Radio Data Transmission Reduction in Power-Constrained WSN
Koffi V. C. Kevin de Souza, Jalal Almhana, Philippe Fournier‐Viger · 2018
Recent studies have shown that radio data transmission is the most power consuming operation in power-constrained wireless sensor networks (WSNs). The power needed to transmit a single bit can be equal to that required for processing more than one thousand bits. Hence, increasing data processing to reduce data radio transmissions can considerably decrease power consumption. Several approaches have been designed to reduce data transmissions. This paper presents a novel approach, which combines partitioning clustering with the ARIMA algorithm. A performance comparison of the proposed approach with other data transmission algorithms, such as prediction using simple autoregressive and Adaptive Distributed Data Gathering (ADiDaG), is presented. Data from a real WSN, composed of more than 20,000 water meters in the City of Moncton, is used as a case study. Experimental results showed that the designed approach outperforms compared methods in terms of power reduction.