Abstract
This paper focuses on the application environment of solar charging in Energy Harvesting Wireless Sensor Networks (EH-WSN), and studies how to effectively use energy prediction to extend the life of sensor networks. Considering the prediction algorithm of the standard Least Mean Square (LMS), the output power error is large when weather changes are fluctuating, and energy collection cannot be accurately predicted. This paper proposes a Correlation Least Mean Square (C-LMS) prediction model that introduces the correlation factor of weather changes. The algorithm has low complexity with a certain flexibility, which can solve it quickly and effectively improve the accuracy of short-term prediction. Experimental results show that the error rate of the C-LMS prediction algorithm is reduced by about 15% compared with the LMS model, and the prediction accuracy is significantly improved dealing with weather fluctuation. At the same time, based on the above lightweight prediction algorithm, the effects of predictive charging and residual energy on the rechargeable sensor network topology are reconsidered. Compared to a routing strategy that does not consider predictive charging, the optimized network lifetime has increased by nearly 31.7%.
Highlights
Wireless sensor networks (WSN) [1] are one of the hottest research areas that have attracted widespread attention today, involving multidisciplinary cross-fusion and new technologies
This paper proposes a C-Least Mean Square (LMS) prediction model that introduces the correlation factor of weather changes, which can improve the accuracy of short-term prediction with low complexity and flexibility
Correlation Least Mean Square (C-LMS) PREDICTION MODEL Based on the analysis of the standard LMS prediction algorithm, this paper introduces the weather correlation coefficient to correct the prediction of the standard adaptive filter
Summary
Wireless sensor networks (WSN) [1] are one of the hottest research areas that have attracted widespread attention today, involving multidisciplinary cross-fusion and new technologies. A great number of WSNs form a multi-hop and self-organizing network [2] and transmit object information in the monitoring area in a cooperative manner of sensing, collecting, processing, and wireless communication [3]. In the actual environment deployment, WSN has energy limitation due to its own battery power supply drawbacks. It is difficult to replace the energy supply equipment in special environments, which seriously restricts the application effect of the wireless sensor network in long-term data monitoring and transmission.
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