Data Collection Services Research Articles

A Configurable Mathematical Model for Single-Gateway LoRaWAN Performance Analysis

LoRaWAN is a Low Power Wide Area Network technology featuring long transmission ranges and a simple MAC layer, which can support sensor data collection, control applications and reliable services thanks to the flexibility offered by a large set of configurable system parameters. However, the impact of such parameters settings on the system’s performance is often difficult to predict, depending on several factors. To ease this task, in this paper, we provide a mathematical model to estimate the performance of a LoRaWAN gateway serving a set of devices that may or may not employ confirmed traffic. The model features a set of parameters that can be adjusted to investigate different gateway and end-device configurations, making it possible to carry out a systematic analysis of various trade-offs. The results given by the proposed model are validated through realistic ns-3 simulations that confirm the ability of the model to predict the system performance with high accuracy, and assess the impact of the assumptions made in the model for tractability.

Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Quality of life of veterinary residents in AVMA-Recognized Veterinary Specialty Organizations using the WHOQOL-BREF instrument.

ObjectiveTo assess whether there is an association between veterinary specialty and the quality of life of residents in AVMA-Recognized Veterinary Specialty Organizations™ using the WHOQOL-BREF instrument.MethodsThis cross-sectional study used an online survey and data collection service for administration of the survey to veterinary residents during April 2021 to June 2021. Veterinary residents were contacted through their respective AVMA-Recognized Veterinary Specialty Organization™ and through social media. Overall quality of life along with the domains of Physical Health, Psychological Health, Social Relationships, and Environment were measured using the WHOQOL-BREF instrument. Additionally, data on the demographics of participants were collected and investigated as potential confounders. Mean standardized scores (0 to 100) were compared among the specialties using the general linear model.Results792 residents from 21 veterinary specialties were included in the analysis. The results showed that overall quality of life and all four domains varied significantly among specialties after adjusting for significant demographic variables (all Ps < 0.001). The mean standardized overall quality of life score was 54.3, ranging from 31.8 in Emergency and Critical Care to 56.3 in Laboratory Animal. The mean standardized quality of life scores were lowest for Psychological Health (50.3), followed by Social Relationships (55.0), Environment (61.4), and Physical Health (62.6). Residents in Emergency and Critical Care had the lowest adjusted average scores in all quality of life domains. Residents in Internal Medicine, Anesthesia and Analgesia, and Surgeons had lower quality of life scores across several domains when compared to other specialties.Clinical relevanceThis study provides insight into the mental health and general well-being of veterinary residents. The results can assist veterinary specialty organizations, universities, and mentors in developing appropriate supporting programs for residents. The results can also assist residents in recognizing and more efficiently caring for their individual mental health and well-being.

PENERAPAN LAYANAN BIMBINGAN KONSELING PAUD

Counseling guidance services in children in PAUD as assistance provided to children in developing optimal aspects of development with the aim that children become responsible and independent in solving problems. The stages of the implementation of counseling guidance services in early childhood include planning, implementation, evaluation, and follow-up. This type of research is a type of field research or case study. And it's qualitatively deskritive. Data collection techniques are carried out by interviewing to find out the school's point of view on the implementation of counseling guidance services in early childhood education while additional data in the form of observation and documentation. The result of this study is the implementation of counseling guidance services in early childhood education n has been going well, which is illustrated from the planning that includes the identification of learners' problems through the child's personal data form. Implementation includes data collection, information, extension, placement and devaluation services. Evaluation includes conformity between the program and implementation, compliance with the program, obstacles, responses of children, teachers and parents and changes in progress in the provision of guidance also running as a whole. Follow-up includes other methods performed if an action that has been performed has not been successful. Counseling guidance services include nonprofessional counseling guidance services, because those who become counseling guidance teachers are classroom teachers appointed by the principal as BK teachers.

