Development of Energy Efficient Routing Protocol in Wireless Sensor Networks using Probabilistic Neural Network (ERPPNN)

Mariappan, E.; Paramasivan, B.
July 2014
Australian Journal of Basic & Applied Sciences;Jul2014, Vol. 8 Issue 10, p302
Academic Journal
Background: A wireless sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows in network becomes difficult to manage. It is essential that the network be able to self-organize. Clustering is an efficient technique to address the network scalability problem. Objective: We proposed a Probabilistic Neural Network (PNN) model to classify the sensor nodes in networks into minimum Weighted Connected Dominated Sets (WCDS) which consists of nodes having more connectivity and higher energy. The PNN consists of shrinking the hidden layers which is accomplished by performing K-means clustering on the training data of each WCDS separately. The means of the resultant clusters are presented in each class or cluster, instead of presenting energy parameter of all the sensor nodes in networks in the hidden layer nodes. Load balancing is achieved by selecting WCDS as random basis. The training parameter and WCDS are derived based on nodes having more connectivity and higher energy. Results :This technique achieved better results when the transmission range is short and equal performance when the transmission range becomes larger. The results show that it is important to select a suitable transmission range to make the network stable and to extend the lifespan of the network.. Conclusion: ERPPNN enhances the energy efficiency of the sensor nodes by selecting optimal WCDS consists minimum hops and higher energy sensor node and also provides effective routing that increase network lifetime.


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