Yazar "Çalhan, Ali" seçeneğine göre listele

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    Apache Spark Tabanlı Duygu Analizi
    (2021) Yıldırım, Emre; Çalhan, Ali
    Bu çalışmada, büyük verileri bellek içi hesaplama yöntemi ile hızlı bir şekilde işleyebilen Apache Spark açık kaynak kodlu çerçeve kullanılarak duygu analizi gerçekleştirilmiştir. Duygu analizi işleminde Spark içerisinde bulunan MLlib makine öğrenimi kütüphanesi kullanılmıştır. Lojistik regresyon (LR), destek vektör makinesi (SVM) ve Naive Bayes (NB) makine öğrenmesi sınıflandırma algoritmaları kullanılmıştır. Çalışmada, duygu analizinde kullanılan makine öğrenmesi algoritmaları doğruluk, kesinlik ve duyarlılık performanslarına göre değerlendirilmektedir. Sonuçlar, SVM algoritmasının çalışmada kullanılan iki farklı veri setinde de sırasıyla %91, %88 doğruluk, %91, %90 kesinlik ve %91, %87 duyarlılık değerleri ile en iyi performansa sahip olduğunu göstermektedir.
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    Artificial bee colony-based spectrum handoff algorithm in wireless cognitive radio networks
    (Wiley, 2018) Bayrakdar, Muhammed Enes; Çalhan, Ali
    In this work, we proposed a new artificial bee colony-based spectrum handoff algorithm for wireless cognitive radio systems. In our wireless cognitive radio system, primary users, secondary users, and related base stations exist in the same communication environment. For our artificial bee colony-based algorithm, secondary users that always struggle to discover an idle channel have a leading role. While honey bees work hard to find the best-quality nectar source for foraging, secondary users try to find idle channels for making communication. In this way, secondary users are organized for different missions such as sensing and handoff similar to honey bees to minimize spectrum handoff delay by working together. In the spectrum handoff stage, some secondary users must sense the spectrum so that the interrupted secondary user may perform the spectrum handoff process. In our developed spectrum handoff algorithm, the spectrum availability characteristic is observed on the basis of the missions of the bees in the artificial bee colony algorithm with the aim of minimizing the spectrum handoff delay and maximizing probability of finding an idle channel. With the help of the algorithm that is developed using the artificial bee colony, spectrum handoff delay of secondary users is considerably decreased for different number of users without reducing probability of finding an available channel.
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    Artificial Neural Network Based Vertical Handoff Algorithm for Reducing Handoff Latency
    (Springer, 2013) Çalhan, Ali; Çeken, Celal
    One of the most challenging topics for next generation wireless networks is vertical handoff concept since several wireless technologies are assumed to cooperate. Plenty of parameters related to user preferences, application requirements, and network conditions, such as; data rate, service cost, network latency, speed of mobile, battery level, interference ratio and etc. must be considered in vertical handoff process along with traditional RSSI information. In this study, a new artificial neural network based handoff decision algorithm is proposed in order to reduce the handoff latency of smart terminal deployed in aforementioned wireless heterogeneous infrastructures. The prominent parameters data rate, monetary cost and RSSI information are taken as inputs of the developed vertical handoff decision system. Performance results of the proposed system are also compared with those of classical Multiple Attribute Decision Making method Simple Additive Weighting, and of some other artificial intelligence based algorithms. According to the results obtained, the proposed neural network based vertical handoff decision algorithm is able to determine whether a handoff is necessary or not properly, and selects the best candidate access network considering the abovementioned parameters. The results also show that, the neural network based algorithm developed significantly reduces the handoff latency while the number of handoffs, which is another vital performance metric, is still reasonable.
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    Bilişsel radyo ağlarında spektrum el değiştirme
    (2015) Bayrakdar, Muhammed Enes; Çalhan, Ali
    Bilişsel radyo, frekans spektrumundaki kullanılmayan boşlukların fırsatçı bir şekilde kullanılmasını sağlayan yeni bir teknoloji olarak geliştirilmiştir. Sabit spektrum tahsisleri, radyo frekans spektrumunda kullanılmayan frekans kanallarının ortaya çıkmasına neden olmaktadır. Bilişsel radyo teknolojisi, ikincil kullanıcıların ortamı sezerek spektrumun boş ya da kullanılmayan kısımlarından verimli bir şekilde faydalanmasını amaçlamaktadır. Spektrum yönetimi, birincil (lisanslı) ve ikincil (lisanssız) kullanıcılar arasındaki etkileşimi kontrol eden bir tekniktir. İkincil kullanıcıların birincil kullanıcılara girişim oluşturmaması, bilişsel radyo ağlarının başarımı açısından oldukça önemlidir. İkincil kullanıcılara tahsis edilen frekans kanallarının tekrar birincil kullanıcılara tahsisi durumunda ikincil kullanıcıların yeni bir frekans kanalına geçmeleri gerekmektedir. Bu işlem spektrum el değiştirme olarak ifade edilmektedir. Bu çalışmada, bilişsel radyo ağlarında spektrum el değiştirme olarak bilinen ikincil kullanıcıların kanal değiştirmesi ile ilgili çalışmalar incelenmiştir.
