Tolga Ayav

Ayav, Tolga

Name Variants

Ayav T.
Ayav,Tolga
Ayav, Tolga
Ayav, T.
Tolga Ayav

Job Title

Prof. Dr.

Email Address

tolgaayav@iyte.edu.tr

Scholarly Output

69

Articles

17

Citation Count

189

Supervised Theses

23 Scholarly Output Search Results

Now showing 1 - 10 of 68

Implementation and performance analysis of contex-aware role-based access controls for cloud-based IoT platform
(Izmir Institute of Technology, 2019-09) Döşemeci, Mete Merthan; Ayav, Tolga; Ayav, Tolga
IoT has received substantial attention in both industry and the scholarly world in the recent years. The main idea is to interconnect the physical world with the digital world. Sensors read physical world and present processible data. This data needs to be secured. Currently, most of the cloud based IoT platforms use some sort of Role-Based Access Control (RBAC) , which is one of the approaches to control access to the devices, hence the data. In some cases RBAC is insufficient for fulfilling constantly changing requirements of IoT. ABAC (Attribute Based Access Control) can be flexible enough for fulfilling. However ABAC requires higher level of maintenance. We wanted to implement a access control method that uses both RBAC’s and ABAC’s advantages. We called it OBAC(Operation Based Access Control). Authorization is being implemented in a plug and play manner. We implemented that way because; It is designed for cloud platforms and we wanted to switch between access control methods easily. The results of the experiment shows that proposed access control(OBAC) had minimum latency and management steps across other access control methods.
Block-chain based remote update for embedded devices
(Izmir Institute of Technology, 2019-12) Kaptan, Melike; Ayav, Tolga; Ayav, Tolga; Erten, Yusuf Murat
This research work is an attempt to devise a platform to send automatic remote updates for embedded devices. In this scenario there are Original Equipment Manufacturers (OEMs), Software suppliers, Block-Chain nodes, Gateways and embedded devices. OEMs and software suppliers are there to keep their software on IPFS (Inter Planetary File System) and send the meta-data and hashes of their software to the Block-Chain nodes in order to keep this information distributed and ready to be requested and used. There are also gateways which are also the members of the Block-Chain and IPFS network. Gateways are responsible for asking for a specific update for specific devices from IPFS database using the meta-data standing on the Block-Chain. And they will send those hashed secure updates to the devices. In order to provide a traceable data keeping platform gateway update operations are handled as a transactions in the second block-chain network which is the clock-chain of the gateways. In this study implementation of the two block chain shows us that, even though the calculation overhead of the member devices, with regulations specific to the applications block-chains provide applicable platforms.
Exploring house price dynamics: An agent-based simulation with behavioral heterogeneity
(İzmir Institute of Technology, 2016-07) Özbakan, Ahmet Tolga; Ayav, Tolga; Kale, Serdar
In contemporary capitalist economies, housing is not only a shelter that satisfies a basic human need. It is also a commodity produced for exchange in markets and an asset for storing and enhancing wealth. As such, its mispricing can have repercussions for individuals, firms, industries, nationwide economies, and for the global economy. The purpose of this dissertation is to explore the price dynamics of this complex entity in an analytically tractable framework. In agreement with a recent but growing body of literature, the study shares the view that incorporating insights from behavioral economics can be valuable in such an undertaking. To support this statement, the dissertation first presents a theoretical framework that situates differing views on house prices in a wider split between neoclassical and behavioral economists. Then, the study proposes an agent-based simulation by extending a prominent real estate market model known as the Four Quadrant Model. Specifically, the extended model seeks to explore the extent to which behavioral heterogeneity and dynamic market behavior enhance the existing explanations of house price dynamics. The dissertation validates the proposed model by running a set of experiments with empirical data obtained from Istanbul’s housing market between January 2010 and September 2015. The results suggest that both the inclusion of behavioral heterogeneity and dynamic behavior are relevant in the exploration of house price dynamics. Based on the theoretical framework and the simulation results, the dissertation calls for action on the part of policy makers, researchers, and members of civic and professional organizations.
Parallelization of a novel frequent itemset hiding algorithm on a CPU-GPU platform
(Izmir Institute of Technology, 2014) Heye, Samuel Bacha; Ayav, Tolga; Ayav, Tolga
