On the feasibility of using wireless ethernet for indoor localization

Ladd, Andrew M.; Bekris, Kostas E.; Rudys, Algis; Wallach, Dan S.; Kavraki, Lydia E.

doi:10.1109/tra.2004.824948

Cited by 205 publications

(120 citation statements)

References 22 publications

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“…However, such an assumption breaks under the presence of walls or furniture (cf. Campos et al (2014)) and different experiments have arrived at different distributions (Ladd et al (2004)). A general model remains unavailable and discrete Gaussian approximations are common (cf.…”

Section: Formulation and Reduction Of The Labelling Problemmentioning

confidence: 99%

“…Labels must be assigned in an spatially informative manner; and in this particular context, we are restricted to the use of observations on WiFi access points (APs) and their received signal strength indicators (RSSIs), along with metadata linked to the activities undertaken by device carriers. In standard wireless networks, especially within large indoor environments, there commonly are multiple APs serving end-users, uniquely identifiable through their MAC address; such signals can be received by hardware in many mobile devices and there exists literature on their use for (mostly supervised) location estimation (see Roos et al (2002); Youssef et al (2003); Ladd et al (2004) and references therein). In addition, the rise of ubiquitous computing permits the feed of substantial contextual information for use within an unsupervised learning scheme.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

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In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets.Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

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Section: Formulation and Reduction Of The Labelling Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…provide software access to the amplitude of the received signal. Another use for RSS is profiling [54], [55], in which a map of RSS values is constructed during an initial training phase. Sensors then estimate their position by matching observed RSS values with the training data.…”

Section: Measurement Phasementioning

confidence: 99%

“…Such methods are used in [55], [53], and [70]. Like MLE, the data likelihood is computed using a measurement model.…”

Section: Cellularmentioning

confidence: 99%

A Survey on Localization for Mobile Wireless Sensor Networks

Amundson

Koutsoukos

2009

Lecture Notes in Computer Science

252

142

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Abstract. Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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“…A number of localization methods using the signal strength from wireless LAN access points have been proposed, as these methods do not require many devices to determine a location [4][5][6][7][8]. However, there is some uncertainty associated with such localizations because of the reflection of wireless LAN signals due to the structure of the building and the humans inside them.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Wireless Mobile Client based on Sensor Data Fusion for Localization in Multistory Building

Umetani

Yamane

Yamashita

et al. 2013

Transactions of ISCIE

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This paper describes a state space model for estimating the location of a mobile wireless client based on sensor data fusion. Data pertaining to the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. We use the strength of signals received by wireless LAN access points to locate mobile clients in multistory buildings. We also use the acceleration of the mobile client as sensor data. The output of an accelerometer is prone to uncertainty; we use a probabilistic model of the location estimated by wireless LAN signals and the acceleration acquired by a sensor to create an observation model. In addition, a simplified measurement method is proposed for use by a fingerprinting-type estimation system based on human footsteps. Experimental results show the feasibility of the proposed method.

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On the feasibility of using wireless ethernet for indoor localization

Cited by 205 publications

References 22 publications

Unsupervised labelling of sequential data for location identification in indoor environments

Unsupervised labelling of sequential data for location identification in indoor environments

A Survey on Localization for Mobile Wireless Sensor Networks

State Estimation of Wireless Mobile Client based on Sensor Data Fusion for Localization in Multistory Building

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