Edge computing-Based mobile object tracking in internet of things

High-Confidence Computing

卷:2

Wei Yu¹  Yalong Wu²  Yuwei Cao³  Pu Tian⁴  Linqiang Ge⁵ 

[1] Corresponding author.;

[2] Department of Computer Science and Engineering, North Central College, Naperville, USA;

[3] Department of Computer Science, University of Illinois at Chicago, Chicago, USA;

[4] Department of Computer and Information Sciences, Towson University, Towson, USA;

[5] School of Computer Science, Columbus State University, Columbus, USA;

关键词: Internet of things;    Edge computing;    Architecture;    Mobile object tracking;    Vector auto regression;   

DOI  :  

来源: DOAJ

【 摘 要 】

Mobile object tracking, which has broad applications, utilizes a large number of Internet of Things (IoT) devices to identify, record, and share the trajectory information of physical objects. Nonetheless, IoT devices are energy constrained and not feasible for deploying advanced tracking techniques due to significant computing requirements. To address these issues, in this paper, we develop an edge computing-based multivariate time series (EC-MTS) framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks. Specifically, EC-MTS leverages statistical technique (i.e., vector auto regression (VAR)) to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction. Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure. We have validated the efficacy of EC-MTS and our experimental results demonstrate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects. In addition, we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.

【 授权许可】

Unknown