A Cooperative Indoor Localization Enhancement Framework on Edge Computing Platforms for Safety-Critical Applications

Abstract

With the maturity and popularity of the Internet of Things (IoT), wireless communication techniques have been vastly applied in daily lives. However, indoor localization has been remained as a challenge due to the insufficient accuracy. In this paper, a cooperative localization method called "Reliable And Cooperative Indoor Localization (RACIL)" framework is proposed to determine a target location under the coverage of multiple WSN schemes like WiFi, Blutooth/BLE, Zigbee, and so on. The calculated "intermediate result" of target location from each WSN scheme are further evaluated by a confidence degree mechanism on the edge computing platforms for a "weighted center" as the final target location. In such a way, both the accuracy and reliability of localization are improved. In order to evaluate the proposed RACIL framework, Matlab simulation and a real tunnel environment emulating coal mining scenario are set up separately for the analysis of location accuracy and capability. The experimental results show that RACIL improves not only the location accuracy but also the location rate in the coverage area with the presence of unreliable anchor nodes in the network.