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The current review explores multiple definitions of school shootings used by myriad data collection platforms and by various scholars. Importantly, the impacts of definitional discrepancies on inclusion criteria, data divergence, research, policy, and public perception are discussed at length. The review concludes with a call to Criminologists and school gun violence scholars to better collaborate on what should be considered a "school shooting" and lists five benefits that may result from modifying school gun violence definitions and data collection methodologies. [ABSTRACT FROM AUTHOR]

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability. [ABSTRACT FROM AUTHOR]

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks. [ABSTRACT FROM AUTHOR]

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. [ABSTRACT FROM AUTHOR]

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics. [ABSTRACT FROM AUTHOR]

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent's Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time. [ABSTRACT FROM AUTHOR]

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683. [ABSTRACT FROM AUTHOR]

Background: Managing repaired tetralogy of Fallot (TOF) patients is still challenging despite the fact that published studies identified prognostic clinical or imaging data with rather good negative predictive accuracy but weak positive predictive accuracy. Heterogeneity of the initial anatomy, the surgical approach, and the complexity of the mechanism leading to dilation and ventricular dysfunction explain the challenge of predicting the adverse event in this population. Therefore, risk stratification and management of this population remain poorly standardized. Design: The CMR/CT WG of the Italian Pediatric Cardiology Society set up a multicenter observational clinical database of repaired TOF evaluations. This registry will enroll patients retrospectively and prospectively assessed by CMR for clinical indication in many congenital heart diseases (CHD) Italian centers. Data collection in a dedicated platform will include surgical history, clinical data, imaging data, and adverse cardiac events at 6 years of follow-up. Summary: The multicenter repaired TOF clinical database will collect data on patients evaluated by CMR in many CHD centers in Italy. The registry has been set up to allow future research studies in this population to improve clinical/surgical management and risk stratification of this population. [ABSTRACT FROM AUTHOR]

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications. [ABSTRACT FROM AUTHOR]

Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot's facial appearance. The final measurement scale comprised four dimensions, "ethics concern", "capability", "positive affect", and "anthropomorphism", consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale's reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users. [ABSTRACT FROM AUTHOR]

This article discusses the key factors that influence quality in contemporary surgical nursing. The factors include education and training, technology and innovation, patient-centered care, and patient safety. The article emphasizes the importance of continuous improvement and adaptation in healthcare to ensure high-quality care and patient safety. It also highlights the need for ongoing education in the use of new technologies and the importance of communication skills and cultural awareness in providing individualized care. The article concludes by emphasizing the role of surgical nurses in promoting quality and safety in modern medical practice. [Extracted from the article]

The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results. [ABSTRACT FROM AUTHOR]

The construction industry requires comprehensive and accurate as-built information for a variety of applications, including building renovations, historic building preservation and structural health monitoring. Reality capture technology facilitates the recording of as-built information in the form of point clouds. However, the emerging development trends of scan planning and multi-technology fusion in point cloud acquisition methods have not been adequately addressed in research regarding their effects on point cloud registration quality and data quality in the built environment. This study aims to extensively investigate the impact of scan planning and multi-technology fusion on point cloud registration and data quality. Registration quality is evaluated using registration error (RE) and scan overlap rate (SOR), representing registration accuracy and registration coincidence rate, respectively. Conversely, data quality is assessed using point error (PE) and coverage rate (CR), which denote data accuracy and data completeness. Additionally, this study proposes a voxel centroid approach and the PCP rate to calculate and optimize the CR, tackling the industry's challenge of quantifying point cloud completeness. [ABSTRACT FROM AUTHOR]

To address the challenges of inefficient intelligent parking performance and reduced efficiency in complex environments, this study presents an end-to-end intelligent parking control model based on a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture incorporating multi-source sensory information fusion to improve decision-making and adaptability. The model can produce real-time intelligent parking control decisions by extracting spatiotemporal features, including comprehensive 360-degree panoramic images and ultrasonic sensor distance measurements. To enhance the coverage of real-world environments in the dataset, a data collection platform was developed, leveraging the PreScan simulation platform in conjunction with actual parking environments. Consequently, a comprehensive parking environment dataset comprising various types was constructed. A deep learning model was devised to ameliorate horizontal and vertical control in intelligent parking systems, integrating Convolutional Neural Networks and Long Short-Term Memory in a parallel configuration. By meticulously accounting for parking environment characteristics, sliding window parameters were optimized, and transfer learning was employed for secondary model training to fortify the prediction accuracy. To ascertain the system's robustness, simulation tests were performed. The ultimate results from the actual environment experiment revealed that the end-to-end intelligent parking model substantially surpassed the existing approaches, bolstering parking efficiency and effectiveness in complex contexts. [ABSTRACT FROM AUTHOR]

In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture. [ABSTRACT FROM AUTHOR]

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. [ABSTRACT FROM AUTHOR]

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort. [ABSTRACT FROM AUTHOR]

Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users' demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed. [ABSTRACT FROM AUTHOR]

To address power transmission lines (PTLs) traveling through complex environments leading to misdetections and omissions in fitting recognition using cameras, we propose a fitting recognition approach combining depth-attention YOLOv5 and prior synthetic dataset to improve the validity of fitting recognition. First, datasets with inspection features are automatically synthesized based on prior series data, achieving better results with a smaller data volume for the deep learning model and reducing the cost of obtaining fitting datasets. Next, a unique data collection mode is proposed using a developed flying-walking power transmission line inspection robot (FPTLIR) as the acquisition platform. The obtained image data in this collection mode has obvious time-space, stability, and depth difference, fusing the two data types in the deep learning model to improve the accuracy. Finally, a depth-attention mechanism is proposed to change the attention on the images with depth information, reducing the probability of model misdetection and omission. Test field experiments results show that compared with YOLOv5, the mAP5095 (mean average precision on step size 0.05 for thresholds from 0.5 to 0.95) of our depth-attention YOLOv5 model for fitting is 68.1%, the recall is 98.3%, and the precision is 98.3%. Among them, AP, recall, and precision increased by 5.2%, 4.8%, and 4.1%, respectively. Test field experiments verify the feasibility of the depth-attention YOLOv5. Line field experiments results show that the mAP5095 of our depth-attention YOLOv5 model for fittings is 64.6%, and the mAPs of each class are improved compared with other attention mechanisms. The inference speed of depth-attention YOLOv5 is 3 ms slower than the standard YOLOv5 model and 10 ms to 15 ms faster than other attention mechanisms, verifying the validity of the depth-attention YOLOv5. The proposed approach improves the accuracy of the fitting recognition on PTLs, providing a recognition and localization basis for the automation and intelligence of inspection robots. [ABSTRACT FROM AUTHOR]

Natural illumination has an important place in home automation applications. Among other advantages, it contributes to better visual health, energy savings, and lower CO2 emissions. Therefore, it is important to measure illuminance in the most accurate and cost-effective way. This work compares several low-cost commercial sensors (VEML 7700, TSL2591, and OPT3001) with a professional one (ML-020S-O), all of them installed outdoors. In addition, a platform based on the Internet of Things technology was designed and deployed as a centralized point of data collection and processing. Summer months have been chosen for the comparison. This is the most adverse situation for low-cost sensors since they are designed for indoor use, and their operating range is lower than the maximum reached by sunlight. The solar illuminance was recorded every minute. As expected, the obtained bias depends on the solar height. This can reach 60% in the worst circumstances, although most of the time, its value stays below 40%. The positive side lies in the good precision of the recordings. This systematic deviation makes it susceptible to mathematical correction. Therefore, the incorporation of more sensors and data that can help the global improvement of the precision and accuracy of this low-cost system is left as a future line of improvement. [ABSTRACT FROM AUTHOR]

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes. [ABSTRACT FROM AUTHOR]

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer's disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants' light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer's disease and related conditions. [ABSTRACT FROM AUTHOR]

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the "multiscale model to model cloud comparison using precision maps" plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole. [ABSTRACT FROM AUTHOR]

New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite this, more and more decision support systems (DSSs) tools are used in agriculture, and digital platforms as data aggregators could be useful technologies for helping farmers make better decisions. However, as these systems develop, the efficiency of these platforms becomes more challenging (sharing, ownership, governance, and transparency). In this paper, we conduct a case study for an accessible and scalable digital data platform that is focused on adding value to smallholders. The case study research is based on meta-governance theory and multidimensional multilayered digital platform architecture, to determine platform governance and a data development model for the Andalusian (Spain) fruit and vegetable sector. With the information obtained from the agents of this sector, a digital platform called farmdata was designed, which connects to several regional and national, and public and private databases, aggregating data and providing tools for decision making. Results from the interviews reflect the farmer's interests in participating in a centralized cloud data platform, preferably one that is managed by a university, but also with attention being paid toward security and transparency, as well as providing added value. As for future directions, we propose further research on how the benefits should be distributed among end users, as well as for the study of a distributed model through blockchain. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them. [ABSTRACT FROM AUTHOR]

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals' activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition. [ABSTRACT FROM AUTHOR]

In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the "Spot the Jellyfish" initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f 1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report. [ABSTRACT FROM AUTHOR]

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city's future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective. [ABSTRACT FROM AUTHOR]

This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements. [ABSTRACT FROM AUTHOR]

The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs' group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents' observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents' coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs' cooperative decision making. [ABSTRACT FROM AUTHOR]

In order to improve the response performance of a proportion integration differentiation (PID) controller for magnetorheological fluids (MRF) brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is identified. Moreover, an improved fruit fly optimization algorithm (IFOA) through integration of PID control strategy and cloud model algorithm is proposed to design a PID controller for MRF brake. Finally, the simulation and experiment are carried out. The results show that IFOA, with a faster response output and no overshoot, is superior to the conventional PID and fruit fly optimization algorithm (FOA) PID controller. [ABSTRACT FROM AUTHOR]

