Classification of Cellular Phone Mobility using Naive Bayes Model

Puntumapon, Kamthorn; Pattara-Atikom, Wasan

doi:10.1109/vetecs.2008.324

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…For further communication, at the initial stage, to set up a route, the initialization of the cellular network took place. After the set up of the route, the optimal routing algorithm [8] is carried out. Subsequently, the selection of optimal route, a dynamic mode switching model [7] is used for the initialization of communication.…”

Section: System Modelmentioning

confidence: 99%

Mobility Management and Mode Switching using Support Vector Machine

Jagtap¹

2021

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In the communication fields, the service provides by the cellular networks received huge interest among researchers. Hence, mobile industries handle significant technological rivalry as regard service quality. On the basis of superiority, consistency, and a rapid service the quality, is determined, and finally, it is distributed to the user. Hence, one of the major factors here is mobility management because it is coping with the crucial information in order to manage the user's mobility. Nevertheless, because of the increase of linked devices, the users establish heavily motivates the researchers to formulate the new mode switching technique. Therefore, this work formulates a new mode switching technique by exploiting the Support Vector Machine (SVM). Based on numerous parameters quality such as bandwidth, link consumption, delay, energy utilization and signal strength, the mode switching depends. The parameter quality should maintain during the network switches communication connect to user mode from the cellular mode. A model for mobility management is formulated to enhance network performance in that user mobility is calculated. Hence, the adopted technique is important to aid the enhanced user mobility at the time of communication. By exploiting the adopted SVM model attains better performance with minimum delay, maximum power, link utilization ratio, and throughput correspondingly.

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Section: System Modelmentioning

confidence: 99%

Mobility Management and Mode Switching using Support Vector Machine

Jagtap¹

2021

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“…At the time of writing the product is available on the market. In [8] a software installed on volunteers' mobile phones is used for collecting Cellular Dwell Time (CDT). The latter is then used to characterize walking users and sky trains.…”

Section: A Active Techniquesmentioning

confidence: 99%

“…need to be identified and excluded from the processing. In [8] a model for differentiating walking users from sky train passengers is proposed by statistically analyzing the user permanence in a cell. Experimental results showed promising performance with accuracy up to 93%.…”

Section: A Single Out Road Usersmentioning

confidence: 99%

Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap

Valerio

D’Alconzo

Ricciato

et al. 2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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In this contribution we address the problem of using cellular network signaling for inferring real-time road traffic information. We survey and categorize the approaches that have been proposed in the literature for a cellular-based road monitoring system and identify advantages and limitations. We outline a unified framework that encompasses UMTS and GPRS data collection in addition to GSM, and prospectively combines passive and active monitoring techniques. We identify the main research challenges that must be faced in designing and implementing such an intelligent road traffic estimation system via third-generation cellular networks.

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“…The amount of data is unstable due to the limitation of districts and predefined routes for the fleets or taxis. With the popularization of mobile network and for the sake of conquering the difficulties occurred in the previous two methods, Cellular-Based Vehicle Probe (CVP) emerges [1], [2], [3], [4], [5]. The comparisons for various techniques are summarized as follows [6].…”

Section: Introductionmentioning

confidence: 99%

“…When a mobile equipment has a ongoing voice call and moves from a cell to the other one, the base station subsystem (BSS)/Universal Terrestrial Radio Access Network (UTRAN) will automatically report a new serving cell to the core network for it, which is known as a handover procedure. Using the two Inter-VLR LAU events with various LAC borders constitutes the algorithm of freeways for a longer distance, and the average speed is defined as the distance of two Inter-VLR LAU events with various LAC borders divided by the time difference of them [1], [2], [3], [4]. For a shorter distance, two handover events shall be adopted, and the average speed is defined as the distance of two handover events divided by the time difference of them [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A novel traffic information estimation method based on mobile network signaling

Kao

Tsai

2014

The 16th Asia-Pacific Network Operations and Management Symposium

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Two commonly used methods for traffic information rely on Vehicle Detector (VD) and Global Positioning SystemBased Vehicle Probe (GVP); however, they have some confinements, such as high cost for construction and maintenance, and limited coverage. For the sake of overcoming dilemmas happened on VD and GVP, Cellular-Based Vehicle Probe (CVP) comes into being. However, the current applications for CVP mainly focus on arteries or freeways, where traffic information for longer distance is derived from two Inter-Visitor Location Register Location Area Update (Inter-VLR LAU) events with various Location Area Code (LAC) borders, and the one for shorter distance is from two consecutive handover events. The perplexity of available CVP techniques comes about is that there are no two Inter-VLR LAU events with various LAC borders and few handover events in scenic spots. In order to expand the applications for CVP to scenic spots, a cost-effective and flexible method utilizing mobile network signalling called Enhanced CVP (ECVP) is proposed. The key concept for ECVP is that we adopt Inter-VLR LAU events at the origin and all kinds of communication events at the destination to retrieve traffic information. The inaccuracy of ECVP consists inthe uncertainty of event occurred time at the destination. Therefore, with a view to acquiring more accurate traffic information, three novel Reinforced CVP (RCVP) algorithms, inclusive of Fixed r percent samples CVP (F-RCVP), Dynamic r percent samples (D-RCVP), and Dynamic r percent samples with Discarding former samples (DD-RCVP), are presented. Numerical results show that F-RCVP is suitable for scenic spots that the LAC border only contain samples resulting from cars. By contrast, if the samples consist of both cars and motorcycles, it is recommended that D-RCVP and DD-RCVP are introduced.

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Classification of Cellular Phone Mobility using Naive Bayes Model

Cited by 20 publications

References 8 publications

Mobility Management and Mode Switching using Support Vector Machine

Mobility Management and Mode Switching using Support Vector Machine

Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap

A novel traffic information estimation method based on mobile network signaling

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