Crowdsourcing data from multiple sensing agents has become a fundamental mechanism for extracting information in large-scale cyber-physical systems (CPS). However, there has been little attention paid to the difficulties crowdsourcing presents when the desired information is represented by continuous signals. Crowdsourcing can result in unique types of noise in the aggregated data sample set. Errors from clock synchronization, GPS location determination, or sensor heterogeneity result in biased and correlated errors in the sampled data from each individual sensor. Furthermore, in CPS sensors are often mobile and operate asynchronously. This affects the spatial sampling rates among sensors and results in nonuniform sample spacing. We refer to these sampling issues as the noisy multi-source, variable-rate (MSVR) sampling problem. This work introduces and investigates signal reconstruction algorithms given MSVR sampling conditions. These signal reconstructions are investigated for vehicular applications, using a joint road inclination and bank angle signal estimation algorithm that has access to MSVR sampled vehicle GPS and accelerometer data. The signal reconstruction and angle determination algorithms are validated on simulated and real-world vehicle data, where we reconstruct a road elevation signal with 0.89 m root-mean-square error.
['Andrew Fox', 'B. V. K. Vijaya Kumar', 'Fan Bai']
Multi-source variable-rate sampled signal reconstructions in vehicular CPS