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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
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Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

机译：智慧城市中的志愿者：以人为本措施的贡献策略比较

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摘要

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.
机译：提供智慧城市服务通常依赖于用户的贡献，例如数据的贡献，这对于用户而言在隐私方面可能是昂贵的。应最大程度地降低隐私风险以及对用户的不公平分配，因为它们会破坏用户的参与，这对于智慧城市应用的成功至关重要。本文通过基于真实数据（即智能电表读数和参与式感应）的计算机模拟研究智能城市应用程序中的隐私，公平和社会福利。通过提出一种适用于跨应用场景的设计原则，在服务的提供取决于用户的贡献的情况下，我们将公共物品理论作为智能城市应用中资源管理的模型进行了概括。我们通过显示其在两种情况下的实现来验证其适用性：智能电网和交通拥堵信息系统。根据这一设计原则，我们以人为中心的措施（即隐私，公平和社会福利）评估了不同类别的资源管理算法，并确定了与方案无关的特定方案。这些结果对于智能城市应用程序设计人员可能是有意义的，它们可以根据给定特定场景的一组需求来选择合适的算法，并且对于用户基于匹配其隐私首选项的算法来选择服务。

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