NDLI: Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization

Content Provider	Springer Nature Link
Author	Wang, Yongli Qian, Jiangbo
Copyright Year	2011
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.
Starting Page	307
Ending Page	318
Page Count	12
File Format	PDF
ISSN	10220038
Journal	Wireless Networks
Volume Number	18
Issue Number	3
e-ISSN	15728196
Language	English
Publisher	Springer US
Publisher Date	2011-12-15
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	RFID data Real-time location tracing service Uncertainty Particle filter Adaptive Cyber-physical system Communications Engineering, Networks Electrical Engineering Computer Communication Networks Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Electrical and Electronic Engineering

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