Space-Time Derivative-Based Prediction: A Novel Trickling Mechanism for WSN - Richa, César Huegel; Fröhlich, Antônio Augusto; Gracioli, Giovani (Anais do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC); 2017: Anais do VII Simpósio Brasileiro de Engenharia de Sistemas Computacionais, 2017)
Time series prediction techniques reduce the number of messages generated at the application level, saving energy spent in the communication and, consequently, extending the network lifetime. Trickle is a well-known time series prediction mechanism commonly used to decrease the number of transmitted messages in Wireless Sensor Networks (WSN) and thus save energy. This paper presents the Space-Time Derivative-Based Prediction (ST-DBP), a novel Trickling mechanism to suppress data transmission in space-time regions in WSNs. We integrate ST-DBP with the Trustful Space-Time Protocol (TSTP), an application-oriented, cross-layer communication protocol, and compare two variations of the ST-DBP with the original DBP using real data from a Solar Farm in terms of suppression data ratio. Our results show that the two variations of the ST-DBP outperform the original DBP.