Problem Of Data Collection Research Articles

The Analysis of Opportunities of the Application of Big Data and Artificial Intelligence Technologies in Public Governance and Social Policy

This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying “the hot spots” of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world.

Energy-Efficient UAV-Enabled Data Collection via Wireless Charging: A Reinforcement Learning Approach

In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit $Q$ -learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which $Q$ -table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.

A Dynamic Survival Modeling Approach to the Prediction of Web Survey Breakoff

Abstract Respondents who break off from a web survey prior to completing it are a prevalent problem in data collection. To prevent breakoff bias, it is crucial to keep as many diverse respondents in a web survey as possible. As a first step of preventing breakoffs, this study aims to understand breakoff and the associated response behavior. We analyze data from an annual online survey using dynamic survival models and ROC analyses. We find that breakoff risks between respondents using mobile devices versus PCs do not differ at the beginning of the questionnaire, but the risk for mobile device users increases as the survey progresses. Very fast respondents as well as respondents with changing response times both have a higher risk of quitting the questionnaire, compared to respondents with slower and steady response times. We conclude with a discussion of the implications of these findings for future practice and research in web survey methodology.

How to Tell the Story?

In this paper I investigate the problems of data collection, data analysis and the final communication of the results of our research, when doing social science that we, ourselves, are part of. Central to this are the concepts life world, language games and stories and narratives. How do we collect stories and narratives in the field, how do we construct scientific narratives that are both reliable and valid? And finally, how do we, as researchers present our newly constructed narrative to a – hopefully – interested audience? That is, how do you, as a consumer of scientific narratives, read what I have been writing? Finally, I will discuss the problem of handing over research results to the people that we are doing research with. This is all done within a framework of a pragmatic constructivist paradigm.

Towards a Smart Privacy-Preserving Incentive Mechanism for Vehicular Crowd Sensing

Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.

Data Collection and Distribution in Nigerian Higher Institutions: Problems and Way Forward

Higher institutions in Nigeria are facing many challenges. One of such challenges is lack ofcreditable data to plan and to take decisions on development of the institutions. This paper is aim todiscuss the problems of data collection and distribution in the Nigerian higher institutions. Secondarydata were employed to discuss the various points raised in the paper. The secondary data were sourcedfrom both hard ware and software resources. The paper identified inadequate funding, inadequateworking materials, shortage of professional data experts, poor capacity development of data mangersand inadequate infrastructural facilities as the problems preventing effective data collection anddistribution in the Nigerian higher institutions. To address these problems, the paper recommended thatthe government should increase the funding of higher institutions, more professional data mangersshould be employed, adequate working tools should be provided, training and retraining programmeshould be constantly provided for data mangers and school administrators should provide allinfrastructural facilities needed by units and departments handling data management in the varioushigher institutions in the country.

Autonomous Data Collection With Timed Communication Constraints for Unmanned Underwater Vehicles

This letter studies the problem of data collection where an autonomous underwater vehicle (AUV) is required to inspect several goals while staying within the communication range of an unmanned surface vehicle (USV) moving along a predefined path. A reward is associated with each inspected goal, and, so the overall objective is to increase the sum of the rewards. The proposed planner takes the AUV dynamics and the obstacles into account in order to generate collision-free and dynamically-feasible trajectories that enable the AUV to inspect multiple goals while maintaining communication with the USV. When not all the goals can be inspected, the planner generates trajectories that increase the sum of the rewards. The proposed planner constructs a roadmap equipped with time information in order to facilitate navigation and maintain communication in complex 3D environments. A crucial aspect is an effective coupling of discrete search with sampling-based motion planning. Specifically, a multi-goal discrete search is proposed to effectively compute roadmap paths that increase the sum of the rewards. These paths then guide the sampling-based expansion of a motion tree. Experiments using complex 3D environments and an AUV model defined by nonlinear second-order differential equations indicate the efficiency of the planner to inspect an increasing number of goals.

On Choosing Structure for a Machine Learning-based Reaction Force Predictor for Walking Robots

This paper focuses on the topic of contact reaction prediction for walking robots, namely on the analysis of performances on different structures of the machine-learning-based predictors. Predicting reaction forces is important due to the fact that it can allow us to retrieve a simplified model of the contact scenario, as was proposed in the literature preciously. This allows to turn the problem of contact model identification into a data collection and processing problem. In order to do it effectively, both a data compression (feature extraction) and regression strategies might be used. This research provides an analysis of both with respect to the discussed problem.

Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

This paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

Research Design in Socially Deviant Tourist Behavior Studies: A Mixed-Method Approach

Most extant research on socially deviant tourist behavior is qualitative in nature and lacks empirical investigation due to the topic's sensitivity and data collection difficulties. This research note addresses the methodological and data collection challenges in deviant tourist behavior studies and provides a comprehensive mixed-method research design by demonstrating an empirical study of drug tourism among Chinese visitors in Amsterdam. A sequential explanatory design is presented, which includes ethnography and two-staged qualitative interview data gathering and analysis, followed by a cross-sectional quantitative survey study. This research note provides methodological contributions to solve the data collection problems in special interest tourism and other sensitive research topics in social science.

Implementation Status of Health Management Information System in Hospitals of South West Shoa Zone, Oromia, Central Ethiopia.

BackgroundHealth Management Information Systems (HMIS) is an information system for health management and planning at district, regional, and national levels. Problems of data collection and processing, poor information use, lack of identifying information required at specific levels, and inadequacy of resources are hindering the implementation of the program.ObjectiveTo assess the implementation status of Health Management Information System (HMIS) in hospitals of South West Shoa Zone, Oromia, Central Ethiopia.MethodsThe assessment was conducted in South West Shoa Zone hospitals using a cross-sectional study design. Using three dimensions, namely, availability of resources, compliance to guideline, and utilization of information were studied. All hospitals in the zone were included and 210 health workers were included in the study. Quantitative data were entered into Epi-data and analyzed using SPSS.v.23. Descriptive summery was done using the proportion and frequencies and qualitative results were analyzed thematically. The study was conducted from February 20, 2020, to March 20, 2020.ResultsAll hospitals have at least one computer specifically assigned for HMIS, while none of the hospitals have an allocated budget for HMIS. Regarding service training, 82% of the respondents had received HMIS training. The overall Implementation status of HMIS was 58%, which showed as a judgment parameter of “poor” implementation. Particularly, availability (58%) and compliance (55%) dimensions are categorized as “poor” implementation, whereas the information utilization dimension is categorized as a judgment parameter of “fair” implementation.ConclusionThe overall implementation status of HMIS in the study area was poorly implemented according to the preset criteria with stakeholders. Therefore, allocation of budget specifically for HMIS, strengthening capacity building activities such as training and supportive supervision with written feedbacks are recommended for effective implementation of the Health Management Information System.

Makine Öğrenmesi Kullanılarak Endüstriyel Pres Makinesine Uygulanan Kestirimci Bakım Çalışmaları

The main purpose of Industry 4.0 applications is to provide maximum uptime throughout the production chain, to reduce production costs and to increase productivity. Thanks to Big Data, Internet of Things (IoT) and Machine Learning (ML), which are among the Industry 4.0 technologies, Predictive Maintenance (PdM) studies have gained speed. Implementing Predictive Maintenance in the industry reduces the number of breakdowns with long maintenance and repair times, and minimizes production losses and costs. With the use of machine learning, equipment malfunctions and equipment maintenance needs can be predicted for unknown reasons. A large amount of data is needed to train the machine learning algorithm, as well as adequate analytical method selection suitable for the problem. The important thing is to get the valuable signal by cleaning the data from noise with data processing. In order to create prediction models with machine learning, it is necessary to collect accurate information and to use many data from different systems. The existence of large amounts of data related to predictive maintenance and the need to monitor this data in real time, delays in data collection, network and server problems are major difficulties in this process. Another important issue concerns the use of artificial intelligence. For example, obtaining training data, dealing with variable environmental conditions, choosing the ML algorithm better suited to a specific scenario, necessity of information sensitive to operational conditions and production environment are of great importance for analysis. In this study, predictive maintenance studies for the transfer press machine used in the automotive industry, which can predict the maintenance need time and give warning messages to the relevant people when abnormal situations approach, are examined. First of all, various sensors have been placed in the machine for the detection of past malfunctions and it has been determined which data will be collected from these sensors. Then, machine learning algorithms used to detect anomalies with the collected data and model past failures were created and an application was made in a factory that produces automotive parts.

