REAL-TIME SELF-TRACKING IN THE INTERNET OF THINGS

We investigate the problem of real-time self-tracking of tagged objects in a new system with low-cost "smart" tags. These tiny and battery-less devices will play a pivotal role in the infrastructure of the Internet of Things (IoT). With capabilities of low-power computation and tag-to-tag backscattered communication, no readers will be needed for running the Radio Frequency Identification (RFID) system. In order to allow for low-cost tags, self-tracking has to be performed with simple algorithms while still exhibiting high accuracy. In this paper we propose a linear observation model for which Kalman filtering (KF) is the optimal method. We also consider a nonlinear model for which we apply particle filtering (PF) of reduced complexity as the tracking method. The performance and computational complexity of the different methods are compared by computer simulations.