Optimal deployment of intelligent mobile air quality systems

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY MASTER’S GRADUATION THESIS Optimal deployment of intelligent mobile air quality systems NGUYEN VIET DUNG Dung.NV202342M@sis.hust.edu.vn Major: Data Science and Artificial Intelligence (Elitech) Thesis advisor: Institute: Assoc.Prof Do Phan Thuan _ School of Information and Communication Technology HA NOI, 09/2022 CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM Độc lập – Tự – Hạnh phúc BẢN XÁC NHẬN CHỈNH SỬA LUẬN VĂN THẠC SĨ Họ tên tác giả luận văn: Nguyễn Việt Dũng Đề tài luận văn: Triển khai tối ưu hệ thống quan trắc khơng khí di động thơng minh Chun ngành: Khoa học liệu Trí tuệ nhân tạo Mã số SV: 20202342M Tác giả, Người hướng dẫn khoa học Hội đồng chấm luận văn xác nhận tác giả sửa chữa, bổ sung luận văn theo biên họp Hội đồng ngày 29/10/2022 với nội dung sau: - Thêm giới thiệu chi tiết nghiên cứu có liên quan chương - Đổi tên chương từ “Problem formulation & hardness” thành “Problem formulation” - Thêm phát biểu toán opportunistic sensing optimization trước viết tắt thành OSO - Đổi tên phần 3.2 thành “Mathematical formulation of OSO” - Thêm giải thích rõ hàm mục tiêu điều kiện mục 3.2 - Thêm lý giải thích sử dụng thuật toán quy hoạch động: “In this simplified scenario, our dynamic programming approach guarantees that the set found by the submaxSet function is always maximum thus the number mentioned in the previous section 5.1.1.2 will be equal to Later we will show that we cannot use dynamic programming in the general scenario, and we will need another greedy sub-process which has a lower performance ratio for that.” - Thêm số giải thích chi tiết thuật toán meta-heuristics lý lựa chọn sử dụng chúng, cụ thể sau: + “They are appropriate methods to verify efficiency of the approximation algorithm, since their tremendous performance in practice was shown in numerous research papers, especially researches related to air monitoring systems If the greedy approximation approach is decent, the experimental results produced SĐH.QT9.BM11 Ban hành lần ngày 11/11/2014 by it should be competitive to the ones produced by the chosen metaheuristics It is indeed true, and we will show the experimental results supporting this observation later in this thesis.” + “Two meta-heuristics, the genetic algorithm and the simulated annealing algorithm, are chosen to solve the OSO problem because of their simplicity and efficiency in practice Related researches about air monitoring systems also deployed these methods to solve challenging problems, and the results usually show that they are good choices for creating a solution.” - Thêm giải thích cho hình vẽ bảng biểu - Thêm mô tả input output cho thuật toán - Thêm mục 6.4 “Comparison of results between the approximation algorithm and the meta-heuristics” chuyển mục 6.4 cũ thành mục 6.5 “Discussion” Ngày Giáo viên hướng dẫn tháng năm Tác giả luận văn CHỦ TỊCH HỘI ĐỒNG SĐH.QT9.BM11 Ban hành lần ngày 11/11/2014 Graduation Thesis Assignment Name: Nguyen Viet Dung Phone: +84 399629097 Email : Dung.NV202342M@sis.hust.edu.vn Student ID: 20202342M Class: 20BKHDL-E Thesis title: Optimal deployment of intelligent mobile air quality systems Thesis code: 2020BKHDL-KH01 Affiliation : Hanoi University of Science and Technology I – Nguyen Viet Dung - hereby warrants that the work and presentation in this thesis performed by myself under the supervision of Assoc.Prof Do Phan Thuan All the results presented in this thesis are truthful and are not copied from any other works All references in this thesis including images, tables, figures and, quotes are clearly and fully documented in the bibliography I will take full responsibility for even one copy that violates school regulations Hanoi, 28th September, 2022 Author Nguyen Viet Dung Attestation of thesis advisor : I certify that the thesis entitled “Optimal deployment of intelligent mobile air quality systems” submitted for the degree of Master of Science (M.S.) by Mr Nguyen Viet Dung is the record of research work carried out by him during the period from 10/2020 to 10/2022 under my guidance and supervision, and that this work has not formed the basis for the award of any Degree, Diploma, Associateship and Fellowship or other Titles in this University or any other University or institution of Higher Learning Hanoi, 28th September, 2022 Thesis Advisor Assoc.Prof Do Phan Thuan Acknowledgements In order to obtain this master's thesis, apart from my own efforts, it is impossible not to mention the help of many other people First, I would like to thank Associate Professor Do Phan Thuan and Dr Nguyen Phi Le, my direct mentors From the time I got my thesis title to the time I finished it, there was not a moment that they didn't encourage me to run to the finish line I am where I am today in large part because of their support Next, I have to mention the funding source of VinIF Their financial support helped me to pay my tuition fees and complete my studies with peace of mind Finally, I would like to express my sincerest thanks to my teachers, friends and family Without them by my side, I wouldn't have made it to the end of the road Two years of wonderful lectures and extremely helpful time doing research will be in my heart forever