An introduction to fuzzy control
An introduction to fuzzy control
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Fuzzy Modelling: Paradigms and Practices
Fuzzy Modelling: Paradigms and Practices
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
From a Genetic Fuzzy Rule-Based System to a Intelligent Sensor Network
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
D-FLER: a distributed fuzzy logic engine for rule-based wireless sensor networks
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
Fuzzy if... then rule models and their transformation into one another
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Nowadays, growing interest exists on the integration of artificial intelligence technologies, such as neural networks and fuzzy logic, into Wireless Sensor Networks. However, few attentions have been paid to integrate knowledge based systems into such networks. The objective of this work is to optimize the design of a distributed Fuzzy Rule-Based System embedded in Wireless Sensor Networks. The proposed system is composed of: a central computer, which includes a module to carry out knowledge bases edition, redundant rules reduction and transformation of knowledge bases with linguistic labels in others without labels; access point; sensor network; communication protocol; and Fuzzy Rule-Based Systems adapted to be executed in a sensor. Results have shown that, starting from knowledge bases generated by a human expert, it is possible to obtain an optimized one with a design of rules adapted to the problem, and a reduction in number of rules without a substantial decrease in accuracy. Results have shown that the use of optimized knowledge bases increases the sensor performance, decreasing their run time and battery consumption. To illustrate these results, the proposed methodology has been applied to model the behavior of agriculture plagues.