Xu, H.; Feng, J.; Ji, M.; Fan, S., and Ji, W., 2020. The seawater quality monitoring and data inconsistency processing system based on LoRa sensor network. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 219–222. Coconut Creek (Florida), ISSN 0749-0208.

With the development of society and the influence of human activity, marine pollution, especially in the coastal area, has become more and more serious. Therefore, research on seawater quality plays an important role in mariculture, marine eutrophication, and algal bloom prediction. The large range and low power consumption conditions of some typical wireless transmission modes used in seawater quality monitoring systems, like ZigBee and 4G, make real-time data collection difficult. In this article, a seawater quality monitoring and data inconsistency processing system based on “long-range” (LoRa) technology is proposed. In the system, the seawater quality information is collected by the LoRa-based sensor network and transmitted to the cloud platform to perform data analysis and processing. In order to reduce the inconsistency of the data collected by multiple sensors, the algorithm of data inconsistency elimination, which is based on evidence theory and thresholds, is presented. The performances of the proposed algorithm and several other typical algorithms are evaluated through experiments. Simulation results show that the accuracy of the proposed algorithm is superior to the other compared typical algorithms, and the LoRa-based sensor network can achieve a reliable and stable performance.