LAUSR

With the explosion of smart devices and the gradual maturation of mobile systems, mobile crowdsensing (MCS) is playing more and more important roles in our daily life. In traditional MCS with a centralized framework, participants directly send perceived information to the task provider alone. This framework greatly increases the burden of cloud-based servers and cannot make full use of the increasing computation and storage capabilities of Internet of Things devices. To offload the computing and storage burden from traditional MCS architecture, a distributed MCS architecture was proposed in this paper, in which participants exchange sensing information with each other rather than forward it to central servers to complete a task together. Then, a diffusion Kalman filtering algorithm with quantized information exchange (QDKF) was proposed to solve the dynamic real-time estimate problem and limited communication resources in distributed MCS, where nodes exchange their quantized observations with neighbors to reduce the consumption of computing and storage resources. To prove the convergence and stability of the QDKF algorithm, an in-depth analysis of the algorithm uncertainty was reported to completely characterize the proposed solution. Moreover, the proposed algorithm achieves a superior performance by simulation.