Tackling the redundancy and sparsity in crowd sensing applications

Driven by the proliferation of sensor-rich mobile devices, crowd sensing has emerged as a new paradigm of gather-ing information about the physical world. In crowd sens-ing applications, user observations are usually unevenly dis-tributed across the monitored entities, and this gives rise to two major challenges-redundancy and sparsity. On one hand, multiple users may observe the same entity, and their observations are sometimes conicting with each other due to the unreliable nature of human-carried sensors. On the other hand, crowd sensing data are usually very sparse, and there may exist considerable number of entities that never receive any observations from users. Some existing work studies these two challenges separately. However, we can gain great benefits by dealing with them jointly. In this paper, we develop an integrated framework to estimate the true values of entities from redundant and sparse data in crowd sensing applications. In this framework, we pro-pose an effective algorithm to infer the \missing" observa-tions for each entity, and aggregate both user-contributed and inferred observations to discover the true values of enti-ties. We conduct extensive experiments on real-world crowd sensing systems to demonstrate the advantages of the pro-posed framework on correctly inferring entity truths from redundant and sparse data.