Desain dan Implementasi Program Layanan Jemaat GKJ Gondokusuman Yogyakarta dengan Metode RAD

The development of GKJ Gondokusuman which became an open church made church boards and pastors required to give satisfaction to the congregation. With these problems, it is necessary to have an application for the GKJ Gondokusuman congregation data collection service program. Where in the GKJ Gondokusuman congregation data collection service application, the church congregation can independently update their personal data. The development of this application will apply the Rapid Application Development (RAD) method by following the stages starting with requirements planning (planning the terms), followed by a RAD design workshop, and ending with implementation. The GKJ Gondokusuman congregation data collection service application program was successfully built in this study. Each stage of the RAD method in developing the GKJ Gondokusuman service application runs smoothly and well, as evidenced by the black box test results that have been carried out by the GKJ Gondokusuman service application, which can be used to update the personal data of the congregation members.

A Systematic Literature Review of Learning-Based Traffic Accident Prediction Models Based on Heterogeneous Sources

Statistics affirm that almost half of deaths in traffic accidents were vulnerable road users, such as pedestrians, cyclists, and motorcyclists. Despite the efforts in technological infrastructure and traffic policies, the number of victims remains high and beyond expectation. Recent research establishes that determining the causes of traffic accidents is not an easy task because their occurrence depends on one or many factors. Traffic accidents can be caused by, for instance, mechanical problems, adverse weather conditions, mental and physical fatigue, negligence, potholes in the road, among others. At present, the use of learning-based prediction models as mechanisms to reduce the number of traffic accidents is a reality. In that way, the success of prediction models depends mainly on how data from different sources can be integrated and correlated. This study aims to report models, algorithms, data sources, attributes, data collection services, driving simulators, evaluation metrics, percentages of data for training/validation/testing, and others. We found that the performance of a prediction model depends mainly on the quality of its data and a proper data split configuration. The use of real data predominates over data generated by simulators. This work made it possible to determine that future research must point to developing traffic accident prediction models that use deep learning. It must also focus on exploring and using data sources, such as driver data and light conditions, and solve issues related to this type of solution, such as high dimensionality in data and information imbalance.

A privacy preserving data aggregation and query for metro passenger flow via mobile crowdsensing

SummaryWith the popularity of mobile Internet networks and mobile terminal equipment, mobile crowdsensing (MCS) has become a popular and economical way of data collection, sharing, and query services. However, the existing privacy‐preserving query schemes MCS‐based less consider the sensing user's privacy leakage at data aggregation phase and querier's privacy leakage at query service phase simultaneously. In this article, we propose a novel privacy‐preserving aggregation and query scheme applied in metro passenger flow via MCS, which achieves the metro passenger flow acquisition and query service and protects the sensing user's identity and location privacy as well as the querier's query privacy. We exploit Paillier cryptosystem and pseudonym mechanism to prevent the privacy leakage, and utilize secure kNN algorithm to protect the query privacy. Meanwhile, we achieve the sensing user accountability based on digital signature algorithm and credible management. The privacy analysis demonstrates that our scheme satisfies the privacy‐preserving and security requirements. The performance evaluation and experimental result show the efficiency of our scheme in practice.

ENERGY UTILIZATION IMPACTS IN DETECTING ABNORMALITY ON WIRELESS SENSOR NETWORKS

With the novel developments in Wireless Sensor Network (WSN) technologies, environmental data collection and processing services are applied in diverse industrial and scientific areas. However, energy limitations and vulnerabilities of WSN nodes are still the main drawbacks of technological developments in the area. Understanding the energy utilization patterns of nodes helps to detect abnormal node behaviors and prevent malicious nodes. In this study, we observe the energy utilization behaviors of nodes and found that nodes have distinctive activity patterns based on their types. We also found that source, sink, and relay nodes on the data propagation path have higher energy consumption patterns compared to other nodes

Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs

With the flexible mobility and deployment, Unmanned Aerial Vehicles (UAVs) have created a new dimension to provide data collection services for a large number of distributed wireless sensory devices. Unfortunately, the broadcast nature of wireless channels make it hard to guarantee the security of data transmission in Internet of Things (IoTs). In this paper, focusing on the scenario where potential eavesdroppers intend to intercept the data uploading of legitimate nodes, we take the advantages of the UAV technology and the physical-layer security approach to study the Secure Data Collection Problem (SDCP). We first explore the flight scheduling and task allocation, that is, determining which areas to be visited and who to be served by UAVs, to establish favorable communication channels. Then we set up the utility function with secrecy rates and non-negative budget constraints. Due to the coupling between the UAV scheduling and task allocation optimization variables in the SDCP, we construct a surrogate function to achieve decoupling and reformulate SDCP into the SDCP-m problem, which is analyzed and proved to be a nonnegative monotone submodular function subject to a general constraint. Finally, an efficient Cost–Benefit Greedy (CBG) algorithm, which is theoretically analyzed to seek the quantitative value of approximation ratio, is proposed. The simulation results show that the CBG algorithm not only outperforms benchmark algorithms in terms of the average secrecy rate and the iteration number, but also generates a nearly optimal trajectory.

The dark side of phubbing in the workplace: Investigating the role of intrinsic motivation and the use of enterprise social media (ESM) in a cross-cultural setting

Despite the increasing phenomena of supervisor phubbing (a counterproductive workplace behaviour of managers), very few studies have explored its outcomes in organisations. This study aims to bridge this gap by investigating the relationships between supervisor phubbing and key employee outcomes. We conducted two studies in cross-cultural settings. In Study 1, which was conducted in Pakistan (a collectivistic culture), we collected 370 useable responses through an online survey from the employees working in the service sector organisations. Study 2, which was conducted in the United States of America (an individualistic culture), utilised the Prolific data collection service to gather 352 responses. Our results from both studies reveal that supervisor phubbing is negatively related to employee job performance and work engagement via intrinsic motivation. Further, enterprise social media (ESM) usage in organisations moderates the relationships between supervisor phubbing and its outcomes such that these relationships are weaker for employees whose ESM usage is higher. Our work offers significant contributions to the literature on technology use at workplace as it discusses a counterproductive workplace behaviour of managers (phubbing) and that behaviour’s association with key employee outcomes in organisations while also considering the moderating role of ESM usage in organisations.

Construction and Empirical Study of Learner Portrait in Online General Education Course

The problems of high course selection rate, low completion rate, and insufficient pertinence of learning support services in online general education courses are the focus of current general education researchers. Based on 3P (presage, process, and product) learning theory, we put forward a “three-stage, four-level” framework for learners’ portrait process of online general education course, including three learning stages of “presage process product” and four levels of “portrait goal, data collection, label analysis, and portrait service.” Then, taking the learners of the online general education course of Zhejiang Shuren College as an example, we make a case analysis based on the portrait framework, evaluate the learning effect from different stages, and put forward targeted teaching strategies and measures. Research results show that the proposed framework can reflect the characteristics of online learning experience, online learning investment, and online learning results of high-risk learners and can provide data support for the design of online learning support services and optimizing learning effects.

Coalitional Formation-Based Group-Buying for UAV-Enabled Data Collection: An Auction Game Approach

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System.

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

Artificial Intelligence and Edge Computing-Enabled Web Spam Detection for Next Generation IoT Applications

For next generation IoT applications, edge devices provides most of the computing resources close to the proximity of the end users. These devices having built-in intelligence using various AI techniques can take independent decisions in the environment where these are deployed. Motivated from these concerns, We suggest a cognitive intrusion security system to maintain the credibility of search engine results, which eliminates the advertising images from penetrating the image database of the web browser. The proposed framework provides edge intelligence for web data filteration and detects the web spam by considering three different layers, i.e., data collection services, edge computing services, and cloud services. The target is to detect the malicious images. Firstly, the features of an image such as mean, image gradient, entropy are fetched and then the retrieved data is processed in the proposed framework. Deep learning algorithms are used for the validation of the proposed system. By evaluating it on real-time collected dataset, it resulted in an accuracy of 98.77%.

What are the Challenges of Teaching Children with Hearing Loss During the COVID-19?

