# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 2 Related Work
### Mobile Crowdsensing  
Crowdsensing networks are an important part of the CPSS paradigm. In [2], a systematic literature review reveals an overview of search terms in Web of Science related to crowdsensing. Existing studies in MCS focus on either the global approach or specific problems, based on theoretical models (game theory, information theory), applied sciences (data science,computational intelligence) and engineering (system architectures, mobile technologies, communication systems). In [49], a review of technical approaches and applications in smartphone-based crowdsensing solutions precedes the proposed reference architecture that can be adapted to the various MCS architecture designs with the major components defined for client-side, server-side and the communications level. Besides the technical complexity of MCS architectures, sustainable citizen involvement in MCS is difficult to achieve when considering sensing costs (battery, mobile data, time, effort) and long-term commitment with indirect benefits for participants. In this sense, the Vehicle Routing Problem (VRP) is a highly studied optimization problem that can be applied for the allocation of tasks in an MCS solution that involves the optimization of sensing costs for smart urban mobility [50]. Transportation and traffic management are some of the domains where vehicle routing is a primary objective, while MCS proves to be essential for real-world applications [51]. While cost-efficient allocation of tasks is important for the overall system, an incentive mechanism should be considered for increasing the participation of citizens in solving large-scale problems at the community level [5]. For this reason, there are many research papers addressing the problem of optimal incentives to achieve higher levels of participation [52]. Game theory was proposed in 1928 by [PERSON], while incentive schemes include reputation-based schemes that promote cooperation, such as PETrust (penalty-incentive mechanism); Peer-to-Peer (P2P) networks, where trust plays an important role; and credit-based schemes, which promote cooperation by rewarding participants for helping others [53]. [PERSON] and [PERSON] [54] define an optimization model for exogenous rewards and punishments in the context of a \"regime change\" game, considering the overall equilibrium, optimal effort and optimal reward schemes. The reward scheme can be determined based on the level of optimism for optimal participation of citizens. While offering higher rewards would induce a higher level of participation from otherwise less involved citizens, the level of participation of otherwise highly involved citizens would decrease. The optimal reward scheme requires adapting to the level of optimism of each citizen to achieve equilibrium. Auction-based incentives are widely used in the context of MCS, requiring an allocation rule and a payment rule for sensing tasks, such as the Vickrey-Clarke-Groves (VCG) auction model. For achieving the overall utility of the platform, trust and reputation can be used as a reward scheme, based on the number of true and false reports for each participant. Considering a user-centric approach, task assignment in MCS relies on the reported sensing costs, while VCG-based auction is used to achieve truthful reporting. The proposed solutions are commonly evaluated in terms of sensing coverage, incentive cost and truthfulness using given scenarios [55]. In this paper, we aim to provide a location-independent evaluation of VCG for truthful participant selection under different initial conditions in terms of geolocation. VCG and proposed alternatives were used by [PERSON] and [PERSON] [56] in MCS for efficient path planning and increasing the level of truthful participation. While the experimental setup is evaluated for a single distribution of tasks, in the current paper, we propose a more comprehensive simulation to evaluate the overall performance in real-world scenarios.