Data Collection Tasks Research Articles

Factors related to the collaboration of cadres in stunting prevention

In the implementation of stunting prevention, the lack of cooperation between stunting cadres, no direct communication media and no one who facilitates the cooperation of stunting cadres to carry out data collection tasks as indicators of stunting achievement and for stunting prevention. This can result in repeated data collection, unequal data, and irrelevant data accuracy. Stunting cadres, who are community health workers selected and trained to monitor and prevent stunting at the village level, play a crucial role in these efforts. If stunting cadres collaborate, the performance of both will be more effective and efficient. This study aims to find out the factors related to the collaboration of stunting cadres in stunting prevention efforts. A quantitative research method with a cross-sectional study design was employed. The research sample consisted of 63 stunting cadres. Data analysis was conducted using univariate and bivariate analysis. The results showed that variables such as trust (p-value 0.032), communication (p-value 0.021), agreement (p-value 0.002) and added public value (p-value 0.001) were significantly related to the collaboration of stunting cadres. The conclusion of this study is that stunting cadre collaboration is important in stunting prevention efforts and has an impact on optimizing stunting prevention programs.

Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System.

This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework's architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

ABSTRACTThe Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L2‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

ElectrApp: Deep learning framework to enhance cardiac arrhythmia detection

Abstract Medical specialists who diagnose patients with cardiovascular arrhythmias are not promptly available in rural areas. The proposed information system provides access to upload the data to a cloud-based processing module in which different arrhythmias are classified, allowing the classification of various arrhythmias to solve the scarcity of physicians in remote areas through an automatic system that helps with the diagnosis. This information system has a controller module for the operation that manages data collection, processing, beautification and visualization tasks. We used the MIT-BIH Arrhythmia Database during the training and validation of the classification module. The module uses deep learning to identify five different types of heartbeats according to standards, reporting 98.9% accuracy. The information system will improve health coverage in the prediagnosis of cardiovascular diseases.

Indeterministic Data Collection in UAV-Assisted Wide and Sparse Wireless Sensor Network

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%.

Federated deep reinforcement learning based trajectory design for UAV-assisted networks with mobile ground devices

Exploiting highly maneuverable unmanned aerial vehicles (UAVs) has been considered as an efficient way to assist wireless systems, e.g., for applications of data collection. However, several challenges remain to be addressed in the design of such UAV-assisted networks, including multi-UAV joint trajectory determination, data privacy protection, and adaption to the complex channel environment particularly with mobile ground devices (GDs). In this paper, we study a multi-UAV assisted data collection system where UAVs collect data locally from mobile GDs. The aim is to minimize the whole operation time cost via jointly optimizing the UAVs’ three-dimensional (3D) trajectory together with the GDs’ communication scheduling, while satisfying the constraints of no-fly zones (NFZs) and collision avoidance. With a nonconvex feasible set (due to the NFZs), the established problem is nonconvex. Moreover, the randomness of GDs movements significantly reduces the performance of a typical redesign, i.e., determining the UAVs’ trajectory and users’ scheduling before starting the data collection task. To tackle these issues, we first transform the established problem into a Markov decision one, and then propose a multi-agent federated reinforcement learning (MAFRL)-based approach to optimize the dynamic long-term objective via jointly determining UAVs’ 3D trajectory and GD’s communication scheduling. A multi-step propagation technique and a dueling network architecture are adopted to enhance the neural network utilized to train agents, i.e., to accelerate the convergence rate of the proposed method and improve its overall stability. Finally, experimental results reveal the effectiveness of our proposed method in the considered practical scenario.

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

Stability Analysis through a Stability Factor Metric for IQRF Mesh Sensor Networks Utilizing Merged Data Collection.

This paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF's Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks. Our findings provide valuable insights, demonstrating the metric's efficacy in achieving robust and reliable network operations, especially in data collection tasks. The inclusion of redundant paths as a factor in the stability metric significantly enhances its practicality and relevance. Furthermore, this research offers practical ideas for enhancing the design and management of wireless mesh sensor networks. The stability metric uniquely assesses the resilience of data collection activities within these networks, with a focus on the benefits of redundant paths, underscoring the significance of stability in network evaluation.

