Evaluation of methods for determining the mobile traffic distribution in cellular radio networks

Collmann, Ralf

doi:10.1109/25.966591

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…Alternatively, onsite RSS propagation model estimation may be used in order to leverage indoor accuracy [15,30,31]. In outdoor cases, topographical maps and propagation-prediction tools, as well as statistical modeling, neural networks and particle filters have been used instead of RSS pre-measurements [2,14,22,32,[33][34][35][36][37][38]. Furthermore, specialized techniques for WSNs have been introduced in order to enhance localization accuracy and reduce uncertainty [22,39].…”

Section: Localization Techniques -Overviewmentioning

confidence: 99%

Indoor Localisation With Wireless Sensor Networks

Mitilineos¹,

Kyriazanos²,

Segou³

et al. 2010

PIER

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Abstract-Wireless Sensor Networks (WSNs) have attracted a great deal of research interest during the last few years. Potential applications make them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Localization is a key aspect of such networks, since the knowledge of a sensor's location is critical in order to process information originating from this sensor, or to actuate responses to the environment, or to infer regarding an emerging situation etc. Indoor localization in the literature is based on various techniques, ranging from simple Received-Signal-Strength (RSS) to the more demanding Time-of-Arrival (ToA) or Directionof-Arrival (DoA) of the incoming signals. In the context of several EU research projects, various WSN platforms for indoor localization have been developed, evaluated and tested within real-world emergency medical services applications. These platforms were selected in order to deal with all principal localization techniques, namely RSSI, ToA and DoA. Deployment and real-world considerations are discussed, measurements results are presented and overall system evaluation conclusions are drawn regarding indoor localization capabilities of WSNs.

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Section: Localization Techniques -Overviewmentioning

confidence: 99%

Indoor Localisation With Wireless Sensor Networks

Mitilineos¹,

Kyriazanos²,

Segou³

et al. 2010

PIER

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“…It is given by (5) For the ToA estimation problem, the matrix is set to be an identity matrix of size .…”

Section: A Zero Memory Estimationmentioning

confidence: 99%

“…For more precise location estimates, the relationship between radio signals' characteristics and the relative positions of the mobile terminal and base stations is exploited to generate location estimation procedures. Measurements that have been proposed for the location of mobile terminals include the angle of arrival (AoA) of the radio signals from the mobile terminals to the base stations, the received signal strength (RSS) of the radio signal of the mobile terminal at the base station, the time of arrival (ToA) of the radio signal from the base station at the mobile terminal, and the time difference of arrival (TDoA) between signals from multiple base stations at the mobile terminal [2]- [5]. No matter which measurements are used to locate the mobile terminal, there are errors in the position estimates resulting from noise in the measurements.…”

mentioning

confidence: 99%

Dynamic model-based filtering for mobile terminal location estimation

McGuire

Plataniotis

2003

IEEE Trans. Veh. Technol.

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Abstract-Mobile terminal location is an important area of research because of its applications in location-sensitive browsing and resource allocation. This paper presents a method for reducing the error in mobile terminal location estimation. A preprocessor using nonparametric estimation is used to reduce the effects of non-line-of-sight and multipath propagation on the location procedure. A model-based dynamic filter is presented that uses an accurate model of mobile terminal motion to combine information from location measurements made at different time instances together to create an improved location estimate. The model of mobile terminal motion has a kinematic state space model describing the physical rules governing terminal motion and a control model that describes the human control input into the motion process. Location dependency in the control input model is used to derive a new dynamic filter. This filter provides greatly improved accuracy over previously known location techniques and is much more robust to variations in the mobile terminal motion and nonlinear effects in the propagation environment.

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“…Alternatively, onsite RSS propagation model estimation may be used in order to leverage indoor accuracy [15]. In outdoor cases topographical maps and propagation-prediction tools, as well as statistical modeling and neural networks, have been used instead of RSS pre-measurements [9,[16][17][18][19][20]. However, topographical maps are not always available, while propagation prediction tools, statistical models and neural networks require calibration.…”

mentioning

confidence: 98%

Blind Position Location via Geometric Loci Construction

Mitilineos¹

2010

Wireless Pers Commun

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A new position location technique is proposed, using received signal strength measurements, which can be implemented using inexpensive off-the-shelf equipment. The proposed technique is based on geometric loci construction, and overcomes the need for onsite calibration measurements or propagation prediction tools and topographical/architectural plans of the covered area. The proposed algorithm exhibits similar simulated performance compared to a recently proposed positioning technique, but without the need of model calibration. In addition, measurements results are also presented, demonstrating the performance of the algorithm.Keywords Radio position measurement · Received signal strength · Geometric loci IntroductionPosition location of wireless units has received considerable attention during the past few years. The US Federal Communications Commission (FCC) and the European Commission suggest location information providing for E-911 and E-112 call services respectively [1,2]. These requirements have resulted to a rapid development of relative services and an increasing demand for ubiquitous positioning with the best possible accuracy. On the other hand, location based services are expected to have a high economic impact in the near future [3]. Location information can add many potential applications to telecommunications systems, such as location-sensitive billing, fraud protection, person/asset tracking, fleet management, intelligent transportation systems, etc.In the literature, several positioning techniques are proposed. Global Positioning System (GPS) provides global coverage but specialized equipment is needed, which is not available to off-the-shelf wireless units. Thus, in the case of an emergency call, the network operator will fail to localize a user if she/he is not equipped with a GPS module. Also, certain S. A. Mitilineos (B) 18, Ydras str.,

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Evaluation of methods for determining the mobile traffic distribution in cellular radio networks

Cited by 11 publications

References 5 publications

Indoor Localisation With Wireless Sensor Networks

Indoor Localisation With Wireless Sensor Networks

Dynamic model-based filtering for mobile terminal location estimation

Blind Position Location via Geometric Loci Construction

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