# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 6 Conclusions and Future Work  
The goal of this paper is to provide a tool for understanding user participation in established smart city services. In order to do this, we first proposed a scenario-independent design principle based on voluntary user contribution and a simulation framework for smart city applications. The applicability of this framework was verified using real-world data from the two application scenarios of traffic congestion monitoring and electric vehicle charging. Secondly, we used this framework to measure the effects, along various dimensions, of citizens' participation. Voluntary contributions empower users to control the ownership of their resource, e.g., by contributing data towards a service, independently of the type of resource and its use. Different contribution strategies produce trade-offs along measures such as efficiency, privacy and fairness, which we quantified in both implemented scenarios. Results suggest that such trade-offs depend on characteristics of the algorithms and not on characteristics of the scenarios. Therefore, they can be used as implementation recommendations to service providers and system designers about the choice of contribution strategies, regardless of the specific smart city domain. Specifically, centralized optimization is found to offer the highest efficiency of contributions and to guarantee the success of the application. It is hence recommendable for mission-critical services that require high availability, e.g., load management on a smart grid. Localized strategies are, on the other hand, less efficient and might cause the system to fail with low probability, but compensate for this by improving on privacy and fairness of contribution. Localized strategies are hence more suitable for privacy-sensitive applications that require user adoption, such as participatory sensing, or applications where fairness in redistribution is crucial, e.g., charging of electric vehicles. Modeling of scenarios with different cost and value characteristics is left to future work--for example, \"negative\" contribution in traffic congestion, where users contribute to the public good by choosing a longer route over the shortest but congested route [69]. Incentive mechanisms help increase user participation [70] but require quantifying privacy [24], which could also be addressed in future work. The current work considers only localized algorithms, i.e., it assumed no communication between user devices. Relaxing this limitation is a worthy avenue for future work. Communication between user devices would introduce privacy concerns due to data exchange but would allow analysis of new classes of algorithms, such as decentralized optimization. Finally, it would be beneficial to verify the proposed design principle based on public good theory in other smart city domains, in order to further evaluate its generality and suggest as a complete blueprint for smart city applications. Other potential application areas could, for example, include pollution or noise monitoring and real-time public transport information, or any other similar data-rich application relying on user contributions in which system performance, fairness and user privacy are of concern. The authors contributed to the research as follows: [PERSON], [PERSON] and [PERSON] contributed to the conceptualization and methodology, [PERSON] and [PERSON] contributed to software creation and validation. [PERSON] and [PERSON] contributed to data curation and visualization, [PERSON], [PERSON] and [PERSON] contributed to writing the manuscript. [PERSON] acknowledges support by the European Commission through the ERC Advanced Investigator Grant 'Momentum' (Grant No. 324247). The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 17: Dependence of results on the range of values and costs (\(x\)-axis).