SmartCS: Enabling the Creation of Machine Learning&amp;ndash;Powered Computer Vision Mobile Apps for Citizen Science Applications without Coding

It is undeniable that citizen science contributes to the advancement of various fields of study. There are now software tools that facilitate the development of citizen science apps. However, apps developed with these tools rely on individual human skills to correctly collect useful data. Machine learning (ML)–aided apps provide on-field guidance to citizen scientists on data collection tasks. However, these apps rely on server-side ML support, and therefore need a reliable internet connection. Furthermore, the development of citizen science apps with ML support requires a significant investment of time and money. For some projects, this barrier may preclude the use of citizen science effectively. We present a platform that democratizes citizen science by making it accessible to a much broader audience of both researchers and participants. The SmartCS platform allows one to create citizen science apps with ML support quickly and without coding skills. Apps developed using SmartCS have client-side ML support, making them usable in the field, even when there is no internet connection. The client-side ML helps educate users to better recognize the subjects, thereby enabling high-quality data collection. We present several citizen science apps created using SmartCS, some of which were conceived and created by high school students.

PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries

Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&amp;P'21) and compare in terms of monetary cost for different input sizes.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. Supplementary data are available at Bioinformatics online.

Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT

ObjectiveChatbots have the potential to improve user compliance in electronic Patient-Reported Outcome (ePRO) system. Compared to rule-based chatbots, Large Language Model (LLM) offers advantages such as simplifying the development process and increasing conversational flexibility. However, there is currently a lack of practical applications of LLMs in ePRO systems. Therefore, this study utilized ChatGPT to develop the Chat-ePRO system and designed a pilot study to explore the feasibility of building an ePRO system based on LLM. Materials and MethodsThis study employed prompt engineering and offline knowledge distillation to design a dialogue algorithm and built the Chat-ePRO system on the WeChat Mini Program platform. In order to compare Chat-ePRO with the form-based ePRO and rule-based chatbot ePRO used in previous studies, we conducted a pilot study applying the three ePRO systems sequentially at the Sir Run Run Shaw Hospital to collect patients’ PRO data. ResultChat-ePRO is capable of correctly generating conversation based on PRO forms (success rate: 95.7 %) and accurately extracting the PRO data instantaneously from conversation (Macro-F1: 0.95). The majority of subjective evaluations from doctors (>70 %) suggest that Chat-ePRO is able to comprehend questions and consistently generate responses. Pilot study shows that Chat-ePRO demonstrates higher response rate (9/10, 90 %) and longer interaction time (10.86 s/turn) compared to the other two methods. ConclusionOur study demonstrated the feasibility of utilizing algorithms such as prompt engineering to drive LLM in completing ePRO data collection tasks, and validated that the Chat-ePRO system can effectively enhance patient compliance.

AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

The advantages of gamification for collecting linguistic data: A case study using Word Ladders

This paper delves into the integration of gamification techniques within the field of linguistics to enhance data collection for academic research purposes. Through an exploration of the Word Ladders mobile application, designed to elicit hierarchical word associations and therefore linguistic data, the study investigates the potential benefits of gamification in terms of data quality, user experience, and motivation in taking part to the research and to the data collection task. The experimental design examines the advantages of a gamified approach compared to traditional research methods (online surveys), through an experimental session followed by a survey (n=189). Results showed that competition between users is a powerful motivator that can be easily integrated in gamified approaches and less so in classic online surveys, driving engagement and potentially enhancing the scalability of data collection while retaining the quality of data collected in classic lab settings. While challenges persist, our research contributes to the understanding of gamification’s impact on data collection, user experience, and motivation, laying the foundation for transformative advancements in the field of language and communication sciences.

Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network.

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN-collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform

Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.