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    BULUT DESTEKLİ MEDİKAL NESNELERİN İNTERNETİ TABANLI UZAKTAN SAĞLIK İZLEME SİSTEMİ
    (2021) Çalhan, Ali; Cicioğlu, Murtaza
    Nesnelerin interneti kavramı günümüzde kendinden sıkça söz ettiren bir kavram olmakla birlikte yeni nesil heterojen haberleşme ağları olarak ifade edilmektedir. Özellikle sağlık alanı başta olmak üzere, yerleşim, tarım, taşıma, endüstri vb. alanlarında her geçen gün çözümler üretmekte ve bulut teknolojileriyle birlikte anılmaktadır. Sağlık alanında Medikal Nesnelerin İnterneti olarak isimlendirilen Nesnelerin İnterneti farklı tipte sağlık uygulamaları ve cihazların birbirleri ile haberleşmesini ön plana çıkarmaktadır. Çalışmamızda bir bireyin çeşitli fizyolojik ölçümlerinin kablosuz haberleşme ile merkezi bir düğümde toplanması ve bu düğümün bir ağ geçidine verileri göndermesi şeklinde bir senaryo Riverbed Modeler benzetim programında gerçekleştirilmiştir. Ağ geçidi aldığı verileri anlık olarak buluta aktarması ve ardından bir mobil uygulama sayesinde eşzamanlı olarak sağlık personelinin ekranında görüntülenmesini sağlayacak bir altyapı tasarlanmıştır. Bu sayede gerçek zamanlı bir Medikal Nesnelerin İnterneti uygulaması önerilmektedir. Kalp ritmi, kan basıncı, oksijen miktarı, vücut sıcaklığı ve solunum oranı verileri anlık olarak birey ve sağlık personeli arasında paylaşılmış olup özellikle pandemi süreçlerinde kullanılabilecek bir uzaktan sağlık izleme sistemi kurulmuştur.
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    Case study on handoff strategies for wireless overlay networks
    (Elsevier, 2013) Çalhan, Ali; Çeken, Celal
    One of the most challenging topics for next-generation wireless networks is the process of vertical handoff since many of wireless technologies overlap each other and build a heterogeneous topology. Several parameters, pertaining to user/application requirements and network conditions, such as data rate, service cost, network latency, speed of mobile, and etc. must be considered in the handoff process of heterogeneous networks along with RSSI information. In this paper, adaptive fuzzy logic-based vertical handoff decision-making algorithms are presented for wireless overlay networks which consist of GSM/GPRS/Wi-Fi/IJMTS/WiMAX technologies. The parameters data rate, monetary cost, speed of mobile and RSSI information are processed as inputs of the proposed fuzzy-based systems. According to these parameters, an output value, which varies between 1 and 10, is produced. This output value is utilized to determine whether a handoff process is necessary or not and to select the best candidate access point in the vicinity. The results show that, compared to the traditional RSSI-based algorithm significantly enhanced outcomes can be achieved for both user and network as a consequence of the proposed fuzzy-based handoff systems. The simulation results are also compared with those of classical MADM (Multiple Attribute Decision Making) method, i.e. SAW (Simple Additive Weighting). According to the results obtained, the proposed vertical handoff decision algorithms are able to determine whether a handoff is necessary or not, properly, and select the best candidate access network considering the aforementioned parameters. Moreover, fuzzy-based algorithm noticeably reduces the number of handoffs compared to SAW-based algorithm. (C) 2012 Elsevier B.V. All rights reserved.
