Design of scheduling algorithms for UAVs to Detect Air Pollution Sources from Chimneys in Industrial Area

• Autorzy: Vinh Le Van , Huyen Ngo Thanh

Identyfikatory

DOI

10.15439/2022R20

• Języki publikacji: EN

Abstrakty

EN

With the rapid development of Unmanned Aerial Vehicle (UAV), many related applications using UAVs is to monitor air quality in urban, rural or industrial areas. They often focus on how to monitor the propagation of air pollution, provided the pollution sources should be positioned with permanently placed wireless sensors. However, it is hard and time-consuming to identify pollution sources due to a number of chimneys in industrial areas. Therefore, to air pollution source detection in the minimum search time from the chimneys with fixed locations in an industrial park using one or more UAVs. In this paper, we propose two heuristics algorithms for air-pollution-source detection by UAVs including Interference-Graph- Based Algorithm (IGBA), and Extended Interference-Graph-Based Algorithm (EIGBA). As a result, the detection time by these proposed algorithms compared with that by the Traveling Salesman Problem (TSP) algorithm air pollution source detection time is significantly reduced.

Słowa kluczowe

EN

Gaussian Plume Model; Unmanned Aerial Vehicle; Air Pollution

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 273--278
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr.

Twórcy

autor

Vinh Le Van

• Faculty of Information Technology, Hung Yen University of Technology and Education, Hungyen, Vietnam

autor

Huyen Ngo Thanh

• Faculty of Information Technology, Hung Yen University of Technology and Education, Hungyen, Vietnam

Bibliografia

• [1] P. W. Birnie and A. E. Boyle, International law and the environment, 1994.
• [2] K. K. Khedo, R. Perseedoss, and A. Mungur, “A wireless sensor network air pollution monitoring system,” arXiv preprint https://arxiv.org/abs/1005.1737, 2010.
• [3] B. Bathiya, S. Srivastava, and B. Mishra, “Air pollution monitoring using wireless sensor network,” in 2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2016, pp. 112–117.
• [4] K. Hu, V. Sivaraman, B. G. Luxan, and A. Rahman, “Design and evaluation of a metropolitan air pollution sensing system,” IEEE Sensors Journal, vol. 16, no. 5, pp. 1448–1459, 2015.
• [5] Z. Qiu, X. Chu, C. Calvo-Ramirez, C. Briso, and X. Yin, “Low altitude uav air-to-ground channel measurement and modeling in semiurban environments,” Wireless Communications and Mobile Computing, 2017.
• [6] M. Benjamin, Drone warfare: Killing by remote control. Verso Books, 2013.
• [7] S. Pochwała, A. Gardecki, P. Lewandowski, V. Somogyi, and S. Anweiler, “Developing of low-cost air pollution sensor—measurements with the unmanned aerial vehicles in poland,” Sensors, vol. 20, no. 12, 2020. [Online]. Available: https://www.mdpi.com/1424-8220/20/12/3582
• [8] S. Ito, K. Akaiwa, Y. Funabashi, H. Nishikawa, X. Kong, I. Taniguchi, and H. Tomiyama, “Load and wind aware routing of delivery drones,” Drones, vol. 6, no. 2, 2022. [Online]. Available: https: //www.mdpi.com/2504-446X/6/2/50
• [9] C. Zhan, Y. Zeng, and R. Zhang, “Energy-efficient data collection in uav enabled wireless sensor network,” IEEE Wireless Communications Letters, vol. 7, no. 3, pp. 328–331, 2017.
• [10] Q. Gu, D. R Michanowicz, and C. Jia, “Developing a modular unmanned aerial vehicle (uav) platform for air pollution profiling,” Sensors, vol. 18, no. 12, p. 4363, 2018.
• [11]O. Alvear, N. R. Zema, E. Natalizio, and C. T. Calafate, “Using uav-based systems to monitor air pollution in areas with poor accessibility,” Journal of Advanced Transportation, vol. 2017, 2017.
• [12] D. Ni, G. Yu, and S. Rathinam, “Unmanned aircraft system and its applications in transportation,” Journal of Advanced Transportation, vol. 2017, 2017.
• [13] A. De Visscher, Air dispersion modeling: foundations and applications. John Wiley & Sons, 2013.
• [14] A. Green, R. Singhal, and R. Venkateswar, “Analytic extensions of the gaussian plume model,” Journal of the Air Pollution Control Association, vol. 30, no. 7, pp. 773–776, 1980.
• [15] G. Reinelt, “Tsplib—a traveling salesman problem library,” ORSA journal on computing, vol. 3, no. 4, pp. 376–384, 1991.
• [16] A. K. Jain, “Data clustering: 50 years beyond k-means,” Pattern recognition letters, vol. 31, no. 8, pp. 651–666, 2010.
• [17] E. Benavent and A. Martı́nez, “Multi-depot multiple tsp: a polyhedral study and computational results,” Annals of Operations Research, vol. 207, no. 1, pp. 7–25, 2013.
• [18] M. Latah, “Solving multiple tsp problem by k-means and crossover based modified aco algorithm.” International Journal of Engineering Research and Technology, vol. 5, pp. 430–434, 2016.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).