Deep Neural Glow Worm Swarm Optimized Soft C-Means Clustering for Energy Aware Route Discovery and Data Gathering in WSN

Abstract

Cluster head selection enacts a prominent role in Wireless Sensor Network to optimize the energy usage during the data collection. Few research works have been designed to choose the best cluster head in wireless network using different optimization techniques. However, cluster head selection performance of conventional algorithms was lower to extend the lifetime of network. Therefore, a Deep Neural Glowworm Swarm Optimized Soft C-Means Clustering (DNGSOSCC) model is proposed. Initially, DNGSOSCC model obtains number of sensor nodes as input at the input layer. After taking input, soft clustering process is carried in DNGSOSCC model at the first hidden layer where it groups the sensor nodes in WSN into a different cluster. Then, the glow worm population is initialized in DNGSOSCC model with the support of number of clusters formed at the second hidden layer. Next, Luciferin value is assessed in DNGSOSCC model for all sensor nodes according to their residual energy level in the third hidden layer. Followed by, the sensor node with higher luciferin value within cluster is selected as optimal cluster head at the fourth hidden layer to perform energy efficient data gathering in WSN. Finally, the output layer provides the selection result of optimal cluster heads in WSN. By using the above process, DNGSOSCC model significantly gathers the data from its cluster member through selected optimal cluster heads with minimal amount of energy. From that, DNGSOSCC model upsurges the lifespan of network through performing efficient route discovery and data collection in WSN. The DNGSOSCC model conducts the simulation work using metrics such as data deliverance ratio, data loss ratio, data transmission delay and network lifetime with reverence to number of data packets and sensor nodes. From the experiments conducted, DNGSOSCC model improves the data deliverance ratio and network lifetime by 19% and 10% as well as minimizes the data loss ratio and data transmission delay by 76% and 34% respectively than existing methods.