Understanding Multiple Features with Hypercube for Distinguishing Uncertain Objects in Mobile Crowdsensing

Uncertain data are inherent in mobile crowd sensing applications, and the objects that they correspond to are usually vaguely specified. In order to improve performance, we often increase the number of features. However, the more features are used, the more redundancy and cost are involved correspondingly. Therefore, the number of features we selected for a specified application is a tradeoffs between the accuracy and the cost. In this paper, we model such tradeoffs between accuracy and cost as an optimization problem. Moreover, for investigating this problem, we propose to model the sensing with multiple features under a hypercube structure. In our scheme, each feature of uncertain objects is represented as a component of the vertex's coordinate in hypercube. At the same time, we prefer to define the edges between vertices with relative entropy rather than Euclidean distance. Because the former one could accurately measures the difference between two probability distributions of data. We evaluate our proposed schemes with real data of a crowd sensing recognition case, which are collected by smartphones with sensors.