Radio Map Update Automation for WiFi Positioning Systems

Lim, Jaechang; Jang, Woo–Hyuk; Yoon, Giwan; Han, Dongsoo

doi:10.1109/lcomm.2013.022213.122885

Cited by 54 publications

(34 citation statements)

References 5 publications

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“…A well-developed fingerprinting method alone may produce lower average positioning errors but is more labor intensive [18,21] also requires WiFi signal reference points information for developing a radio map [22]; in contrast, the trilateration method produces GPS-like positioning accuracy with less computational effort [23]. The distinguishing characteristic of the trilateration method is that unlike the fingerprinting method, trilateration is not susceptible to interference by nominal changes …”

Section: Figure 1 Comparison Results Of Average Errors In Four Diffementioning

confidence: 99%

Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment

Jung

Bell

2013

Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness

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Location Based Services (LBS), which are supported by ubiquitous location finding and positioning information, have become increasingly popular. These services are integrated with various wireless technologies (i.e. cellular, WiFi, and Bluetooth) on mobile devices that establish necessary location information. While each of these technologies contributes to the integration, development and success of LBSs, WiFi has been the most widely employed as an alternative to the Global Positioning System (GPS). Several commercial WiFi-based positioning systems (WPS) are available to the public to extend positioning coverage to places where GPS is unreliable or unavailable; however, these commercial WPSs often fail to deliver GPS-like positioning. The coarse positioning accuracy of commercial WPSs may be caused by unreliable or unsecured databases, which contain the essential WiFi-array information to produce local positioning. Knowing this, the quality of WiFi-based positioning services can be enhanced by improving the quality of a database with well-surveyed and accurate information. The Saskatchewan Enhanced positioning System (SaskEPS) is designed to reduce common errors in WPS. SaskEPS's positioning accuracy and consistency is supported by a thoroughly validated Access Point (AP) database. It has been tested in several buildings at the University of Saskatchewan and successfully provides GPS-like positioning accuracy. Our tests have also begun to elucidate the role of WiFi density in ensuring GPS-like positioning accuracy in indoor spaces. In this paper, we investigate the quantitative relationship between WiFi density and SaskEPS's overall positioning accuracy.

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Section: Figure 1 Comparison Results Of Average Errors In Four Diffementioning

confidence: 99%

Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment

Jung

Bell

2013

Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness

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“…Other approaches have attempted to reduce manual calibration or recalibration cost by using user feedback data [2,3,9,18,5,15]. In these approaches, the user feedback data are used for detecting environment changes and updating the radio map, thus, any additional sensors are not needed.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to this, user feedback approaches are vulnerable to erroneous data as users can input incorrect location information. Another approach [15] uses implicitly acquired fingerprints to detect changes in the Wi-Fi environment and update the radio map. However, the objective of this approach is to distinguish places, thus, it cannot provide an accurate location.…”

Section: Related Workmentioning

confidence: 99%

Crowdsourcing-based radio map update automation for wi-fi positioning systems

Chang

Han

2014

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

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With the popularization of smartphones, the needs for indoor location information are rapidly growing these days. Wi-Fi based positioning technique has been widely used to provide positioning information indoors. In particular, fingerprint-based localization is preferred because of its advantage in accuracy. However, the accuracy of localization gradually degrades as the Wi-Fi environment changes. In order to prevent the accuracy degradation, a radio map, which stores the Wi-Fi environment information, should be updated to accommodate the changes. Recalibration is commonly used to update a radio map, but it usually requires considerable time and effort. In this paper, we propose a method that can update the radio map automatically by using fingerprints collected from numerous users. In order to tag the locations of the collected fingerprints more accurately, the data from various sensors such as accelerometer and gyroscope are used. The proposed method also uses optimization algorithms along with a filtering method to remove erroneous data. The evaluation results showed that the accuracy achieved by the method was comparable to that of manual calibration in spite of using user feedback data. This indicates that without recalibration, the fingerprint-based indoor positioning system can keep up its accuracy if it is used by many users.

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“…Finally, the system passes several data calibration levels and tests in simulated industrial environment, wherein it is proved that it has high system acquisition accuracy, reliable data transmission, and stable operation control center. Being able to complete detection tasks in harsh and hazardous environments, the system makes up for the shortcomings of previous detection methods and enables remote wireless intelligent detection of toxic gases [17,18]. SO 2 ), environmental temperature, and humidity in industrial sites through multisensor modules, integrate all information through microprocessor STM32F107VCT6, and transmit the information to the control center using ZigBee transmission module, while controlling the movement of detection equipment.…”

Section: Introductionmentioning

confidence: 99%

Data Analysis with a Wireless Multisensory Toxic Gas Detection System

Wu¹,

Shi²

2015

International Journal of Distributed Sensor Networks

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This paper proposes a wireless multisensory toxic gas detection system. The system allows remote wireless detection of four types of toxic gases, specifically Cl 2 , CO, NO 2 , and SO 2 , using gas detection technologies and wireless communication technologies such as ZigBee and Wi-Fi. Further, we analyze the data collected from sensors to determine a correspondence between the sensors and the gases and establish recognition models for different gases. Through multiple data calibration levels and industrial experiments, we have verified the feasibility of the system, along with its gas detection accuracy and long-term stability.

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Radio Map Update Automation for WiFi Positioning Systems

Cited by 54 publications

References 5 publications

Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment

Quantitative comparison of indoor positioning on different densities of WiFi arrays in a single environment

Crowdsourcing-based radio map update automation for wi-fi positioning systems

Data Analysis with a Wireless Multisensory Toxic Gas Detection System

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