Multi-agent mobile crowdsensing by pervasive machines: a robust task allocation approach

Mobile crowd sensing (MCS) is an attractive and innovation paradigm in which a crowd of users equipped with smart mobile devices conduct sensing tasks by fully exploiting their carried diverse embedded sensors. With the development of robots and Artificial Intelligence, many MCS studies with pervasive machines (Yu et al. Commun ACM 64:76–80, 2021) (e.g., unmanned vehicles, drones, etc.) as participants have emerged in recent years. Compared to human participants, robot participants have the advantages of being able to perform dangerous and boring tasks, being highly controlled, and not requiring complex incentive mechanisms. However, participants in previous studies usually have only one type of robot, and the use of heterogeneous robots for collaborative sensing was not considered. Second, previous studies have not considered the vulnerability of robots in realistic environments. In this paper, a multi-agent mobile crowdsensing (MA-MCS) system consisting of multiple heterogeneous robots is proposed to address the above two problems, and the task allocation problem of this system is investigated. To enable the robots to overcome the complex real-world environment, this paper proposes the concept of sense area information map (SAIM) and a self-repairing task allocation algorithm based on the information map. The SAIM can reflect the performance of different robots performing sense tasks in different locations and at different times, and provide guidance for task allocation. The self-repairing task assignment algorithm can be used to repair and reassign tasks after the robot has encountered abnormal situations. Through experiments, it is demonstrated that the SAIM and the map-based self-repairing task allocation algorithm can effectively improve task coverage at the expense of certain energy consumption.