Challenges and opportunities of automated data pipelines for environmental sustainability applications in industrial manufacturing

Energy efficiency analyses and life cycle assessments are two examples of data-driven applications focused on environmental sustainability in the industrial manufacturing context. Both use methodologies based on aggregating operational data, extracted from multiple data sources along a value chain. However, the possibility to source, utilize, and share data is often obstructed by heterogeneous or non-transparent data operations across organizations, as well as poor availability of digital interfaces for data collection. In practice, the underlying application processes lack fidelity and granularity, as a trade-of to simplify the modeling and data collection tasks. Therefore, new opportunities arise for automated workflows on data collection (data pipelines) as a relevant component of every digital transformation strategy. This study reflects on the challenges experienced by two industrial actors collecting operational data for an energy efficiency analysis and a Life-cycle Assessment (LCA), each with a different manufacturing context and architectural approach. Finally, it presents the opportunities for new technologies at affordable costs that potentially ease the development and operation of data pipelines to solve such challenges.

Use of Microsoft Excel for Data Collection and Processing to Predict Students’ Performance in EDM

Abstract— Educational Data Mining (EDM) referes to the research designed to classify, analyze, and predict the students’ academic performance from the data collected from educational setting. Data collection and data processing are an important task in any research such as EDM. In this research article, data collection and data processing task are explained in detailed to build the model for predicting students’ performance and provide the recommendation in Educational Data Mining. In data collection step, we have collected the result ledgers in PDF form related to Four Year Computer Science and Engineering (CSE) course from university. The PDF ledgers for two academic years 2014-15 and 2015-16 of Four Years - First Year, Second Year, Third Year, and Final Year are downloaded from site http://www.sus.ac.in/examination/Online-Result-(Ledger) or https://su.digitaluniversity.ac/Content.aspx?ID=29445 to prepare the dataset to predict students’ performance in Educational Data Mining (EDM). In current study, Syllabus structure of Four Year course of Computer Science and Engineering, Credit system pattern, attributes required for preparing dataset, and types of assessment methods such as Types of assessment methods such as Theory + Practical, Theory + Practical + Practical Oral Exam (POE), Practical + POE, Practical + OE, Practical, Term Work, and Theory are explained in detailed. So original data downloaded from university site for two academic years 2014-15 and 2015-16 of Four Years CSE course from Sem-I to Sem-VIII is prepared with the help of Excel and contain approximately 10,616 students data with 544 number of attributes. For data processing, Microsoft Excel is used. Microsoft Excel features such as Text to Column – Delimited, Text to Column - Fixed width, Filter, and Conditional Formatting – Highlight Cells Rules – Text that contains – are considered for preparation of dataset. Also various functions such as SUM, IF, COUNTIF, MOD and % are employed for processing the data. After data processing step, final dataset for two academic years 2014-15 and 2015-16 from Sem-I to Sem-VIII consists of 6906 students data with 970 number of attributes.In addition to the data collection and processing, research gaps related to the dataset size, etc. are also identified and mentioned the same in this article. These two steps - data collection and processing discussed in detailed in this research article will help the researcher working in EDM to prepare the dataset to build the model so that more work in education sector related to students’ performance can be carried out to improve the teaching-learning process. Keywords—Data Collection, Data Processing, Microsoft Excel, Educational Data Mining (EDM)

Smart rover-voice control bluetooth control robot to avoid obstacles

This paper presents the development and implementation of a Smart Rover, integrating robotics and IoT technologies to create a versatile and efficient robotic platform. The Smart Rover is a versatile robotic platform integrating Bluetooth, voice control, and IoT capabilities for seamless navigation and obstacle avoidance. Powered by an Arduino UNO microcontroller and programmed in C++, the Smart Rover provides a user-friendly interface for remote control through Bluetooth-enabled devices. Voice commands add convenience and accessibility, offering an alternative control method. The onboard sensors facilitate autonomous obstacle detection and avoidance, enabling the Smart Rover to navigate its environment intelligently. The prototype demonstrates successful integration and functionality, offering a compact and sturdy design suitable for various applications. IoT integration expands functionality, allowing the rover to communicate with other smart devices or cloud services, opening up possibilities for data collection, remote monitoring, and collaborative tasks.