Volunteers in the Smart City

Comparison of Contribution Strategies on Human-Centered Measures

Journal Article (2018)
Author(s)

S. Bennati (ETH Zürich)

Ivana Dusparic (Trinity College Dublin)

R. Shinde (Student TU Delft)

C.M. Jonker (Universiteit Leiden, TU Delft - Interactive Intelligence)

Research Group
Interactive Intelligence
Copyright
© 2018 S. Bennati, Ivana Dusparic, R. Shinde, C.M. Jonker
DOI related publication
https://doi.org/10.3390/s18113707
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 S. Bennati, Ivana Dusparic, R. Shinde, C.M. Jonker
Research Group
Interactive Intelligence
Issue number
11
Volume number
18
Pages (from-to)
1-22
Reuse Rights

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Abstract

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.