%0 Journal Article %1 Rajinder_2015 %A Singh, Er. Rajinder %A Singh, Er. Manpreet %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Clustering Dynamic Energy Network Sensor Wireless consumption %N 3 %P 1102--1105 %R 10.17762/ijritcc2321-8169.150345 %T To Analyze and Enhance Drina in Dynamic Clustering Using Knowledge Based Learning for WSN %U http://dx.doi.org/10.17762/ijritcc2321-8169.150345 %V 3 %X In previous searches many researches had done work on energy consumption problem. They use different techniques to minimize energy consumption, like clustering, re-girding etc. The energy consumption of wireless nodes is depends upon the transmission distance, optimal routing protocols and amount of data to be transmitted. Energy consumption is the major problem, in this paper we are decreasing the energy consumption to implement a novel approach and compare these results with the exiting technique named DRINA 1. The concept, which is implementing on exiting technique, is neural network approach. In neural networks weights can be adjusts easily by applying some algorithms. This concept becomes key point in our work also. Here we adjust nodes not weights according the sending capacity of those nodes for communication. The node which has the higher sending capacity (means higher battery backup) as compare with other nodes in a cluster that node becomes the cluster head of a cluster. In a cluster one node should be act as a cluster node only. There is only one cluster head present in a cluster and number of cluster heads is present in a network, so, that’s why, we are also working on cluster head selection.

@article{Rajinder_2015, abstract = {In previous searches many researches had done work on energy consumption problem. They use different techniques to minimize energy consumption, like clustering, re-girding etc. The energy consumption of wireless nodes is depends upon the transmission distance, optimal routing protocols and amount of data to be transmitted. Energy consumption is the major problem, in this paper we are decreasing the energy consumption to implement a novel approach and compare these results with the exiting technique named DRINA [1]. The concept, which is implementing on exiting technique, is neural network approach. In neural networks weights can be adjusts easily by applying some algorithms. This concept becomes key point in our work also. Here we adjust nodes not weights according the sending capacity of those nodes for communication. The node which has the higher sending capacity (means higher battery backup) as compare with other nodes in a cluster that node becomes the cluster head of a cluster. In a cluster one node should be act as a cluster node only. There is only one cluster head present in a cluster and number of cluster heads is present in a network, so, that’s why, we are also working on cluster head selection.}, added-at = {2015-08-06T09:02:03.000+0200}, author = {Singh, Er. Rajinder and Singh, Er. Manpreet}, biburl = {https://www.bibsonomy.org/bibtex/2580a3e6023f569ffffa4ca6c9767e937/ijritcc}, doi = {10.17762/ijritcc2321-8169.150345}, interhash = {8f63a221b2ac76b0f557ca09de2e6987}, intrahash = {580a3e6023f569ffffa4ca6c9767e937}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Clustering Dynamic Energy Network Sensor Wireless consumption}, month = {march}, number = 3, pages = {1102--1105}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-06T09:02:03.000+0200}, title = {To Analyze and Enhance Drina in Dynamic Clustering Using Knowledge Based Learning for {WSN}}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150345}, volume = 3, year = 2015 }