P-SUS: Parallel Execution of Sensing Unit Selection for Mobile Crowd Sensing in an Urban Road Network

Abstract

Faced with time-varying traffic state, it is a huge challenge to select the applicable sensing units to ensure the quality of service (QoS) of the mobile crowd sensing in an urban road network (MCS-URN) system. To address the problem, this paper proposes a novel framework for parallel execution of sensing unit selection based on the artificial system, computational experiment, and parallel execution (ACP) approach. The framework is composed of artificial and physical MCS-URN systems and the former is designed to process the data collected from the latter. Through the data completion, traffic state prediction, simulation and evaluation, the optimal unit selection method will be selected from the arithmetic library and recommended to physical MCS-URN system to adapt the time-varying traffic state. Empirical study proves that the proposed framework can effectively improve the QoS of MCS-URN system when traffic state changes.