Çiçek, Cihan Tuğrul

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C.T.Çiçek
Çiçek,C.T.
C.,Cihan Tugrul
Cihan Tugrul, Cicek
C.,Çiçek
C., Cicek
Cicek, Cihan Tugrul
Cihan Tuğrul, Çiçek
C.T.Cicek
C., Cihan Tugrul
Çiçek, Cihan Tuğrul
Ç.,Cihan Tuğrul
Cicek,C.T.
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Doktor Öğretim Üyesi
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cihan.cicek@atilim.edu.tr
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Scholarly Output

5

Articles

4

Citation Count

32

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0

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2020 - 20235
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Now showing 1 - 5 of 5
  • Thumbnail Image
    Article
    Citation Count: 3
    Privacy protection via joint real and reactive load shaping in smart grids
    (Elsevier, 2022) Çiçek, Cihan Tuğrul; Ilic, Marija; Gultekin, Hakan; Cicek, Cihan Tugrul; Tavli, Bulent; Industrial Engineering
    Frequent metering of electricity consumption is crucial for demand-side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers' private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information. (c) 2022 Elsevier Ltd. All rights reserved.
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    Article
    Citation Count: 0
    SANAL MARKET SEKTÖRÜNDE HEDEF MÜŞTERİ KİTLESİNİN TANIMLANMASI VE MAKİNE ÖĞRENMESİ İLE TÜKETİM EĞİLİMLERİNİN TAHMİNİ
    (2023) Çiçek, Cihan Tuğrul; Selçuk, Gözdem Dural; Industrial Engineering
    Tüketici davranışları, değişen yaşam koşulları doğrultusunda farklılaşmakta ve sanal market kullanımı her zamankinden daha yüksek bir hızla artmaktadır. Bu durum, pazarda rekabet eden firmaları yeni iş modelleri arayışına yöneltmekte ve sanal market sektöründe rekabet eden ve/veya sektöre yeni girmeyi hedefleyen firmaların hedef müşteri kitlesini anlayabilmeleri, pazar stratejilerini belirlemeleri açısından önem arz etmektedir. Bu çalışmada, sanal market kullanımına yönelen tüketici yapısını anlayabilmek amacı ile bir anket çalışması uygulanmış olup; anket sonuçları istatistiksel çıkarım yöntemleri ile incelenerek, sanal market kullanımını tercih eden tüketicilerin demografik yapısı belirlenmiştir. Daha sonra belirlenen demografik özellikler üzerinden, sınıflandırma problemleri için kullanılan çeşitli makine öğrenmesi teknikleri ile bireylerin sanal market kullanımları tahmin edilmiş ve kullanılan tahmin modelleri çeşitli performans ölçütleri ile kıyaslanmıştır. Karşılaştırmalar sonucunda rasgele orman tekniği kullanılan dört farklı ölçütün üçünde en yüksek skora ulaşmıştır. Bunlara ek olarak, anket cevapları ışığında sanal market sektöründe faaliyet gösteren/gösterecek olan firmalara müşteri memnuniyeti üzerinde etken olabilecek faktörler hakkında bazı iç görüler sunulmuştur. Elde edilen sonuçlar, hızlı ve kullanımı kolay mobil aplikasyonlarla satış sonrası hizmetlerin müşterilerin sanal market kullanımını artırdığını, minimum tutar ve sınırlı teslimat bölgelerinin kullanımı azalttığını göstermiştir.
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    Conference Object
    Citation Count: 0
    A Reinforcement Learning Algorithm for Data Collection in UAV-aided IoT Networks with Uncertain Time Windows
    (Ieee, 2021) Çiçek, Cihan Tuğrul; Industrial Engineering
    Unmanned aerial vehicles (UAVs) have been considered as an efficient solution to collect data from ground sensor nodes in Internet-of-Things (IoT) networks due to their several advantages such as flexibility, quick deployment and maneuverability. Studies on this subject have been mainly focused on problems where limited UAV battery is introduced as a tight constraint that shortens the mission time in the models, which significantly undervalues the UAV potential. Moreover, the sensors in the network are typically assumed to have deterministic working times during which the data is uploaded. In this study, we revisit the UAV trajectory planning problem with a different approach and revise the battery constraint by allowing UAVs to swap their batteries at fixed stations and continue their data collection task, hence, the planning horizon can be extended. In particular, we develop a discrete time Markov process (DTMP) in which the UAV trajectory and battery swapping times are jointly determined to minimize the total data loss in the network, where the sensors have uncertain time windows for uploading. Due to the so-called curse-of-dimensionality, we propose a reinforcement learning (RL) algorithm in which the UAV is trained as an agent to explore the network. The computational study shows that our proposed algorithm outperforms two benchmark approaches and achieves significant reduction in data loss.
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    Article
    Citation Count: 5
    3-D Dynamic UAV Base Station Location Problem
    (informs, 2021) Çiçek, Cihan Tuğrul; Shen, Zuo-Jun Max; Gultekin, Hakan; Tavli, Bulent; Industrial Engineering
    We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice.
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    Article
    Citation Count: 24
    Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization
    (Ieee-inst Electrical Electronics Engineers inc, 2020) Çiçek, Cihan Tuğrul; Gultekin, Hakan; Tavli, Bulent; Yanikomeroglu, Halim; Industrial Engineering
    Unmanned Aerial Vehicle Base Stations (UAV-BSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) with a potential to create opportunities for enhancing the capacity of the network by dynamically moving the supply towards the demand while facilitating the services that cannot be provided via other means efficiently. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAV-BS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAV-BS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAV-BS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.