RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. In this paper, we consider RFID reader tracking which refers to continuously locating a mobile object by attaching it with a RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty is that the RFID readings gathered from the environment are often noisy. Existing approaches for tracking with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of PF has extremely high computational cost, and may be difficult to be done on mostly resource constrained mobile RFID devices. In this paper, we propose a hybrid method which combines PF with Weighted Centroid Localization (WCL) to achieve high accuracy and low computational cost. Our observation is that WCL has the same accuracy with PF with much lower cost if the object's velocity is low. Our method has two critical features. The first feature is adaptive switching between using WCL and PF based on the estimated velocity of the mobile object. The second feature is the further reduction of computational cost by offloading costly PF algorithm onto nearby servers. We evaluate the performance of our method through extensive simulations and experiments in two real world applications, namely, indoor wheelchair navigation and in-station Light Rail Vehicle (LRV) tracking at one of Hong Kong MTR depots. The result shows that our proposed approach has significantly less computational cost than existing PF based methods, while being as accurate as them.
- Computation offloading
- Particle filter
- RFID tracking
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering