The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

Ping Wang - One of the best experts on this subject based on the ideXlab platform.

  • Privacy management and optimal pricing in people centric sensing
    arXiv: Computer Science and Game Theory, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han
    Abstract:

    With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

  • Privacy Management and Optimal Pricing in People-Centric Sensing
    IEEE Journal on Selected Areas in Communications, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang
    Abstract:

    With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Mohammad Abu Alsheikh - One of the best experts on this subject based on the ideXlab platform.

  • Privacy management and optimal pricing in people centric sensing
    arXiv: Computer Science and Game Theory, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han
    Abstract:

    With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

  • Privacy Management and Optimal Pricing in People-Centric Sensing
    IEEE Journal on Selected Areas in Communications, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang
    Abstract:

    With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Derek Leong - One of the best experts on this subject based on the ideXlab platform.

  • Privacy management and optimal pricing in people centric sensing
    arXiv: Computer Science and Game Theory, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han
    Abstract:

    With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

  • Privacy Management and Optimal Pricing in People-Centric Sensing
    IEEE Journal on Selected Areas in Communications, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang
    Abstract:

    With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Dusit Niyato - One of the best experts on this subject based on the ideXlab platform.

  • Privacy management and optimal pricing in people centric sensing
    arXiv: Computer Science and Game Theory, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han
    Abstract:

    With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

  • Privacy Management and Optimal Pricing in People-Centric Sensing
    IEEE Journal on Selected Areas in Communications, 2017
    Co-Authors: Mohammad Abu Alsheikh, Dusit Niyato, Derek Leong, Ping Wang
    Abstract:

    With the emerging sensing technologies, such as mobile crowdsensing and Internet of Things, people-centric data can be efficiently collected and used for analytics and optimization purposes. These data are typically required to develop and render people-centric services. In this paper, we address the Privacy Implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and Privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal Privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared with the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.

Xia Yang - One of the best experts on this subject based on the ideXlab platform.