Service node selection optimization for mobile crowd sensing in a road network environment

Abstract

Mobile crowd sensing (MCS) can be an effective method for urban traffic sensing applications by collecting data in urban road networks through ubiquitous sensor-mounted vehicles. However, due to the limited network resources and the randomness of automobiles, the quality of service (QoS) of MCS cannot be effectively guaranteed. Some related works noted that optimizing the selection of service nodes can effectively improve the QoS of MCS. However, existing node selection methods are unsuitable for MCS in an urban road network (MCS-URN). An MCS-URN is a unique MCS environment in which the service nodes are vehicles, and the sensing area is road segments. In this paper, we focus on improving the QoS in an MCS-URN by optimizing the selection of service nodes with limited network resources. First, the utility function of the QoS in MCS-URN is proposed based on the coverage and the data score. Then the service node optimization model in the MCS-URN is presented, by selecting an appropriate set of service nodes within the maximum proportion of the total network resources to maximize the utility value of the QoS. Also, an innovative service node selection method which considering the mobility of automobiles and the topological structure of urban road networks is introduced. In the end, a simulation study is carried out to evaluate the service node optimization model in the MCS-URN. The simulation results show that our service node optimization model can effectively improve the QoS of the MCS-URN.