NeTS: Small: Integrating Electronic and Visual Signals for Accurate Localization

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Nowadays, an increasing number of objects can be represented by their wireless electronic identifiers. For examples, people can be recognized by their cellular phones' or laptops' MAC addresses and products can be identified by their RFID numbers. Localizing objects with electronic identifiers is more and more important as our society is becoming increasingly "digitalized". However, traditional wireless localization techniques cannot meet the rapidly mounting requirements of accurate and cost-effective localization. Some of them need expensive hardware to achieve high accuracy, which is impractical for massive deployments, while others such as WiFi RSSI based localization are inaccurate and not robust to environmental noise. This project designs a novel localization methodology called EVLoc. In EVLoc, visual signals (V signals) are used to improve the accuracy of electronic localization, as the accuracy of visual localization is around 0.1-0.5 meter. This technique fully leverages V signals' high accuracy and the pervasiveness of electronic signals (E signals). The project conducts three main research tasks: (1) perform object localization given complete and distinct E and V signals, (2) perform object localization in practical settings when the E and V signals are not complete and distinct, and (3) implement the E-V approach to validate its accuracy and efficacy. This pioneering research develops a transformative approach that integrates E and V signals for accurate localization. The approach can be applied to many emerging applications such as epidemic control and tracking of elderly people, children, and those exhibiting at-risk behaviors for healthcare purposes.