Multi-objective Optimization Model for Service Node Selection Based on Trade-off between Quality of Service and Resource Consumption in Mobile Crowd Sensing

As mobile crowd sensing (MCS) cannot provide reliable services, the quality of service (QoS) is a major research interest. Since the service node makes a decisive impact on two vital factors of QoS, service node selection is becoming a novel research direction in MCS. In this paper, we analyze the factors that need attention when selecting proper service nodes in MCS and define the service node selection problem (SNSP) as follows: finding the optimal set of service nodes, provided that optimizes multiple metrics of QoS simultaneously and satisfies the network resource constraint. Accordingly, we formulate a multiobjective optimization model (MOOM), which converts SNSP to a multiobjective optimization problem (MOOP). Since the MOOM considers the comprehensive effect of all service nodes on one metric as one objective of MOOP, it can handle the diversity of metrics and conflicts between them; in particular, it can flexibly change the metric system of QoS depending on different demands. To demonstrate the value and effectiveness of the proposed MOOM, we propose a paradigm of it and design a corresponding multiobjective optimization selection mechanism. This paradigm focuses on the influence of node spatiotemporal mobility on both data collection and data transmission. Extensive experiments and comparison on a real-world data show that MOOM is an effective model for selecting service nodes with both good coverage and transmission performances.