Sensor Data Gathering Research Articles

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.

Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

A lightweight sensor ontology for supporting sensor selection, deployment, and data processing in forming processes

In the era of smart manufacturing, modern manufacturing systems face high demands for enhancing process performance and reducing machine downtime. Sensors and process data are essential for successfully implementing data-driven approaches to guarantee robust and reliable process monitoring, tool conditioning, or quality assurance. However, the accuracy and performance of such approaches are highly dependent on the quality of the gathered sensor data and influenced by the implemented data acquisition and processing methods. For this purpose, this work proposes a lightweight sensor ontology to provide a comprehensive overview to characterize underlying relationships between the physical environment and the quality of the data sets. The extended sensor ontology, in combination with domain knowledge, aims to support engineers in fully exploiting the potential of sensor data to obtain trustworthy data sets in forming technologies. As a result, this approach can improve the implementation of automated and data-driven process monitoring of forming systems and tools.

Detecting Environmental Conditions in Cultivation Lands Using Bionic Devices

Climate change has an impact on crops, fruits, vegetables, and pest infestation, hence agricultural output is a top priority for most nations. As a result, professional growers have the problem of reaching maximum output results, and greenhouses have emerged as an excellent choice for ensuring these results. Farmers can employ innovative technologies inside greenhouses to prevent insect damage to plants and increase indoor growth through climate management. However, in order to manage agricultural fields and greenhouses successfully, farmers must now use Industry 4.0 technology such as robotics, Internet of Things devices, machine learning software, and so on. In this setting, sensor deployment is critical for gathering data and obtaining knowledge to help farmers make decisions. As a practical option for small farms, this research proposes an autonomous robot that drives along greenhouse crop paths with previously specified routes and can collect environmental data provided by a wireless sensor network when the farmer has no prior knowledge of the crop. An unsupervised learning method is used to cluster the optimal, standard, and deficient sectors of a greenhouse in order to identify improper crop development patterns. Finally, a user interface is built to assist farmers in planning the robot's route and distance while gathering sensor data to monitor crop conditions.

Autonomous mobile robots for outdoor tasks

This paper presents a low-cost hardware platform for outdoor robots, being suitable for education, industrial prototyping and private use. The choice of components isdiscussed, including platform, sensors and controller as well as GPS and image processing hardware. Furthermore, a software approach is proposed, allowing students and researchers to easily implement own algorithms for localization, navigation and the tasks to fulfill. Several robots can be integrated in a framework which connects various different hardware platforms, called the BOSPORUS network. Together with other components, they form an intelligent network for gathering sensor and image data, sensor data fusion, navigation and control of mobile platforms. The architecture of a reference platform on the campus of the Brandenburg University of Applied Sciences is presented and evaluated.

Leveraging Large Language Models for Sensor Data Retrieval

The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it into a reusable structured format, while the second component aims to identify the most relevant sensor data to augment a given user-provided dataset. The evaluation of the proposed approach involved comparing the performance of various large language models in generating representative word embeddings for each table to retrieve related sensor data. The results show promising performance in terms of precision and MRR (0.90 and 0.94 for the best-performing model, respectively), indicating the system’s ability to retrieve pertinent sensor data that fulfil user requirements.

BLUETOOTH ENVIRONMENTAL MONITORING SYSTEM

The primary goal of this project is to construct an environmental monitoring system integrating a Bluetooth HC-06 module. Drawing upon acquired expertise, the project resulted in the development of a functional system capable of gathering sensor data, transmitting it wirelessly to a mobile device via the Bluetooth module, and displaying it in real-time. The utilization of the FreeRTOS operating system facilitated synchronous data collection, orchestrated through tasks synchronized by semaphores, thereby guaranteeing the integrity of real- time data acquisition and transmission.

Heart disease prediction: Improved quantum convolutional neural network and enhanced features

Currently, heart disease is the leading cause of death in world. Since cardiac sickness requires knowledge and detailed information, it is challenging to anticipate. Healthcare systems have been using Internet of Things (IoT) technologies for gathering sensor data for diagnosing the heart disease and prognosis in recent years. Researchers have focused a lot of emphasis on diagnosing heart disease, although the outcomes are not always reliable. This article proposes the automated heart disease prediction model with three main stages, including preprocessing, feature extraction, and prediction. The input data undergoes an improved Z-score normalization as the preprocessing step. The appropriate features needed to train the prediction model are retrieved from the preprocessed data during feature extraction. The features extracted include improved entropy, statistical features, and information gain features. Depends on the features extracted, prediction is determined by the Improved Quantum CNN (IQCNN). The results of the IQCNN are compared to earlier systems for a various metrics. The proposed IQCNN model has achieved better accuracy of 0.91 at a 70% learning rate when evaluated over conventional methods like Bi-LSTM, CNN, QCNN, DNN, NN, RNN, MDCNN and E-KNN for better performance in the prediction of heart disease.

