Cooperative Stackelberg game based optimal allocation and pricing mechanism in crowdsensing. (2018) - Liu, Chunchi; Du, Rong; Wang, Shengling; Bie, Rongfang
Crowdsensing has been earning increasing credits for effectively integrating the mass sensors to achieve significant tasks that one single sensor cannot imagine. However in many existing works in this field, some key information of the participants is incomplete to each other, hence causing the non-optimality result. Noticing that a potential cooperation between the players, we propose the cooperative Stackelberg game based optimal task allocation and pricing mechanism in a crowdsensing scenario. Aiming at different optimising criteria, we propose two optimal Stackelberg games that are either with no budget constraint (No-Budget OpSt Game) or with budget constraint (Budget-Feasible OpSt Game). Analysis of their corresponding Stackelberg Equilibrium is then presented. Lastly, we perform extensive simulations to test the impact of the parameters on our model. Results of our two proposed games are progressively compared to show their optimisations in their respective criteria.