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime. [ABSTRACT FROM AUTHOR]

The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

The aim in the current work is the development of a method to characterize force sensors under sinusoidal excitations using a primary standard as the source of traceability. During this work the influence factors have been studied and a method to minimise their contributions, as well as the corrections to be performed under dynamic conditions have been established. These results will allow the realization of an adequate characterization of force sensors under sinusoidal excitations, which will be essential for its further proper use under dynamic conditions. The traceability of the sensor characterization is based in the direct definition of force as mass multiplied by acceleration. To do so, the sensor is loaded with different calibrated loads and is maintained under different sinusoidal accelerations by means of a vibration shaker system that is able to generate accelerations up to 100 m/s2 with frequencies from 5 Hz up to 2400 Hz. The acceleration is measured by means of a laser vibrometer with traceability to the units of time and length. A multiple channel data acquisition system is also required to simultaneously acquire the electrical output signals of the involved instrument in real time. [ABSTRACT FROM AUTHOR]

Here, I take the position that information is a result of interactions between observers. In order to proceed with this, I construct a simple physical example, with forces standing in for observers. That example leads me to consider the relation between investigative work and energy constraints, which in turn leads toward a surprising suggestion concerning the most general motivation for work. [ABSTRACT FROM AUTHOR]

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The "outside world", in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models. [ABSTRACT FROM AUTHOR]

Activity recognition systems using simple and ubiquitous sensors require a large variety of real-world sensor data for not only evaluating their performance but also training the systems for better functioning. However, a tremendous amount of effort is required to setup an environment for collecting such data. For example, expertise and resources are needed to design and install the sensors, controllers, network components, and middleware just to perform basic data collections. It is therefore desirable to have a data collection method that is inexpensive, flexible, user-friendly, and capable of providing large and diverse activity datasets. In this paper, we propose an intelligent activity data collection tool which has the ability to provide such datasets inexpensively without physically deploying the testbeds. It can be used as an inexpensive and alternative technique to collect human activity data. The tool provides a set of web interfaces to create a web-based activity data collection environment. It also provides a web-based experience sampling tool to take the user's activity input. The tool generates an activity log using its activity knowledge and the user-given inputs. The activity knowledge is mined from the web. We have performed two experiments to validate the tool's performance in producing reliable datasets. [ABSTRACT FROM AUTHOR]

The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies. [ABSTRACT FROM AUTHOR]

We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime. [ABSTRACT FROM AUTHOR]

Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy. [ABSTRACT FROM AUTHOR]

The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low- and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices. [ABSTRACT FROM AUTHOR]

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers' data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way. [ABSTRACT FROM AUTHOR]

This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. First, we exploited fine-tuning using a Deep Recurrent Neural Network to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. Second, we utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed model's capability and feasibility, we developed and deployed the proposed system to realistic settings. For our experimental setup, we gathered more than 16,800 activity windows from 12 activity classes using smartphone sensors. We empirically evaluated the proposed quality by comparing it with a baseline using machine learning. Our results indicate that the proposed system effectively improved activity accuracy recognition for individual users and reduced cost and latency for inference for mobile devices. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with on-device personalization. [ABSTRACT FROM AUTHOR]

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca2+), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca2+(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients' urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes. [ABSTRACT FROM AUTHOR]

With the growing interest in big data technology, mobile IoT devices play an essential role in data collection. Generally, IoT sensor nodes are randomly distributed to areas where data cannot be easily collected. Subsequently, when data collection is impossible (i.e., sensing holes occurrence situation) due to improper placement of sensors or energy exhaustion of sensors, the sensors should be relocated. The cluster header in the sensing hole sends requests to neighboring cluster headers for the sensors to be relocated. However, it can be possible that sensors in the specific cluster zones near the sensing hole are continuously requested to move. With this knowledge, there can be a ping-pong problem, where the cluster headers in the neighboring sensing holes repeatedly request the movement of the sensors in the counterpart sensing hole. In this paper, we first proposed the near-uniform selection and movement scheme of the sensors to be relocated. By this scheme, the energy consumption of the sensors can be equalized, and the sensing capability can be extended. Thus the network lifetime can be extended. Next, the proposed relocation protocol resolves a ping-pong problem using queues with request scheduling. Another crucial contribution of this paper is that performance was analyzed using the fully-customed OMNeT++ simulator to reflect actual environmental conditions, not under over-simplified artificial network conditions. The proposed relocation protocol demonstrates a uniform and energy-efficient movement with ping-pong free capability. [ABSTRACT FROM AUTHOR]

The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation. [ABSTRACT FROM AUTHOR]

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities. [ABSTRACT FROM AUTHOR]

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources. [ABSTRACT FROM AUTHOR]