Abstract
In wireless sensor networks (WSN), sink node needs to store and track data efficiently in a timely manner. As data is stored in a regular time interval, massive data is being collected which results in poor data tracking at sink node. To overcome this problem, an online data tracking and estimation (ODTE) algorithm is developed. ODTE algorithm captures continuous data and does online data tracking at regular time interval. It also measures distortion factor (DF) which estimates an optimal data collected at sink node. Moreover, data transmission overhead is another major issue at sink node while transmitting continuous data from sensor nodes. To solve this, we develop data prediction systems (DPS) which allows sensor nodes to transmit data to a sink node within a limit, controlled by a switching operation. This make sink node for predicting data for sensor nodes thereby reducing transmission overhead in WSN.We validate ODTE algorithm and prediction models using simulation results.
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