Li, H., Liu, T., Kai-Ho Chan, H. orcid.org/0000-0002-5312-6083 et al. (1 more author) (2022) Spatial data analysis for intelligent buildings: awareness of context and data uncertainty. Frontiers in Big Data, 5. 1049198. ISSN 2624-909X
Abstract
Intelligent buildings are among the most active Internet-of-Things (IoT) verticals, encompassing various IoT-enabled devices and sensing technologies for digital transformation. Analysis of spatial data, a very common type of data collected in intelligent buildings, offers a lot of insights for many purposes such as facilitating space management and enhancing the utilization efficiency of buildings. In this paper, we recognize two major challenges in spatial data analysis for intelligent buildings (SDAIB): (1) the complicated analytical contexts that are related to the building space and internal entities and (2) the uncertainty of spatial data due to the limitations of positioning and other sensing technologies. To address these challenges, we identify and categorize different kinds of analytical contexts and spatial data uncertainties in SDAIB, and propose a unified modeling framework for handling them. Furthermore, we showcase how the proposed framework and the associated modeling techniques are used to enable context-aware and uncertainty-aware SDAIB, in the tasks of hotspot discovery, path planning, semantic trajectory generation, and distance monitoring. Finally, we offer several research directions of SDAIB, in line with the emerging trends of the IoT.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | spatial data uncertainty; context-aware computing; indoor spaces; smart buildings; mobility analysis; IoT data quality |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Nov 2022 14:57 |
Last Modified: | 29 Nov 2022 01:19 |
Status: | Published |
Publisher: | Frontiers Media S.A. |
Refereed: | Yes |
Identification Number: | 10.3389/fdata.2022.1049198 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193221 |