance and 2–5 sensor constraints. We prove that a clustering process alone is inadequate, and a strict graph r-regularity constraint is essential for forming valid sensor clusters. The goodness of fit measurements revealed HDBSCAN was best for initiating the pipeline. On average 44 % of the sensors fit well within the geometric bounds with a 45 % cluster quality score. With the regularity relaxed, 60 % of the sensors resulted in a 42 % node-degree weighted mean absolute error. The method can be applied to any proximity and density-constrained sensor network.

Charging Protocol for Partially Rechargeable Mobile Sensor Networks.

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.

Acoustic micronektonic distribution and density is structured by macroscale oceanographic processes across 17–48° N latitudes in the North Atlantic Ocean

This study investigates the large-scale distribution patterns of the acoustic scattering layers and micronekton density across the Northeast Atlantic Ocean during daylight hours. A research cruise on board R/V “Kronprins Haakon” was conducted during May 2019 from Cape Verde to Bay of Biscay. Hydrological data were obtained at 20 conductivity-temperature-depth sensor (CTD) stations. To estimate the micronekton densities in front of the trawl, an autonomous echo sounder (120 or 200 kHz) on the headrope of the macroplankton trawl was used. Acoustic data were also collected along the cruise track using ship-mounted multi-frequency echo sounders (backscatter at 18 and 38 kHz was analyzed). Acoustic observations (both at 18, 38 and 120/200 kHz) showed clear patterns in the horizontal distribution of the micronekton during daytime with higher backscatter and echo densities in the south of the study area (from 17 to 37° N), and the absence of high backscatter in the surface from 37 to 45° N. Backscatter and echo densities were found to be significantly influenced by: temperature, salinity, and oxygen, as well as depth and time of the day.

Estimation of the Space–Time Spectrum of Waves Based on Circular Measuring Gratings

In this paper, a method for estimating the spectra of wave processes based on a special type of measuring lattices, which are a set of rotating and static sensors, called “circular,” is developed. A method for correcting spectra estimated on the basis of discrete digital series obtained from circular lattice sensors is described. Spectra estimates are based on the multidimensional maximum entropy method. The data of test calculations for several types of circular lattices are presented as applied to wave processes in the form of harmonics distorted by white noise. The effectiveness of the proposed method for correcting spectra is shown. The general principles of using circular lattices for estimating the spectra of wave processes with the help of sensors located on rotating satellites are outlined. The possibility of using this approach for estimating the spectra of wave processes using sensors installed on micro- and nanosatellites is discussed.

Optimal Sensor Scheduling Under Intermittent Observations Subject to Network Dynamics

Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-term average cost problems and show that the value iteration algorithm converges to a solution of the average cost problem. We further show that the suboptimal policies provided by the rolling horizon truncation of the value iteration also guarantee stability and provide near-optimal average cost. Lastly, we provide qualitative characterizations of the multidimensional set of measurement loss rates for which the system is stabilizable for a static network, thus extending earlier results on intermittent observations.

Turbulence induced self-noise on cylinders in low velocity flow

Nominally stationary acoustic sensors, e.g., buoy suspended or bottom anchored, are subject to various environmental flows. These flows in turn generate turbulence along the surface of acoustic enclosures that give rise to self-noise. A continuous-flow water tunnel replicates at-sea flows for evaluation of acoustic enclosures for such self-noise. In preparation for the tunnel experiments, direct numerical simulations of hydrodynamic flow around the acoustic sensor housing have been performed, and show turbulence along the housing surface. Time-space analysis of pressure variation along the surface of the housing shows significant acoustic-like surface interaction at low frequencies, even at very low mean flow velocity. The broad frequency content of the turbulence is due to intermittently generated, irregular, coherent vortex structures that separate from the surface of the housing and advect downstream. These structures drive rapid surface pressure transients that propagate within the housing to the acoustic sensors as noise. The potential for noise reduction using foil shaped housing is explored. This research is supported by 6.2 NRL base program sponsored by the Office of Naval Research. Distribution Statement A: Approved for public release. Distribution unlimited.

