Types Of Sensor Data Research Articles

Circadian rhythms are not captured equal: Exploring Circadian metrics extracted by differentcomputational methods from smartphone accelerometer and GPS sensors in daily life tracking.

ObjectiveTo identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods.MethodsWe used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor regression, non-parametric method, Fourier analysis) on two types of smartphone sensor data (GPS, accelerometer) to characterize CR. We explored the inter-relations among the extracted circadian metrics as well as between the circadian metrics and participants’ self-reported mood and sleep outcomes.ResultsCompared to GPS signals, smartphone accelerometer activity follows an intradaily distribution that starts earlier in the day, winds down later, reaches half cumulative activity about the same time, conforms less to a sinusoidal wave, and exhibits more intradaily fragmentation but higher CR strength and lower interdaily disruption. We found a notable negative correlation between intradaily variability and CR strength especially pronounced in GPS activity. Self-reported sleep and mood outcomes showed significant correlations with particular CR metrics.ConclusionsWe revealed significant inter-relations and discrepancies in the circadian metrics discovered from two smartphone sensors and four CR algorithms and their bearings on wellbeing indicators such as sleep quality and loneliness.

High-performance Edwards curve aggregate signature (HECAS) for nonrepudiation in IoT-based applications built on the blockchain ecosystem

“Blockchain” is a buzzword that has captured attention from researchers in almost every possible domain. In a blockchain ecosystem, multiple parties across the network have to establish trust in an untrusted environment to ensure seamless nonrepudiation. Nonrepudiation is a crucial factor for secure information auditing in the blockchain. To achieve this, we require an optimal digital signature. In this work, we design a digital signature to address the nonrepudiation challenge in blockchain ecosystems. Our approach developed in the current work completed signing and verification tasks with 10% and 13% shorter processing times, respectively than the conventional digital signature scheme. The adoption of the high-performance Edwards curve aggregate signature (HECAS) in a blockchain ecosystem can yield a 10% improvement in the transaction flow, a 10% improvement in block validation and a 40% decrease in storage costs relative to the same system implemented without HECAS. Finally, we simulated various blockchain-based Internet of Things (IoT) ecosystems and experimented with our scheme. The results show that our work can produce convincing, consistent results across various sensor data types from smart IoT-based blockchain solutions.

Identifying malicious nodes in wireless sensor networks based on correlation detection

The wireless sensor network (WSN) is a multi-hop wireless network that comprises multiple sensor nodes arranged in a self-organized manner. It is usually deployed in unattended areas where sensor nodes can easily be infiltrated by attackers who can affect the detection results by injecting false data. This paper proposes a malicious-node identification method based on correlation theory that prevents fault data injection attacks. First, anomalies among similar types of sensor data are detected based on time correlation. Second, malicious nodes are identified based on spatial correlation. Third, the identified malicious nodes are verified based on event correlation. The experimental results and their comparison with those of existing methods show that the proposed scheme has better recall with lower false-positive and false-negative rates than those of the traditional fuzzy reputation model and weighted-trust-based methods.

Multilink Internet-of-Things Sensor Communication Based on Bluetooth Low Energy Considering Scalability

As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on Bluetooth low energy (BLE) enables only the processing of up to 19 peripheral nodes per central device. This study suggested an alternative to increasing the number of IoT sensor nodes while minimizing the addition of a central processor by expanding the number of peripheral nodes that can be processed per central device through a new group-switching algorithm based on Bluetooth low energy (BLE). Furthermore, this involves verifying the relevancy of application to the industry field. This device environment lowered the possibility of data errors and equipment troubles due to communication interference between central processors, which is a critical advantage when applying it to industry. The scalability and various benefits of a group-switching algorithm are expected to help accelerate various services via the application of BLE 5 wireless communication by innovatively improving the constraint of accessing up to 19 nodes per central device in the conventional multilink IoT sensor communication.