IoT and Cloud Based Sustainable Smart Irrigation System

This paper addresses the concerns in irrigation mechanisms and employs a Sensors and cloud-based platform to monitor and control an irrigation system. In order to monitor the soil’s moisture level in real time, moisture sensors are placed in the field. These sensors wirelessly provide data to the NodeMCU, which processes and relays the information. The NodeMCU gathers the sensor data and processes it, considering standard deviations and crop-specific parameters. Using this information, the system starts the water pump and opens the solenoid valves to begin the irrigation operation. The NodeMCU coordinates with these valve-opening devices via wireless communication. In addition, the system uses online weather prediction data to provide more precise watering schedule optimisation. The system dynamically modifies the irrigation schedule based on analysis of weather patterns, evapotranspiration rates, and crop water needs to save water during times of rain or high humidity. The solution makes use of cloud-based platforms to improve scalability and accessibility. The gathered sensor data and command instructions are safely transferred to the cloud server. This allows farmers to remotely check on and adjust the irrigation system by means of the web or mobile apps. In addition, data analytics methods may be used to infer information and provide suggestions for improved methods of water management and crop care. In conclusion, the Internet of Things (IoT) NodeMCU smart irrigation system provides an automated and intelligent approach to water management in agriculture. Water is saved, crop yields are boosted, and sustainability is enhanced thanks to the capacity to irrigate precisely based on real-time soil moisture data, weather predictions, and crop needs.

Heart health prediction and classification: An IoMT and AI collaborative model

Internet of Things (IoT) technology has been used in medical care as the Internet of Medical Things (IoMT) to gather sensor data for diagnosing and predicting cardiac disease. IoMT allows users to access real-time tracking information and manually estimate the person's health using Machine Learning (ML) algorithms. The primary goal of the study proposal is to categorize data and forecast heart illness using health information and medical imagery. The suggested IoMT-based Heart Health Prediction and Classification (IoMT-HHPC) model is a medical data categorization and forecasting framework in two phases. If the first stage's outcome effectively predicts heart disease, the second step is image classification. Data collected from medical equipment attached to the person's body were initially categorized. Echocardiography (ECG) images were analyzed to forecast cardiac problems. This article used many ML techniques to forecast cardiac disease. An IoMT-HHPC model with ANN achieved an accuracy of 99.02%, surpassing the performance of other ML algorithms.

Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices

Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition system based on a common wearable device: a smartwatch. This is one of the most inconspicuous devices suitable for activity recognition as it is very common and worn for extensive periods of time. We propose a human activity recognition system that is extensible, due to the wide range of sensing devices that can be integrated, and that provides a flexible deployment system. The machine learning component recognizes activity based on plot images generated from raw sensor data. This service is exposed as a Web API that can be deployed locally or directly in the cloud. The proposed system aims to simplify the human activity recognition process by exposing such capabilities via a web API. This web API can be consumed by small-network-enabled wearable devices, even with basic processing capabilities, by leveraging a simple data contract interface and using raw data. The system replaces extensive pre-processing by leveraging high performance image recognition based on plot images generated from raw sensor data. We have managed to obtain an activity recognition rate of 94.89% and to implement a fully functional real-time human activity recognition system.

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.

Design and Development of a 3D-Printed CubeSat by Go Science

This project outlines the design, development, and construction of a 3D-printed CubeSat, a miniature satellite, equipped with an ESP32 microcontroller, an MPU9250 inertial measurement unit, and a BMP280 barometric pressure sensor module. The CubeSat adheres to standard CubeSat dimensions and incorporates a range of systems, including power management, communication, and data collection, to enable its functionality in space. The CubeSat's structure was meticulously designed using CAD software, and 3D printing technology was employed to fabricate its lightweight, space-grade casing. The ESP32 microcontroller serves as the central processing unit, interfacing with the MPU9250 and BMP280 sensors via I2C communication. Custom firmware was developed to gather sensor data and manage power resources efficiently. Solar Panels are also used for redundant Power Support. The CubeSat features a wireless transceiver for communication with ground stations, facilitating telemetry data transmission and command reception. This CubeSat can collect sensor’s data and transmit it to Ground Station for analysis, contributing valuable insights into its surrounding environment. Maintenance and potential upgrades will be considered to extend its operational lifespan. This project exemplifies the intricate process of designing and developing a CubeSat, highlighting the integration of advanced sensors and microcontrollers in a 3D-printed structure. The CubeSat's deployment underscores its potential for scientific research and data collection in the realm of space exploration. Developed by Go Science Technical Team. IndexTerms – Go Science, CubeSat, ESP32, MPU9250, BMP280, Environmental Monitoring, Space Technology, Sensor Integration, Data Collection, Climate Research, Microsatellites, Miniature Satellites, Space Sensors, Atmospheric Data, Earth Observation, Remote Sensing, Environmental Science, Climate Monitoring, Sensor Fusion, Space Exploration.

