Performance analysis of efficient pipeline architectures for underwater big data analytics

Underwater sensor networks (UWSNs) have emerged as an essential technology for various undersea applications. However, the use of acoustic links in UWSNs poses critical challenges in terms of overall delay, energy consumption and low bandwidth, which make the transmission of raw data in large sizes impractical. In our previous work, we employed various nodes (processing nodes, gateway nodes, and sensing nodes) that are used to construct different candidate architectures (i.e. single, pipeline, and hybrid of parallel/pipeline). We exploit the idea of in-network data processing in order to reduce the volume of data and extract only valuable information. In this paper, we analytically calculate and compare the performance of the various types of proposed architectures. The tradeoff between the cost of processing and the expected speedup is also analyzed. The results confirm our previous research hypothesis that processing the collected data locally and applying the idea of pipeline/parallel processing leads to significant improvement in the performance of UWSNs.