Traffic Management in Wireless Sensor Network Based on Modified Neural Networks

Abstract

Wireless Sensor Networks (WSNs) are event-driven network systems consist of many sensors node which aredensely 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 nodeis the bottleneck of network there may be chance for congestion due to heavy data traffic. Due to congestion, it leads to dataloss; it may be important data also. To achieve this objective, soft computing based on Neural Networks (NNs) CongestionController approach is proposed. The NN is activated using wavelet activation function that is used to control the traffic ofthe WSN. The proposed approach which is called as Modified Neural Network Wavelet Congestion Control (MNNWCC), hasthree main activities: the first one is detecting the congestion as congestion level indications; the second one is estimated thetraffic rate that the upstream traffic rate is adjusted to avoid congestion in next time, the last activates of the proposedapproach is improved the Quality of Services (QoS), by enhancement the Packet Loss Ratio (PLR), Throughput (TP), BufferUtilization (BU) and Network Energy (NE) . The simulation results show that the proposed approach can avoid the networkcongestion and improve the QoS of network.