An Adaptive Weighted Fuzzy Controller Applied on Quality of Service of Intelligent 5G Environments

in computational intelligence area, it is suitable to fulfill the analysis in order to interpret the concept and sources of uncertainty and the conditions of its incidence, and hence pursuit for reliable techniques of dealing with it. Dealing with uncertainties in this case is a challenging and multidisciplinary activity. So, there is a need for a capable tool for modeling, control, and analytics to test and evaluate uncertainties for different configurations of the same IoT system. This article emphasis on the analysis of the uncertainty in one of the most important technology trends, the IOT on case study of green environment, such as smart homes. In this research a Mamdani Fuzzy Inference System has been proposed with a Gaussian MFs to handle the uncertainty in intelligent environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved.