264 research outputs found

    An Explorative Study of the Effectiveness of Mobile Advertising

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    This study examines factors related to the effectiveness of mobile advertising. Using a large data set with 115, 899 records of ad tap through from a mobile advertising company, we identify that the influencing factors for ad tap through are application type, mobile operators, scrolling frequency, and the regional income level. We use a logit model to analyze how the probability of ad tap through is related to the identified factors. The results show that application type, mobile operators, scrolling frequency, and the regional income level all have significant effects on the likelihood whether users would tap on certain types of advertising. Based on the findings, we propose strategies for mobile advertisers to engage in effective and targeted mobile advertising

    Building a Mobile Advertising System for Target Marketing

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    Mobile advertising has become one of the most exciting new technological frontiers in advertising area in recent years. The ubiquitous nature of mobile phones makes it possible for advertisers to target users effectively. This paper proposes a targeted mobile advertising system (TMAS) that works as a platform to provide consumers personalized ads based on the consumersโ€™ contextual and preference. The platform allows shops to provide contextual and time-sensitive ads and consumers to locate ads and promotion information using their smart phone. A demonstration is conducted to show the validity of the key process in the TMAS

    Novel Packet Switching for Green IP Networks

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    A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy-efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing-related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using wellknown real network topologies and achieved a more energy-efficient performance than that achieved in previous studies.Peer reviewe

    Autonomous Coordinator Selection in Beamformed 60GHz Wireless Networks

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    In 60 GHz wireless networks, autonomous coordinator selection is required to find a device to coordinate the transmissions among devices. In order to minimize the power consumption for the coordinator, we utilize the direction information extracted from beamformed transmissions in finding the coordinator automatically. The problem is formulated as a K-center problem, which is a NP-hard problem in general. Analysis is carried out to find optimal solutions in certain tractable topologies. Numerical algorithms and simulation results are further presented for random two dimensional topologies

    Approximation Algorithms for Resource Allocation

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    This thesis is devoted to designing new techniques and algorithms for combinatorial optimization problems arising in various applications of resource allocation. Resource allocation refers to a class of problems where scarce resources must be distributed among competing agents maintaining certain optimization criteria. Examples include scheduling jobs on one/multiple machines maintaining system performance; assigning advertisements to bidders, or items to people maximizing profit/social fairness; allocating servers or channels satisfying networking requirements etc. Altogether they comprise a wide variety of combinatorial optimization problems. However, a majority of these problems are NP-hard in nature and therefore, the goal herein is to develop approximation algorithms that approximate the optimal solution as best as possible in polynomial time. The thesis addresses two main directions. First, we develop several new techniques, predominantly, a new linear programming rounding methodology and a constructive aspect of a well-known probabilistic method, the Lov\'{a}sz Local Lemma (LLL). Second, we employ these techniques to applications of resource allocation obtaining substantial improvements over known results. Our research also spurs new direction of study; we introduce new models for achieving energy efficiency in scheduling and a novel framework for assigning advertisements in cellular networks. Both of these lead to a variety of interesting questions. Our linear programming rounding methodology is a significant generalization of two major rounding approaches in the theory of approximation algorithms, namely the dependent rounding and the iterative relaxation procedure. Our constructive version of LLL leads to first algorithmic results for many combinatorial problems. In addition, it settles a major open question of obtaining a constant factor approximation algorithm for the Santa Claus problem. The Santa Claus problem is a NPNP-hard resource allocation problem that received much attention in the last several years. Through out this thesis, we study a number of applications related to scheduling jobs on unrelated parallel machines, such as provisionally shutting down machines to save energy, selectively dropping outliers to improve system performance, handling machines with hard capacity bounds on the number of jobs they can process etc. Hard capacity constraints arise naturally in many other applications and often render a hitherto simple combinatorial optimization problem difficult. In this thesis, we encounter many such instances of hard capacity constraints, namely in budgeted allocation of advertisements for cellular networks, overlay network design, and in classical problems like vertex cover, set cover and k-median

    2021 International Conference on Optical Network Design and Modeling (ONDM)

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    Producciรณn CientรญficaThe planning of multi-access edge computing (MEC) systems does not only consist in distributing MEC servers among the base stations (BSs) but also in designing the network to interconnect BSs, MEC resources and the wide area network (WAN) gateway. Due to their high bandwidth, fiber links are the best option for those connections in 5G environments. In contrast to previous works, which only solve the server placement problem, in this paper, an integer linear programming (ILP) formulation is proposed for solving both problems while reducing the installation cost (servers and fibers). The fiber deployment cost is especially important in sparsely populated areas as the distance between BSs are much longer than in urban environments. The model was tested using real BSs locations and population data showing that the formulation considerably reduces the installation cost.Interreg V-A Spain-Portugal (POCTEP) programme 2014- 2020 (project 0667_DISRUPTIVE_2_E)Ministerio de Economรญa, Industria y Competitividad (projects TEC2017-84423-C3-1-P and RED2018- 102585-T)Junta de Castilla y Leรณn - Fondo Europeo de Desarrollo Regional (project VA231P20

    Li-Fi based on security cloud framework for future IT environment

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    This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).Peer reviewedPublisher PD

    Location Estimation in Wireless Communication Systems

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    Localization has become a key enabling technology in many emerging wireless applications and services. One of the most challenging problems in wireless localization technologies is that the performance is easily affected by the signal propagation environment. When the direct path between transmitter and receiver is obstructed, the signal measurement error for the localization process will increase significantly. Considering this problem, we first propose a novel algorithm which can automatically detect and remove the obstruction and improve the localization performance in complex environment. Besides the environmental dependency, the accuracy of target location estimation is highly sensitive to the positions of reference nodes. In this thesis, we also study on the reference node placement, and derive an optimum deployment scheme which can provide the best localization accuracy. Another challenge of wireless localization is due to insufficient number of reference nodes available in the target\u27s communication range. In this circumstance, we finally utilize the internal sensors in today\u27s smartphones to provide additional information for localization purpose, and propose a novel algorithm which can combine the location dependent parameters measured from sensors and available reference nodes together. The combined localization algorithm can overcome the error accumulation from sensor with the help of only few number of reference nodes

