In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.
Today's networks are often hierarchically modeled for both scalability and security considerations. Topology aggregation is introduced for the purpose of simplifying routing by summering and compressing information of lower levels and advertising them to higher logical levels. However, the balance between performance and accuracy of state information must be taken into account since topology aggregation will ineluctably give rise to distortion. In this paper, new approximation methods based on approximation staircase formed by power-transformed linear regression model and hyperbolic-transformed linear regression model are proposed to approximate the service efficient frontier in the full mesh approach, and then the full mesh topology is transformed to a simpler tree structure and star structure. The proposed methods are shown to be valuable by performance analysis and comparison with existing methods.
SUMMARY With the rapid development of Internet of things (IoT), Radio Frequency Identification (RFID) has become one of the most significant information technologies in the 21st century. However, more and more privacy threats and security flaws have been emerging in various vital RFID systems. Traditional RFID systems only focus attention on foundational implementation, which lacks privacy protection and effective identity authentication. To solve the privacy protection problem this paper proposes a privacy protection method with a Privacy Enhancement Model for RFID (PEM4RFID). PEM4RFID utilizes a "2+2" identity authentication mechanism, which includes a Two-Factor Authentication Protocol (TFAP) based on "two-way authentication". Our TFAP employs "hardware information + AES-ECC encryption", while the ""two-way authentication" is based on improved Combined Public Key (CPK). Case study shows that our proposed PEM4RFID has characteristics of untraceability and nonrepeatability of instructions, which realizes a good trade-off between privacy and security in RFID systems.
In this paper, we propose two efficient topology aggregation (TA) methods to deal with the scalability problem of hierarchical quality of services (QoS) routing. Our methods first use line fitting staircase or quadratic curve fitting staircase to approximate the original service staircase in the full mesh compaction. Then we respectively transform the restrictive parameters and the additive parameters of each full mesh topology into tree structure and star structure respectively for further compaction. Experimental results with randomly generated networks show that our methods perform far better than other methods in terms of achieving balance between the level of compaction and accuracy consideration.
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