Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.