Abstract Monitoring air quality plays a critical role in the sustainable development of developing regions where the air is severely polluted Air quality monitoring systems based on static monitors often not provide information about the area each monitor represents or represent only small areas In addition, they have high deployment costs that reflect the efforts needed to ensure sufficient quality of measurements Meanwhile, the mobile air quality monitoring system, such as the one in this work, shows the feasibility of solving those challenges The system includes environmental sensors mounted on buses that move along their routes, broadening the monitoring areas In such a system, we introduce a new optimization problem named opportunistic sensing that aims to find (1) optimal buses to place the sensors and (2) the optimal monitoring timing to maximize the number of monitored critical regions We investigate the optimization problem in two scenarios: simplified and general bus routes Initially, we mathematically formulate the −targeted1 problem and prove its NP-hardness Then, we propose a polynomial-time -, −1 - approximation algorithm for the problem with the simplified, general routes, respectively To show the proposed algorithms’ effectiveness, we have evaluated it on the real data of real bus routes in Hanoi, Vietnam The evaluation results show that the former algorithm guarantees an average performance ratio of 75.70%, while the latter algorithm achieves the ratio of 63.96% Notably, −1 when the sensors can be on (e.g., enough energy) during (1 the−1)whole route, the -approximation algorithm achieves the approximation ratio of Such ratio, which is almost twice as 78.42% −1 −1 −1 , enlarges the average performance ratio to To further test the efficiency of the greedy approximation algorithm and optimize the results, we propose two more meta-heuristic algorithms for this problem: genetic algorithm and simulated annealing algorithm Experiments show that the above meta-heuristic algorithms only increase the goodness of the results by 1% to 3% on average, but have a much larger running time than the greedy algorithm From there, we see that the approximation algorithm in particular is already a feasible solution in practice without mentioning any other complicated tools Content Graduation Thesis Assignment Acknowledgements Abstract Content List of Figures List of Tables Acronyms 10 Chapter Introduction 11 1.1 Mobile air quality monitoring systems 11 1.2 Opportunistic sensing optimization (OSO) problem 12 1.3 Thesis contribution 12 1.4 Structure of thesis 12 Chapter Related works 13 Chapter Problem formulation 17 3.1 Problem statement 17 3.2 Mathematical formulation of OSO 18 3.3 Hardness of OSO 22 Chapter Theoretical background 24 4.1 Approximation algorithms 24 4.2 Meta-heuristic algorithms 24 4.3 Research methodology 27 Chapter Proposed solution 29 5.1 Approximation algorithms 29 5.2 Meta-heuristic algorithms 38 Chapter Experimental results 42 6.1 Experimental settings 42 6.2 Numerical results of approximation algorithms 45 6.3 Numerical results of meta-heuristic algorithms 51 6.4 Comparison of results between the approximation algorithm and the meta-heuristics 61 6.5 Discussion 61 Chapter Conclusion 63 Published papers 64 References 65 List of Figures Figure A map of size × with bus routes and critical squares When = 2, an Figure Illustration of observable positions, example of the sensor’s turn-on positions on bus is shown With such selected that sensor can observe critical squares , , , and 17 boundary, observable square, and observable segment 19 Figure Illustration of Theorem 3.1’s proof ( is an arbitrary point on a bus route segment is the leftmost observable bound to then it is also observable by and ={ , } Figure A corresponding If is a critical square observable by closest , ) bus map when 20 = 3, 1={ , , , , }, ={ , , , }, 23 Figure The remaining map after removing bus from the map in Fig 1, and the greedy process continues 29 observed by Figure (a) [lAb, Ab] is the unique close segment that contains all sensor’s turn-on positions observed by a sensor turned on at somewhere on the bus route where the critical square turning on sensor from bus route is observed (b) There are critical squares (in this figure, = 5) Each square can be in the middle of the interval [lib, have critical points which are the left endpoints (l , where = 1, … , ) of ib] We then ib such intervals 33 Figure Efficiency heatmap 45 Figure Performance in the simplified scenario with = 10, = 12 46 Figure Performance in the simplified scenario with = 25, = 30 47 Figure 10 Performance in the simplified scenario with = 30, = 36 47 = 42, = 50 47 Figure 11 Performance in the simplified scenario with Figure 12 Performance in the general and special scenario with Figure 14 Performance in the general and special scenario with Figure 15 Performance in the general and special scenario with = 10, = 12 48 = 30, = 36 49 = 42, = 50 50 List of Tables Table Notation list……………………………………………………………… …… 18 Table Meta-heuristics performance compared to the approximation algorithm’s results in the simplified scenario………………………………………………… ……………… 51 Table Meta-heuristics performance compared to the approximation algorithm’s results in the general scenario 55