Towards crowd-aware indoor path planning

Liu, Tiantian; Li, Huan; Lu, Hua; Cheema, Muhammad Aamir; Shou, Lidan

doi:10.14778/3457390.3457401

Cited by 17 publications

(19 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the analytical contexts in SDAIB are complicated and diverse, involving many aspects related to the building environment and the internal entities like human beings and sensory devices. Many existing works (Yang et al, 2010;Kamkarian and Hexmoor, 2012;Lin et al, 2016;Teng et al, 2017;Li et al, 2018cLi et al, , 2020bLiu et al, 2021c) find relevant analytical contexts according to the need of the analytical task and design specific data structures for them.…”

Section: Figurementioning

confidence: 99%

“…for keyword-aware path planning (Feng et al, 2020), temporalvariation aware path planning (Liu et al, 2021a), and crowdaware path planning (Liu et al, 2021c).…”

Section: Path Planningmentioning

confidence: 99%

“…In this study, we pay particular attention to such spatial data analysis for intelligent buildings (SDAIB). Referring to the data analysis platform in Figure 1 , general data analysis tasks include anomaly detection, frequent pattern mining, and forecasting, while typical SDAIB tasks are hotspot discovery (Li et al, 2018b , c ), path planning (Feng et al, 2020 ; Chan et al, 2021 ; Liu et al, 2021a , c ), semantic trajectory generation (Li et al, 2020a , b ), and distance monitoring (Chan et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Liu²,

Chan³

et al. 2022

Front. Big Data

Self Cite

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: Figurementioning

confidence: 99%

“…for keyword-aware path planning (Feng et al, 2020), temporalvariation aware path planning (Liu et al, 2021a), and crowdaware path planning (Liu et al, 2021c).…”

Section: Path Planningmentioning

confidence: 99%

Section: Introductionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent approaches to indoor localization [ 3 , 4 ], there have been promising results in pinpointing exact locations using Bluetooth. However, some approaches have shown the need to know the floorplan or dimensions of an area [ 5 , 6 ]. They rely on beacons [ 7 , 8 , 9 ], which are not always accessible and wield less computational power.…”

Section: Introductionmentioning

confidence: 99%

Path Forming of Healthcare Practitioners in an Indoor Space Using Mobile Crowdsensing

Panlaqui

Fuad

Deb

et al. 2022

Sensors

View full text Add to dashboard Cite

While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization.

show abstract

“…Pedestrian routing is typically decoupled into outdoor routing along paths and pavements and indoor routing that allows navigating (multi-level) buildings. A plethora of algorithms have been developed for each scenario separately [3]. But there is a lack of scalable solution approaches that integrate both and allow to compute routes that traverse paths as well as buildings.…”

Section: Introductionmentioning

confidence: 99%

Barrier-Free Pedestrian Routing with Contraction Hierarchies

Storandt

Müller³

et al. 2021

Proceedings of the 29th International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

Figure 1: Pedestrian routing example at the train station of Bern, Switzerland. Google Maps (left) recommends a route with a huge detour that suggests hopping over train tracks to reach the destination. In the middle and right image, routes computed with our approach are shown. The route in the middle is the shortest route. It uses two stairs and a pedestrian bridge. The right route is the shortest barrier-free route and uses elevators to get from one platform to another.

show abstract

Towards crowd-aware indoor path planning

Cited by 17 publications

References 51 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

Path Forming of Healthcare Practitioners in an Indoor Space Using Mobile Crowdsensing

Barrier-Free Pedestrian Routing with Contraction Hierarchies

Contact Info

Product

Resources

About