Mobile cluster head selection using soft computing technique in wireless sensor network

Wireless sensor networks generally consist of static sensor node, which can be deployed to monitor the environment. The network is built by the sensor nodes, and the data from the source reach base station by passing through a number of sensor nodes. This causes loss of energy at the sensor nodes. To reduce energy loss of the sensor nodes in WSN, cluster-based topology can be used. Sensor nodes are grouped into clusters. Each cluster contains one cluster head for effective communication. Cluster head collects the data from the sensor nodes and sends to the base station. This causes fast depletion of cluster heads energy. To overcome energy problem, we have proposed a novel mobile data gathering in WSN by soft computing-based CH selection and clustering. It is based on fuzzy inference system. The smart CH selection and vehicular data gathering reduce the time and energy loss due to uploading. Due to reduced energy loss, the lifetime of network is also increased. In this paper, we compared the smart CH selection technique with high-energy CH selection on the quality measures packet loss, collection delay, residual energy, distance travelled and network lifetime. The proposed smart CH selection-based vehicular data gathering is produced better results compared to the other methods.