ISSN 2738-0971 | eISSN 2738-1013
Bulletin of Natural Sciences Research
Open access journal | Double-blind peer review

Izzet Fatih Senturk

Bursa Technical University, Bursa, Turkey

Articles

Open Access Original Scientific Paper

COVID-19 RISK ASSESSMENT IN PUBLIC TRANSPORT USING AMBIENT SENSOR DATA AND WIRELESS COMMUNICATIONS

by Izzet Fatih Senturk, Nurettin Gökhan ADAR, Stefan Panić, Časlav Stefanović, Mete Yağanoğlu, Bojan Prilinčević

Covid-19 causes one of the most alarming global health and economic crises in modern times. Countries around the world establish different preventing measures to stop or control Covid-19 spread. The goal of this paper is to present methods for the evaluation of indoor air quality in public transport to assess the risk of contracting Covid-19. The first part of the paper involves investigating the relationship between Covid-19 and various factors affecting indoor air quality. The focus of this paper relies on exploring existing methods to estimate the number of occupants in public transport. It is known that increased occupancy rate increases the possibility of contamination as well as indoor carbon dioxide concentration. Wireless data collection schemes will be defined that can collect data from public transportation. Collected data are envisioned to be stored in the cloud for data analytics. We will present novel methods to analyze the collected data by considering the historical data and estimate the virus contagion risk level for each public transportation vehicle in service. The methodology is expected to be applicable for other airborne diseases as well. Real-time risk levels of public transportation vehicles will be available through a mobile application so that people can choose their mode of transportation accordingly.

View Full-Text DOI 10.5937/bnsr10-29239 Published: 08 Nov 2020
Open Access Original Scientific Paper

COOPERATIVE MOBILE DATA COLLECTION IN SMART CITIES

by Izzet Fatih Senturk

Smart cities are driven by huge amount of data collected from sensors deployed across the city. Sensors typically form a multi-hop network with a base station (BS) in order to send their data to the command and control center. However, sparse deployment of sensors can leave subsets of the network partitioned from the rest of the network. In such a case, isolated partitions cannot forward their data to the BS. Consequently, network coverage and data fidelity decline. A possible solution to link partitions and provide connectivity is employing mobile data collectors (MDCs). A smart vehicle supporting wireless communication can act as an MDC and carry data between sensors and the BS. Using a single MDC extends the average tour length. To minimize the maximum tour length, multiple MDCs can be employed. To identify sensors to be visited by each MDC, this paper clusters partitions as many as the number of MDCs and assigns an MDC for each cluster. Then two different cooperative data collection schemes are considered based on the availability of inter-MDC data exchange. If MDCs collaborate in data delivery, they meet at certain meeting points for data exchange. Such a cooperation avoids the requirement of visiting the BS for some MDCs and reduces tour lengths. On the other hand, MDCs closer to the BS can experience data loss due to buffer overflow given the higher volume of the accumulated data. Presented approaches are evaluated in terms of maximum tour length, data latency, and data loss. The smart city application is simulated with deployment of sensors on certain amenity types. Geographic data is obtained from a volunteered geographic information system and MDC mobility is restricted with the road network. Obtained results indicate that MDC cooperation decreases maximum tour length at the expense of increased rate of data loss and data latency.

View Full-Text DOI 10.5937/univtho10-25351 Published: 07 Apr 2020