Data mining is used to extract useful information from large data. But the organizations which mine the data might not be the owner of the data. So, before the owners can make their data accessible for data mining they want to make sure that no sensitive information can be mined from the released data whose discovery by others might harm them. Itemset hiding is one mechanism to prevent the disclosure of sensitive itemsets. In this thesis, a new integer programing based itemset hiding algorithm was developed and a mechanism to speed up the computation time of its implementation was proposed by using parallel computation on Graphical Processing Units (GPUs).
Predictive maintenance for smart industry
(01. Izmir Institute of Technology, 2020-12) Asadzade, Asad; Ayav, Tolga; Ayav, Tolga; 01. Izmir Institute of Technology
After the internet of things developed rapidly, it started to be used in many several industrial areas. Thanks to IoT, data that affect the health of any equipment or other important systems are collected. When these data are processed correctly, important information about the production process is obtained. For example, thanks to this data, systems based on machine learning are created to predict when various components will fail. Thus, maintenance operations are carried out before the component's breakdown, and replacement operations are performed if necessary. This strategy, called predictive maintenance, provides industries with advantages such as maximizing the life of components, reducing extra costs, and time saving. In this study, we applied ARF method, which is based on stream learning, on Turbofan Engine Degradation Simulation Datasets which are provided by NASA to estimate the remaining useful lifetime of jet engines. As a result, we mentioned about the advantages of streaming learning over batch learning and compared our results with other batch learning based studies which are applied on the same datasets.
Citation Count: 0
A Review of Cloud Deployment Models for E-Learning Systems
(Ieee, 2013) Leloglu, Engin; Ayav, Tolga; Aslan, Burak Galip; Ayav, Tolga; Bilgisayar Mühendisliği Bölümü
With the significant growth in the cloud-based systems, many industries give their attention to cloud computing solutions. E-learning is a promising application area since its typical requirements such as dynamically allocation of computation and storage resources, coincide well with cloud characteristics. This paper presents some possible cloud solutions in e-learning environments by emphasizing its pros and cons. It is of paramount importance to choose the most suitable cloud model for an e-learning application or an educational organization in terms of scalability, portability and security. We distinguish various deployment alternatives of cloud computing and discuss their benefits against typical e-learning requirements.
Blockchain application on loyalty card
(Izmir Institute of Technology, 2020-04) Sönmeztürk, Osman; Ayav, Tolga; Ayav, Tolga; Erten, Yusuf Murat
Today, traditional loyalty systems are insufficient to meet the needs of users. The users need to stay within the loyalty system for a long time and accumulate points in order to win prizes and besides, the rewards they receive may be out of their interest. In addition, users usually forget the awards they have won in traditional loyalty systems and have difficulty in following up rewards. In addition to that, users usually do not prefer to share their personal information to join loyalty systems due to privacy concerns. Therefore, the number of customers in the loyalty systems is decreasing day by day. The designed loyalty program mentioned in this thesis works with IZTECH Tokens, which works on the Ethereum chain and are created by following ERC20 standards. Thanks to the new generation loyalty system, users can convert their earned tokens to Ethereum on the stock exchange without accumulating them or can receive services or products with the accumulated tokens according to their interests from a market that has been contracted by the manufacturer. Additionally, users in the designed system do not need to carry many cards, it is adequate to have only one Ethereum wallet. Furthermore, users do not need to share any personal data to join the loyalty system. Markets can request Ether from the manufacturer according to the number of tokens they have accumulated. The loyalty system mentioned in this thesis not only aims to establish a win-win relationship between the manufacturer, market, and client but also to find solutions to the customer problems mentioned above.
Vibration analysis of pre-twisted rotating beams
(Izmir Institute of Technology, 2003) Yıldırım, Tolga; Ayav, Tolga; Yardımoğlu, Bülent
A new linearly pretwisted rotating Timoshenko beam element, which has two nodes and four degrees of freedom per node, is developed and subsequently used for vibration analysis of pretwisted beams with uniform rectangular cross-section. First, displacement functions based on two coupled displacement fields (the polynomial coefficients are coupled through consideration of the differential equations of equilibrium) are derived for pretwisted beams. Next, the stiffness and mass matrices of the finite element model are obtained by using the energy expressions. Finally, the natural frequencies of pretwisted rotating Timoshenko beams are obtained and compared with previously published both theoretical and experimental results to confirm the accuracy and efficiency of the present model. The new pretwisted Timoshenko beam element has good convergence characteristics and excellent agreement is found with the previous studies.
