A Reprocessing Model for Complete Execution of RFID Access Operations on Tag Memory

This paper investigates the problem of inconsistent states of radio frequency identification （RFID） tag data caused by incomplete execution of read/write operations during access to RFID tag memory. Passive RFID tags require RF communication to access memory data. This study is motivated by the volatility of RF communication, where instability is caused by intermittent connections and uncertain communication. If a given tag disappears from the communication area of the reader during the reading or writing of tag data, the operation is incomplete, resulting in an inconsistent state of tag data. To avoid this inconsistency, it is necessary to ensure that any operations on tag memory are completed. In this paper, we propose an asynchronous reprocessing model for finalizing any incomplete execution of read/write operations to remove inconsistent states. The basic idea is to resume incomplete operations autonomously by detecting a tag＇s re-observation from any reader. To achieve this, we present a concurrency control mechanism based on continuous query processing that enables the suspended tag operations to be re-executed. The performance study shows that our model improves the number of successful operations considerably in addition to suppressing inconsistent data access completely.

A query index for continuous queries on RFID streaming data

RFID middleware collects and filters RFID streaming data to process applications＇ requests called continuous queries, because they are executed continuously during tag movement. Several approaches to building an index on queries rather than data records, called a query index, have been proposed to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification （ECSpec） model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index, because it is necessary to insert a large number of segments into the index. To solve this problem, we propose a transform method that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. Comparing with existing query indexes, the performance of proposed index outperforms the others on various datasets.

Aggressive Complex Event Processing with Confidence over Out-of-Order Streams

In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.