Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

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 succes...

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Main Authors: Stefano Bennati, Ivana Dusparic, Rhythima Shinde, Catholijn M. Jonker
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/18/11/3707
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spelling doaj-841b0baed5094d768c225e4d5c514c8c2020-11-24T21:46:38ZengMDPI AGSensors1424-82202018-10-011811370710.3390/s18113707s18113707Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered MeasuresStefano Bennati0Ivana Dusparic1Rhythima Shinde2Catholijn M. Jonker3Chair of Computational Social Science, ETH Zürich, Clausiusstrasse 50, 8092 Zürich, SwitzerlandSchool of Computer Science and Statistics, Trinity College Dublin, Dublin 2, IrelandInteractive Intelligence Group, TU Delft, Mekelweg 4, 2628 Delft, The NetherlandsInteractive Intelligence Group, TU Delft, Mekelweg 4, 2628 Delft, The NetherlandsProvision 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.https://www.mdpi.com/1424-8220/18/11/3707participatory sensingsmart citiespublic goodprivacyfairness
collection DOAJ
language English
format Article
sources DOAJ
author Stefano Bennati
Ivana Dusparic
Rhythima Shinde
Catholijn M. Jonker
spellingShingle Stefano Bennati
Ivana Dusparic
Rhythima Shinde
Catholijn M. Jonker
Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
Sensors
participatory sensing
smart cities
public good
privacy
fairness
author_facet Stefano Bennati
Ivana Dusparic
Rhythima Shinde
Catholijn M. Jonker
author_sort Stefano Bennati
title Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
title_short Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
title_full Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
title_fullStr Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
title_full_unstemmed Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
title_sort volunteers in the smart city: comparison of contribution strategies on human-centered measures
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-10-01
description 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.
topic participatory sensing
smart cities
public good
privacy
fairness
url https://www.mdpi.com/1424-8220/18/11/3707
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