UAV-Aided Data Collection for Information Freshness in Wireless Sensor Networks

In this work, we study the UAV-enabled data collection problem for high information freshness in wireless sensor networks, where one UAV is dispatched to collect information of ground Sensor Nodes (SNs). The information freshness is measured by the Age of Information (AoI) of each SN, which is defined as the sum of the SN's data uploading time and the UAV's flight time after leaving this SN. Two optimization problems of age-optimal data collection are formulated to minimize the SNs' maximal AoI and average AoI, respectively. An iterative SN association and trajectory planning policy is proposed to seek the age-optimal solutions via an iterative two-step procedure. Firstly, SN association is performed based on the affinity propagation clustering method with an appropriate weight to find a set of data Collection Points (CPs) at which the UAV hovers to collect data and schedules which SNs to upload in what order. Based on this result, trajectory planning is performed to find the max-AoI-optimal and ave-AoI-optimal trajectories of the UAV along the CPs using dynamic programming or genetic algorithm. With the optimized clustering weight, the proposed scheme can always strike a balance between the SNs' uploading time and the UAV's flight time in various scenarios. Simulation results show that the proposed strategy can improve the freshness of information collected from all the SNs.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

Joint 3-D Trajectory and Resource Optimization in Multi-UAV-Enabled IoT Networks With Wireless Power Transfer

This article studies the data collection problem in an Internet-of-Things (IoT) network with multiple unmanned aerial vehicles (UAVs) where UAVs first power multiple IoT devices by wireless power transfer, and then IoT devices utilize the harvested energy to transmit data to UAVs. Different from most of the existing works that often assume the channel between the UAV and the IoT device is a simplified Line-of-Sight (LoS) channel, a more practical and accurate probabilistic LoS channel model is adopted, in which both the elevation angle and the distance between the UAV and the IoT device determine the channel gain. Our objective is to maximize the UAV's minimum data collection rate among all IoT devices by jointly optimizing time allocation and 3-D trajectory of UAVs within a limited time duration. This results in a nonconvex optimization problem, which is challenge to solve. To tackle this difficulty, we transform the nonconvex problem to a difference of convex (D.C.) optimization problem by subtly using several methods. To solve the D.C. optimization problem, an efficient iterative algorithm is designed via a successive convex approximation method. Numerical simulation results are provided to verify the performance of the proposed algorithm compared to two benchmark algorithms, the algorithm with simplified LoS model and that with 2-D trajectory optimization, under various conditions.

Data Collection Technology for Ambient Intelligence Systems in Internet of Things

Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance are essential. A novel technology is proposed, which includes the following steps: (1) definition of the data collection (DC) problem (considering the model of the observed system, DC conditions, etc.); (2) DC policy assignment; (3) construction of DC models; (4) evaluation and presentation of the data processing results. The proposed DC technology supports the development of data collecting subsystems in AmIS. Such subsystems provide data that reflect the changes in structure, state, situation, and behavior of participants in their IoT environment in time. Therefore, we show how this “cognitive” function of the DC process increases the intelligence level of IoT environment.

Energy-Efficient Cluster-Based Data Collection by a UAV with a Limited-Capacity Battery in Robotic Wireless Sensor Networks

In this work, our motivation focuses on an energy-efficient data collection problem by a mobile sink, an unmanned aerial vehicle (UAV) with limited battery capacity, in a robot network divided into several robot clusters. In each cluster, a cluster head (CH) robot allocates tasks to the remaining robots and collects data from them. Our contribution is to minimize the UAV total energy consumption coupled to minimum cost data collection from CH robots by visiting optimally a portion of the CH robots. The UAV decides the subset of CH robots to visit by considering not only the locations of all CH robots but also its battery capacity. If the UAV cannot visit all CH robots, then the CH robots not visited by the UAV transmit their data to another CH robot to forward it. The decision of transmission paths of transmitting robots is included in the cost optimization. Our contribution passes beyond the existing paradigms in the literature by considering the constant battery capacity for the UAV. We derive the optimal approach analytically for this problem. For various numbers of clusters, the performance of our strategy is compared with the approach in the close literature in terms of total energy consumed by CH robots, which affects network lifetime. Numerical results demonstrate that our strategy outperforms the approach in the close literature.

Geographical Correlation-Based Data Collection for Sensor-Augmented RFID Systems

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the Geographical correlation-based RF-data Collection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only 1/28~1/3 of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.

MANAGEMENT MODELS OF DATA COLLECTION PROCESSES IN IOT NETWORKS WITH THE DYNAMIC STRUCTURE

The collection of data from the network with dynamic structure is a complex process that must be performed with considering of security, energy efficiency and latency requirements. To determine the optimal data collection models that meet the stated requirements, the authors analyzed models and methods of data collection in dynamic networks, as well as management processes of data collection. The study allows to determine the most effective technologies for data collection in dynamic networks, which include Fog technologies and clustering technologies. Based on the analysis, the authors have developed the model for data collection managment, which allows to construct and rebuild the structures of data collection models in accordance with the requirements and conditions of data collection. The developed approaches and principles were successfully implemented in practice: a system of data collection was tested for the crane complexes, which is designed to work at production sites. In general, the study allows to identify methods and tools that effectively solve the problems of data collection in the networks with dynamic structure, and to demonstrate the solution of these problems in practice.