Abstract
The location-based services (LBS) aid the next-generation Internet of Things (IoT) by adding the location context to IoT data. A novel real-time indoor localization and navigation (ILN) system is proposed in this paper. The passive ultra-high frequency Radio Frequency Identification (UHF RFID) tags are mounted on the walls of known locations throughout the indoor environment as reference tags, then the localization of RFID reader is calculated by the inverse synthetic aperture radar (ISAR) algorithm exploiting tags’ backscatter phase information, which would have an accuracy within 1 meter. The resultant trajectory can be obtained to support the accurate navigation. A cloudlet mobile edge computing (MEC) framework is designed to properly process the first-stage sensor data and differentiatedly handle the various ILN tasks between the mobile device and remote server. Therefore, a highly accurate localization and turn-by-turn floor-cognitive navigation real-time solution would be feasibly constituted. It is highly adoptable by the mass-market due to the wide-range, low cost and energy-efficient features. This solution would benefit the future LBS such as indoor delivery robots, unmaned vehicles, auto-inventory checking etc.
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