Large Sensor Research Articles

Data Prediction Based Encoder-Decoder Learning in Wireless Sensor Networks

Wireless sensor networks are typically characterized by large network size and sensor nodes with low energy capacity and a limited bandwidth for data transmission. Over-activity of the sensor nodes will therefore cause many issues to the network, such as an increase in the network depletion rate and poor data transmission. Data prediction methods that exploit the inter-relationship between sensor nodes can be used to reduce data traffic across wireless networks. Several related work in data prediction do not consider the time-series distribution of the sensing data. However, exploiting sequential features of the historical observations can improve prediction accuracy, and increase the number of sensing data predicted per sequence. This work propose a sequence to sequence data prediction model, using a one dimensional layer convolutional neural network to extract spatial features from the pre-processed sensing data, and an encoder-decoder model to predict the next two outputs in the sequence by exploiting the temporal distribution of the data. The aforementioned approach has the capacity of generating more accurate information, which can reduce network traffic and energy expenditure in WSNs. Furthermore, the experimental results reveals that, based on a suitable choice of nodes, our proposed model perform accurate predictions, with reduced root mean squared error as compared to related work. We also propose an approach to regulate and control the data traffic toward the base station.

First bent wafer-scale sensor in truly-cylindrical geometry for the ALICE ITS3 detector

An upgrade of the three innermost layers of the ALICE ITS, intended to be installed during the LHC Long Shutdown 3, is under development. The detector concept foresees the usage of curved, wafer-scale Monolithic Active Pixel Sensors. Extensive characterization studies of bent single ALPIDE chips (used for the current ITS), have been carried out to evaluate their performance under the mechanical stress involved in the bending process. These tests on small sensors have opened the way to the investigation of a large scale sensor: a full size demonstrator of a half-layer in a truly cylindrical shape based on so called super-ALPIDE chips. Such activity has required the development of special tools and procedures dedicated to bend and read out the new pixel matrix.

False Event Message Detection Robust to Burst Attacks in Wireless Sensor Networks

A sensor device can be used to detect target events at low cost. Moreover, there is a significant risk of sensor nodes being compromised or captured within large wireless sensor networks (WSNs). The transmission of valid event messages to users by a WSN can be hindered by network congestion due to false event messages in a compromised node. Several methods are available for detecting false event messages. However, they rely on long bits of authentication codes and hence do not provide a fundamental solution to prevent network congestion. In the proposed method, various hashing vectors, which are space-efficient data structures that can determine whether the given data are an element of a set, are created based on the correct combination of authentication codes and placed in each node in advance. An event message contains an XOR of the authentication codes, and each node verifies it based on its hashing vector. If a node is acquired illegally, the information of the hashing vector and the XOR information of the authentication codes assigned to the correct event message is compromised, so we propose an algorithm to update the information securely. Compared to existing research, the number of hops required to detect a false event message increases by only about one hop, but the amount of traffic that a malicious node can generate can be reduced by about 60% or more. In other words, the proposed method effectively reduces the amount of traffic an attacker can generate with false event messages, which also reduces the overall network congestion.

Skin-Inspired Large Area Iontronic Pressure Sensors with Ultra-Broad-Range and High Sensitivity

Skin-Inspired Large Area Iontronic Pressure Sensors with Ultra-Broad-Range and High Sensitivity

Change Detection for Large Distributed Sensor Networks With Multitriggered Local Sensors

The emergence of large sensor networks and the Internet of Things has reinvigorated interest into distributed quickest change detection. Important shortcomings of existing approaches are ease of design, flexibility in communication, and applicability to larger networks. The new approach proposed in this work features local sensors that can be triggered multiple times, i.e., can reset and continue monitoring after transmitting their decisions. With larger sensor networks as a focus, the system allows for multiple simultaneous transmissions to a fusion center within bandwidth limitations. The proposed system uses the cumulative-sum procedure at local sensors to binarize local decisions, which are then transmitted to the fusion center that also employs cumulative-sum quickest detection. Test overdesign due to sequential test overshoot is avoided, and global and local thresholds are chosen to meet a desired false alarm rate constraint using numerical computation of expected delay performance. The system compares favourably to several existing methods while offering greater flexibility in the amount of fusion center communication.

