Cloud/Fog Computing System Architecture and Key Technologies for South‐North Water Transfer Project Safety

Abstract

In view of the real-time and distributed features of Internet of Things (IoT) safety system in water conservancy engineering, this study proposed a new safety system architecture for water conservancy engineering based on cloud/fog computing and put forward a method of data reliability detection for the false alarm caused by false abnormal data from the bottom sensors. Designed for the South-North Water Transfer Project (SNWTP), the architecture integrated project safety, water quality safety, and human safety. Using IoT devices, fog computing layer was constructed between cloud server and safety detection devices in water conservancy projects. Technologies such as real-time sensing, intelligent processing, and information interconnection were developed. Therefore, accurate forecasting, accurate positioning, and efficient management were implemented as required by safety prevention of the SNWTP, and safety protection of water conservancy projects was effectively improved, and intelligential water conservancy engineering was developed. BackgroundWater conservancy engineering presents the national fundamental industry and is vital in national economic development. The construction of water conservancy projects is generally large in scale, with high investment, wide geographical distribution, and decentralized management, in a long construction period and contains a large amount of information. It may be also affected by unfavorable terrain conditions, complex geological structure, and flood, which increase the difficulty in supervision. Therefore, the construction of Internet safety information system is of great significance for improving safety monitoring and ensuring project safety and water quality safety in water conservancy engineering.With the rapid development of Internet of Things (IoT) technology in recent years, an important source of big data is the tremendous amount of data that are collected, transferred, stored, and processed through the IoT system [1][2][3]. Due to the general dense geographical distribution of water conservancy projects, the IoT-based data in water conservancy engineering have typical geographical attributes.Taking the Middle Route of the SNWTP as an example, its main line starts at Taocha Sluice of Danjiangkou Reservoir, crosses the Yangtze River, Huaihe River, Yellow River, and Haihe River, passes through Provinces of Henan, Hebei, Beijing and Tianjin, and provides water for tens of large-and medium-sized cities, like Pingdingshan, Xuchang, Zhengzhou, Jiaozuo, Xinxiang, Hebi, Anyang, Handan, Xingtai, Shijiazhuang, Baoding, Beijing, Tianjin, and so on. Therefore, it is necessary to build the safety information network system for the water conservancy project.Combined with the research work of the National Major Project "Research and Development of Sensor Network Technology Oriented to the Safety of the South-North Water Transfer Project (SNWTP)," this study investigated the cloud/fog network system architecture for project safety of the Middle Route of SNWTP. This paper first ana...

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

“…Adopting fog computing is more comprehensive and flexible compare to other similar computing paradigm proposed by research community such as Cloudlets, cloud of things, mist computing and edge computing (Yousefpour et al, 2018). Before making any decision, future user of fog computing should identify the current problem with cloud computing (Fan et al, 2018). Therefore, they can proceed with exploring the adoption solution and effective design in order for them to identify suitable factors for adoption (Peng et al, 2018; Liu, Chan, Yang, & Niu, 2018) Despite the importance of this technology, there is limited study in the adoption, implementation and usage of the fog computing in organization (Alshamaila, Papagiannidis, & Li, 2013).…”

“…Consistency of data analysis performance must elevated if the data streams from devices handled in huge amount. Assessment on reliability outcome will help organisation to improve overall performance of data analysis (Ahamed et al, 2013;Fan et al, 2018;Lapin, 2014;Verba et al, 2019). In addition, to validate the identified dimensions, survey will be conducted between companies that adopted fog computing.The questionnaire constructed from literature review and modified to fit the context of fog computing adoption will be distributed through email to respondents within quarter of a year in order to increase the questionnaire response rate.…”

Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

“…It not only reduces the network delay for mobile users but also significantly reduces the link bandwidth backbone [9,10]. Although there are many advantages of fog computing, some security issues still need to be solved.…”

“…For instance, when the number of users has increased dramatically, these users can obtain the service by visiting the contents of the cache in the fog servers so as to reduce network delay [8]. And it also significantly reduces the bandwidth of the backbone link load [9,10]. Unfortunately, the nodes in fog environment are close to the mobile users, and fog computing nodes are usually composed of devices with weak computing ability.…”

Managing Crowds with Wireless and Mobile Technologies

“…Therefore, unlike Clouds, Fog is close to the end-user and supports the distributed computing model. More information about Fog is available in [40,41]. Here we provide a comparison between Fog and Cloud.…”