Abstract
In resource constraint wireless sensor networks (WSN), an important design concern is the optimization of the data transmission reduction of each sensor node (SN) to extent the overall network lifetime. Numerous cited works claim that the Data Prediction Method (DPM) is the most competent method for data transmission reduction among data aggregation, data regression, neural networks models, spatiotemporal correlation, clustering methods, adaptive sampling and data compression. The big data is generally communicated across the WSN which leads to packet collisions, packet drops and unnecessary energy consumption. Thus, we propose a machine learning model-based on Data Prediction Method (MLM-DPM) to solve these problems. The proposed work is simple yet efficient in terms of processing and needs a small memory footprint in SN. The proposed approach reduces the data transmission rates while maintaining data accuracy. The proposed work is estimated on real dataset attained from the Life Under Your Feet (LUYF) project and compared to two recent Data Prediction Methods. The simulation results were promising and justify the proposed claims.
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Jain, K., Gupta, M., Abraham, A. (2022). Data Prediction Model in Wireless Sensor Networks: A Machine Learning Approach. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_13
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DOI: https://doi.org/10.1007/978-3-030-96299-9_13
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