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    Comparative Performance Evaluation of Efficient Spectrum Handoff Methods in Wireless Cognitive Networks
    (Ieee, 2018) Bayrakdar, Muhammed Enes; Çalhan, Ali
    There are a variety of techniques and methods used for spectrum handoff in cognitive radio networks. With the help of these techniques and methods, it is ensured that the secondary users can switch to other spectrum without interrupting their communication. Optimization and artificial intelligence techniques in spectrum handoff process are discussed because they are current and open-minded topics. In this work, it has been possible to carry out the spectrum handoff process in various ways, taking into account the needs of the secondary users, and artificial intelligence techniques have been utilized in the decision making process with multiple parameters. By means of the developed hybrid and artificial intelligence based spectrum handoff methods, comparative network parameters such as total number of handoff and total spectrum handoff duration are obtained. In this way, it is possible for the secondary user to select the appropriate method according to the environment conditions.
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    Decısıon System For Rule Based Spectrum Handoff Process Of Secondary Users
    (2017) Bayrakdar, Muhammed Enes; Çalhan, Ali
    Bilişsel radyo ağları, gelecek nesil kablosuz ağlar alanında öne çıkan teknolojilerden biridir. Geleneksel kablosuz ağlardaki sabit spektrum atamalarına karşılık, bilişsel radyo ağları dinamik spektrum tahsis esasına göre çalışmaktadır. Dinamik spektrum tahsis işlemlerinde, ikincil kullanıcıların spektrum bandı değiştirmesi spektrum el değiştirme olarak tanımlanmaktadır. Bu çalışmada, ikincil kullanıcılar için bulanık mantık tabanlı spektrum el değiştirme karar sistemi önerilmiştir. Sistemimiz, üç giriş parametresi ve bir de çıkış parametresinden oluşmaktadır. Çıkıştaki spektrum el değiştirme olasılığı; birincil kullanıcıların ortam kullanım yoğunluğu, ikincil kullanıcıların veri oranı ve ortamdaki gürültü etkisi giriş parametrelerine göre elde edilmektedir. Her bir giriş parametresinin çıkışa olan etkisi ayrı ayrı irdelenmiştir. Elde edilen sonuçlardan, birincil kullanıcıların spektrum kullanım yoğunluğunun diğer giriş parametrelerinden daha baskın olduğu gözlemlenmiştir
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    Deep learning and machine learning based anomaly detection in internet of things environments
    (Gazi Univ, Fac Engineering Architecture, 2022) Gökdemir, Ali; Çalhan, Ali
    Graphical/Tabular Abstract Classical machine learning and deep learning were compared in detecting attacks on IoT environments. Due to its success in anomaly detection in the literature, Support Vector Machines (SVM) and Naive Bayes (NB) algorithms from classical machine learning algorithms were preferred. As a deep learning algorithm, the Long Short-Term Memory (LSTM) algorithm, which is mostly used in fields such as natural language processing and text processing, and which has very few studies in anomaly detection, has been chosen. With the LSTM algorithm, higher values were obtained in accuracy and f1 scores. Figure A. Proposed system model for anomaly detection in IoT environments with LSTM-SVM-NB algorithms Purpose: As the use of Internet of Things (IoT) systems has become widespread, cyber-attacks against these systems have also increased. Cyber-attacks occurring in IoT environments can include different types of attacks, such as the inability of their devices to serve, corruption, data capture, modification, or deletion. In this study, it is tried to predict duplication, interception, and modification attacks in Message Queuing Telemetry Transport (MQTT) messages using an IoT dataset with artificial intelligence techniques. Theory and Methods: In this study, compared to the performance metrics of SVM and NB, which are machine learning algorithms, and LSTM, which is a deep learning algorithm. Results: Experimental results show that the LSTM algorithm can be used in anomaly detection in the cyber security area, apart from natural language processing and text processing, which are the areas widely used in the literature. Besides, it was concluded that the LSTM algorithm achieved higher accuracy than the classical machine learning algorithms. Conclusion: In this paper, a comparison of deep learning and machine learning algorithms for anomaly detection in IoT environments is made. The results show that the LSTM algorithm, gives more effective results in anomaly detection than classical machine learning algorithms, but has some disadvantages in terms of working time.
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    Delay Characteristics of TDMA Medium Access Control Protocol for Cognitive Radio Networks
    (Ieee, 2016) Bayrakdar, Muhammed Enes; Çalhan, Ali
    In this work, we have evaluated the delay characteristics of Time Division Multiple Access (TDMA) protocol. In our simulation scenarios, primary users and secondary users exploit TDMA as a medium access control protocol. We have designed a network environment in Riverbed (OPNET) simulation software that consists of primary users, secondary users, and base stations. In our network model, secondary users sense the spectrum and inform the base station about empty channels. Then, the base station decides accordingly which secondary user may exploit the empty channel. Energy detection technique is employed as a spectrum sensing technique because it is the best when information about signal of primary user is obtained. Besides, different number of users is selected in simulation scenarios in order to achieve accurate delay results. Comparing analytical model with simulation results, we have shown that delay analysis of our system model is reliable and correct.