Citation Count: 5
Test input generation from cause-effect graphs
(Springer, 2021) Ufuktepe, Deniz Kavzak; Ayav, Tolga; Belli, Fevzi; Ayav, Tolga; Bilgisayar Mühendisliği Bölümü
Cause-effect graphing is a well-known requirement-based and systematic testing method with a heuristic approach. Since it was introduced by Myers in 1979, there have not been any sufficiently comprehensive studies to generate test inputs from these graphs. However, there exist several methods for test input generation from Boolean expressions. Cause-effect graphs can be more convenient for a wide variety of users compared to Boolean expressions. Moreover, they can be used to enforce common constraints and rules on the system variables of different expressions of the system. This study proposes a new mutant-based test input generation method, Spectral Testing for Boolean specification models based on spectral analysis of Boolean expressions using mutations of the original expression. Unlike Myers' method, Spectral Testing is an algorithmic and deterministic method, in which we model the possible faults systematically. Furthermore, the conversion of cause-effect graphs between Boolean expressions is explored so that the existing test input generation methods for Boolean expressions can be exploited for cause-effect graphing. A software is developed as an open-source extendable tool for generating test inputs from cause-effect graphs by using different methods and performing mutation analysis for quantitative evaluation on these methods for further analysis and comparison. Selected methods, MI, MAX-A, MUTP, MNFP, CUTPNFP, MUMCUT, Unique MC/DC, and Masking MC/DC are implemented together with Myers' technique and the proposed Spectral Testing in the developed tool. For mutation testing, 9 common fault types of Boolean expressions are modeled, implemented, and generated in the tool. An XML-based standard on top of GraphML representing a cause-effect graph is proposed and is used as the input type to the approach. An empirical study is performed by a case study on 5 different systems with various requirements, including the benchmark set from the TCAS-II system. Our results show that the proposed XML-based cause-effect graph model can be used to represent system requirements. The developed tool can be used for test input generation from proposed cause-effect graph models and can perform mutation analysis to distinguish between the methods with respect to the effectiveness of test inputs and their mutant kill scores. The proposed Spectral Testing method outperforms the state-of-the-art methods in the context of critical systems, regarding both the effectiveness and mutant kill scores of the generated test inputs, and increasing the chances of revealing faults in the system and reducing the cost of testing. Moreover, the proposed method can be used as a separate or complementary method to other well-performing test input generation methods for covering specific fault types.
Density grid based stream clustering algorithm
(Izmir Institute of Technology, 2019-11) Ahmed, Rowanda Daoud; Ayav, Tolga; Ayav, Tolga; Dalkılıç, Gökhan
Recently as applications produce overwhelming data streams, the need for strategies to analyze and cluster streaming data becomes an urgent and a crucial research area for knowledge discovery. The main objective and the key aim of data stream clustering is to gain insights into incoming data. Recognizing all probable patterns in this boundless data which arrives at varying speeds and structure and evolves over time, is very important in this analysis process. The existing data stream clustering strategies so far, all suffer from different limitations, like the inability to find the arbitrary shaped clusters and handling outliers in addition to requiring some parameter information for data processing. For fast, accurate, efficient and effective handling for all these challenges, we proposed DGStream, a new online-offline grid and density-based stream clustering algorithm. We conducted many experiments and evaluated the performance of DGStream over different simulated databases and for different parameter settings where a wide variety of concept drifts, novelty, evolving data, number and size of clusters and outlier detection are considered. Our algorithm is suitable for applications where the interest lies in the most recent information like stock market, or if the analysis of existing information is required as well as cases where both the old and the recent information are all equally important. The experiments, over the synthetic and real datasets, show that our proposed algorithm outperforms the other algorithms in efficiency.

Ayav, Tolga

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Scholarly Output

69

Articles

17

Citation Count

189

Supervised Theses

23

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