Distributed Data Collection System Research Articles

RETRACTED ARTICLE: Application of hardware system of temporal big data in enterprise human resource management in distributed environment

Distributed data collection systems are often used in many fields, such as industrial control, power systems, seismic monitoring, and ocean exploration. In a distributed data collection system, the time of data collection and the efficiency of data collection are all related to the accuracy of the data. Subsequent analysis and processing show that the distributed data collection system is a very important performance indicator: this article combines the timing factors of the timing rules in the dataset, and after normalization and expansion, the rules will be edited according to the effective voting rate. Perform a weighted sum calculation on the rules in the edited sub-database to obtain the final timing rules. Finally, the algorithm is implemented by pseudo code. With the rapid development of information technology, we have entered the era of big data. Big data has the characteristics of resource sharing, large storage capacity, and high speed, so it has been widely used in various fields. In the context of the rapid development of the modern economy, the human resources department has provided a relatively complete infrastructure system for many large companies. It can be seen that the work of the personnel department is gradually becoming an important factor in the development of the company. Appropriate implementation of big data in the company’s human resource model can integrate resources into the industry and the company, improving operational efficiency, and laying a solid foundation for the company’s sustainable and long-term development. This article first outlines the hardware system and company management of temporal big data in a distributed environment, discusses in detail the needs of human resource management in companies in the era of big data, and proposes a suitable human resource management model.

An Unstructured Supplementary Service Data System for Daily Tracking of Patient Samples and Diagnostic Results in a Diagnostic Network in Malawi: System Development and Field Trial.

BackgroundDiagnostics in many low- and middle-income countries are conducted through centralized laboratory networks. Samples are collected from patients at remote point-of-care health facilities, and diagnostic tests are performed at centralized laboratories. Sample transportation systems that deliver diagnostic samples and test results are crucial for timely diagnosis and treatment in such diagnostic networks. However, they often lack the timely and accurate data (eg, the quantity and location of samples prepared for collection) required for efficient operation.ObjectiveThis study aims to demonstrate the feasibility, adoption, and accuracy of a distributed data collection system that leverages basic mobile phone technology to gather reports on the quantity and location of patient samples and test results prepared for delivery in the diagnostic network of Malawi.MethodsWe designed a system that leverages unstructured supplementary service data (USSD) technology to enable health workers to submit daily reports describing the quantity of transportation-ready diagnostic samples and test results at specific health care facilities, free of charge with any mobile phone, and aggregate these data for sample transportation administrators. We then conducted a year-long field trial of this system in 51 health facilities serving 3 districts in Malawi. Between July 2019 and July 2020, the participants submitted daily reports containing the number of patient samples or test results designated for viral load, early infant diagnosis, and tuberculosis testing at each facility. We monitored daily participation and compared the submitted USSD reports with program data to assess system feasibility, adoption, and accuracy.ResultsThe participating facilities submitted 37,771 reports over the duration of the field trial. Daily facility participation increased from an average of 50% (26/51) in the first 2 weeks of the trial to approximately 80% (41/51) by the midpoint of the trial and remained at or above 80% (41/51) until the conclusion of the trial. On average, more than 80% of the reports submitted by a facility for a specific type of sample matched the actual number of patient samples collected from that facility by a courier.ConclusionsOur findings suggest that a USSD-based system is a feasible, adoptable, and accurate solution to the challenges of untimely, inaccurate, or incomplete data in diagnostic networks. Certain design characteristics of our system, such as the use of USSD, and implementation characteristics, such as the supportive role of the field team, were necessary to ensure high participation and accuracy rates without any explicit financial incentives.

Deploying SDN Control in Internet of UAVs: Q-Learning-Based Edge Scheduling

Nowadays, wilderness monitoring provides massive data output for supporting agricultural production, environmental protection, and disaster monitoring. However, smart upgrading alone for these wireless nodes cannot meet the softwarized network needs today, relating to the explosion of multi-dimensional data and multi-species equipment. In this article, we conduct a comprehensive solution for the UAV based data collection strategy in an “air-to-ground” intelligent softwarized collection system. The innovation in this article is that after using the IoT nodes to complete the data collection process through the proposed bandwidth-weighted traffic pushing optimization (BWPTO) algorithm, the system infers the future changes according to the current network state using a deep Q-learning (DQL) network. Then, by developing the proposed AIIPO (Air-to-Ground Intelligent Information Pushing Optimization) algorithm, the entire network can “forward-looking” the uploaded information to potentially idle nodes in the future, thus achieve the optimized system performance. Through the final mathematical experiments, we prove the optimality of our proposed routing algorithm and forwarding strategy, which are more applicable in the dynamic “air-to-ground” distributed data collection system than other benchmark solutions.