Detection by Block- and Band-Permuted Data

Unlabeled detection is an important problem in large sensor networks faced with big-data applications. The focus of this paper is on decision problems in which the data vector received at the fusion center (FC), tasked to perform the final inference, undergoes an unknown permutation. Two representative permutation models are considered, whose structure is imposed by practical considerations. The first is the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -block permutation model, where the observations are split into different blocks of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , and independent data permutations affect each block. The second is the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> -banded permutation model, in which each sample of the vector received by the FC lies at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> positions ahead or behind its original location. To solve these challenging detection tasks, three detectors for both permutation models are proposed. The first is inspired by the uniformly most powerful invariant principle; the second relies on a distribution-averaged strategy; and the third is designed according the generalized likelihood ratio approach. The performance of these detectors is investigated by computer experiments, and their relative merits are discussed.

Secure and Energy Efficient Intrusion Detection in 3D WSN

Large area wireless sensor network deploys enormous number of nodes for intrusion detection. The possibility that some of the nodes become malicious is inevitable in the unattended environment. It is required to perform intrusion detection process with potential nodes (nodes with at least minimum threshold energy and minimum trust). This paper presents a method for intrusion detection with potential nodes. Energy consumption of the nodes is measured and the detection process is distributed among the nodes which enhances the network life time. Selection of optimum number of nodes for detecting the intruder in rounds which cover the deployment area reduces average energy consumption in the network. It is found that our simulation validates analytical results.

Optimization of large scale concentric-annulus electrical capacitance tomography sensor with internal electrodes

To improve the spatial resolution of electrical capacitance tomography (ECT) images for visualizing large-scale concentric-annulus measuring area, ECT sensors with 12 external electrodes (EE) and different number of internal electrodes, 4, 6 and 8 are designed. The outer radius of the sensors is 500 mm and inter radius is 100, 150, 200, 250 mm respectively. Two excitation strategies are used, which are external-opposite-internal (EOI) and external-opposite (EO). The obtained capacitance data are used to reconstruct images using linear back projection (LBP) and Landweber iteration. Based on numerical simulation and experimental measurement, it is found that increasing the number of internal electrodes does not improve the quality of images. When LBP is used with normalized sensitivity matrix, the 12–4 internal-external-electrode (IEE) sensor with EOI excitation strategy has advantages over the EE sensor and other IEE sensors in terms of image quality and contrast. When Landweber iteration is used, the IEE sensors with EO excitation strategy can effectively eliminate the influence of abnormal capacitances. Landweber iteration can reconstruct high quality images when the sensitivity matrix is normalized according to discrete elements of measurement area, while the reconstructed images are more sensitive to the noise level.

DeepQuake — An application of CNN for seismo-acoustic event classification in The Netherlands

Recent developments in instrumentation and methodology are major driving forces in the advances of solid Earth sciences. The deployment of large and dense sensor networks enables data centres to acquire data of increased volume and quality. The analysis of such data provides scientists with a better understanding about natural phenomena in the subsurface. At the same time new challenges arise to exploit the growing information potential. Innovative methods based on Artificial Intelligence offer concrete opportunities to tackle those challenges. In this paper we investigate Convolutional Neural Networks (CNN) for seismo-acoustic event classification in The Netherlands. A supervised deep learning approach is applied to continuous seismic waveforms in order to automatically detect seismo-acoustic events and discriminate their sources. Our region of interest is characterised by high seismic noise conditions and intense anthropogenic activity which deeply influence the recorded signals. Site conditions and our aim for a tool that could support operational earthquake analysis motivated us to devise a novel specialised CNN architecture.Building on existing approaches, we designed, trained and evaluated two CNN models: arch-time and arch-spect. Our results suggest that as CNN inputs, spectrograms are more suitable than continuous waveforms. Therefore, we propose a model (arch-spect) that achieves a performance among the highest in modern source detection systems.Furthermore, we focus on explainability aspects and propose a method to gain insights in the behaviour of a ‘black-box’ deep learning classification system. By generating visualisations of activations we aim to empower domain experts with a useable cross-checking tool and pave the way for a broader operational uptake of Artificial Intelligence based methods.