Serial correlated–uncorrelated concurrent space method for process monitoring

Due to the closed-loop feedback control system, the data collected in the industry process is always temporal serial correlated. Moreover, different types of sensor data (such as temperature, pressure, liquid level, flow and so on) would show different temporal serial correlations. Meanwhile, the current process monitoring method considers all types of sensor data equally, ignoring the difference in temporal serial correlations caused by various sensor types. In response to this, serial correlated–uncorrelated concurrent space method is proposed in this work, where process variable original space is divided into serial correlated space and serial uncorrelated space via the degree of temporal serial correlations. In addition, the proposed method not only considers the local monitoring within space via principal component analysis and slow feature analysis respectively, but also monitors the information between-space based on the moving window and the mutual information. Finally, on the basis of the monitoring statistic within space and between-space, the comprehensive monitoring indicator is constructed via the local outlier factor method. The advantage and superiority of the proposed method is illustrated via testing on Tennessee Eastman process and Continuous Stirred Tank Reactor process.

A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation

Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.

INIM: Inertial Images Construction with Applications to Activity Recognition

Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone’s inertial sensor readings are used as input to a machine learning algorithm which performs the classification. There are several approaches to tackle such a task: feature based approaches, one dimensional deep learning algorithms, and two dimensional deep learning architectures. When using deep learning approaches, feature engineering is redundant. In addition, while utilizing two-dimensional deep learning approaches enables to utilize methods from the well-established computer vision domain. In this paper, a framework for smartphone location and human activity recognition, based on the smartphone’s inertial sensors, is proposed. The contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, and transfer learning from computer vision domain to the inertial sensors classification problem. Four different datasets are employed to show the benefits of using the proposed approach. In addition, as the proposed framework performs classification on inertial sensors readings, it can be applied for other classification tasks using inertial data. It can also be adopted to handle other types of sensory data collected for a classification task.

Deep learning-based tree species mapping in a highly diverse tropical urban setting

Spatially explicit information on urban tree species distribution is crucial for green infrastructure management in cities. This information is usually acquired with ground-based surveys, which are time-consuming and usually cover limited spatial extents. The combination of machine learning algorithms and remote sensing images has been hailed as a promising way to map tree species over broad areas. Recently, convolutional neural networks (CNNs), a type of deep learning method, have achieved outstanding results for tree species discrimination in various remote sensing data types. However, there is a lack of studies using CNN-based methods to produce tree species composition maps, particularly for tropical urban settings. Here, we propose a multi-task CNN to map tree species in a highly diverse neighborhood in Rio de Janeiro, Brazil. Our network architecture takes an aerial photograph (RGB bands and pixel size = 0.15 m) and delivers two outputs: a semantically segmented image and a distance map transform. In the former, all pixel positions are labeled, while in the latter, each pixel position contains the Euclidean distance to the crown boundary. We developed a post-processing approach that combines the two outputs, and we classified nine and five tree species with an average F1-score of 79.3 ± 8.6% and 87.6 ± 4.4%, respectively. Moreover, our post-processing approach produced a realistic tree species composition map by labeling only pixels of the target species with high class membership probabilities. Our results show the potential of CNNs and aerial photographs to map tree species in highly diverse tropical urban settings, providing valuable insights for urban forest management and green spaces planning.

Optimize the early warning time of coal and gas outburst by multi-source information fusion method during the tunneling process

Accurate and advanced early warning of coal and gas outburst is an important means to ensure the process safety of coal mining. It is a difficult problem to realize the reliable early warning of coal and gas outburst because the cause of the complex causes and disaster-causing mechanism has not been clearly defined. At present, the traditional early warning models have some problems, such as low degree of information fusion, non-dynamic decision-making, and short early warning time. To optimize the early warning time and improve the process safety of tunneling, a new early warning model is proposed. This new model is a multi-source information fusion dynamic early warning model based on combining Autoregressive Integrated Moving Average (ARIMA) and the Transferable Belief Model (TBM). The ARIMA described the changes of different types of sensor data with spatial heterogeneity over time, and the TBM fused different types of sensor data and makes dynamic decisions to achieve early warning of Coal and gas outburst. Finally, Acoustic Emission (AE), Electromagnetic Resonance (EMR), and Gas concentration data of NO.12223 conveyor roadway of Jinjia coal mine from August 3, 2017, to August 7, 2017, were used as the experimental data set. The proposed model was applied to the NO.12223 conveyor roadway of Jinjia Coal Mine to verify the dynamic early warning for the risk of coal and gas outburst. The results showed that the gas gush out with tunneling is identified 1 and 38 min in advance. It was about 9 min earlier than the earliest response EMR signal obtained by the linear regression prediction model of a single indicator used in the original monitoring and early warning system. The research results are of practical significance to optimize the time of early warning of coal and gas outburst and improve process safety risk control in coal mine production.

