Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Abstract

Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

“…Real-time is equally as important as accuracy and precision in some localization-based applications. When criminals are being rehabilitated, for example, the police need to know where they are being rehabilitated in real-time so that they can have additional possibilities for more rehabilitation [68]. A catastrophe occurs inside buildings, panicked trapped individuals can move in real-time, and the rescue team must locate the location of the trapped individuals in real-time during an emergency rescue.…”

Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem

“…This is because, with higher measurement noise, the DM will be initially less capable to correctly resolving the ambiguity of the x-coordinate (both x solutions might be acceptable, and their mean value would be considered) of the UAV 3D position, Equation ( 12), with the non-coplanar ANs. With coplanar ANs the horizontal plane is the plane of symmetry of the ANs' layout, which is a more effective plane of symmetry to resolve the ambiguity of the z-coordinate of the UAV 3D position, Equation (15), since there will always be a negative z solution to be rejected and, thus, the other correct z solution will always be considered without averaging, irrespective of the SNR level. always be a negative z solution to be rejected and, thus, the other correct z solution will always be considered without averaging, irrespective of the SNR level.…”

“…TSoA arises in MIMO [8] and multistatic [12] systems consisting of two sets of ANs (or sensors), i.e., transmitters and receivers [1]. Radiofrequency (RF)-based positioning systems usually use wireless technologies such as Wi-Fi [13][14][15][16], ZigBee [17,18], Bluetooth [19][20][21], ultra-wideband (UWB) [22][23][24], radiofrequency identification (RFID) [25][26][27][28], pseudolites [29], the fifth-generation (5G) communication system [30], and millimeter-wave (mmWave) and terahertz (THz) frequency bands in the envisioned sixth-generation (6G) communication networks [31]. Further technologies used for positioning include visible light communication (VLC) [32], ultrasound [33,34], acoustic [35,36], infrared [37,38], vision [39], magnetic field [40], and dead reckoning [41].…”

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

“…Beyond the mathematics behind these models, the VET or undergraduate student must be aware of the existence of these methods, as well as certain rules for the selection of the appropriate algorithm and the parameterization based on the problem to be faced in order to use them in practical applications. Although usually the problem of location based on Wi-Fi signals is solved by trilateration [39], due to the difficulty of modeling the propagation model within buildings, some authors have used Machine Learning techniques even based on cloud services [40][41][42]. As a didactic example of the application of Machine Learning with Andruino-R2, a supervised machine learning model was created to implement a simple positioning system, which allows identifying which room the robot is in, based on the Wi-Fi signals received, in a three-room environment.…”