Tuncay Ercan

Bio: Tuncay Ercan is an academic researcher from Yaşar University. The author has contributed to research in topics: Cloud computing & Wireless sensor network. The author has an hindex of 7, co-authored 40 publications receiving 707 citations. Previous affiliations of Tuncay Ercan include Siemens.

Host Based Dynamic Throughput Maximization Model for IEEE 802.11 WLAN

Abstract: As the demand for uninterrupted Internet access grows, the popularity of wireless communication increases. However, wireless communication has some problems compared to conventional wired communication. Especially, if widely used Wireless Local Area Network (WLAN) applications are taken into consideration, it becomes an important issue to balance the load among available access points. It is impossible to balance the load when wireless hosts associate with an access point by using the classical approach of Received Signal Strength Index (RSSI). Some solutions containing a central server, requiring a specific brand of access point or protocol revisions have been proposed previously, but none of them has been favored as a generally accepted solution. In this study, a proposal which is central server free and requires no modifications to the existing infrastructure is presented. The proposed model is based on a dynamic determination of the least loaded access point to associate with, in order to balance load and maximize throughput.

ANFIS Analysis of Wireless Sensor Data with FPGA

Abstract: Applications related with WSNs may include thousands of separate sensor nodes, production and control data for different industrial sectors. It is important to manage these applications, monitor the network and reprogram the nodes to avoid operational problems. In this study, we propose a smart wireless sensor network using a reconfigurable embedded system of Field-Programmable Gate Arrays (FPGAs) with a soft-core processor. This processor can be programmed dynamically and synthesized to implement the preprocessing of sensed data by ensemble Hybrid Neuro-Fuzzy algorithms such as Adaptive Neuro-Fuzzy Inference System (ANFIS). The first part of the proposed work is based on Matlab software to develop and train the ANFIS algorithm. Two different types of data sets (temperature and humidity) downloaded from Internet have been used in order to make a comparison between the Matlab Toolbox and modified ANFIS algorithm with momentum factor. The results obtained in this study have shown that the modified ANFIS algorithm is the convenient choice in terms of speed, accuracy.

Training professional smart city workforces through a partnership with technology firms

Abstract: Smart cities need professionals who are well trained in basic technologies. However, the existing workforce in the cities is unable to meet this new and complex job demand due to the ever-evolving and diverse technological changes. Professionals trained in high technology are few in number and demand high wages. As a result, the need for a workforce is increasing, and it becomes a necessity to train the existing workforce to adapt to high-quality jobs. It is necessary to change the training programs in technical schools and universities to train a highly qualified workforce, but the results of this can be seen in a long period. Naturally, to achieve an effective result in a short time, existing technology employees should be educated on the needed subjects and adapt to new technologies. New graduates trained with old education programs should also be included in these programs. Resources available for education are universities, technical schools, professional organizations, and companies that produce technology. Due to their official programs and institutional nature, the adaptation of the first three sources to the new conditions will take a longer time. Today, technology-producing companies are more advanced in technology than most educational institutions. Therefore, in the training of the current workforce, technology companies are an important alternative that the state or private institutions can offer to get fast results and to train a sufficient number of qualified workforce, together with both the main mechanism of the master–apprentice approach for the company and the cooperation between companies working in the same sector.

Understanding the determinants of cloud computing adoption

Abstract: – The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high‐tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure., – A questionnaire‐based survey was used to collect data from 111 firms belonging to the high‐tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis., – The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing., – The research was conducted in the high‐tech industry, which may limit the generalisability of the findings., – The findings offer cloud computing service providers with a better understanding of what affects cloud computing adoption characteristics, with relevant insight on current promotions., – The research contributes to the application of new technology cloud computing adoption in the high‐tech industry through the use of a wide range of variables. The findings also help firms consider their information technologies investments when implementing cloud computing.

Progress in Outlier Detection Techniques: A Survey

Abstract: Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

A Descriptive Literature Review and Classification of Cloud Computing Research

Abstract: We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.

Molecularly Imprinted Electrochemical Sensors

Abstract: In this review, the applications of molecularly imprinted polymer (MIP) materials in the area of electrochemical sensors have been explored. The designs of the MIPs containing different polymers, their preparation and their immobilization on the transducer surface have been discussed. Further, the employment of various transducers containing the MIPs based on different electrochemical techniques for determining analytes has been assessed. In addition, the general protocols for getting the electrochemical signal based on the binding ability of analyte with the MIPs have been given. The review ends with describing scope and limitations of the above electrochemical based MIP sensors.