META-MCS: A Meta-knowledge Based Multiple Data Inference Framework

Zijie Tian, En Wang, Wenbin Liu, Baoju Li, Funing Yang

Abstract: Mobile crowdsensing (MCS) is a paradigm for data collection with the limitation of budgets and worker availability. The central strategy of MCS is recruiting workers to sense a part of data and subsequently infer the unsensed data. To infer unsensed data, prior research has proposed several algorithms that do not require historical data, but their inference accuracy is very limited. More effective works are training a model with sufficient historical data. However, such methods can’t infer data with few to none historical data. A more promising strategy is training models from other similar datasets that have been sensed. However, such datasets are different in terms of sensing locations, numbers of sensed data and data types. Such variance introduces the complex issue of integrating knowledge from these datasets and then training inference models. To solve these, we propose a meta-knowledge based multiple data inference framework named META-MCS. In META-MCS, we propose a similarity evaluation model TMFS. Following this, we cluster similar datasets and train generalized models for each cluster. Finally, META-MCS selects an appropriate model to infer unsensed data. We validate our proposed methods through extensive experiments using ten different datasets, which substantiate the effectiveness of our framework.