Open AccessFormalizing and securing relationships on multi-task metric learning for IoT-based smart cities
Author(s) -
B I Savelyev,
S V Solodov,
D V Tropin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2094/3/032062
Subject(s) - internet of things , smart city , computer science , metric (unit) , task (project management) , software , the internet , channel (broadcasting) , transport infrastructure , computer security , systems engineering , telecommunications , transport engineering , world wide web , engineering , operations management , programming language
The use of modern information and communication technologies is an essential condition for the formation of the transport infrastructure of a smart city. Scientific and methodological approaches are developed to effectively monitor the transport infrastructure of a smart city based on multi-channel metric learning in the Internet of Things. The proposed solutions provide invariance to the type and nature of the movement of objects. The principles of technical implementation of the proposed method are substantiated using the characteristics of unmanned aerial vehicles of a smart city. Adaptive automatic switching of transport infrastructure monitoring channels is implemented in the form of a neural network analyzer software.