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M.Tech / M.E / PhD Thesis | Information Technology | India | Volume 6 Issue 1, January 2017
Transmission Policies for Data Aggregation using Cooperate Node in Wireless Sensor Networks
Shrikant A. Shinde | Shailaja N. Uke
Abstract: Wireless Sensor Networks (WSNs) consist of sensor nodes and the sensor nodes are capable of collecting, sensing and gathering data from the environment. These networks have extensive application in disaster management, habitat monitoring, security, and military, etc. Wireless sensor nodes are very small in size and very low battery power and have limited processing capability. Data aggregation is a very important technique in WSNs and helps in reducing the energy consumption by eliminating redundancy. The main aim of data aggregation is to collect and gather data in an energy efficient manner and due to this the network lifetime is improved. One data aggregation method in a WSN is sending local representative data to the Sink node based on the spatial-correlation of sampled data. Based on this correlation degree, a data density correlation degree (DDCD) clustering method is presented in detail so that the representative data have a low distortion on their correlated data in a WSN. The proposed system uses a co-operate node or data centre node which cooperates among the sensor nodes in a particular area of WSN and which improves transmission policies as well as energy efficiency. To design a modified data density correlation degree clustering algorithm for energy balanced network by using clustered data aggregation methodology. The simulation results show that the resulting representative data achieved using the proposed modified data density correlation degree clustering method have better throughput, packet delivery ratio, dropping ratio, delay, normalized overhead and average energy consumption than those achieved using the data density correlation degree clustering method.
Keywords: Data aggregation, data density, correlation degree, energy efficiency, wireless sensor networks
Edition: Volume 6 Issue 1, January 2017,
Pages: 1168 - 1172