Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications

As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed.

Earth and Rock-Filled Dam Monitoring by High-Resolution X-Band Interferometry: Gongming Dam Case Study

Middle-sized earth- and rock-filled dams with clay cores continue to settle by approximately 0.5–1.5% of their height for approximately 1–3 years after their construction phase. This paper investigates the use of high-resolution spaceborne Synthetic aperture Radar (SAR) interferometry to monitor this settlement process, with the case of the Gongming dam in China. The varieties of slope foreshortening and stretching in the radar coordinates are attributed to the radar’s local incidence angle and the dam’s slope heading, which are analysed in detail. Focusing on the embankment slope settlement analysis, the equations for calculating foreshortening and the line-of-sight deformation decomposition are derived in detail for the adjustment and data fusing. The scattering characteristics of different materials on the dam surface are analysed, including the grass slope, concrete slope, top road (crest), top wall, step, and ditch. According to the analysis of the precipitation data from a local meteorological station, the coherence losses on the slopes are mainly caused by surface moisture. Both the TerraSAR-X Spotlight (TSX-SL) data and the COSMO-SkyMed Strip Mode (CSK-SM) data are analysed by the stacking method to assess the slopes’ deformations. The TSX-SL data results show the highest rate of settlement as 2 cm/yr on the top of the dam slope, consistent with the clay core shrinking process. The CSK-SM data show a similar trend in the lower part of the dam slope but underestimate the deformation in the upper part of the slope.