Positioning Methods and Technologies in Mobile and Pervasive Computing

Stojanović, Dragan; Barshan, Billur; Papadopoulos, Apostolos N.; Weghe, Nico Van de; Claramunt, Christophe

doi:10.4018/978-1-4666-5888-2.ch564

Cited by 2 publications

(2 citation statements)

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“…This representation also takes into account the broad trends of positioning techniques. Since raw mobility data can be received from different types of sensors (Stojanovic et al, 2015), different data sensors might exist for representing a given trajectory (RFID, WiFi, Bluetooth, etc. for indoor mobility, and GPS for outdoor mobility).…”

Section: Spatial Modelling Principlesmentioning

confidence: 99%

“…While outdoor spaces denote geographical spaces at large, they are generally much less constrained than indoor spaces, as indeed a building generally has a well-designed set of rooms and corridors that denote some functional relations. While human movements in outdoor spaces can be easily tracked using GPS-based devices, there is a need in indoor spaces for some specific location-aware devices such as WiFi or RFID sensors (Stojanovic, Barshan, Papadopoulos, Van de Weghe, & Claramunt, 2015). Data integration between trajectory data from multiple devices, spatial data and other data can build more comprehensive city profiles (Wang, Bao, Culpepper, 2020).…”

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confidence: 99%

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A hierarchical indoor and outdoor model for semantic trajectories

Noureddine

Ray

Claramunt

2021

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With the growth of location-based information and the widespread adoption of mobile devices and connected sensors, human mobility has recently emerged as an important research area. Nowadays, the exponential development of mobile sensors and the Internet of Things offers many opportunities for the integration of real-time data on humans acting in indoor and outdoor environments. Moreover, mobile crowd-sensing allows volunteers to actively provide real-time trajectory and activity data (Guo et al., 2015). However, such crowd-sourcing data are most often heterogeneous in space and time and require a flexible data model that can integrate the data as they are and provide data manipulation and analysis capabilities to reformat the data at the appropriate level of abstraction. Understanding urban mobility patterns, together with associated contextual information, requires a sound integration of the modelling level within current information infrastructures. Such development appears as

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Section: Spatial Modelling Principlesmentioning

confidence: 99%

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confidence: 99%