促进物联网边缘 UWB 节点集成通信定位技术的碳中和研究

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Ouhan Huang;Huanle Rao;Zhongyi Zhang;Renshu Gu;Hong Xu;Gangyong Jia
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引用次数: 0

摘要

减少碳排放,提高燃油汽车的经济性,是实现 "碳中和 "的有效途径之一。车联网(IoV)是物联网与交通深度融合的发展中技术。工业物联网(IIoT)可以结合车辆信息,精确定位车辆碳排放,为后续的碳中和决策过程提供基础。然而,要实现物联网的精确需求,需要的不仅仅是传统的全球导航卫星系统(GNSS)。要实现碳排放检测,提供高精度定位,并为后续的碳中和决策过程奠定基础,就必须设计一个具有车联网功能的碳排放检测和定位系统。在本研究中,我们建议采用边缘超宽带(UWB)节点的地理邻近性和各种数据源的合并这两种方法来提高物联网情况下的定位精度。在仔细研究了单边缘节点的定位误差和 UWB 通信系统实现的范围误差后,我们选择了一种合适的滤波策略来提高单节点的精度。在提高单节点精度后,我们在空间维度上使用加权最小二乘法算法融合多个边缘节点的位置信息;在时间维度上,由于节点间通信的时间相关性,我们使用扩展卡尔曼滤波法融合一段时间内的数据。实验结果表明,与之前的研究相比,我们结合了时间和空间信息的协同定位方法实现了更高的定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosting Research for Carbon Neutral on Edge UWB Nodes Integration Communication Localization Technology of IoV
Reducing carbon emission to improving the economy of fuel vehicle is one of the effective ways to achieve Carbon Neutrality. The Internet of Vehicles (IoV) is a developing technology for deep integration of the Internet of Things (IoT) and transportation. The Industrial Internet of Things (IIoT) can incorporate vehicle information to pinpoint vehicle carbon emissions and provide a foundation for the subsequent carbon-neutral decision-making process. To achieve the precision needs of IoT, however, more than conventional Global Navigation Satellite Systems (GNSS) are required. To achieve carbon emission detection, provide high-precision positioning, and provide a foundation for subsequent carbon-neutral decision-making, it is essential to design a carbon emission detection and positioning system with the capability of vehicle networking. The geographic proximity of edge Ultra Wide Band (UWB) nodes and the merging of various data sources are two methods we suggest employing in this study to increase location accuracy in IIoT situations. After carefully examining the positioning error of the single-edge node and the range error achieved in the UWB communication system, we choose a suitable filtering strategy to enhance single-node accuracy. Following the improvement of single-node accuracy, we fuse the location information of multiple edge nodes using a Weighted Least Squares algorithm in the spatial dimension; in the temporal dimension, we use Extended Kalman filtering to fuse the data over a period of time due to the temporal correlation of inter-node communication. Experimental results demonstrate that our co-localization method, which combines temporal and spatial information, achieves higher localization accuracy in comparison with previous work.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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