IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning

Peiran Li, Haoran Zhang, Wenjing Li, Keping Yu, Ali Kashif Bashir, Ahmad Ali Al Zubi, Jinyu Chen, Xuan Song, Ryosuke Shibasaki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Tracking demographic dynamics for the built environment is important for a smart city. As a kind of ubiquitous Industrial Internet of Things (IIoT) device, portable devices (e.g., mobile phones) afford a great potential to achieve this goal. Tracking the demographic dynamics illuminates two things: populations mobility (where do people go) and the related demographics (who are they). Many past studies have investigated the tracking of population dynamics; however, few of them tried tracking the demographic dynamics. In this context, our study proposed a ubiquitous IIoT based trustworthy approach for built environment demographic dynamics tracking. First, we employed a meta-graph-based data structure to represent users life patterns and projected them into a low-dimension space as uniform features. Then, based on the life-pattern features, we derived a variation-inference-based advanced Bayesian model to infer the demographics. Finally, taking a region in Tokyo as a case study, we compared our methods with baseline methods (heuristic algorithm, deep learning), and the result proved a superior accuracy (the MAPE improved by 0.07 to 0.28) as well as reliability (0.78 Pearson correlation coefficient with survey data).

Original languageEnglish
JournalIEEE Transactions on Network Science and Engineering
Publication statusAccepted/In press - 2022


  • Data models
  • Deep learning
  • GPS trajectory
  • Global Positioning System
  • IIoT
  • Industrial Internet of Things
  • Mathematical models
  • Mobile handsets
  • Trajectory
  • demographics
  • variation inference

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications


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