Open AccessUSING MOBILE CROWD SENSING FOR SOCIAL DISTANCING REAL-TIME NAVIGATION
Author(s) -
Nataliia Kunanets,
Nataliia Kunanets,
Olga Artemenko,
Pavlo Fedorka,
Ruslan Nebesnyi
Publication year - 2021
Publication title -
upravlìnnâ rozvitkom skladnih sistem
Language(s) - English
Resource type - Journals
eISSN - 2412-9933
pISSN - 2219-5300
DOI - 10.32347/2412-9933.2021.47.57-62
Subject(s) - computer science , context (archaeology) , social distance , mobile device , global positioning system , social media , task (project management) , human–computer interaction , world wide web , covid-19 , engineering , geography , telecommunications , medicine , disease , archaeology , pathology , infectious disease (medical specialty) , systems engineering
The study focuses on current trends in the use of smartphones and other smart devices to provide information support to users in a COVID pandemic. The perspective adaptations of mobile crowd sensing (MCS) information technologies to ensure the social distance of pedestrians while walking through the city are described. Specifics of navigation and data collection from users' smartphone’s sensors in the case of open space and in large buildings (stations, hospitals, government agencies and other social infrastructure) navigation are considered. A project approach to the creation of a mobile recommender application for a safe walking route in real time with the support of social distancing based on hybrid tourist recommender systems, mobile crowd sensing and big data analysis is proposed. The main problems and challenges presented with the implementation of such a project are outlined. Safe navigation of users in large rooms is also an increase in the urgent task. After all, even the best strict guarantees of the ban do not make it possible to determine the assessment of health care facilities, construction of social, educational, trade, transport infrastructure, etc. The main problems and stages of data extraction from the user 's device are considered, as well as the approaches to the organization of user navigation within the city are analyzed: both indoors and outdoors. The algorithm was proposed for building a safe route recommendation with mobile crowd sensing application with multi-criteria context evaluation for social distancing real-time navigation.