Geological disaster information sharing based on Internet of Things standardization

被引:2
作者
Zhang, Guocai [1 ,2 ]
Liu, Xue [1 ,2 ]
Zheng, Fangkun [1 ,2 ]
Sun, Ying [1 ,2 ]
Liu, Guihong [1 ,2 ]
机构
[1] CCCC Fourth Harbor Engn Inst Co Ltd, Guangzhou 510230, Peoples R China
[2] CCCC Key Lab Environm Protect & Safety Fdn Engn Tr, Guangzhou 510230, Peoples R China
关键词
Internet of Things; Landslide monitoring; OGC standard; Sensor things; Landslides; Geological; Disaster;
D O I
10.1007/s12665-023-11353-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Nowadays the recent development of new information generation processes in the Internet of Things (IoT) is applied in various factors such as the development of modern sensors, wireless networks, big data applications, automatic monitoring systems, etc. Landslide disaster monitoring is an important and practical direction of Internet of Things technology in a disaster monitoring system, which has the advantages of low cost and mature technology. With the continuous development of IoT technology, standardized data sharing and interoperability have been put forward on the agenda. Based on the Open Geospatial Consortium (OGC) Sensor Thing Application Programming Interface (API) standard, this article analyzes landslide monitoring data sharing and interoperability from the data model, shared service content, and system construction and provides a reference for the standardization of geological disaster data sharing and interoperability regarding IoT applications. Due to the characteristics of a simple layout, strong anti-damage and self-healing ability, self-organization, etc., based on the ZigBee multi-sensor and wireless Mesh network, it is widely used in the data collection and transmission of geological disaster monitoring systems. The experimental analysis is carried out based on various parameters namely computational cost, power utilization, fitness function, danger rate, evacuation time, total travel time as well as root mean square error. Finally, the results conclude that the proposed approach attained better performances in terms of cost, power utilization, and error rate.
引用
收藏
页数:12
相关论文
共 17 条
[1]   Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city [J].
Chen, Chen ;
Hui, Qiang ;
Xie, Wenxuan ;
Wan, Shaohua ;
Zhou, Yang ;
Pei, Qingqi .
COMPUTER NETWORKS, 2021, 186
[2]   Research on location fusion of spatial geological disaster based on fuzzy SVM [J].
Chen, Guobin ;
Li, Shijin .
COMPUTER COMMUNICATIONS, 2020, 153 :538-544
[3]  
Dachyar M, 2020, International Journal of Advanced Science and Technology, V29, P3654
[4]   Design and implementation of geological hazard monitoring system via the Internet of things [J].
Gao, Xiangmin ;
Wu, Guixian ;
Chen, Jian ;
Zeng, Qingyan .
ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (21)
[5]   A hybrid intelligent model for network selection in the industrial Internet of Things [J].
Goudarzi S. ;
Anisi M.H. ;
Abdullah A.H. ;
Lloret J. ;
Soleymani S.A. ;
Hassan W.H. .
Applied Soft Computing Journal, 2019, 74 :529-546
[6]   Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm [J].
Kaur, Amandeep ;
Sood, Sandeep K. .
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2020, 32 (02) :273-289
[7]   Exploring a Design of Landslide Monitoring System [J].
Khan, Rahat ;
Yousaf, Suhail ;
Haseeb, Abdul ;
Uddin, Muhammad Irfan .
COMPLEXITY, 2021, 2021
[8]   Reliability analysis of the internet of things using Space Fault Network [J].
Li, Shasha ;
Cui, Tiejun ;
Alam, Muhammad .
ALEXANDRIA ENGINEERING JOURNAL, 2021, 60 (01) :1259-1270
[9]   Internet of Things technology in mineral remote sensing monitoring [J].
Liu, Yanbing ;
Dhakal, Sanjev .
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 2020, 48 (12) :2065-2077
[10]  
Lvqing Yang, 2019, 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE), P91, DOI 10.1109/ICCCE48422.2019.9010789