Abstract: To address the information gap between RFID data and the requirements of upstream applications,the chara-cter of real time of sensor data,an algorithm for choosing the optimal uncertain data of complete data streams was proposed．The drawbacks of generic particle filter were analyzed．Then an entropy-based method was adopted to estimate the most likely attribute weight for each object,by using possibility degree matrix to select optimal particles,to efficiently capture the possible locations and containment for tagged objects.The performance of the generic particle filter is improved．In this method,though particle optimization,particles are moved to the regions where they have larger values of posterior density function．The experimental results show the accuracy and efficiency and the number of particles needed for accurate location are reduced dramatically．Finally,a numerical example was given to show the feasibility and effectiveness in terms of measurement of underlying uncertainties over RFID data.

Key words: Internet of things,Radio frequency identification data streams,Optimal estimation,Particle filter