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    Derin öğrenme tabanlı modülasyon tanıma
    (2023) Leblebici, Merih; Çalhan, Ali; Cicioğlu, Murtaza
    Haberleşme teknolojilerinde her geçen gün artan sinyal çeşitliliği, bu sinyallerin tanımlanması ve sınıflandırılması gerekliliğini ortaya çıkarmıştır. Beşinci nesil (fifth generation, 5G) ve ötesi kablosuz haberleşme teknolojileri, birçok uygulama için vazgeçilmez iletişim araçları haline gelmiştir. Otomatik modülasyon tanıma (automatic modulation recognition, AMR) tekniği, özellikle yeni nesil nesnelerin interneti, akıllı şehirler, otonom araçlar ve bilişsel radyo gibi birçok uygulama için temel bileşen haline gelmiştir. Bu çalışmada sekiz farklı modülasyon türü kullanılarak bir veri seti oluşturulmuş ve derin öğrenme (deep learning, DL) algoritmalarından olan evrişimli sinir ağları (convolutional neural network, CNN) kullanılarak farklı sinyal-gürültü oranlarında (signal-to-noise ratio, SNR) modülasyon türü sınıflandırılması yapılmıştır. Sonuç olarak SNR değerleri 10 dB, 20 dB ve 30 dB iken CNN ile sınıflandırma işleminde sırasıyla %80,76, %99,89 ve %100 doğruluk sağlanmıştır.
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    Drone-assisted smart data gathering for pandemic situations
    (Pergamon-Elsevier Science Ltd, 2022) Çalhan, Ali; Cicioğlu, Murtaza
    In this study, a new approach is proposed based on drone-assisted smart data gathering for pandemic situations. Drones can play important roles in highly dynamic and dense disaster areas for the data gathering process. Under these conditions, if big data gathering is necessary, the network traffic can be lightened and balanced with smart techniques. For these reasons, the drones construct the aerial network and scan the frequency bands in their coverage area. Then the collected data on the related drone is processed in terms of importance and priority levels. The drones take on fog computing capabilities for the specific duties. So, the unnecessary data will not be transmitted to the related destinations and the most priority data will be transferred immediately to the related units. The proposed mechanism is developed and examined with various scenarios. The throughput, delay and energy consumption performance metrics are considered for performance evaluation.
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    Dynamic HUB Selection Process Based on Specific Absorption Rate for WBANs
    (Ieee-Inst Electrical Electronics Engineers Inc, 2019) Cicioğlu, Murtaza; Çalhan, Ali
    Wireless body area networks (WBANs) play an important role in remote health monitoring applications nowadays. WBANs consist of several sensor nodes and a fixed HUB on, in, or around the human body. HUB collects the data from the sensor nodes and sends the received data to a gateway. High data rates from HUB cause to increase in temperature of tissues. If an organ receives electromagnetic signals for longer time period, then it will be affected by heat and might be damaged. In this paper, specific absorption rate, battery level, and priority of the sensor nodes are taken into account for dynamical HUB selection process in WBANs. Therefore, the task of the HUB is shared among the sensor nodes to reduce the negative effects of electromagnetic signals due to fixed HUB placement.
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    An effective routing algorithm for spectrum allocations in cognitive radio based internet of things
    (Wiley, 2022) Cicioğlu, Murtaza; Çalhan, Ali; Miah, Md Sipon
    The Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad-hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR-based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on-demand distance vector routing protocol is proposed for CR-based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.
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    Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring
    (Springer Heidelberg, 2023) Yıldırım, Emre; Cicioğlu, Murtaza; Çalhan, Ali
    The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed.
