Osamu Muta

Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this article presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.