Development of Hybrid model for data prediction and improving network performance in wireless sensor networks
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Abstract
In last decade lots of work done in the field of data prediction, various approaches used for data prediction like ARMA, Kalman Filter, LMS and recently work on Deep Learning based LSTM approach. In current scenario sensor is best resource for data collecting and used in various place like border surveillance, security purpose, monitoring and so on. The limitation of sensor nodes are low battery power, low memory and low computation power.
In this paper we proposed a hybrid model which is work in two steps in first step Deep learning-based LSTM approach for data prediction in wireless sensor network and in second step apply feed forward filter with gateway to improve network performance, simulation process is done by Spyder 3.8 with intel dataset it gives better result.
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