3 research outputs found

    Approximation algorithms for mobile multi-agent sensing problem

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.Multi-agent systems are generally applicable in a wide diversity of domains, such as robot engineering, computer science, the military, and smart cities. In particular, the mobile multi-agent sensing problem can be defined as a problem of detecting events occurring in a large number of nodes using moving agents. In this thesis, we introduce a mobile multi-agent sensing problem and present a mathematical formulation. The model can be represented as a submodular maximization problem under a partition matroid constraint, which is NP-hard in general. The optimal solution of the model can be considered computationally intractable. Therefore, we propose two approximation algorithms based on the greedy approach, which are global greedy and sequential greedy algorithms, respectively. We present new approximation ratios of the sequential greedy algorithm and prove tightness of the ratios. Moreover, we show that the sequential greedy algorithm is competitive with the global greedy algorithm and has advantages of computation times. Finally, we demonstrate the performances of our results through numerical experiments.๋‹ค์ค‘ ์—์ด์ „ํŠธ ์‹œ์Šคํ…œ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋กœ๋ด‡ ๊ณตํ•™, ์ปดํ“จํ„ฐ ๊ณผํ•™, ๊ตฐ์‚ฌ ๋ฐ ์Šค๋งˆํŠธ ๋„์‹œ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ๋Š” ์›€์ง์ด๋Š” ์—์ด์ „ํŠธ๋ฅผ ์ด์šฉํ•ด ๋งŽ์€ ์ˆ˜์˜ ๋…ธ๋“œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ด๋ฒคํŠธ๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๋ฌธ์ œ๋กœ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ์˜ ์ˆ˜ํ•™์  ๊ณต์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ NP-๋‚œํ•ด ๋ฌธ์ œ์ธ ๋ถ„ํ•  ๋งคํŠธ๋กœ์ด๋“œ ์ œ์•ฝ ํ•˜์—์„œ ํ•˜์œ„ ๋ชจ๋“ˆ ํ•จ์ˆ˜์˜ ์ตœ๋Œ€ํ™” ๋ฌธ์ œ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฌธ์ œ์˜ ์ตœ์ ํ•ด๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ์˜ ํฌ๊ธฐ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ๋ณดํ†ต ํ•ฉ๋ฆฌ์ ์ธ ์‹œ๊ฐ„ ์ด๋‚ด์— ๊ณ„์‚ฐํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํƒ์š•์  ์ ‘๊ทผ ๋ฐฉ์‹์— ๊ธฐ์ดˆํ•œ ๋‘ ๊ฐ€์ง€ ๊ทผ์‚ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜)์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ƒˆ๋กœ์šด ๊ทผ์‚ฌ ๋น„์œจ์„ ์ฆ๋ช…ํ•˜๊ณ  ๊ทผ์‚ฌ ๋น„์œจ์— ์ •ํ™•ํ•˜๊ฒŒ ์ผ์น˜ํ•˜๋Š” ์ธ์Šคํ„ด์Šค๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ, ์ˆ˜์น˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋กœ ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํšจ๊ณผ์ ์ธ ํ•ด๋ฅผ ์ฐพ์•„์ค„ ๋ฟ ์•„๋‹ˆ๋ผ, ์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋น„๊ตํ•ด ๊ณ„์‚ฐ ์‹œ๊ฐ„์˜ ์ด์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค.Chapter 1 Introduction 1 Chapter 2 Literature Review 4 Chapter 3 Problem statement 7 Chapter 4 Algorithms and approximation ratios 11 Chapter 5 Computational Experiments 22 Chapter 6 Conclusions 30 Bibliography 31 ๊ตญ๋ฌธ์ดˆ๋ก 40Maste

    Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

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    We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem

    Maximizing lifetime in wireless sensor networks with multiple sensor families

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    Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times
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