    Online Advertising Assignment Problems Considering Realistic Constraints

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.With a drastic increase in online communities, many companies have been paying attention to online advertising. The main advantages of online advertising are traceability, cost-effectiveness, reachability, and interactivity. The benefits facilitate the continuous popularity of online advertising. For Internet-based companies, a well-constructed online advertisement assignment increases their revenue. Hence, the managers need to develop their decision-making processes for assigning online advertisements on their website so that their revenue is maximized. In this dissertation, we consider online advertising assignment problems considering realistic constraints. There are three types of online advertising assignment problems: (i) Display ads problem in adversarial order, (ii) Display ads problem in probabilistic order, and (iii) Online banner advertisement scheduling for advertising effectiveness. Unlike previous assignment problems, the problems are pragmatic approaches that reflect realistic constraints and advertising effectiveness. Moreover, the algorithms the dissertation designs offer important insights into the online advertisement assignment problem. We give a brief explanation of the fundamental methodologies to solve the online advertising assignment problems in Chapter 1. At the end of this chapter, the contributions and outline of the dissertation are also presented. In Chapter 2, we propose the display ads problem in adversarial order. Deterministic algorithms with worst-case guarantees are designed, and the competitive ratios of them are presented. Upper bounds for the problem are also proved. We investigate the display ads problem in probabilistic order in Chapter 3. This chapter presents stochastic online algorithms with scenario-based stochastic programming and Benders decomposition for two probabilistic order models. In Chapter 4, an online banner advertisement scheduling model for advertising effectiveness is designed. We also present the solution methodologies used to obtain valid lower and upper bounds of the model efficiently. Chapter 5 offers conclusions and suggestion for future studies. The approaches to solving the problems are meaningful in both academic and industrial areas. We validate these approaches can solve the problems efficiently and effectively by conducting computational experiments. The models and solution methodologies are expected to be convenient and beneficial when managers at Internet-based companies place online advertisements on their websites.์˜จ๋ผ์ธ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ธ‰๊ฒฉํ•œ ์„ฑ์žฅ์— ๋”ฐ๋ผ, ๋งŽ์€ ํšŒ์‚ฌ๋“ค์ด ์˜จ๋ผ์ธ ๊ด‘๊ณ ์— ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ด๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ๊ด‘๊ณ ์˜ ์žฅ์ ์œผ๋กœ๋Š” ์ถ”์  ๊ฐ€๋Šฅ์„ฑ, ๋น„์šฉ ํšจ๊ณผ์„ฑ, ๋„๋‹ฌ ๊ฐ€๋Šฅ์„ฑ, ์ƒํ˜ธ์ž‘์šฉ์„ฑ ๋“ฑ์ด ์žˆ๋‹ค. ์˜จ๋ผ์ธ์— ๊ธฐ๋ฐ˜์„ ๋‘๋Š” ํšŒ์‚ฌ๋“ค์€ ์ž˜ ์งœ์—ฌ์ง„ ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น๊ฒฐ์ •์— ๊ด€์‹ฌ์„ ๋‘๊ณ  ์žˆ๊ณ , ์ด๋Š” ๊ด‘๊ณ  ์ˆ˜์ต๊ณผ ์—ฐ๊ด€๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์˜จ๋ผ์ธ ๊ด‘๊ณ  ๊ด€๋ฆฌ์ž๋Š” ์ˆ˜์ต์„ ๊ทน๋Œ€ํ™” ํ•  ์ˆ˜ ์žˆ๋Š” ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น ์˜์‚ฌ ๊ฒฐ์ • ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ˜„์‹ค์ ์ธ ์ œ์•ฝ์„ ๊ณ ๋ คํ•œ ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น ๋ฌธ์ œ๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋‹ค๋ฃจ๋Š” ๋ฌธ์ œ๋Š” (1) adversarial ์ˆœ์„œ๋กœ ์ง„ํ–‰ํ•˜๋Š” ๋””์Šคํ”Œ๋ ˆ์ด ์• ๋“œ๋ฌธ์ œ, (2) probabilistic ์ˆœ์„œ๋กœ ์ง„ํ–‰ํ•˜๋Š” ๋””์Šคํ”Œ๋ ˆ์ด ์• ๋“œ๋ฌธ์ œ ๊ทธ๋ฆฌ๊ณ  (3) ๊ด‘๊ณ ํšจ๊ณผ๋ฅผ ์œ„ํ•œ ์˜จ๋ผ์ธ ๋ฐฐ๋„ˆ ๊ด‘๊ณ  ์ผ์ •๊ณ„ํš์ด๋‹ค. ์ด์ „์— ์ œ์•ˆ๋˜์—ˆ๋˜ ๊ด‘๊ณ  ํ• ๋‹น ๋ฌธ์ œ๋“ค๊ณผ ๋‹ฌ๋ฆฌ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฌธ์ œ๋“ค์€ ํ˜„์‹ค์ ์ธ ์ œ์•ฝ๊ณผ ๊ด‘๊ณ ํšจ๊ณผ๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ์‹ค์šฉ์ ์ธ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ๋˜ํ•œ ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น ๋ฌธ์ œ์˜ ์šด์˜๊ด€๋ฆฌ์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•œ๋‹ค. 1์žฅ์—์„œ๋Š” ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น ๋ฌธ์ œ์— ๋Œ€ํ•œ ๋ฌธ์ œํ•ด๊ฒฐ ๋ฐฉ๋ฒ•๋ก ์— ๋Œ€ํ•ด ๊ฐ„๋‹จํžˆ ์†Œ๊ฐœํ•œ๋‹ค. ๋”๋ถˆ์–ด ์—ฐ๊ตฌ์˜ ๊ธฐ์—ฌ์™€ ๊ฐœ์š”๋„ ์ œ๊ณต๋œ๋‹ค. 2์žฅ์—์„œ๋Š” adversarial ์ˆœ์„œ๋กœ ์ง„ํ–‰ํ•˜๋Š” ๋””์Šคํ”Œ๋ ˆ์ด ์• ๋“œ๋ฌธ์ œ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. worst-case๋ฅผ ๋ณด์žฅํ•˜๋Š” ๊ฒฐ์ •๋ก ์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ค๊ณ„ํ•˜๊ณ , ์ด๋“ค์˜ competitive ratio๋ฅผ ์ฆ๋ช…ํ•œ๋‹ค. ๋”๋ถˆ์–ด ๋ฌธ์ œ์˜ ์ƒํ•œ๋„ ์ž…์ฆ๋œ๋‹ค. 3์žฅ์—์„œ๋Š” probabilistic ์ˆœ์„œ๋กœ ์ง„ํ–‰ํ•˜๋Š” ๋””์Šคํ”Œ๋ ˆ์ด ์• ๋“œ๋ฌธ์ œ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋‚˜๋ฆฌ์˜ค ๊ธฐ๋ฐ˜์˜ ํ™•๋ฅ ๋ก ์  ์˜จ๋ผ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ Benders ๋ถ„ํ•ด๋ฐฉ๋ฒ•์„ ํ˜ผํ•ฉํ•œ ์ถ”๊ณ„ ์˜จ๋ผ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•œ๋‹ค. 4์žฅ์—์„œ๋Š” ๊ด‘๊ณ ํšจ๊ณผ๋ฅผ ์œ„ํ•œ ์˜จ๋ผ์ธ ๋ฐฐ๋„ˆ ๊ด‘๊ณ  ์ผ์ •๊ณ„ํš์„ ์„ค๊ณ„ํ•œ๋‹ค. ๋˜ํ•œ, ๋ชจ๋ธ์˜ ์œ ํšจํ•œ ์ƒํ•œ๊ณผ ํ•˜ํ•œ์„ ํšจ์œจ์ ์œผ๋กœ ์–ป๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋Š” ๋ฌธ์ œํ•ด๊ฒฐ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. 5์žฅ์—์„œ๋Š” ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฒฐ๋ก ๊ณผ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ๋ฐฉํ–ฅ์„ ์ œ๊ณตํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฌธ์ œํ•ด๊ฒฐ ๋ฐฉ๋ฒ•๋ก ์€ ํ•™์ˆ  ๋ฐ ์‚ฐ์—… ๋ถ„์•ผ ๋ชจ๋‘ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ์ˆ˜์น˜ ์‹คํ—˜์„ ํ†ตํ•ด ๋ฌธ์ œํ•ด๊ฒฐ ์ ‘๊ทผ ๋ฐฉ์‹์ด ๋ฌธ์ œ๋ฅผ ํšจ์œจ์ ์ด๊ณ  ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ์ด๋Š” ์˜จ๋ผ์ธ ๊ด‘๊ณ  ๊ด€๋ฆฌ์ž๊ฐ€ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฌธ์ œ์™€ ๋ฌธ์ œํ•ด๊ฒฐ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ์˜จ๋ผ์ธ ๊ด‘๊ณ  ํ• ๋‹น๊ด€๋ จ ์˜์‚ฌ๊ฒฐ์ •์„ ์ง„ํ–‰ํ•˜๋Š” ๋ฐ ์žˆ์–ด ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1 Introduction 1 1.1 Display Ads Problem 3 1.1.1 Online Algorithm 4 1.2 Online Banner Advertisement Scheduling Problem 5 1.3 Research Motivations and Contributions 6 1.4 Outline of the Dissertation 9 Chapter 2 Online Advertising Assignment Problem in Adversarial Order 12 2.1 Problem Description and Literature Review 12 2.2 Display Ads Problem in Adversarial Order 15 2.3 Deterministic Algorithms for Adversarial Order 17 2.4 Upper Bounds of Deterministic Algorithms for Adversarial Order 22 2.5 Summary 28 Chapter 3 Online Advertising Assignment Problem in Probabilistic Order 30 3.1 Problem Description and Literature Review 30 3.2 Display Ads Problem in Probabilistic Order 33 3.3 Stochastic Online Algorithms for Probabilistic Order 34 3.3.1 Two-Stage Stochastic Programming 35 3.3.2 Known IID model 37 3.3.3 Random permutation model 41 3.3.4 Stochastic approach using primal-dual algorithm 45 3.4 Computational Experiments 48 3.4.1 Results for known IID model 55 3.4.2 Results for random permutation model 57 3.4.3 Managerial insights for Algorithm 3.1 59 3.5 Summary 60 Chapter 4 Online Banner Advertisement Scheduling for Advertising Effectiveness 61 4.1 Problem Description and Literature Review 61 4.2 Mathematical Model 68 4.2.1 Objective function 68 4.2.2 Notations and formulation 72 4.3 Solution Methodologies 74 4.3.1 Heuristic approach to finding valid lower and upper bounds 75 4.3.2 Hybrid tabu search 79 4.4 Computational Experiments 80 4.4.1 Results for problems with small data sets 82 4.4.2 Results for problems with large data sets 84 4.4.3 Results for problems with standard data 86 4.4.4 Managerial insights for the results 90 4.5 Summary 92 Chapter 5 Conclusions and Future Research 93 Appendices 97 A Initial Sequence of the Hybrid Tabu Search 98 B Procedure of the Hybrid Tabu Search 99 C Small Example of the Hybrid Tabu Search 101 D Linearization Technique of Bilinear Form in R2 104 Bibliography 106Docto
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