WaCoDiS: Automated Earth Observation data processing within an event-driven architecture for water monitoring

Climate changes and the ongoing intensification of agriculture effect in increased material inputs in watercourses and dams. To ensure an efficient and environmentally friendly water resource management, water management associations need means for efficient water monitoring as well as novel strategies to reduce the pollution of surface and ground water. Traditionally, water management associations operate large sensor networks to suffice their needs for hydrological and meteorological measurement data to monitor and model physical processes within the catchments they are responsible for. Implementing a comprehensive monitoring system often suffers from sparse coverage of in-situ data. Due to the evolvement of the Copernicus satellite platforms, the broader availability of satellite data provides a great potential for deriving valuable, complementary information from Earth Observation data, that contributes to a detailed understanding of hydrological processes. Although the number of satellite data platforms that provide online processing environments is growing, it is still a big challenge to integrate those platforms into traditional workflows of users from environmental domains such as hydrology. Thus, in this paper, we introduce a software architecture to facilitate the generation of Earth Observation information targeted towards hydrology. The presented WaCoDiS System comprises several microservices as well standardized interfaces that enable a platform-independent processing of satellite data. First, we discuss the contribution of Earth Observation data to water monitoring and derive several challenges regarding the facilitation of satellite data processing. We then describe our system design with a brief overview about the different system components which form an automated processing pipeline. The suitability of our system is proven as part of a pre-operational deployment for a German water management association. In addition, we demonstrate how our system is capable of integrating satellite data platforms, using the Copernicus Data and Exploitation Platform - Deutschland (CODE-DE) as a reference example.

A comparison of instrument response correction methods: Post-processing and real-time methods

Industrial vibration monitoring often requires sensors with adjustable sensitivity and suitable frequency range. In practice, most industrial studies utilize either geophones (velocimeters) or accelerometers. In some cases, where low frequency content is of interest, larger sensor will be required. In difficult installation conditions, it can be advantageous to utilize smaller, higher frequency sensor elements (geophones or accelerometers) to simplify installation and maintenance. A frequency correction of sensors or the recorded waveforms will be needed to accommodate the frequency range of interest. Most accelerometers have relatively smaller sensitivity at low frequency which can affect the calculation of vibration velocity and displacement at low frequencies. Geophones are limited by their frequency response, which drops-off significantly their sensitivity below the resonant frequency of the sensor. Structural and ground vibrations that occur under the resonant frequency could be observed at test sites, but the recorded waveforms cannot be used directly for real-time assessment, and therefore it can be beneficial to artificially expand the frequency range below the sensor's frequency cutoff. Methods for such expansion, were developed and are well established in seismological studies and exploratory geophysics. Usually, these procedures are applied in data post-processing. These methods are not applicable when an operator requires real-time feedback of the measured vibrational amplitude, for example, monitoring of machinery foundations, where excitation control is necessary to avoid infrastructure damage.This paper presents an approach for instrument frequency extension in the necessary low-frequency range of common geophone elements in real-time applications and compares the results of the proposed technique in post-processing and in real time data collection systems.

AirSensor v1.0: Enhancements to the open-source R package to enable deep understanding of the long-term performance and reliability of PurpleAir sensors

As low-cost air quality sensors become more widely utilized, more tools and methods are needed to help users access/process sensor data, identify poorly performing sensors, and analyze/visualize sensor data. Free and open-source software (FOSS) packages developed for use on FOSS data science platforms are well-suited to support this need by offering replicable and shareable tools that can be adapted to meet a user or project's specific needs. This paper describes enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 1.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor. This paper also demonstrates how these enhancements may be used to track and assess the health of air sensors in real-time or for large historical datasets. The dataset used for this analysis was collected during a multi-year project (with sensors deployed from October 2017 to October 2020) involving the distribution of approximately 400 PA-II sensors across 14 communities in southern, central, and northern California. Applying the tools in the AirSensor package revealed a dramatic variability in sensor performance, mainly driven by seasonal trends or particulate matter source type. These results also indicate that this sensor can provide useful data for at least three years with little evidence of substantial or consistent drift. Further, high agreement was observed between co-located sensors deployed at different times, indicating that it may be reasonable to compare data from old and new PA-II sensors. In addition to assessing the long-term performance and reliability of the PA-II sensor, this analysis serves as a model for how data from large sensor networks may be effectively processed, evaluated, interpreted, and communicated.