Design and analysis of organic thin film gas sensor based on bottom gate elevated electrode structure

Environmental pollution especially the air pollution has proved to be a threat to all lifeforms on earth. Due to various anthropogenic activities, toxic and ozone depleting gases are released in our environment (like ammonia, Sulphur di oxide, carbon monoxide, nitrogen di oxide etc.). To check the prevalence of such gases in the atmosphere we require sensors that could accurately detect their presence in our surroundings. For the detection of these gases various type of sensors like metal oxide-based gas sensor, optical gas sensor, electro-chemical gas sensor etc. are used. Such gas sensors are largely based on inorganic electronics technology that makes them rigid, expensive, and exclusive in nature. With advent of organic electronics such sensors have become incompatible with modern electronic devices developed with organic materials like smart watches, smart fabrics etc. Also, these sensors are stationary in nature and are operational only in a fixed position. Therefore, a new organic thin film gas sensor (OTFGS) is proposed in this article that could operate at low power and could readily be integrated with the modern personal electronic gadgets like smartphones and smart watches. This will provide the mobility to the sensor and toxic gases could be detected in real time at multiple locations rather than at specific points where stationary gas sensors are installed.

Data collection system with signal optimal-routine for the mining and environmental monitoring in Vietnam

Purpose. To develop a data acquisition which is optimized for suitable purposes in monitoring/warning or decision-making assistance in the field of geography accident in mining activities or agroforestry. Methodology. In activities related to mining, agroforestry and environmental monitoring, many climate factors must be taken into account including humidity, soil moisture or level of rainfall. Those data play a key role in pre-warning or assisting in decision-making for operators who are responsible for risk warning. In this paper, we present an optimized data acquisition system which is reasonable in cost, simple and easy in installation. The system includes a sensor station (SS) and a central station to collect data from specified monitoring points. The latter one is used to gather data from SS through a new and optimized Lora WAN communication system. When sending gathered data to a cloud sever, an assistant system based on 3G module is established to warn of abnormal scenarios of the mining procedure or environmental parameters. The system is experimented, tested and implemented in the North Mountain area (Northwest) of Vietnam. Findings. A development of an acquisition system with optimized-data for alarming/warning and monitoring in the field of geography accident, mining activities and environment. The system could get sensing signal in both direct way and indirect way despite of bad weather. Originality. An improved Dijkstra algorithm is implemented to optimize and simulate the routing paths of a network. The optimization could show the best way for getting signal from sensor station indirectly to other ones, then to the central station. Practical value. A simple, reasonable-cost and easy-installation system is formed for monitoring, risk warning in the field of geography, mining, and climate.

Online transportation network cyber-attack detection based on stationary sensor data

Penetration of connected vehicles and crowdsourced mapping applications give rise to security vulnerabilities in transportation networks. Accurate detection of cyber-attacks on transportation networks is critical to minimize impacts on transportation systems. This task is particularly challenging because the impacts of regional cyber-attacks can be invisible on aggregated traffic data, especially when only sensor data is accessible to transportation agencies. We propose an analytical framework that leverages real-time road link sensory data to conduct online data-driven transportation network anomaly detection using non-parametric long short-term memory (LSTM) and parametric Gaussian process model. The online anomaly detection models can continuously update model coefficients as real-time sensory data arrives. We utilize a city-scale microscopic traffic simulation to validate our cyber-attack detecting framework. The cyber-attack detection model achieves a F1 score, which is a harmonic mean of the precision and recall of classifiers, between 84% to 96% considering different initial training data sizes. We compare with major offline models to demonstrate the effectiveness and robustness of online models. In addition, we devised a meta-heuristic method to solve the multi-objective sensor location problem to simultaneously enhance anomaly detection efficiency and maximize traffic information gain. This study demonstrates a systematic approach to address the emerging concerns of cyber-security in transportation networks with minimum requirements for infrastructure upgrades. Our results can help transportation security authorities identify potential cyber-attacks and protect transportation infrastructure from malicious cyber-hackers.

Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network.

Soils are a complex ecosystem that provides critical services, such as growing food, supplying antibiotics, filtering wastes, and maintaining biodiversity; hence monitoring soil health and domestication is required for sustainable human development. Low-cost and high-resolution soil monitoring systems are challenging to design and build. Compounded by the sheer size of the monitoring area of interest and the variety of biological, chemical, and physical parameters to monitor, naive approaches to adding or scheduling more sensors will suffer from cost and scalability problems. We investigate a multi-robot sensing system integrated with an active learning-based predictive modeling technique. Taking advantage of advances in machine learning, the predictive model allows us to interpolate and predict soil attributes of interest from the data collected by sensors and soil surveys. The system provides high-resolution prediction when the modeling output is calibrated with static land-based sensors. The active learning modeling technique allows our system to be adaptive in data collection strategy for time-varying data fields, utilizing aerial and land robots for new sensor data. We evaluated our approach using numerical experiments with a soil dataset focusing on heavy metal concentration in a flooded area. The experimental results demonstrate that our algorithms can reduce sensor deployment costs via optimized sensing locations and paths while providing high-fidelity data prediction and interpolation. More importantly, the results verify the adapting behavior of the system to the spatial and temporal variations of soil conditions.

