Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

With the rapid development of Industry 5.0 and mobile devices, the research of mobile crowdsensing networks has become an important research focus. Task allocation is an important research content that can inspire crowd workers to participate in crowd tasks and provide truthful sensed data in mobile crowdsourcing systems. However, how to inspire crowd workers to participate in crowd tasks and provide truthful sensed data still has many challenges. In this article, based on the Markov model and collaborative filtering model, the similarities, trajectory prediction, dwell time, and trust degree are considered to propose the Markov and Collaborative filtering-based Task Recommendation (MCTR) model. Then, based on the Walrasian equilibrium, the optimum solution is researched to maximize the social welfare of mobile crowdsourcing systems. Finally, the comparison experiments are carried out to evaluate the performance of the proposed multiobjective optimization and the Markov-based task allocation with other methods. Through comparison experiments, the efficiency and adaptation of mobile crowdsourcing systems could be improved by the proposed task allocation.

As a kind of point-to-point distributed public ledger technology, blockchain has been widely concerned in recent years. The privacy protection of blockchain technology has always been the core issue of people’s attention. In this paper, some existing solutions to the current problems of user identity and transaction privacy protection are surveyed, including coin mixing mechanism, zero knowledge proof, ring signature and other technologies. Secondly, five typical applications of privacy protection technology based on blockchain are proposed and analyzed, which are mainly divided into technology applications based on coin mixing protocol, encryption protocol, secure channel protocol and so on. Finally, in view of the shortages of the existing blockchain privacy protection technology, we explore future research challenges that need to be studied in order to preserve privacy in blockchain system, and looks forward to the future development direction.