Mobile big data fault-tolerant processing for ehealth networks

Abstract

In daily life, people tend to use mobile networks for more accurate overall data. With intelligent mobile devices, almost all kinds of data can be collected automatically, which contributes directly to the blooming of eHealth. However, large amounts of data are also leading us into the era of big data, in which new data collection, transmission, and processing techniques are required. To ensure ubiquitous data collection, the scale of mobile eHealth networks has to be expanded. Also, networks will face more pressure to transmit large amounts of eHealth data. In addition, because the processing time increases with data volume, even powerful processors cannot always be regarded as efficient for big data. To solve these problems, in this article, an interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed. In this three-layer architecture, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer. Then the data integrating layer checks the accuracy of data, and also prepares for data processing. In the end, an RVNS queue is adopted for rapid data processing in the data analyzing layer. After processing with relevant rules, only valuable data will be reported to health care providers, which saves their effort to identify these data. Simulation shows that IRQA is steady and fast enough to process large amounts of data.