Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization |
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Authors: | Yongli Wang Jiangbo Qian |
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Affiliation: | (1) School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;(2) School of Information Science and Engineering, Ningbo University, Ningbo, China |
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Abstract: | The management of the uncertainties over data is an urgent problem of novel applications such as cyber-physical system, sensor
network and RFID data management. In order to adapt the characteristics of evolving over time of sensor data in real-time
location tracing service based on RFID, a measuring algorithm for the Uncertainty of RFID Data-PPMU (a particle filter and
particle swarm optimization-based measuring uncertainty algorithm for RFID Data) is proposed in this paper. PPMU can change
the number of samples adaptively on the basis of K–L distance to adapt the evolution of RFID data, and PPMU introduces an
improved PSO (particle swarm optimization) method to enhance the efficiency of re-sampling phase of SIRPF (sequential importance
re-sampling particle filter). Meanwhile, PPMU defines a fitness function base on Conventional Weighted Aggregation for PSO
that balances the importance between the priori density and likelihood density to detect the most optimal samples among candidate
sample sets. It provides a measurement with confidence factor for initial tuples in the probability RFID database. Experiments
on real dataset show the proposed method can effectively measure the underlying uncertainty over RFID data. Compared with
existing algorithms, PPMU can be further improved particle degradation and particle impoverishment problem. |
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