Fast tag identification for mobile RFID robots in manufacturing environments

Purpose In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection. Design/methodology/approach A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection. Findings The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission. Originality/value In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

A tnGAN-Based Leak Detection Method for Pipeline Network Considering Incomplete Sensor Data

Due to the widely deployed sensors in the pipeline network, the data-driven detection method is a natural choice with multiple sensor measurements. However, the incomplete data problem caused by device failure or network interruption seriously hinders the implementation of pipeline status monitoring. Aiming at this difficulty, this article proposes a generative adversarial network based on trinetworks form (tnGAN) to handle leak detection problems with incomplete sensor data. First, the generative model is proposed to recover incomplete data through fully exploiting the same-level nature similarity of data features. Therein, the same type of sensor data, obtained from the pipeline network, is used as the input. Next, to further boost the temporal evolvement characteristics and the spatial similarity, a multiview awareness strategy is incorporated in the established model to facilitate the integration of inherent information. Then, a dual-discriminative network architecture is proposed to detect pipeline status through computing the similarity of the latent features of samples. With the abovementioned structure, the proposed method can achieve different incomplete data recovery situations, such as individual lost and random missing. In addition, it can also aggregate the output and features of the discriminative networks to obtain the pipeline leak detection result. Finally, the experiment results on a pipeline network demonstrate the capability and effectiveness of the proposed method in both data recovery and leak detection.

GAN-based Style Transformation to Improve Gesture-recognition Accuracy

Gesture recognition and human-activity recognition from multi-channel sensory data are important tasks in wearable and ubiquitous computing. In these tasks, increasing both the number of recognizable activity classes and the recognition accuracy is essential. However, this is usually an ill-posed problem because individual differences in the same gesture class may affect the discrimination of different gesture classes. One promising solution is to use personal classifiers, but this requires personal gesture samples for re-training the classifiers. We propose a method of solving this issue that obtains personal gesture classifiers using few user gesture samples, thus, achieving accurate gesture recognition for an increased number of gesture classes without requiring extensive user calibration. The novelty of our method is introducing a generative adversarial network (GAN)-based style transformer to 'generate' a user's gesture data. The method synthesizes the gesture examples of the target class of a target user by transforming of a) gesture data into another class of the same user (intra-user transformation) or b) gesture data of the same class of another user (inter-user transformation). The synthesized data are then used to train the personal gesture classifier. We conducted comprehensive experiments using 1) different classifiers including SVM and CNN, 2) intra- and inter-user transformations, 3) various data-missing patterns, and 4) two different types of sensory data. Results showed that the proposed method had an increased performance. Specially, the CNN-based classifiers increased in average accuracy from 0.747 to 0.822 in the CheekInput dataset and from 0.856 to 0.899 in the USC-HAD dataset. Moreover, the experimental results with various data-missing conditions revealed a relation between the number of missing gesture classes and the accuracy of the existing and proposed methods, and we were able to clarify several advantages of the proposed method. These results indicate the potential of considerably reducing the number of required training samples of target users.

A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms

Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

Integration of Semantics Into Sensor Data for the IoT

The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies enables more expressive data representation that supports knowledge discovery. In this paper, a systematic review of integration of semantics into sensor data for the IoT is provided. The conducted review is focused on analyzing the main solutions of adding semantic annotations to the sensor data, standards that enable all types of sensor data via the web, existing models of stream data annotation, and the IoT trend domains that use semantics.

Wearable muscle movement information measuring device based on acceleration sensor

In this paper, a wearable device designed using acceleration sensors to obtain acceleration information generated by the user's muscles during exercise follows data collection, data pre-processing, feature extraction and feature selection, classification model training, and evaluation of this muscle information of human movement. Designed a relevant model verification system and wearable device, and carried out verification experiments on the model in real-time, accuracy, interactivity, and parallelism with other sensor data types. Transitional motion detection and segmentation algorithms can effectively segment out the transition actions included in the acceleration sequence, and using this method to classify and recognize nine kinds of human motion information, the average recognition rate reaches 98.56%.

Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery

AbstractRegular biomass estimations for natural and plantation forests are important to support sustainable forestry and to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with information derived from Pléiades very high resolution (VHR) stereo images applied to plantation forests in an arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and 500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but constant number of samples. In the second experiment, we fixed the total area to be sampled to account for the additional effort to collect large field plots. Our results for the first experiment show that model performance metrics Spearman’s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51 and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with a plot size of 100 m2 (most samples) with Spearman’s r = 0.77, RMSErel = 0.59 and RMSEnor = 0.15. Results from an analysis of variance type-II suggest that the overall most important factors to explain model performance metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing data types (e.g. LiDAR).

Smoothed LSTM-AE: A spatio-temporal deep model for multiple time-series missing imputation

Missing values widely exist in time-series data owing to sensor or communication failure. It is indispensable to impute the missing data for equipment state monitoring and advanced data analysis. In this study, we propose a deep spatiotemporal time-series missing data imputation model, called LSTM-AEs, to enhance the imputation performance and handle multiple missing patterns. Generally, it is practically challenging to determine when, which, and how many sensors fail. Most previous algorithms cannot handle multiple-sensor missing data owing to insufficient attributes. As different missing patterns require different models and may appear simultaneously, it is inadvisable to employ a large number of models for handling all these patterns. A deep auto-encoder (DAE) can eliminate the diversity of missing patterns by restoring the information from the sensor data. The proposed model combines DAE and LSTM for extracting spatio-temporal features to estimate missing values in multiple time series. Moreover, a smoothing regularization term is added into the combined model, leading to a more stable estimation. Experiments are conducted on three types of sensor data: the Tennessee Eastman process simulation data, gas turbine data from the offshore oil Corporation, and power plant simulation data. The results demonstrate that the proposed technique is effective for different missing patterns and provides more accurate predictions than the existing techniques.

Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.

DESIGN AND ANALYSIS OF AN INTELLIGENT FIRE DETECTION SYSTEM FOR AIRCRAFT

Fire detection system and fire warning are design features of an aircraft. Fire detection system protects the aircraft and passengers both in case of actual fire during flight. But spurious fire warning during flight creates a panic situation in flight crews and passengers. The conventional fire alarm system of an aircraft is triggered by false signal. ANN based fire detection system provides real observation of deployed zones. An intelligent fire detection system is developed based on artificial neural network using three detection information such as heat (temperature), smoke density and CO gas. This Information helps in determining the probability of three representative of Fire condition which is Fire, smoke and no fire. The simulated MATLAB results Show that the errors in identification are very less. The neural network based fire detection system integrates different types of sensor data and improves the ability of system to correct prediction of fires. It gives early alarm when any kind of fire broke out and helps to decrease in spurious warning.

A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series

Data-driven methods for machinery health monitoring and prediction, such as machine learning and statistical pattern recognition techniques, normally requires high quality (less noised), frequently-sampled and large volume time-series data to estimate proper models between system inputs and outputs. However, most of the industrial time-series are highly noisy and only few types of sensory data are available, which challenges the estimation accuracy. This paper proposes a data-driven spectral decomposition framework (denoted as Koopman-CBM) for the machinery health monitoring and prediction problem. Specifically, considering noisy industrial signals in the form of one dimensional time-series, we use the higher-order dynamic mode decomposition (DMD) embeds time-lagged snapshots to increase the spatial complexity of low-dimensional time series and use the total-least-square algorithm to compensate the effect of measurement noise, thereby, extracting accurate dynamical features. The obtained de-noised model characteristics (i.e., eigenvalues, eigenfunctions, Koopman operator) is effective in predicting the system health stages. In parallel, using the Koopman operator as the linear predictor associated with the nonlinear dynamics, we can then perform remaining useful life (RUL) predictions with high accuracy. The experimental validation of the proposed framework is carried out on a rolling bearing datasets for degradation health-stage inspection and RUL prediction. Results show that– compared with other mainstream methods– our approach is capable of identifying the critical degradation stages and achieving higher RUL prediction accuracies.