Implementing Fuzzy Logic in Cognitive Sensor Networks for Environmental Monitoring

Cognitive Sensor Networks are sophisticated systems that use adaptive mechanisms, fuzzy logic, fuzzy clustering algorithms, and other cutting-edge technologies to improve environmental monitoring through flexibility, accurate decision-making, and efficient handling of sensor data uncertainties. The goal of this research is to better monitor the environment by integrating fuzzy logic into cognitive sensor networks. Fuzzy Inference Systems (FIS), rule-based decision making, fuzzy logic controllers (FLC), adaptive fuzzy systems, and fuzzy clustering techniques are some of the components of the complete framework suggested to address the issues given by uncertainties and imprecisions inherent in environmental data. By creating language variables and membership functions, the FIS represents the complex interactions between input sensor data and ambient conditions. Fuzzy rules and input sensor data provide the foundation for the well-informed judgements made by FLCs. Fuzzy output is transformed into useful control signals through the use of defuzzification techniques. The system has adaptive mechanisms that allow fuzzy logic parameters to be dynamically adjusted in response to changing environmental conditions. With 89.8% accuracy, 91.2% precision, 88.6% recall, and an F1 score of 89.9%, the suggested system outperforms the current approaches, PN-WSNA and CogLEACH, after much testing and analysis.