# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 5 Proposed Solution
### Crowdsensing Model  
For georeferenced serious games where a set of challenges are distributed on the map, the game design for achieving optimal distribution of players to complete the tasks can be defined as a dispatch optimization problem combined with a reward-based incentive  
Figure 7: Raft algorithm. mechanism. The tasks involve issue reporting in the context of urban water and can be extended for many use cases. The dispatch optimization problem can be formulated as an application of the Vehicle Routing Problem (VRP) as shown in Figure 8, where the players, i.e., \((P_{1}-P_{3})\) are modeled as vehicles and tasks, i.e., \((T_{1}-T_{10})\), are modeled as customers, with the objective of cost optimization (e.g., distance traveled) under specific constraints, as described in Section 4.1. Therefore, geolocation and other constraints can serve as a filter for dispatching tasks to players to achieve a cost-effective routing strategy for increased platform utility in terms of truthful reporting and quality of sensing data. However, to achieve an overall platform utility, the reward-driven incentive must be counterbalanced by a regulation strategy. The reputation score is the main currency in the game and must be defined in order to achieve optimal incentives for crowdsensing tasks while reducing the risk of overreporting or false reporting. In this sense, the VRP can be defined with the capacity constraints for each player according to the reputation. At each iteration of the optimization algorithm, the capacities are updated. In the case of truthful reporting, the capacity is increased according to the reward, which allows for routing towards the next tasks. We considered three types of players: 100% match (truthful and accurate reporting), 80% match (truthful and mostly accurate reporting) and 50% match (false/inaccurate reporting). To evaluate the model and different types of behavior, we run the optimization problem on the entire range of distributions in terms of geographical coordinates of the players. We considered fixed coordinates for the tasks and variable starting coordinates for players to evaluate the average cost of the system (total distance traveled) for each player  
Figure 8: Crowdsensing model with Vehicle Routing Problem (VRP) solution. behavior and therefore to assess the platform utility for encouraging truthful participation. The results are presented in Section 7.1. To translate the problem formulation into VRP constraints, we considered the following: (1) player reputation is represented by vehicle capacity; (2) player effort is represented by vehicle fuel level; (3) task priority is represented by customer demand. Therefore, an effective routing strategy that stimulates truthful reporting through reputation incentives accounts for the reputation of players that report the issue as well as players that validate the issue, allowing for an overall regulation of the system towards improved platform utility. The proposed framework allows for the evaluation of reward strategies based on the estimated level of trust/reputation of players. The rewards can be defined based on the required capacities that satisfy a given distribution of issues. To achieve the optimal solution in terms of the assignment of the most truthful players to a higher number of priority issues, a balance must be achieved between the relative priorities and the reputation of players. An optimization problem can be defined when comparing the results for each player type with the relative task priority adjusted by a reward factor to achieve an optimal allocation of tasks. The VCG auction model can be used to provide incentives according to the player type and estimated cost. Therefore, the players submit their bids according to the expected cost for each task and their own reputation level. The algorithm provides the optimal assignments and rewards according to the auction model, which incentivizes truthful bidding.