# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
The first contribution of this paper is to introduce a design principle that enables such a comparison by means of a scenario-independent model of user contributions to a smart city application, which is based on the theory of public goods and voluntary contribution games [8]. This principle is based on the concept of contribution strategy: the orchestration of demand and supply of some common resource, i.e., the smart city service, is driven by independent contributions of individuals in a population. This approach is independent of the characteristics of the resource; hence it can be combined with existing mechanisms that work at the content level, e.g., privacy-preserving algorithms. This also makes data sharing across multiple smart city applications easier, thanks to the uniform way of dealing with different kinds of data. Following this design principle, a simulation framework is developed (available on Github at [9]) with the goal of understanding user participation in smart city services by addressing the research question: how do different contribution strategies compare in terms of privacy, fairness and social welfare? The second contribution of this paper is to verify the applicability of the design principle in two different examples of smart city application scenarios, i.e., traffic congestion information and charging of electric vehicles, using real-world datasets. Similar validation in different scenarios can provide useful insights for the design of other resource-based smart city applications. The third contribution is to compare different contribution strategies--centralized optimization and two flavors of localized learning, with and without contextual awareness--in terms of trade-offs between system efficiency, user privacy or resource distribution fairness, and to identify scenario-independent trade-offs that characterize a specific algorithm. Specifically, localized solutions, which work only on local data, are found to deliver higher privacy and equality than a centralized solution. Centralized optimization offers instead higher efficiency thanks to the global knowledge of data. Localized algorithms with and without contextual knowledge are evaluated, and those considering the current context in the decision-making are found to deliver higher fairness. This work can be of interest to designers of smart city applications and services that look for a guideline for the choice of algorithm, given specific scenario priorities in terms of different measures. Another target audience is that of citizens that are concerned with the risk of abuse of their data, and particularly privacy-aware prosumers. The results help quantify different service implementations along measures such as privacy and fairness; thus help the citizens choose a service provider that best matches their preferences, reducing the concerns about the system and fostering user participation. The rest of the paper is organized as follows: Section 2 introduces the proposed design principle and describes how it can be applied to two smart city scenarios, Section 3 describes the contribution strategies and the measures used to evaluate them, Section 4 describes and discusses the main results obtained from simulating voluntary contributions in different scenarios, Section 5 situates the work in the literature, and Section 6 concludes the paper discussing possible avenues for future work and giving general recommendations about the choice of contribution strategies.