Multi-camera joint spatial self-organization for intelligent interconnection surveillance

The construction of smart city makes information interconnection play an increasingly important role in intelligent surveillance systems. Especially the interconnection among massive cameras is the key to realizing the evolution from current fragmented monitoring to interconnection surveillance. However, it remains a challenging problem in practical systems due to large sensor quantity, various camera types, and complex spatial layout. Aimed at this problem, this paper proposes a novel multi-camera joint spatial self-organization approach, which realizes interconnection surveillance by unifying cameras into one imaging space. Differing from existing back-end data association strategy, our method takes front-end data calibration as a breakthrough to relate surveillance data. Specifically, this paper first initials camera spatial parameter by sequence complementary feature integration. Through integrating complementarity and redundancy among sequence features, our method has robustness under scene dynamic changes and noise. Then, we propose a multi-camera joint optimization method based on common monitoring coverage correlation analysis to estimate a more accurate relative relationship. By leveraging the two strategies, the spatial relationship and visual data association across monitoring cameras are returned finally. Our system organizes all cameras into a unified imaging space by itself. Extensive experimental evaluations on an actual campus environment demonstrate our method achieves remarkable performance.

Interrater and Intrarater Reliability of Electrical Impedance Myography: A Comparison between Large and Small Handheld Electrode Arrays.

The objective of this study was to evaluate the interrater and intrarater reliability of electrical impedance myography (EIM) using handheld sensors of different sizes. Electrical impedance myography of the biceps brachii muscle of twenty healthy individuals was performed by two raters using both large and small sensors. The procedures were also repeated 5 to 8 days after the first recording session. The repeatability of the resistance, reactance, and phase angle at two different current frequencies (50 and 100 kHz) was assessed by the intraclass correlation coefficient (ICC). The ICCs of the large sensor were higher than those of the small sensor for both the intrarater and interrater reliabilities. High-frequency current tended to improve the ICC for the small sensor. These results indicate reasonable repeatability of the handheld electrode arrays for EIM measurements. The findings suggest that electrode array should be selected appropriately according to the size of the tested muscle.

Mechanism of Ionization Damage in Large Eight-Transistor Complementary Metal–Oxide–Semiconductor Color Image Sensors

To study the degradation of remote-sensing cameras in terms of responsivity, dynamic range, and imaging performance due to exposure to space radiation, a γ-irradiation experiment was performed on a large eighttransistor (8T) complementary metal–oxide–semiconductor (CMOS) color image sensor. An in-depth investigation was thus conducted on the mechanisms by which ionization damage degrades radiation-sensitive parameters (i.e., dark current, dark signal non-uniformity, output signal, saturated output, and responsivity) of an image sensor, in its red, green, and blue channels. The findings of this study will serve as a theoretical and technical basis for the design of large radiation-hardened 8T-CMOS image sensors for space-based remote-sensing satellites.

Potentiometric Free Chlorine Detection without Using Conventional Reference Electrodes

Chlorine disinfection of water is one treatment method to supply safe tap water. The quantitative monitoring of free chlorine concentration as an important water quality index is effective to maintain tap water hygienic safety. In this study, a simple potentiometric sensor which is composed of two dissimilar metal electrodes (a pair of platinum and austenitic stainless steel: SUS316) was evaluated in synthetic and actual tap water. It was demonstrated that the open circuit potential of each electrode had a different sensitivity to free chlorine and that the difference could be extracted as the sensing signal without using conventional reference electrodes. In a flowing system using tap water, more than 300 mV of large sensor signal and good reproducibility were observed at a free chlorine concentration of 0.6 mg l−1. The long-term baseline drift was within the range of ±10 mV and the sensor was almost immune to various environmental factors such as temperature, pressure, flow rate, and conductivity, but not for pH. These results would pave the way to develop various sensor applications that could be directly applied to tap water pipelines for continuous in-line monitoring of residual chlorine with low cost and maintenance.

