Browsing by Author "Hu, Wan-Hsun"

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A Comparison of 4G Telecommunications Tariff Plans in Asia Countries
(2014)
Huang, Huai-Sheng
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Su, Tzu-Chung
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Wu, Jo-Ching
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Tsai, Wan-Du
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Tseng, Yu-Chee
Long Term Evolution (LTE) has been widely adopted by many 4G network operators. Many countries in America, Europe, and Asia have started LTE services since 2010. In the meantime, some countries (such as Taiwan and China) are in the process of transferring from 3G or Worldwide Interoperability for Microwave Access (WiMAX) to 4G services. In this paper, we investigate the tariff plans of the nine telecommunications operators which own top-three market shares in Singapore, Japan, and Hong Kong, respectively. Several issues, such as market share, tariff strategy, and even customs are discussed. Also, Tier-pricing strategies of LTE, such as Fair Use Policy (FUP), monthly data allowance, and data overage allowance, are discussed and compared. Moreover, we compare different charging plans and examine their price-performance ratios.
A study on Shared and non-Shared Billing for Mobile Service
(2015)
Wu, Jo-Ching
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Lin, Yi-Bing
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Lin, Yun-Wei
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Wang, Yung-Ching
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Lin, Bo-Ting
In this paper, we study on the price comparison between shared plan and non-shared plan for mobile service. A non-shared data plan is a traditional data plan for a single user with a limited monthly data allowance. A shared data plan allows a user to add different devices to one inclusive plan and receive one bill at the end of every month, which the monthly data allowance is shared by a group of users across multiple devices. Each data plan is charged by a fixed price for a monthly data allowance and there will be an extra charge if a user consumes more than the monthly data allowance. Therefore, the charged price is affected by the user\'s behaviors. We study the users\' behaviors by observing the measured data from Chunghwa Telecom and derive stochastic models for shared plan and non-shared plan to evaluate monthly prices based on different users\' behaviors. We recommend the most appropriate plan for a group of users based on users\' behaviors. On the other hand, mobile operators can attract more customers and earn more profit. We show that the shared plan is a \'win-win\' plan for users and operators.
A Tool for Defining Charging Models for M2M Communications
(2018-01-01)
Lin, Fuchun J.
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Tsai, Kun Lun
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Song, Shih-Ying
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Hsu, Wen-Cheng
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La, Yueh-Ting
The traditional charging models such as those based on amount of data transfer and number of short messages are no longer adequate for M2M communications. Consequently, new charging models based on newly identified charging factors have been proposed to address this issue. Nevertheless, due to the diversity and complexity of charging factors involved in M2M communications, an automatic tool is deemed necessary to assist defining appropriate charging models for a given type of M2M services. This paper reports the design and application of such a tool to ease the adoption of new charging models in M2M communications.
An Hourly Day-Ahead Paris Metro Pricing Scheme for Mobile Data Networks
(2015)
Huang, Huai-Sheng
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Lee, Po-Han
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Tseng, Yu-Chee
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Lin, Bo Ting
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Hu, Wan-Hsun
Since static pricing models (such as flat-rate or tiered-rate models) can not improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing (SDP) has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service (QoS) versus the prices that they are willing to pay. Even though FMB can not guarantee the actual QoS during service time, a balance between users\' utilities and operators\' revenue is achieved. In this paper, we propose a dynamic PMP scheme, so-called DPMP, which determines the prices and capacities of different classes for the next 24 hours. The prices should optimize the revenues and utilities for operators and subscribers, respectively. Our simulation results show that DPMP can better balance those two factors and determine the appropriate log period for operators.
Charging Architecture for M2M Communications
(2016-01-01)
Lin, Fuchun Joseph
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Chen, Bo-Yan
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Lin, Bo-Ting
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Hu, Wan-Hsun
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交大名義發表
Machine to Machine (M2M) communications or the Internet of Thing (IoT) is emerging as a new form of communications in our society. With such proliferation of the IoT/M2M, we have started to experience various smart services such as smart home, connected car, e-health, smart grid, etc. These IoT/M2M services are set to improve our life significantly. However, in IoT/M2M many things only exchange a small amount of data while generate a large amount of requests such as network connectivity requests, notification requests and storage requests. The traditional charging models such as those based on amount of data transfer are no longer adequate for M2M communications. As new charging factors and charging models are identified for M2M communications, a new charging architecture needs to be designed for M2M communications. This paper proposes such an M2M charging architecture that is designed to collect charging information for M2M communications based on the notions of charging factors and charging models.
Charging Models for M2M Communications
(2016)
Lin, Fuchun Joseph
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Tsai, Wandu
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Lin, Bo-Ting
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Hu, Wan-Hsun
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交大名義發表
The emerging of M2M communications enables many new applications such as smart grid, connected vehicle, eHealth and smart city. The telecom network is an ideal environment to support the implementation of these applications. However, the property of M2M communications is very different from that of traditional telecomm. Consequently, charging models designed for traditional telecomm such as pricing by amount of data transfer are no longer adequate for M2M communications. More general charging models are needed to accommodate new charging factors identified for M2M communications. With appropriate charging models, the amount of resources consumed by M2M communications can be assessed fairly and charged accordingly. This paper proposes general charging models for M2M communications based on two innovative ingredients: charging factors and pricing parameters.
Identification and Analysis of Charging Factors in M2M Communications
(2015)
Lin, Fuchun J.
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Tsai, Wandu
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Cerritos, Eduardo
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Lin, Bo-Ting
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Hu, Wan-Hsun
The widespread application of M2M communications is obvious. There are already many useful services enabled by M2M communications such as video surveillance, smart grid, smart home, connected vehicle, industry automation and smart city. These services are different from traditional Internet services such as e-mail, web browsing and video watching. As a result, we need to understand new charging factors beyond those defined in the past such as amount of data transfer, number of short messages and duration of voice communication. In this paper, we identify new charging factors for M2M communications and analyze their differences with traditional voice and Internet networks. Based on the result of our analysis, new charging models specific to M2M communications are proposed to enable smart data pricing that creates a win-win situation for operators and users.
Time-Dependent Smart Data Pricing Based on Machine Learning
(2017-01-01)
Tsai, Yi-Chia
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Cheng, Yu-Da
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Wu, Cheng-Wei
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Lai, Yueh-Ting
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Hu, Wan-Hsun
The purpose of time-dependent smart data pricing (abbreviated as TDP) is to relieve network congestion by offering network users different prices over varied periods. However, traditional TDP has not considered applying machine learning concepts in determining prices. In this paper, we propose a new framework for TDP based on machine learning concepts. We propose two different pricing algorithms, named TDP-TR (TDP based on Transition Rules) and TDP-KNN (TDP based on K-Nearest Neighbors). TDP-TR determines prices based on users' past willingness to pay given different prices, while TDP-KNN determines prices based on the similarity of users' past network usages. The main merit of TDP-TR is low computational cost, while that of TDP-KNN is low maintenance cost. Experimental results on simulated datasets show that the proposed algorithms have good performance and profitability.