Continuous monitoring of volatile organic compounds through sensorization. Automatic sampling during pollution/odour/nuisance episodic events

Volatile organic compounds (VOCs) are a highly diverse class of chemical contaminants and between 50 and 300 of them may be found in ambient air. In urbanized areas, VOCs are emitted from industrial activities, as well as from vehicle-related and combustion sources. VOCs outdoors can be detected in a broad range of concentrations, usually varying seasonally. The presence of VOCs at relatively high concentrations has been related to poor air quality, discomfort and odorous nuisances. Additionally, they can have negative health effects to the human organism. Hence, in locations where recurrent sporadic situations of high VOCs levels take place, episodic samples' evaluation is necessary instead of 24 h or longer sampling period's evaluations. The use of commercially available metal oxide semiconductor gas sensors for a continuous monitoring of VOCs concentrations in outdoor air is an interesting and innovative technology. Additionally, the use of these sensors for the activation of a VOCs sampler when episodic events of nuisance/odorous annoyance occur was successfully evaluated. The sensor activation is induced by higher VOCs concentrations from a wide number of VOC chemical families. Two sensor stations, developed at our laboratory and provided with sampling pumps, were located in the municipality of Santa Margarida i els Monjos (Catalunya, Spain) in January 2021. The stations started recording data continuously from two different types of VOCs sensors, temperature, relative humidity and pressure in 1.5-min periods. Automatic VOCs sampling was conducted, using multi-sorbent bed tubes, during the months of June–July when the sensors electronic values exceeded a set point value. Samples were analysed through TD-GC/MS. TVOC concentrations in episode samples ranged between 78-669 and 12–159 μg m−3 in Site 1 and Site 2, respectively. Although TVOC concentrations were not high in all cases, relevant concentrations of chloroform were observed, especially in Site 1, with concentrations ranging from 19 to 159 μg m−3.

ChaseCharge: Charging Sensors by Chasing in Sensor-Cloud Systems

The recent breakthrough in wireless power transfer technology enables power to be delivered between transceivers, which is quite helpful for sensor-cloud systems. Traditional charging schemes are only suitable for static sensors, while the issue of charging mobile sensors is ignored. In this paper, we make the first attempt to serve mobile sensors in the sensor-cloud systems in a “chasing” way, where a mobile charger can chase mobile sensors to replenish them. We formalize the charging utility MAximization Problem for dynamic sensors with a mobile charger (MAP) and propose a ChaseCharge algorithm based on RNN to solve it. Theoretical analyses are presented to explore the features of the proposed scheme. We carry out simulations, and the results show that the performance of our algorithm outperforms comparison algorithms by 33% in utility on average. Test-bed experiments are conducted to validate the applicability of the proposed scheme in oceanic monitoring applications.

Real-Time Monitoring for Effects of Vibration and Temperature of Construction Site on Steel Assembly Bracing of Foundation Pit

In this paper, a real-time monitoring system—including vibration acceleration sensors, temperature sensors, and static and dynamic strain sensors—is used to monitor the safety status of a steel assembly bracing in a practical project. It uses 5G wireless networking technology to transmit monitoring data to a cloud server for early warning of abnormal changes and development trends. Real-time monitoring data obtained in a construction site are used as the inputs of the finite element model and the corresponding results of numerical simulation are compared with the results from the real-time monitoring. It can be concluded that: (1) the stress caused by environmental temperature is very significant which can be higher than the initial prestress of the steel assembly bracing; (2) the stress caused by the vertical vibration mainly from construction vehicles is not remarkable, however, the vertical frequency-weighted acceleration of support vibration is relatively large which can affect the sense of safety of engineering technicians on site; (3) the combination of the environmental temperature and vertical vibration does not affect the safety of the steel assembly bracing.