Demand-Driven Data Acquisition for Large Scale Fleets.

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

E-optimum sensor selection for estimation of subsets of parameters

The design of a network of observation locations is addressed in the setting of estimating unknown parameters of a spatiotemporal system modelled by a partial differential equation. Interest is in estimating only a subset of these parameters as accurately as possible. The other parameters, called nuisance parameters, must also be estimated although we are interested in neither their values, nor accuracies. The maximal eigenvalue of the covariance matrix of the maximum-likelihood estimator of the parameters of interest is used as the measure of the identification accuracy. In order to make selection of a best subset of gauged sites from a possibly very large set of candidate sites computationally tractable, its convex relaxation is introduced. Two major problems to be tackled are the potential singularity of the optimal information matrix associated with all unknown parameters and the nondifferentiability of the optimality criterion. The former is settled by imposing a constraint on the minimal allowable value of the determinant of the information matrix. The latter is resolved by reformulating the problem as a convex semi-infinite programming problem whose solution is sought by solving a sequence of finite low-dimensional min–max problems using extremely efficient generalized simplicial decomposition. The excellent performance of the proposed technique is illustrated by an example involving optimal sensor node activation in a large sensor network collecting measurements to identify a moving contaminating source.

Location-Dependent Performance of Large-Area Piezoresistive Tactile Sensors Based on Electrical Impedance Tomography

The technique of electrical impedance tomography (EIT) has been recognized as a promising method to design tactile sensors with continuous sensing capability over a large area. The mechanism of electrical impedance tomography allows reconstructing tactile information within the sensing area based on measurements made only at the boundary. However, spatial performance of EIT-based tactile sensors has demonstrated location dependency, which severely affects correct interpretation of tactile stimuli. Here, we analyzed the effect of hyperparameter on the spatial performance, in terms of amplitude, size, position error, and shape deformation in the reconstructed images. To obtain uniform sensitivity throughout the entire sensing area, we developed an intensity scaling method to correct reconstructed amplitudes based on simulation studies. A diagonal scaling matrix was developed for a symmetric circular sensing area, and the scaling value were constructed according to the radial positions of the finite elements. The correction method was further evaluated on a compliant EIT-based touch sensor made of polymer filled composites with underlying paddings. We found that the developed method effectively produced a more uniform sensitivity distribution, and improved spatial profiles of shape deformation. The findings shown here help better interpret the strength information of tactile stimuli located at different positions of large area EIT-based sensors.

The Operational Inflight Radiometric Uniform Calibration of a Directional Polarimetric Camera

The directional polarimetric camera (DPC) on-board the GF-5A satellite is designed for atmospheric or water color detection, which requires high radiometric accuracy. Therefore, in-flight calibration is a prerequisite for its inversion application. For large field optical sensors, it is very challenging to ensure the consistency of radiation detection in the whole field of view in the space environment. Our work proposes a vicarious in-flight calibration method based on sea non-equipment sites (visible bands) and land non-equipment sites (all bands). Combined with environmental parameters and radiation transmission calculations, we evaluated the radiation detection accuracy of the 0° to 60° view zenith angle of the DPC in each band. Our calibration method is based on the single-day normalized radiance data measured by the DPC. Through data selection, enough calibration samples can be obtained in a single day (the number of desert samples is more than 5000, and the number of calibration samples of the ocean is more than 2.8×106). The measurements are compared with the simulation of 6SV VRT code or look-up tables. The massive amount of data averages the uncertainty of a single-point calculation. Although the uncertainty of a single sample is significant, the final fitting of the curve of the variation in the radiometric calibration coefficient with the observation angle can still keep the root mean squared error at approximately 2–3% or even lower, and for visible bands, the calibration results for both ocean sites and desert sites are in good agreement regarding the non-uniformity of the sensor.