Localization of Wi-Fi Access Point using smartphone's GPS information

Chun, Seung-Man; Lee, Seung-Mu; Nah, Jae-Wook; Choi, Jaeho; Park, Jong-Tae

doi:10.1109/icost.2011.6085822

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…In the literature, efforts for AP localization have been made. Most existing studies were conducted based on the well known LDPL model [12]- [15].…”

Section: Related Workmentioning

confidence: 99%

“…In [12], a method called AP_Area was proposed to infer the location of a target AP. This method obtained the propagation parameters of the LDPL model offline, and acquired the locations of RSS measurement points by GPS.…”

Section: Related Workmentioning

confidence: 99%

“…Most existing studies [12]- [16] employed the lognormal distance path loss (LDPL) model to localize WiFi APs.…”

Section: Introductionmentioning

confidence: 99%

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On the Reliable Localization of WiFi Access Points

et al. 2019

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Acquiring the locations of WiFi access points (APs) not only plays a vital role in various WiFi related applications, such as localization, security and AP deployment, but also inspires the emergence of novel applications. Thus, many efforts have been invested in studying AP localization. Most existing studies adopt the well-known lognormal distance path loss (LDPL) model, which only accounts for largescale fading but ignores small-scale fading induced by multipath propagation. In this paper, we tackle the problem of AP localization based on the Rayleigh lognormal model which characterizes the influence of both large-scale fading and small-scale fading. In addition, particle filtering is used to sequentially narrow the scope of possible locations of the target AP. In order to label the locations of received signal strength (RSS) measurements in real time, manual configuration or certain indoor and outdoor localization techniques, including GPS, pedestrian dead reckoning (PDR) and WiFi fingerprinting, can be leveraged. Moreover, due to the bias caused by unavailable or inaccurate state space, a particle area dynamic adjustment strategy (PADAS) is designed to improve the AP localization accuracy. Extensive experiments were carried out in typical indoor and outdoor scenarios. It is shown that, if accurate location labels are available, the proposed method is able to achieve an average localization accuracy of 2.48 m indoors and 4.21 m outdoors. In comparison with the LDPL based solutions, the proposed method improves by 23.82% -70.38% indoors and 14.13% -35.94% outdoors; more importantly, the proposed method enhanced by using PDR, PADAS, GPS and WiFi fingerprinting can achieve localization accuracy comparable to that with accurate location labels. In addition, an Android application (APP) was developed to demonstrate the feasibility of the proposed algorithm on smartphones.INDEX TERMS WiFi access points localization, Rayleigh lognormal model, particle filtering, pedestrian dead reckoning.

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“…In the literature, efforts for AP localization have been made. Most existing studies were conducted based on the well known LDPL model [12]- [15].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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On the Reliable Localization of WiFi Access Points

et al. 2019

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“…To reduce the implementation cost and energy consumption, special sensor nodes, called anchor nodes, are installed with known positions [4–11] to localize a group of sensor nodes in the vicinity. The simplest way to determine the positions of anchor nodes is to use Global Positioning System (GPS) devices [4, 12–18] or is to be manually measured [8, 10, 11] in advance. However, GPS devices fail to offer precision in an indoor environment or GPS-denied area (poor signal reception).…”

Section: Introductionmentioning

confidence: 99%

Locating Anchors in WSN Using a Wearable IMU-Camera

Huang

Tseng

2014

International Journal of Distributed Sensor Networks

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Localization in a wireless sensor network (WSN) becomes important for many modern applications, like landslide detection, precision agriculture, health care, and so forth. The more precise the position of an anchor node is, the more accurate the localization of a sensor node can be measured. Since the Global Positioning System (GPS) device cannot work properly indoor, some existing localization methods configure anchor nodes in a manual fashion. However, neither applying GPS modules nor manually configuring anchor nodes is suitable for a WSN and especially artificial errors of manual configuration may be propagated and affect the results of localization. In this paper, we propose an alternative method to estimate anchor node locations in an indoor environment. We collect the Received Signal Strength Indicator (RSSI) data from the anchor node when human is walking around them. Meanwhile, we use a wearable IMU-camera device to assist the moving trajectory estimation. We implement a monocular Visual Odometry with a human walking model to estimate moving trajectories. An Unscented Kalman Filter (UKF) is used to estimate the anchor node location by fusing the RSSI data and moving trajectory. The experiment results show that the proposed method has lower estimation error when locating anchors.

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A novel method for measurement points selection in access points localization

Yang

Chen

2016

Wireless Netw

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Localization of Wi-Fi Access Point using smartphone's GPS information

Cited by 8 publications

References 6 publications

On the Reliable Localization of WiFi Access Points

On the Reliable Localization of WiFi Access Points

Locating Anchors in WSN Using a Wearable IMU-Camera

A novel method for measurement points selection in access points localization

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