Abstract-Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume. RFID is a promising electronic identification technology that enables a real-time information infrastructure to provide timely, high-value content to monitoring and tracking applications. However, RFID data a triplet <tag id, reader id, timestamp> in its most basic form raises new challenges since it may be insufficient, incomplete, and voluminous. Insufficient information: Since RFID is inherently an identification technology designed to identify individual objects, a stream of RFID readings does not capture inter-object relationships such as co-location and containment. Incomplete data: Despite technological advances, RFID readings are inherently noisy with observed read rates below 100% in actual deployments. Missed readings result in a lack of information about an object's location, significantly impairing the tasks of determining both location and containment. High volume streams: Perhaps the key distinguishing characteristic of RFID streams are their high data volumes which can easily overwhelm a data stream system. Hence, it is imperative that data be filtered and compressed close to the hardware while preserving all useful information. In this paper, we present SPIRE, a system that addresses the above challenges. SPIRE departs from the prior work by building an interpretation and compression substrate over RFID data streams which employs three key techniques: (1) a time-varying graph model that captures possible object locations and containment relationships with its stream-driven construction, (2) a probabilistic algorithm that infers the mostlikely locations and containment relationships of obujects, and (3) an output algorithm that transforms an input stream to a compressed, yet informative output stream. We have implemented our interpretation and compression substrate and have evaluated it using synthetic RFID streams emulating an enterprise supply-chain environment Our results show that our inference techniques provide good accuracy while retaining efficiency and our compression algorithm yields significant reduction in data volume. Architecture of SPIRE: Our substrate con sists of (i) a data capture module for stream-driven construction of a time-varying graph model encoding possible object locations and containments; (ii) an interpretatioa module to probabilistically infer the most likely location and containment for an object, and (iii) a compression module that outputs stream data in an compressed format with smoothing.
A data interpretation and compression substrate has been presented in REF over RFID streams to address these challenges in enterprise supply-chain environments.
Efficient Data Interpretation and Compression over RFID Streams
2008 IEEE 24th International Conference on Data Engineering
2008 IEEE 24th International Conference on Data Engineering
Computer Science