Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
[15] Peer-to-Peer Architecture Case Study: Gnutella Network
P2P '01 Proceedings of the First International Conference on Peer-to-Peer Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Building the Internet of Things Using RFID: The RFID Ecosystem Experience
IEEE Internet Computing
Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach
IEEE Transactions on Knowledge and Data Engineering
Fast track article: A temporal RFID data model for querying physical objects
Pervasive and Mobile Computing
RFID enabled traceability networks: a survey
Distributed and Parallel Databases
P2P Object Tracking in the Internet of Things
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Developing RFID database models for analysing moving tags in supply chain management
ER'11 Proceedings of the 30th international conference on Conceptual modeling
PeerTrack: a platform for tracking and tracing objects in large-scale traceability networks
Proceedings of the 15th International Conference on Extending Database Technology
Hi-index | 0.00 |
In the emerging environment of the Internet of Things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented amount of transactions and data that requires novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we present a fully distributed model for sovereign RFID data streams. This model combines Tilted Time Frame and Histogram to represent the patterns of object flows. It is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose algorithms that use statistically optimistic number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.