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This work combines an efficient prediction model with a Kalman filter (PKF) to reduce the communication cost in Wireless Sensor Networks (WSNs) with a guaranteed data quality. The hardware accelerator requires fewer resources than previous approaches, while achieving higher energy reductions. Exhaustive experimental results based on datasets from a real WSN application confirm the advantages of the proposed mechanism. Energy efficiency is a primary concern for wireless sensor networks (WSNs).This work uses a predictor combined with a Kalman filter (KF) to reduce the communication energy cost for cluster-based WSNs. The technique, called PKF, is suitable for typical WSN applications with adjustable data quality and tens of pico joule computation cost. However, it is challenging to precisely quantify its underlying process from a mathematical point of view.

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Suguna Angamuthu, S. Kaviya, S. Suvitha, & N. Saranya. (2017). Lifetime enhancement using apkf algorithm for large scale wsn . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(2), 1516–1519. Retrieved from