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

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.

Devising Digital Twins DNA Paradigm for Modeling ISO-Based City Services

“…In our analysis of the literature, humans are identified as one of the influencing factors [ 10 , 27 ]–citizens who are the targets of services, and stakeholders and authorities who regulate services–consistent with our entity-inclusive requirement outlined in Section 1 , in terms of human inclusion. Indeed, many studies emphasize the role of the human factor in smart cities such as citizens’ contribution in the provision of services through providing data [ 10 ], and citizens’ engagement in the validation of the provided services [ 27 ]. In terms of entities other than human, the Internet of Things (IoT) is the backbone of smart cities according to [ 28 ], hence all its four components: the things, the local area network, the internet and the cloud are considered to be influencing factors on the smart cities and their services.…”

“…The T base in the proposed DT-DNA model represents different tasks included in the provided data to represent the service required to model its DT-DNA. Since we adopted ISO 37120 [ 10 ], different indicators under each city service represent the tasks required to model the DT-DNA of each service, and to evaluate its status. Using the ISO 37120 standard indicators, our proposed code format (depicted below) requires a core (C) or supportive (S) indicator type, followed by a service initial, followed by an indicator’s clause initials.…”

“…In our analysis of the literature, humans are identified as one of the influencing factors [10,27]-citizens who are the targets of services, and stakeholders and authorities who regulate services-consistent with our entity-inclusive requirement outlined in Section 1, in terms of human inclusion. Indeed, many studies emphasize the role of the human factor in smart cities such as citizens' contribution in the provision of services through providing data [10], and citizens' engagement in the validation of the provided services [27].…”

“…However, there is a gap in the literature: currently no unified model for city services has been proposed that can guarantee interoperability across cities, capture each city’s unique characteristics, and act as a base for modeling digital twins. A general, standardized model such as this would need to meet the following requirements: Unified and unique , accounting for all city services while protecting the unique identity of each city and providing the means to model special services (being unified implies that the model also needs to be standardized to guarantee interoperability); Entity-inclusive, taking into account human such as citizens, stakeholders and regulatory authorities-due to the central role of users contribution in the provision of smart city services [ 10 ], in addition to other entities such as IoT entities early on in the analysis and design stages, to identify correlations between them and city services based on the expanded definition of DTs that incorporates living and non-living physical entities into the definition of DTs [ 7 ]; Contextualized and customizable , tracking the dynamic state of the city by collecting geo-temporal data as well as emergent data from interactions between citizens and city services; Visualizable , providing visual representations of results and analysis to citizens, stakeholders and regulatory authorities who care about the performance and sustainability of city services and the enhancement of those services for improved quality of life. …”

“…Entity-inclusive, taking into account human such as citizens, stakeholders and regulatory authorities-due to the central role of users contribution in the provision of smart city services [ 10 ], in addition to other entities such as IoT entities early on in the analysis and design stages, to identify correlations between them and city services based on the expanded definition of DTs that incorporates living and non-living physical entities into the definition of DTs [ 7 ];…”