Calibrating Indoor Positioning Systems with Low Efforts

Ficco, Massimo; Esposito, Christian; Napolitano, Antonio

doi:10.1109/tmc.2013.29

Cited by 51 publications

(41 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gu et al [5] and Lui et al [6] provided survey studies of indoor positioning systems for wireless personal networks. Shin et al [7] introduced an indoor user tracking system. The system constructs a topological map with Wi-Fi signal calibrations, assigns semantically meaningful labels into the map, and estimates the semantic location of the user based on the current Wi-Fi signal strength observation.…”

Section: Related Workmentioning

confidence: 99%

“…Eventually, the proposed system was the first Wi-Fi fingerprinting-based SLAM implementation. Lau et al [7] presented the Density-based Cluster Combined Localisation Algorithm that uses the unsupervised technique to recognise meaningful locations. The results prove that the DCCLA is able to detect fingerprints of Wi-Fi signal strengths and does not require users' input.…”

Section: Related Workmentioning

confidence: 99%

“…However, using the RM definition database helps in accuracy to track devices. The RSS values are immediately compared with the recent signal strength while a moving device receives any signal from an AP [7] [8]. RM definition table does not store any new data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Indoor Taxi-Cab: Real-Time Indoor Positioning and Location-Based Services with Ekahau and Android OS

Kilinc

Mostafa

Islam

et al. 2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

View full text Add to dashboard Cite

Abstract-Positioning and routing in outdoors environment is still challenging especially in complex buildings, where a number of buildings are combined with tunnels and bridges, and the GPS signal is unreachable. Looking for a particular room in an unfamiliar huge indoor environment or a product in an enormous store is time wasting and a real life problem that everybody faces on a daily basis. This paper presents a solution using the Ekahau positioning system and Android OS with an intermediary server, which acts between these two systems to provide actual room level positioning by a mathematical modeling technique. The system also provides the shortest path to a target destination by using Dijkstra's search algorithm. The optimal distance between the locations is modeled based on the Taxi-cab geometry distance definition for the mobile clients. Additionally, the users can also display the shortest path for items in the vicinity such as coffee machines, etc. The proposed solution has been evaluated in a real life setting at basement floor level.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Taxi-Cab: Real-Time Indoor Positioning and Location-Based Services with Ekahau and Android OS

Kilinc

Mostafa

Islam

et al. 2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

View full text Add to dashboard Cite

show abstract

“…Then, the distances of the terminal to at least some of the reference nodes can be calculated and the approximate position of the terminal can be estimated. Among the most commonly used signal properties are propagation time, angle of arrival, and signal strength, however due to low deployment and maintenance costs, majority of the solutions reported in the literature rely on received signal strength measurements [2,[23][24][25][26][27][28][29][30][31][32].…”

Section: Positioning Techniquesmentioning

confidence: 99%

“…From that time the problem of indoor positioning and navigation has been widely addressed around the world [2][3][4][5][6][7][8][9][10][11][12][13][14]. Hence maintaining low deployment and maintenance costs is among the most important objectives of the research, majority of the solutions reported in the literature rely on radio signal strength measurements [23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Mixed-Mode Wireless Indoor Positioning System Using Proximity Detection and Database Correlation

Wawrzyniak

Korbel

Skulimowski

et al. 2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The paper presents a prototype mixed-mode wireless indoor positioning and navigation system. The main goal of the system is to provide accurate and reliable location information for visually impaired users. The system also enables access to location related context information. The radio nodes of the network can operate in two power modes providing basis for both rough and precise user positioning. The paper describes an overview of the system architecture and the user interface suited to the needs of the visually impaired. Then, the details of the implemented positioning methods are presented. Finally, the results of experimental evaluation of positioning accuracy obtained using different classification strategies are discussed.

show abstract

Semantic wireless localization of WiFi terminals in smart buildings

et al. 2016

View full text Add to dashboard Cite

The wireless localization of mobile terminals in indoor scenarios by means of a semantic interpretation of the environment is addressed in this work. A training‐less approach based on the real‐time calibration of a simple path loss model is proposed which combines (i) the received signal strength information measured by the wireless terminal and (ii) the topological features of the localization domain. A customized evolutionary optimization technique has been designed to estimate the optimal target position that fits the complex wireless indoor propagation and the semantic target‐environment relation, as well. The proposed approach is experimentally validated in a real building area where the available WiFi network is opportunistically exploited for data collection. The presented results point out a reduction of the localization error obtained with the introduction of a very simple semantic interpretation of the considered scenario.

show abstract

Calibrating Indoor Positioning Systems with Low Efforts

Cited by 51 publications

References 34 publications

Indoor Taxi-Cab: Real-Time Indoor Positioning and Location-Based Services with Ekahau and Android OS

Indoor Taxi-Cab: Real-Time Indoor Positioning and Location-Based Services with Ekahau and Android OS

Mixed-Mode Wireless Indoor Positioning System Using Proximity Detection and Database Correlation

Semantic wireless localization of WiFi terminals in smart buildings

Contact Info

Product

Resources

About