Abstract-While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive.In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths.We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

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Indoor environment inference is of great importance to mobile and pervasive computing. As high-level metadata of indoor environment, floor maps contain rich information and are widely required in many pervasive systems. However, despite significant research progress, automatic inference of indoor maps has been less studied.In this paper, we present iMap, a smartphone-based opportunistic sensing system that automatically constructs the indoor maps by merging crowdsourced walking trajectories from smartphone users. Most importantly, indoor semantics, such as stairs, escalators, elevators and doors are also automatically detected and annotated to the constructed map in the same inference process. The evaluation result shows that iMap can accurately detect different indoor semantics and be applied to different indoor environments. With the capability of generating semanticannotated indoor maps without requiring any prior knowledge of the indoor environment, iMap has the potential to be widely deployed in practice.

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