Implementation of an adaptive data logging algorithm in low-cost IoT nodes for supply chain transport monitoring

IF 1.3 4区工程技术 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI:10.1109/TLA.2024.10705972

Jose Yael Lopez Hernandez;Enrique Gonzalez;Raul Pena;Antonio Carlos Bento;Sergio Camacho-Leon

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引用次数: 0

Abstract

In IoT-based supply chain transportation, low rates for data loss, minimizing time to destination, and optimizing energy consumption are paramount. These factors can be influenced by variable parameters, data volume, logging procedures, positioning complexities, and communication hiccups during transit. This study introduces an adaptive data logging algorithm for a cost-effective IoT node, addressing these challenges. This innovation enables real-time data acquisition and remote display via a web interface. Experimental tests demonstrate the prototype's reliability in both controlled indoor and dynamic outdoor environments, particularly in environmental and GPS data collection. Results reveal 5.24% data loss indoors and 23.24% via the web interface. Outdoors, data loss peaks at 55.34%, increasing to 82.76% with the web interface. However, the obtained information is adequate for prototype validation. The algorithm reduces data by 74%, leading to lower data processing and power transmission needs. Moreover, determining the distance from GPS coordinates is essential for predicting travel times and monitoring vehicle velocity to maximize efficiency. The results from this prototype are expected to enhance the development of advanced models, thus enriching future scientific research initiatives that aim to incorporate IoT technology into transportation systems.

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在用于供应链运输监控的低成本物联网节点中实施自适应数据记录算法

在基于物联网的供应链运输中，降低数据丢失率、最大限度地缩短到达目的地的时间以及优化能源消耗至关重要。这些因素可能会受到运输过程中可变参数、数据量、记录程序、定位复杂性和通信故障的影响。本研究针对这些挑战，为经济高效的物联网节点引入了一种自适应数据记录算法。这项创新可通过网络界面进行实时数据采集和远程显示。实验测试证明了原型在受控室内和动态室外环境中的可靠性，特别是在环境和 GPS 数据收集方面。结果显示，室内数据丢失率为 5.24%，通过网络接口丢失率为 23.24%。在室外，数据丢失率最高为 55.34%，通过网络接口则增加到 82.76%。不过，获得的信息足以用于原型验证。该算法减少了 74% 的数据，从而降低了数据处理和电力传输需求。此外，根据 GPS 坐标确定距离对于预测行驶时间和监控车辆速度以最大限度地提高效率至关重要。该原型的成果有望促进先进模型的开发，从而丰富未来旨在将物联网技术融入交通系统的科研计划。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Latin America Transactions COMPUTER SCIENCE, INFORMATION SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

3.50

自引率

7.70%

发文量

192

审稿时长

3-8 weeks

期刊介绍： IEEE Latin America Transactions (IEEE LATAM) is an interdisciplinary journal focused on the dissemination of original and quality research papers / review articles in Spanish and Portuguese of emerging topics in three main areas: Computing, Electric Energy and Electronics. Some of the sub-areas of the journal are, but not limited to: Automatic control, communications, instrumentation, artificial intelligence, power and industrial electronics, fault diagnosis and detection, transportation electrification, internet of things, electrical machines, circuits and systems, biomedicine and biomedical / haptic applications, secure communications, robotics, sensors and actuators, computer networks, smart grids, among others.