Efficient fuzzy methodology for congestion control in wireless sensor networks

In wireless sensor networks (WSNs), ensuring reliable and efficient communication between nodes requires effective congestion control. This study presents an innovative approach, called FSFG, which combines FCM fuzzy clustering, Fuzzy Inference System (FIS), Support Vector Regression (SVR), and Genetic Algorithm (GA) to address congestion in WSNs. FSFG consists of two phases: Structure Identification and Parameter Identification. In the first phase, fuzzy clustering techniques are employed to determine the desirability of outputs in different classes. Subsequently, support vector regressions are used to determine membership functions with a minimal error rate. In the parameter identification phase, the obtained rules from the FCM method are used to design the desired system, enabling the estimation of necessary outputs based on relevant inputs. Through simulations, the results demonstrate that the FSFG algorithm excels in congestion control, exhibiting a lower error rate and outperforming other algorithms in terms of delay and packet delivery rate.