A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc ...

@article{10209,
   author = {Lánský, Jan and Rahmani, Amir Masoud and Zandavi, Seid Miad and Chung, Vera and Yousefpoor, Efat and Yousefpoor, Mohammad Sadegh and Khan, Faheem and Hosseinzadeh, Mehdi},
   article_location = {Berlin},
   article_number = {1},
   doi = {http://dx.doi.org/10.1038/s41598-022-20353-x},
   keywords = {Air pollution; flying ad hoc networks; Internet of things},
   language = {eng},
   issn = {2045-2322},
   journal = {Scientific Reports},
   title = {A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks},
   url = {https://www.nature.com/articles/s41598-022-20353-x},
   volume = {12},
   year = {2022}
}

TY  - JOUR
ID  - 10209
AU  - Lánský, Jan - Rahmani, Amir Masoud - Zandavi, Seid Miad - Chung, Vera - Yousefpoor, Efat - Yousefpoor, Mohammad Sadegh - Khan, Faheem - Hosseinzadeh, Mehdi
PY  - 2022
TI  - A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks
JF  - Scientific Reports
VL  - 12
IS  - 1
SP  - 1-17
EP  - 1-17
PB  - Springer Nature Limited
SN  - 20452322
KW  - Air pollution
KW  - flying ad hoc networks
KW  - Internet of things
UR  - https://www.nature.com/articles/s41598-022-20353-x
N2  - Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN.
ER  - 

LÁNSKÝ, Jan, Amir Masoud RAHMANI, Seid Miad ZANDAVI, Vera CHUNG, Efat YOUSEFPOOR, Mohammad Sadegh YOUSEFPOOR, Faheem KHAN and Mehdi HOSSEINZADEH. A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks. \textit{Scientific Reports}. Berlin: Springer Nature Limited, 2022, vol.~12, No~1, p.~1-17. ISSN~2045-2322. Available from: https://dx.doi.org/10.1038/s41598-022-20353-x.