Incentivizing for Truth Discovery in Edgeassisted Largescale Mobile Crowdsensing The recent development of humancarried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edgeassisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

Unknown Worker Recruitment with Budget and Covering Constraints for Mobile Crowdsensing Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multiarmed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.