ISSN 1330-3651 (Print), ISSN 1848-6339 (Online)

Original scientific paper Recently, RFID technology has been widely used in many applications such as object monitoring and tracing due to the unique features such as noncontact, automatic, fast and multi-target identification simultaneously. However, because of the interference of environmental factors and the requirement of real-time detection, the data collected by the RFID readers are often full of redundancy, which may reduce the processing efficiency of RFID application servers, even lead to making false decisions. Therefore, it is of definite necessity to filter the redundant data in RFID systems before transmitting them to the upper applications. In order to support approximate filtering of RFID data streams in mobile environment, this paper intends to study effective redundant filtering mechanism in the sliding window model. Firstly, we introduce the application background of RFID data streams and the RFID system architecture based on middleware. Then, we propose a temporal-spatial Bloom filter based on sliding windows, which extends the onedimension array in the standard bloom filter to a two-dimension array, storing both reader IDs and the observed timestamps of original observation items. Meanwhile, in order to guarantee the false positive rate does not increase due to the reason that the space of the filter becomes full, we suggest a random decay strategy for deleting the expired elements. The error rates of the suggested filter, including false positives and false negatives, are analysed in theory. Experimental results show that the suggested filter can filter time redundant data effectively and has a good performance to deal with location movement of RFID objects.