ABSTRACT This study aimed to determine the difficulties of teaching children with hearing loss in the special education centre during the COVID-19 Pandemic in Turkey. Sixty-three professionals who works children with hearing loss participated in this study. The demographic form included personal information about the professional’ and a checklist to determine the effects of the Pandemic on special education. Lastly, the professionals and the teachers completed ‘The Special Education Teachers’ Sense of Professional Self-Efficacy Scale’. An email or a phone message was sent to potential participants, and interested individuals were requested to complete an online questionnaire by clicking on a link that led them to a secure online data collection service (Google Forms). Statistically significant relationships were determined between professionals using virtual communication tools in the distance education, contact with their students, continuing the rehabilitation with them and providing consultancy support to their parents. While a positive correlation was observed between the professionals contacted with their students during the Pandemic, evaluating their students’ development, a negative correlation was found between their competencies to meet their rehabilitation needs and their counselling needs for families. Special education specialists should be supported under special conditions such as the Pandemic and informed about telerehabilitation options.

Infodemic vs. Pandemic Factors Associated to Public Anxiety in the Early Stage of the COVID-19 Outbreak: A Cross-Sectional Study in China.

Introduction: Every outbreak of an epidemic or pandemic disease is accompanied by the tsunami of information, which is also known as the infodemic. Infodemic makes it hard for people to find trustworthy sources and reliable guidance when they need it, and causes social panic about health, widens the gaps between races and regions, and even brings the social chaos all over the world. While most researchers and related parties made efforts to control the inaccurate information spreading online during the COVID-19 pandemic, the infodemic influence caused by the overload of accurate information were almost or completely ignored, and this will hinder the control of infodemic in future public health crises. This study aims to explore the infodemic vs. pandemic influence on people's psychological anxiety across different media sources in the early stage of the COVID-19 outbreak in China.Methods: A cross-sectional study using online survey method was conducted by a data-collection service provider in April 2020. A total of 1,117 valid samples were finally collected from 5,203 randomly invited members via webpages and WeChat. The sample distribution covered the 30 provincial administrative divisions of mainland China.Results: Hierarchical regression analysis for the potential pandemic sources and infodemic sources of psychological anxiety showed that the infodemic factors of attention to the coronavirus information (β = 0.154, p < 0.001) and commercial media exposure (β = 0.147, p < 0.001) is positively related to the level of anxiety. Statistics indicated that influence of the infodemic factors is over and above that of the pandemic factors (ΔR2 = 0.054, F = 14.199, and p < 0.001). Mediation analysis showed that information overload (B = 0.155, Boot SE = 0.022, and 95% Boot CI [0.112, 0.198]) mediates the link between attention to coronavirus information and anxiety; both information overload (B = 0.035, Boot SE = 0.014, and 95% Boot CI [0.009, 0.062]) and media vicarious traumatization (B = 0.106, Boot SE = 0.017, and 95% Boot CI [0.072, 0.140]) mediate the link between commercial media exposure and anxiety.Conclusion: This study suggested that the influence of infodemic with mixed accurate and inaccurate information on public anxiety does exist, which could possibly go beyond that of the pandemic. Information overload and vicarious traumatization explain how infodemic may be associated to public anxiety. Finally, commercial media could be a major source of infodemic in the Chinese media context. Implications for the related parties were discussed.

Improving the Quality of Inpatient Services with Lean Service and Six Sigma Methods at Eshmun Hospital

Hospitals play an important role in the life of the nation and state, because every country has an obligation to meet all the physical and spiritual health needs of every citizen. The main focus of Eshmun Hospital is the accuracy of doctors in diagnosing diseases, responsive patient care, hospital security, hospital access roads, completeness of facilities, and medicines. The research method used in this research is six sigma, pareto diagram, failure mode effect analysis (FMEA), fishbone diagram, and value stream mapping. The conclusions obtained from this research are after repairs are made to the inpatient service activities of the hospital. Eshmun eats service activities which were originally 15 activities reduced to 11 activities, this can save time in carrying out services. Activities that are non-value add are very detrimental, including: the data collection service process is still done manually, patients are still waiting to receive treatment, there are some doctors who are not professional. The main cause of waste is usually caused by hospitals not keeping up with the times, human resources that are not optimal.

Lesbian, gay and bisexual homelessness in Australia: Risk and resilience factors to consider in policy and practice.

