SISE: Self-Updating of Indoor Semantic Floorplans for General Entities

Teng, Xiaoqiang; Guo, Deke; Guo, Yulan; Zhao, Xiang; Liu, Zhong

doi:10.1109/tmc.2018.2812752

Cited by 11 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For most models that augment semantic information to a base spatial model, the event-based updating approach and the life span interval definition can handle the dynamic changes of building semantics. Recently, some studies (Teng et al, 2018 ; Guo et al, 2021 ) consider extracting updates of semantics from crowdsourced images and videos for building self-updating indoor semantic floorplan models.…”

Section: Modeling Techniques In Sdaibmentioning

confidence: 99%

“…In terms of context modeling , the framework should be able to represent more complex and diverse IoT devices and building environments. On the one hand, compared to current manually designed models, automated means such as semantic parsing and extraction (Teng et al, 2018 ; Guo et al, 2021 ) can be developed to extract contexts from the physical world; on the other hand, data structures with more powerful expression capabilities such as heterogeneous graphs and hypergraphs (Zhou et al, 2006 ) can be introduced to the modeling framework. In terms of data uncertainty modeling , the framework should support the decentralized data setting (Li et al, 2022 ), i.e., heterogeneous computing nodes generate and consume data with different mechanisms.…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

Liu²,

Chan³

et al. 2022

Front. Big Data

View full text Add to dashboard Cite

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.

show abstract

Section: Modeling Techniques In Sdaibmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

Liu²,

Chan³

et al. 2022

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Additionally, it used the alpha shape to obtain the overall floorplan shape [15]. SISE proposed enGraph, a new abstraction data model for representing indoor entities and corresponding semantics [16]. In 2015, Zhou et al [17] developed a link-node optimization model for indoor mapping, with pathways representing links and activity landmarks representing nodes.…”

Section: Crowdsourcing-based Map Constructionmentioning

confidence: 99%

“…Due to the prevalence of smartphones, the efficiency of crowdsourcing data collection is also enhanced. In complex environments, numerous smartphone-based map construction and localization methods via crowdsourcing have been proposed [14], such as CrowdInside [15], SISE [16], and Zhou et al also proposed methods [17][18][19]. By processing smartphone-collected crowdsourcing data, a map is constructed by combining semantic/feature information with the estimated trajectories.…”

Section: Introductionmentioning

confidence: 99%

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

Chai

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.

show abstract

“…This heuristic approach (Srinivasan and Venkatesan 2021; Guler and Jha 2020) could be used for a global solution. The floor layouts are being shown using a rectangular dissection, and the borders have a rectangle form as well (Teng et al 2018;Chen et al 2018;Lin et al 2021b;Mohapatra et al 2020;Vehring et al 2020). These lines are given vertically or horizontally, and the modules are dumped in a rectangle form to improve the automation process.…”

Section: Introductionmentioning

confidence: 99%

Very Large-Scale Integration Floor Planning on FIR and Lattice Filters Design With Multi-Objective Hybrid Optimization

Pondreti

Kaparapu

2023

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

Floor planning is indeed an obvious design process in VLSI physical layout since it specifies the dimensions, structure, as well as positions of components upon the chip; in addition, information regarding the overarching silicon area, interlinks, and latency is also provided. VLSI floor planning is an NP-hard issue as the floor plan representations are a crucial component in this process. The intricacy, as well as solution space of the floor plan layout, is influenced by the floorplan visualizations. To tackle the VLSI floor plan challenge, numerous researchers have developed numerous meta-heuristic optimization techniques. The main objective of this work presents a novel multi-objective hybrid optimization method for solving the floor plan optimization issue. Standard DOX and ALO are conceptually combined in the proposed hybrid optimization referred to as Dingo Updated Ant Lion Optimization (DUALO) model. The multi-objectives like wire length, area, and penalty function are taken into consideration.

show abstract

SISE: Self-Updating of Indoor Semantic Floorplans for General Entities

Cited by 11 publications

References 21 publications

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

Very Large-Scale Integration Floor Planning on FIR and Lattice Filters Design With Multi-Objective Hybrid Optimization

Contact Info

Product

Resources

About