Wireless Sensor Networks (WSNs) are event-driven network systems consist of many sensors node which are
densely deployed and wirelessly interconnected that allow retrieving of monitoring data. In Wireless sensor network,
whenever an event is detected, the data related to the event need to be sent to the sink node (data collection node). Sink node
is the bottleneck of network there may be chance for congestion due to heavy data traffic. Due to congestion, it leads to data
loss; it may be important data also. To achieve this objective, soft computing based on Neural Networks (NNs) Congestion
Controller approach is proposed. The NN is activated using wavelet activation function that is used to control the traffic of
the WSN. The proposed approach which is called as Modified Neural Network Wavelet Congestion Control (MNNWCC), has
three main activities: the first one is detecting the congestion as congestion level indications; the second one is estimated the
traffic rate that the upstream traffic rate is adjusted to avoid congestion in next time, the last activates of the proposed
approach is improved the Quality of Services (QoS), by enhancement the Packet Loss Ratio (PLR), Throughput (TP), Buffer
Utilization (BU) and Network Energy (NE) . The simulation results show that the proposed approach can avoid the network
congestion and improve the QoS of network.