A Data Aggregation Algorithm based on Adaptive Step Size Least Mean Square Prediction for WSNs

To address the problem of imbalance between network energy efficiency and data accuracy in wireless sensor networks, a prediction model for data aggregation based on adaptive step size least mean square(ASLMS) is proposed. The ASLMS algorithm minimizes the communication cost and prolongs the network lifetime by accurately predicting the data value and then ensuring the accuracy of the data involved in aggregation. At the same time, the ASLMS algorithm constantly adjusts the step factor of the algorithm with the changing trend of the sensed data, to improve the accuracy and success rate of the prediction model. Simulation results show that the proposed approach can effectively improve network energy efficiency and aggregate data accuracy compared with existing algorithms.