Joint privacy and data quality aware reward in opportunistic Mobile Crowdsensing systems

Mobile Crowdsensing (MCS) is a paradigm involving a crowd of participants, called workers, into sensor data gathering campaigns through their personal devices. Some campaigns require workers to contribute with small amounts of geolocalized data at a constant rate, while being not directly aware of the global conditions of the system. In the scope of this reduced awareness, it is crucial to consider the privacy preservation of single workers at design time, as the disclosure of their exact location may lead to severe privacy issues. In this paper we design a privacy by design MCS framework that leverages variable rewards for workers willing to submit their location with an higher precision than others. Privacy is ensured through a negotiation phase that estimates the reward of the workers for different levels of location precision. This way, it helps them decide autonomously the spatial granularity of their data in order to preserve their privacy, yet obtaining a reward for their data. We design a metric based on k-anonymity to evaluate the level of privacy achieved, and validate the proposed framework over a real dataset. Our results show the efficacy of the framework as well as interesting effects caused by the topology of the environment.

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS and energy efficiency in the Wireless Sensor Network (WSN). The MS collects the data from the Cluster Head (CH), and the RL incentive values select CH. The incentives value is computed by the QoS parameters such as minimum energy utilization, minimum bandwidth utilization, minimum hop count, and minimum time delay. The MS is used to collect the data from CH, thus minimizing the network delay. The sleep and awake scheduling is used for minimizing the CH dead in the WSN. This work is simulated, and the results show that the RLMQ scheme performs better than the baseline protocol. Results prove that RLMQ increased the residual energy, throughput and minimized the network delay in the WSN.

Ecosense: An IoT System for Detecting Suitable and Sustainable Living Conditions

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment.

Lion Based Butterfly Optimization with Improved YOLO-v4 for Heart Disease Prediction Using IoMT

The Internet of Medical Things (IoMT) has subsequently been used in healthcare services to gather sensor data for the prediction and diagnosis of cardiac disease. Recently image processing techniques require a clear focused solution to predict diseases. The primary goal of the proposed method is to use health information and medical pictures for classifying the data and forecasting cardiac disease. It consists of two phases for categorizing the data and prediction. If the previous phase's results are practical heart problems, then there is no need for phase 2 to predict. The first phase categorized data collected from healthcare sensors attached to the patient's body. The second stage evaluated the echocardiography images for the prediction of heart disease. A Hybrid Lion-based Butterfly Optimization Algorithm (L-BOA) is used for classifying the sensor data. In the existing method, Hybrid Faster R-CNN with SE-Rest-Net-101 is used for classification. Faster R-CNN uses areas to locate the item in the picture. The proposed method uses Improved YOLO-v4. It increases the semantic knowledge of little things. An Improved YOLO-v4 with CSPDarkNet53 is used for feature extraction and classifying the echo-cardiogram pictures. Both categorization approaches were used, and the results were integrated and confirmed in the ability to forecast heart disease. The LBO-YOLO-v4 process detected regular sensor data with 97.25% accuracy and irregular sensor data with 98.87% accuracy. The proposed improved YOLO-v4 with the CSPDarkNet53 method gives better classification among echo-cardiogram pictures.

Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data.

This paper makes a contribution to research on digital twins that are generated from robot sensor data. We present the results of an online user study in which 240 participants were tasked to identify real-world objects from robot point cloud data. In the study we manipulated the render style (point clouds vs voxels), render resolution (i.e., density of point clouds and granularity of voxel grids), colour (monochrome vs coloured points/voxels), and motion (no motion vs rotational motion) of the shown objects to measure the impact of these attributes on object recognition performance. A statistical analysis of the study results suggests that there is a three-way interaction between our independent variables. Further analysis suggests: 1) objects are easier to recognise when rendered as point clouds than when rendered as voxels, particularly lower resolution voxels; 2) the effect of colour and motion is affected by how objects are rendered, e.g., utility of colour decreases with resolution for point clouds; 3) an increased resolution of point clouds only leads to an increased object recognition if points are coloured and static; 4) high resolution voxels outperform medium and low resolution voxels in all conditions, but there is little difference between medium and low resolution voxels; 5) motion is unable to improve the performance of voxels at low and medium resolutions, but is able to improve performance for medium and low resolution point clouds. Our results have implications for the design of robot sensor suites and data gathering and transmission protocols when creating digital twins from robot gathered point cloud data.

DEVELOPMENT OF DATA ACQUISITION SYSTEM FOR MOBILE ULTRAVIOLET (UV) MONITORING DEVICE

In this age of modernization, smart or self-sufficient frameworks in all domains of work are becoming increasingly important as innovation advances. This involves improving data collecting for UV monitoring system using metal oxide sensors, an underdeveloped market niche. The Data Acquisition (DAQ) system available at the moment is antiquated and frustrating. This project aims to establish a framework for acquiring information about UV monitoring devices using metal oxide sensors. Metal oxide-based sensor uses titanium dioxide nanorod arrays (TNAs). Self-powered UV photosensor creates electricity when exposed to UV light. UV photosensors are photoelectrochemical (PEC). Monitoring the sensor's real-time output voltage in relation to UV intensity evaluates the data acquisition system's performance. A Raspberry Pi is used to evaluate the data security architecture for the Cytron-UNO development board. The sensor's data gathering system detected the current produced and stamped the date and time on it. This paper discusses trait formation and experimental data.

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors.

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.