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Water crises have been among the most serious environmental problems worldwide since the twenty-first century. A water crisis is marked by a severe shortage of water resources and deteriorating water quality. As an important component of water resources, lake water quality has deteriorated rapidly in the context of fast urbanization and climate change. This deterioration has altered the water ecosystem structure and influenced lake functionality. To curb these trends, various strategies and procedures have been used in many urban lakes. Among these procedures, accurate and responsive water environment monitoring is the basis of the forecasting and prevention of large-scale cyanobacteria outbreaks and improvement of water quality. To dynamically monitor and predict the outbreak of cyanobacteria in Dianchi Lake, in this study, wireless sensors networks (WSNs) and the geographic information system (GIS) are used to monitor water quality at the macro-scale and meso-scale. Historical, real-time water quality and weather condition data were collected, and a combination prediction model (adaptive grey model (AGM) and back propagation artificial neural network (BPANN)) was proposed. The correlation coefficient (R) of the simulation experiment reached 0.995. Moreover, we conducted an empirical experiment in Dianchi Lake, Yunnan, China using the proposed method. R was 0.93, and the predicting error was 4.77. The results of the experiment suggest that our model has good performance for water quality prediction and can forecast cyanobacteria outbreaks. This system provides responsive forewarning and data support for lake protection and pollution control.

Nodes in sensor networks are often prone to failure, particularly when deployed in hostile territories, where chances of damage/destruction are significantly higher. In many applications it is necessary to have some guarantees on the coverage, connectivity and lifetime of the sensor network. The network should also be able to adapt to single and/or multiple node failures as well as disruptions due to the inherent limitations of the wireless communication medium. In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher-powered relay nodes can be used as cluster heads. In this paper, we propose an integer linear program (ILP) for determining the minimum number of relay nodes, along with their locations and a suitable communication strategy such that the network is able to meet specified performance guarantees with respect to coverage, connectivity and lifetime. To the best of our knowledge, this is the first formulation that jointly optimizes energy-aware placement and routing of relay nodes in two-tiered sensor networks.

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