Lesbian, gay and bisexual (LGB) people are more likely than heterosexual people to experience homelessness. The study aimed to compare risk and resilience factors commonly associated with homelessness according to sexual identity to inform more LGB-inclusive and targeted policy and service provision in this area. The study involved analysis of data from two Australian surveys: the General Social Survey 2014 (n=17,401) and the Journeys Home study (n=1,659). Chi-squared analyses of the survey data compared LGB with heterosexual respondents. Bisexual respondents had a significantly earlier average age of first homelessness, and were more likely to have repeated episodes of homelessness than lesbian, gay or heterosexual respondents. Risk factors that were higher among bisexual people included family violence, conflict and rejection and substance use issues. LGB respondents were twice as likely to have experienced discrimination as heterosexual respondents, more likely to have experienced violence and have mental health problems. Conversely, resilience factors for LGB respondents included being more likely to access friends for support in times of crisis, and to be involved in civic or political groups. We suggest that LGB people at risk of homelessness need effective responses based on their risk and resilience factors, including targeted peer support and mainstream services that affirm and acknowledge their diverse sexual identities. Australian policies should encourage improved LGB data collection and specific service responses. Primary prevention approaches include educating families of origin about LGB identities and assertive outreach to prevent housing loss.

Collecting samples from online services: How to use screeners to improve data quality

Increasingly, marketing and consumer researchers rely on online data collection services. While actively-managed data collection services directly assist with the sampling process, minimally-managed data collection services, such as Amazon’s Mechanical Turk (MTurk), leave researchers solely responsible for recruiting, screening, cleaning, and evaluating responses. The research reported here proposes a 2 × 2 framework based on sampling goal and methodology for screening and evaluating the quality of online samples. By sampling goals, screeners can be categorized as selection, which involves matching the sample with the targeted population; or as accuracy, which involves ensuring that participants are appropriately attentive. By methodology, screeners can be categorized as direct, which screens individual responses; and as statistical, which provides quantitative signals of low quality. Multiple screeners for each of the four categories are compared across three MTurk samples, two actively-managed data collection samples (Qualtrics and Dynata), and a student sample. The results suggest the need for screening in every online sample, particularly for the MTurk samples, with the fewest supplier-provided filters. Recommendations are provided for researchers and journal reviewers that provide greater transparency with respect to sample practices.

Utilization of a mobile phone application to increase access to sexual and reproductive health information, goods, and services among university students in Uganda

BackgroundInnovations to increase access to sexual and reproductive health (SRH) information, goods, and services are needed, particularly in low-income settings. This study assessed the utilization of a mobile phone application (MPA) to increase access to SRH information, goods, and services among university students in Uganda.MethodsWe conducted a cross-sectional analysis of data from: (1) an endline survey performed as a consequence of a randomized controlled trial (RCT) of the effectiveness of the MPA, and (2) data from use of the MPA for accessing information, goods, and services over the 6-month time period of the RCT, obtained from in-MPA data collection service providers. We performed descriptive analysis of participant characteristics and their association with the utilization of the MPA using logistic regression; analyses of MPA use for accessing different types of information, goods, and services by gender; and analyses of functionality attributes of the MPA and related services.ResultsIn the study population of young (median 22 years) predominantly female (61%) students, the utilization of the MPA by those who downloaded it was high (81% overall, 82% female, and 82% male). The most popular information portal was the frequently asked questions (71% utilization); the most popular goods were condoms for males (77% utilization) and sanitary pads for females (94% utilization); and the most popular service was HIV testing and counseling (60% utilization). The MPA demonstrated predominantly positive (responsiveness, non-distracting in-app advertisements, and ease of use) attributes.ConclusionA mobile phone app to increase access to SRH information, goods, and services among university students in Uganda demonstrated high utilization. The results of this study support ongoing and future technical improvement efforts and research on effectiveness, economic efficiency, and scalability, along the continuum of activities to scale this intervention in order to improve SRH in low-income settings.Trial RegistrationMUREC1/7 No. 07/05-18. Registered; June 29, 2018.