Real-time Pedestrian Dead Reckoning For IoT Based Platform-Independent Positioning System

This paper introduces a real-time pedestrian dead reckoning (PDR) algorithm for Internet of Things (IoT) devices. It is motivated by a practical challenge encountered during the development and operation of a positioning system named KAILOS, which provides positioning services via a smartphone application. Its dependence on smartphones causes data collection constraints imposed by operating systems. To mitigate this issue, we designed and incorporated a dedicated IoT device into the system, enabling full access to sensing data. This invention demands developing and porting a PDR algorithm into the IoT device to address the communication bottleneck with a remote positioning server. Our goal was to process a large amount of data from an Inertial Measurement Unit (IMU) instantly on the device to produce precise relative location information without forwarding all of it to the server. To this end, we propose a new approach for detecting steps using acceleration differential in our PDR. Additionally, a dynamic gyroscope bias update strategy is also included to enhance the capability of heading estimation. These advancements not only enhance the accuracy of the PDR algorithm but also facilitate its implementation on IoT devices. We practically deployed the PDR algorithm into our IoT hardware platform called Kailos Tag (K-Tag). Via the extensive experiments conducted both indoors and outdoors, we found that our real-time PDR outperformed the conventional methods. It reduces step detection errors (SDE) to approximately 1.6%, travel distance errors (TDE) to below 1.8%, and end/start errors(E/SE) to about 3.2m regardless of environment. Moreover, it enables an average positioning latency of 2.49 ms, while consuming only 20% of CPU usage and 8.6% of total power consumption.