Wireless Positioning Techniques – A Developers Update

Rooney, Seamus; Gardiner, Keith; Carswell, James D.

doi:10.1007/978-3-540-76925-5_12

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The LocateMe module is based on a hybrid positioning calculation that utilizes GSM, Wi‐Fi and Bluetooth radio signals in addition to GPS to determine location. A more comprehensive description of this technology can be found in Rooney et al. (2007b).…”

Section: Tellmementioning

confidence: 99%

Mobile Visibility Querying for LBS

Carswell¹,

Gardiner²,

Yin³

2010

Transactions in GIS

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This article describes research carried out in the area of mobile spatial interaction (MSI) and the development of a 3D mobile version of a 2D web-based directional query processor. The TellMe application integrates location (from GPS, GSM, WiFi) and orientation (from magnetometer/accelerometer) sensor technologies into an enhanced spatial query processing module capable of exploiting a mobile device's position and orientation for querying real-world spatial datasets. This article outlines our technique for combining these technologies and the architecture needed to deploy them on a sensor enabled smartphone (i.e. Nokia Navigator 6210). With all these sensor technologies now available on off-the-shelf devices, it is possible to employ a mobile query system that can work effectively in any environment using location and orientation as primary parameters for directional queries. Novel approaches for determining a user's visible query space in three dimensions based on their line-of-sight (ego-visibility) are investigated to provide for "hidden query removal" functionality. This article presents demonstrable results of a mobile application that is location, direction, and orientation aware, and that retrieves database objects and attributes (e.g. buildings, points-of-interest, etc.) by simply pointing, or "looking", at them with a mobile phone.

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Section: Tellmementioning

confidence: 99%

Mobile Visibility Querying for LBS

Carswell¹,

Gardiner²,

Yin³

2010

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“…ToA and TDoA are based on the measurement of the time flight of the signal, the implementation of this technique requires clock synchronization between the Wireless Access Points (WAP) (Kawauchi et al, 2009), where as for RSS pattern matching algorithms are used to compute the location using RSS (Rooney et al, 2007;Papapostolou and Chaouchi, 2009a).…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System Using Artificial Neural Network

Mehmood¹

2010

Journal of Computer Science

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Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS) using Artificial Neural Networks (ANN), which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP), hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method for collecting the training data can help in addressing the noise and interference, which are one of the major factors affecting the accuracy of IPS.

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EgoViz – A Mobile Based Spatial Interaction System

Gardiner¹,

Yin²,

Carswell³

2009

Web and Wireless Geographical Information Systems

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Abstract. This paper describes research carried out in the area of mobile spatial interaction and the development of a mobile (i.e. on-device) version of a simulated web-based 2D directional query processor. The TellMe application integrates location (from GPS, GSM, WiFi) and orientation (from digital compass/tilt sensors) sensing technologies into an enhanced spatial query processing module capable of exploiting a mobile device's position and orientation for querying real-world 3D spatial datasets. This paper outlines the technique used to combine these technologies and the architecture needed to deploy them on a sensor enabled smartphone (i.e. Nokia 6210 Navigator). With all these sensor technologies now available on one device, it is possible to employ a personal query system that can work effectively in any environment using location and orientation as primary parameters for directional queries. In doing so, novel approaches for determining a user's query space in 3 dimensions based on line-of-sight and 3D visibility (ego-visibility) are also investigated. The result is a mobile application that is location, direction and orientation aware and using these data is able to identify objects (e.g. buildings, points-of-interest, etc.) by pointing at them or when they are in a specified field-of-view.

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Wireless Positioning Techniques – A Developers Update

Cited by 4 publications

References 7 publications

Mobile Visibility Querying for LBS

Mobile Visibility Querying for LBS

Indoor Positioning System Using Artificial Neural Network

EgoViz – A Mobile Based Spatial Interaction System

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