Optimal and Robust Data Aggregative Fusion in Internet of Things for Data Collection on Equipment Status

Abstract

Internet of Things has been regarded as a promising technology and architecture for instant maintenance of equipment status in large marine ships. Data aggregation and data fusion are essential operations in sensing data collection. The energy efficiency of aggregative tree affects the lifetime of the data collection, which should maintain robustness and optimization. Current researches have not explored how to distinguish and rely on the node energy potential for tree construction. In this paper, we propose a bunch of algorithms by using random network for data aggregation. The energy efficiency is improved by our algorithms, which is justified extensively by analysis and proof.