Enhancing Localization Scalability and Accuracy via Opportunistic Sensing - 2018


Employing a mobile phone for fine-grained indoor localization remains an open drawback. Low-complexity approaches without infrastructure haven't achieved correct and reliable results thanks to numerous restrictions. Existing correct solutions depend upon dense anchor nodes for infrastructure and are therefore inconvenient and cumbersome. The problem of beacon signal blockage additional reduces the effective coverage. During this Project, we have a tendency to investigate the issues related to improving localization scalability and accuracy of a portable via opportunistic anchor sensing, a new sensing paradigm which leverages opportunistically connected anchors. One key motivation is that the scalability of the infrastructure-primarily based localization system can be improved by lifting the minimum requirement for anchor numbers or constellations in trilateration. At the same time, location accuracy beneath insufficient anchor coverage will be improved by exploring the chance of various information varieties rather than deploying a lot of anchor nodes. To enable this highly scalable and correct style, we tend to leverage low-coupling hybrid ranging using our low-price anchor nodes with centimeter-level relative distance estimation. Activity patterns extracted in users' smartphones are used for displacement compensation and direction estimation. The system also scales to finer location resolution when anchor access is improved. We tend to introduce robust delay-constraint semidefinite programming in location estimation to understand optimized system scalability and backbone flexibility. We conduct extensive experiments in varied eventualities. Compared with existing approaches, opportunistic sensing could improve the placement accuracy and scalability, furthermore robustness, beneath various anchor accessibilities.

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