Jafar Amiri, Masoud Sabaei, and Bahman Soltaninasab
clustering, sensor network, data correlation, time series, energy prediction
Data gathering in wireless sensor networks is one of the important operations in these networks. These operations require energy consumption. Due to the limited energy of nodes, the energy productivity should be considered as a key objective in design of sensor networks. Therefore the clustering is a suitable method that used in energy consumption management. For this purpose many methods have been proposed. Between these methods the LEACH algorithm has been attend as a basic method. This algorithm uses distributed clustering method for data gathering and aggregation. The LEACH-C Method that is the improvement of LEACH, perform the clustering in centralized mode. In this method, collecting the energy level information of every node directly in each period increases the energy cost. Also phenomenon that have seen by sensor nodes continually change over time. Thereby the information received by nodes is correlated. Sending time correlated data in the network cause to energy dissipation. TINA method and its improvement have been proposed in order to not sending correlated data. These approaches have report errors. In this paper, the idea of not sending time correlated data of nodes has been considered by using the time series function. Also, a model to estimate the remaining energy of nodes by the base station has been presented. Finally, a method has been proposed to aware the base station from the number of correlated data in each node as the estimation of energy will be more precise. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results show that the proposed methods have a better performance in energy consumption and lifetime of the network in comparison with similar methods.
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