Manchester Metropolitan University's Research Repository

    IIoT based trustworthy demographic dynamics tracking with advanced Bayesian learning

    Li, Peiran, Zhang, Haoran, Li, Wenjing, Yu, Keping, Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522, Ali Al Zubi, Ahmad, Chen, Jinyu, Song, Xuan and Shibasaki, Ryosuke (2022) IIoT based trustworthy demographic dynamics tracking with advanced Bayesian learning. IEEE Transactions on Network Science and Engineering. ISSN 2327-4697

    Accepted Version
    Download (9MB) | Preview


    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).

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Actions (login required)

    View Item View Item