Smartphone-Based Distributed Data Collection Enables Rapid Assessment of Shorebird Habitat Suitability.

Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone application called iPlover that addresses these difficulties in collecting biogeomorphic information at piping plover (Charadrius melodus) nest sites on coastal beaches. This paper describes iPlover development and evaluates data quality and utility following two years of collection (n = 1799 data points over 1500 km of coast between Maine and North Carolina, USA). We found strong agreement between field user and expert assessments and high model skill when data were used for habitat suitability prediction. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring and modeling.

A Cloud-based Distributed Data Collection System for Decentralised Wastewater Treatment Plants

Abstract Compliance with increasingly stringent legislation is one of the key challenges in the wastewater technology sector in the European Union (EU). Many EU countries have chosen to use decentralised wastewater treatment plants (WWTPs) because of reduced infrastructural requirements. However, achieving compliance of regulatory standards using a decentralised strategy can be difficult and costly due to inefficient operating cost-efficiency, infrequent monitoring capability, as well as the logistical challenges faced by operators travelling between sites. With these problems in mind, this paper presents a distributed information system that was developed as part of an Irish-based research project that was funded by Enterprise Ireland. The system focuses on the acquisition and hosting of data from decentralised WWTP's to facilitate reliable and timely access to data that can be used for monitoring and analysis, without incurring the inherent logistical and technical costs associated with manual data collection methods.

Distributed control of performance management traffic with accuracy objectives

AbstractOne of the important functions of a network management system (NMS) is performance management (PM). PM deals with collecting statistical information to track the effectiveness and utilization of the network and network elements (NE). While this may be done offline, it is often necessary to monitor the network statistics online in real time. The aim of real‐time monitoring is to achieve high accuracy of the statistics while minimizing the use of scarce network bandwidth. The accuracy objective can vary depending on the priority and dynamic severity state of the NE. We define cost, which needs to be optimized, as a function of network traffic and achieved accuracy. Based on the cost, a comparison of push and pull data collection alternatives is done. We have developed a push‐based distributed data collection system that tunes the accuracy objectives, based on the network traffic and the dynamic severity state of the NE. Copyright © 2009 John Wiley & Sons, Ltd.

Adapting to Urban Warfare

Urban operations currently are of great concern to the defense community. J9, the Experimentation Directorate of USJFCOM, and the Joint Advanced Warfighting Program currently are conducting an experiment to investigate concepts for applying future technologies to joint urban operations. The first phase of the experiment focused on employing future sensors to remotely monitor and understand enemy operations in a foreign city. Characteristics of the urban environment include high building density, a large civilian population, and a cultural environment. These characteristics pose significant challenges for simulation designers. This paper describes the modifications required to adapt the simulations supporting the experiment, JSAF and SLAMEM, to the urban environment. A landscape with a large number of buildings had to be automatically generated and represented in a space efficient manner. Large concentrations of vehicles and pedestrians had to be modeled moving realistically through the city. This behavior had to be automatically generated since it would be impossible to individually control 100,000 entities. Embedding cultural features within the database in the form of building function codes allow civilian entities to automatically plan their movements based on generic daily schedules. Sensor models had to be modified to detect building properties, such as whether a building was fortified. The density of both entities and structures made both movement and intervisibility calculations significantly more expensive, requiring optimization combined with the application of large amounts of hardware. Computation and control was distributed between three CONUS sites and the High Performance Computing Centers at Maui and Wright-Patterson AFB. Limiting and balancing simulation traffic required a major effort. Source squelching was enabled by a distributed data collection system developed to collect data locally on each simulation node while still allowing analysts to perform real-time queries during the experiment.