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Energy efficient data aggregation technique using load shifting policy for wireless sensor network
S. Anavatti, , M. Chandak
Published in Springer Verlag
Volume: 439
Pages: 299 - 310
Data redundancy is quite common in wireless sensor networks (WSNs) where nodes are deployed densely. The reason behind such deployment is to achieve reliability from communication failure. Communication failure happens when particular node transmitting data fails. In WSN there is no other way than keeping redundant nodes to solve communication failure problem. If redundant nodes are available then at the time of node failure the data of failed node can be recovered from its redundant nodes. Though we can achieve reliability through redundant nodes presence of redundant nodes will generate more number of redundant packets which will consume more energy of network. Because nodes which are densely deployed will sense the same information and send it to sink node and sink will waste its energy in processing redundant data and also redundancy will generate heavy traffic in network. Hence, there is a need to trade-off between energy conservation and reliability. To do this trade-off we need to find optimization point of redundancy in WSN. So that reliability and energy conservation both will be maintained. In this paper we have used clustering-based load shifting policy (LSP) to eliminate redundancy up to an adequate level to achieve optimization point of redundancy. Data aggregation eliminates redundancy from WSN. We are performing data aggregation at two levels and at the same time we are keeping redundant nodes up to 50 % to achieve reliability. In this paper, we have done comparison of traditional cluster-based data aggregation with our LSP-based data aggregation. Simulation result shows that LSPDA has lesser average energy consumption and longer lifetime than traditional cluster-based data aggregation method. © Springer Science+Business Media Singapore 2016.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag