IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning

Li，Peiran1; Zhang，Haoran2; Li，Wenjing3; Yu，Keping4; Bashir，Ali Kashif5; Ali Al Zubi，Ahmad6; Chen，Jinyu7; Song，Xuan8; Shibasaki，Ryosuke9

2022

10.1109/TNSE.2022.3145572

IEEE Transactions on Network Science and Engineering   影响因子和分区

2334-329X

2327-4697

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).

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

EI ; SCI

英语

其他

20220511570106

Bayesian networks ; Correlation methods ; Deep learning ; Dynamics ; Graphic methods ; Heuristic methods ; Telephone sets

Ergonomics and Human Factors Engineering:461.4 ; Telephone Systems and Equipment:718.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Mathematical Statistics:922.2

2-s2.0-85123708140

Scopus

1.The University of Tokyo Center for Spatial Information Science, 222781 Kashiwa, Chiba, Japan, (e-mail: lipeiran_csis@csis.u-tokyo.ac.jp)
2.Center for Spatial Information Science, University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: zhang_ronan@csis.u-tokyo.ac.jp)
3.Center for Spatial Information Science, University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: liwenjing@csis.u-tokyo.ac.jp)
4.Global Information and Telecommunication Institute, Waseda University, 13148 Tokyo, Tokyo, Japan, (e-mail: keping.yu@aoni.waseda.jp)
5.Computing and Mathematics, Manchester Metropolitan University, 5289 Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland, M15 6BH (e-mail: dr.alikashif.b@ieee.org)
6.Computer Science, King Saud University, 37850 Riyadh, Riyadh Province, Saudi Arabia, (e-mail: aalzubi@ksu.edu.sa)
7.Center for Spatial Information Science, University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: miraclec@csis.u-tokyo.ac.jp)
8.SUSTech, 255310 Shenzhen, Guangdong, China, (e-mail: songxuan@csis.u-tokyo.ac.jp)
9.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: shiba@csis.u-tokyo.ac.jp)

Li，Peiran,Zhang，Haoran,Li，Wenjing,et al. IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning[J]. IEEE Transactions on Network Science and Engineering,2022,10(5):2745-2754.

Li，Peiran.,Zhang，Haoran.,Li，Wenjing.,Yu，Keping.,Bashir，Ali Kashif.,...&Shibasaki，Ryosuke.(2022).IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning.IEEE Transactions on Network Science and Engineering,10(5),2745-2754.

Li，Peiran,et al."IIoT based Trustworthy Demographic Dynamics Tracking with Advanced Bayesian Learning".IEEE Transactions on Network Science and Engineering 10.5(2022):2745-2754.