Optimal incentive strategy in blockchain-based mobile crowdsensing using game theory

Abstract

Mobile crowdsensing (MCS) is a cutting-edge technology that leverages the computing power of mobile devices to gather environmental data, efficiently utilizing underutilized resources to complete sensing tasks. However, the lack of initiative and motivation among participants in existing MCS systems is a crucial factor limiting its development. As a result, examining the incentive mechanism in MCS becomes essential for attracting a greater number of participants to join. In addition, existing MCS systems typically employ a centralized platform, but this structure poses security issues, as the platform can manipulate MCS operations to gain inappropriate profits and is prone to single point of failures. In response to the above-mentioned issues, this paper proposes a novel blockchain-based MCS system, referred to as CrowdBS. Firstly, the system adopts a decentralized structure and distributed consensus methods to enhance the reliability and security of the system. Additionally, the use of smart contracts allows the system to automatically execute predefined operations without the need for human intervention. In MCS, this means that it can automatically trigger operations such as data collection, reward distribution, task assignment, etc., thereby improving efficiency and reducing the risk of human intervention. Secondly, the system introduces a two-stage Stackelberg game model, attracting more sensing users to participate in tasks through reasonable incentive mechanisms and ensuring fairness in reward distribution, thereby promoting efficient, reliable, and sustainable execution of crowdsensing tasks. Finally, the correctness of the model is verified through simulation experiments, and compared with the existing MCS system. The comparison results show that the system and its incentive mechanism have more comprehensive advantages in terms of reliability, security and performance.