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    Fuzzy Logic Based Channel Selection for Mobile Secondary Users in Cognitive Radio Networks
    (Ieee, 2015) Bayrakdar, Muhammed Enes; Çalhan, Ali
    Cognitive radio is a new technology that aims to solve the spectrum scarcity problem. In cognitive radio networks; because secondary users utilize the spectrum in an opportunistic manner, it is crucial not to cause any interference to the primary users. In this work, a new approach for channel assignment to mobile secondary users by means of fuzzy logic is proposed. In the proposed fuzzy logic system; by using input parameters of density, mobility, and noise, channel selection probability is obtained. Density parameter expresses how intense the spectrum is used by primary users. Mobility parameter is a distance among secondary users, primary users, and base station. Noise parameter represents the noise, and other disruptive effects in the spectrum. Channel selection probability shows which frequency band of primary users to use according to the parameters of mobile secondary user. In the proposed system, the most suitable channel selection is provided for mobile secondary users.
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    A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance
    (Springer India, 2015) Çalhan, Ali
    This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSA(FL) satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway.
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    Fuzzy Logic Based Spectrum Handoff Decision for Prioritized Secondary Users in Cognitive Radio Networks
    (Ieee, 2015) Bayrakdar, Muhammed Enes; Çalhan, Ali
    Recent studies have revealed that due to the increasing number of wireless users, spectrum scarcity problem has arisen. In order to alleviate this problem, cognitive radio technology has emerged with the aim of using available spectrum in an opportunistic manner by secondary users. While utilizing licensed spectrum, it is inevitable for secondary users not to cause any interference to the primary users. Once the primary user activity is detected on the licensed channel at the time of on-going transmission of secondary user, secondary user must either change the licensed spectrum or stop its transmission. This case of changing spectrum by secondary user is known as spectrum handoff. In this paper, fuzzy logic based spectrum handoff decision is carried out by using data rate, channel usage, and priority. Different data traffics such as audio, and video are considered for secondary users. In order to simulate data traffics, two different simulation scenarios are realized. Besides, several priority classes are taken into account for urgent and real-time communications. In the proposed system, it is seen that accurate spectrum handoff decisions are made for different traffic types.
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    Handover management in software-defined 5G small cell networks via long short-term memory
    (Wiley, 2022) Cicioğlu, Murtaza; Çalhan, Ali
    5G and beyond communication technologies have started to spread around the world. Higher frequencies lead 5G base stations to have small coverage areas. Besides, the wireless network users have mobility and may move fast among the base stations. Software-defined networking (SDN) is a promising network solution for dynamic and dense networks such as 5G networks. The handover process defines the transfer of mobile users' connections among the base stations and the handover has to happen frequently in ultra-dense networks. In this study, we aim to construct a more robust handover based on long short-term memory (LSTM) with SDN in terms of the number of handover and handover failures. LSTM, linear regression, support vector machine, and tree algorithms performances have been investigated for handover. According to the R-2 values of LSTM, SVM, tree, linear regression results are obtained as 0.998, 0.980, 0.980, and 0.75, respectively. Root mean square error, coefficient of determination (R), mean squared error, and mean absolute deviation statistics prove the improvement of the handover mechanism. In the proposed approach, approximately 30% reduction in the HO failure ratio and 22.22% reduction number of handover have been observed.
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    HUBsFLOW: A novel interface protocol for SDN-enabled WBANs
    (Elsevier, 2019) Cicioğlu, Murtaza; Çalhan, Ali
    Wireless Body Area Network (WBAN) concept is one of the most promising technologies for healthcare applications. In WBANs, sensor nodes are capable of sensing, gathering the human body signs and sending them to the HUB; the communication between nodes and HUB is called as intra-WBAN communications. Inter-WBAN communication manages all HUBs for communications of various WBANs. WBANs have inherently heterogeneous structures and limited energy sources, and also, installation/configuration network management processes are increasingly quite complex. New approaches are required to implement WBANs in order to overcome these challenges. We propose the Software Defined Networking (SDN) approach aims at constructing a flexible and manageable structure for inter-WBAN communications. Therefore, a new SDN-enabled WBAN architecture with HUBsFIow interface protocol is proposed in this paper. The proposed architecture provides a flexible, manageable, and an energy sensitive structure. Hence, a controller that is a key component for SDN undertakes all management and control processes about network. HUBsFlow interface protocol is utilized on the controller that provides the communications among the controller and HUBs in inter-WBAN communications. All components, protocols, and algorithms of the proposed architecture are developed and simulated using Riverbed Modeler software. Throughput, delay, packet loss ratio, bit error rate, and energy consumption parameters are taken into account for performance evaluation of the proposed architecture. The results show that the proposed architecture outperforms when comparing with traditional WBAN architecture and satisfies IEEE/ISO 11073 service quality requirements. (C) 2019 Elsevier B.V. All rights reserved.