A Generic Participatory Sensing Framework for Multi-modal Datasets

Abstract—Participatory sensing has become a promising data collection approach to crowdsourcing data from multi-modal data sources. This paper proposes a generic participatory sensing framework that consists of a set of well-defined modules in support of diverse use cases. This framework incorporates a concept of “human-as-a-sensor ” into participatory sensing and allows the public crowd to contribute human observations as well as sensor measurements from their mobile devices. We specifically address two issues: incentive and extensibility, where the former refers to motivating participants to contribute high-quality data while the latter refers to accommodating heterogeneous and uncertain data sources. To address the incentive issue, we design an incentive engine to attract high-quality contributed data independent of data modalities. This engine works together with a novel social network that we introduce into participatory sensing, where participants are linked together and interact with each other based on data quality and quantity they have contributed. To address the extensibility issue, the proposed framework embodies application-agnostic design and provides an interface to external datasets. To demonstrate and verify this framework, we have developed a prototype mobile application called imReporter, which crowdsources hybrid (image-text) reports from participants in an urban city, and incorporates an external dataset from a public data mall. A pilot study was also carried out with 15 participants for 3 consecutive weeks, and the result confirms that our proposed framework fulfills its design goals