Yazar "Cengiz, Korhan" seçeneğine göre listele

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    Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment
    (Elsevier, 2023) Kumari, Rani; Sah, Dinesh Kumar; Cengiz, Korhan; Ivkovic, Nikola; Gehlot, Anita; Salah, Bashir
    The rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.
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    Blockchain-assisted post-quantum privacy-preserving public auditing scheme to secure multimedia data in cloud storage
    (Springer, 2024) Gautam, Deepika; Prajapat, Sunil; Kumar, Pankaj; Das, Ashok Kumar; Cengiz, Korhan; Susilo, Willy
    With the escalation of multimedia data, cloud technology has played a very important role in its management with its promising computing and storage capabilities. Cloud computing leverages high scalability, resources, and on-demand services, which have a remarkable impact on assisting multimedia data storage. Meanwhile, any alteration in the outsourced multimedia data is a severe menace. Numerous researchers have focused on providing validation of integrity through the auditing mechanism. Regrettably, their mechanisms are focused on the traditional public-key cryptosystem and cannot withstand quantum attacks. Additionally, the centralised approach of completely relying on a third-party auditor (TPA) of existing mechanisms may lead to biased auditing results. Hence, employing the advantage of resistance against quantum attack and removing the liability of certificates with lattice cryptography and an identity-based cryptosystem, we have presented a new auditing framework by utilising blockchain technology. Blockchain technology is employed in order to track the activities of the cloud service provider and TPA, resulting less dependency on TPA. Security analysis portrays security of the framework under the random oracle model, which depends is based on the Shortest Integer Solution (SIS) problem of the lattices. Reduced computation, communication and storage overheads with efficient features and performance is observed for the proposed mechanism through analysis conducted with similar mechanisms. As a result, better operability is achieved by the proposed mechanism.
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    BukaGini: A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Bouke, Mohamed Aly; Abdullah, Azizol; Frnda, Jaroslav; Cengiz, Korhan; Salah, Bashir
    Feature interaction is a vital aspect of Machine Learning (ML) algorithms, and gaining a deep understanding of these interactions can significantly enhance model performance. This paper introduces the BukaGini algorithm, an innovative and robust approach for feature interaction analysis that capitalizes on the Gini impurity index. By exploiting the unique properties of the BukaGini index, our proposed algorithm effectively captures both linear and nonlinear feature interactions, providing a richer and more comprehensive representation of the underlying data. We thoroughly evaluate the BukaGini algorithm against traditional Gini index-based methods on various real-world datasets. These datasets include the High School Students' Performance (HSSP) dataset, which examines factors affecting student performance; Cancer Data, which focuses on identifying cancer types based on gene expression; Spambase, which targets spam email classification; and the UNSW-NB15 dataset, which addresses network intrusion detection. Our experimental results demonstrate that the BukaGini algorithm consistently outperforms traditional Gini index-based methods in terms of accuracy. Across the tested datasets, the BukaGini algorithm achieves improvements ranging from 0.32% to 2.50%, underscoring its effectiveness in handling diverse data types and problem domains. This performance gain highlights the potential of the BukaGini algorithm as a valuable tool for feature interaction analysis in various ML applications.
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    Energy efficient medium access control protocol for data collection in wireless sensor network: a q-learning approach
    (Elsevier Ltd, 2022) Sah, Dinesh Kumar; Amgoth, Tarachand; Cengiz, Korhan
    In emerging business markets, the data is becoming new gold for the industries. There are several places such as marketing, manufacturing, and analysis of future trends becoming data-intensive to achieve growth. The tiny sensor placements in the field, cities, industry, buildings and sea are helping to collect data and process it either for information retrieval or decision making. The periodic scheduling of radio transceivers assists in accomplishing efficient energy utilization in sensors often uses Time-division multiple access (TDMA) protocols for node scheduling. Our objective is to reduce the amount of time; the receiver node is in the wake-up state. Besides, the slot which potentially not being used for an extended period can utilize by other nodes. The efficient utilization of slots can help to achieve low power duty cycles with low latency. To accomplish that, we propose the emulation of the classroom learning environment with the Q-learning grading for node scheduling. We proposed an analytical mapping of WSNs to classroom learning. The initial benchmark of the performance has compared to the IEEE.802.11 (TDMA-scheduling). Further, the TDMA driven protocols such as Z-MAC, learning-driven protocols (E-MAC or aloha-Q), i-Queue are compared to evaluate the parameters such as energy consumption, throughput, and latency.
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    An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks
    (Tech Science Press, 2023) Mustafa, Mohammed Mubarak; Khalifa, Ahmed Abelmonem; Cengiz, Korhan; Ivkovic, Nikola
    The performance of Wireless Sensor Networks (WSNs) is an important fragment of the Internet of Things (IoT), where the current WSNbuilt IoT network's sensor hubs are enticing due to their critical resources. By grouping hubs, a clustering convention offers a useful solution for ensuring energy-saving of hubs and Hybrid Media Access Control (HMAC) during the course of the organization. Nevertheless, current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively. In this investigation, we recommend an Improved Energy-Proficient Algorithm (IEPA) for HMAC throughout the lifetime of the WSN-based IoT. Three consecutive segments are suggested. For the covering of adjusted clusters, an ideal number of clusters is determined first. Then, fair static clusters are shaped, based on an updated calculation for fluffy cluster heads, to reduce and adapt the energy use of the sensor hubs. Cluster heads (CHs) are, ultimately, selected in optimal locations, with the pivot of the cluster heads working among cluster members. Specifically, the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure, where the IEPA is reasonable for systems that need a long time. The assessment results demonstrate that the IEPA performs better than existing conventions.
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    Integrating CLDs and machine learning through hybridization for human-centric wireless networks
    (Wiley, 2024) Kumari, Binita; Yadav, Ajay Kumar; Cengiz, Korhan; Salah, Bashir
    Wireless sensor networks, more commonly abbreviated as WSNs, have been regarded as helpful tool for managing human-centric applications. Nevertheless, the design of wireless systems that are accurate, efficient, and robust remains difficult due to the variables and dynamics of the wireless environment as well as the requirements of the users. Cross-layer designs along with the machine-learning techniques need to be integrated into a novel hybridization framework for human-centric wireless networks in order to simplify the process and make it more manageable. The purpose of the proposed framework is to enhance wireless sensor networks (WSNs) in terms of their energy efficiency, robustness, real-time performance, and scalability. In particular, machine learning are employed for the purpose of extracting features from sensor data, and the framework combines cross-layer optimization and RL in order to facilitate effective and adaptable communication and networking. In comparison to previous work in this field, the accuracy, energy consumption, robustness, real-time performance, and scalability of the proposed framework are all significantly improved. The hybridization framework that has been proposed provides a promising approach to addressing the challenges, and it can be of use to a variety of applications. A diagram of the proposed hybridization framework. The wireless sensor network (WSNs) collects sensor data and uses a convolutional neural network to extract features. The features are then used by a machine learning algorithm to generate control signals for the system. The control signals are then used to adjust the behavior of the wireless network, which in turn sends configuration commands back to the sensor network.image
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    A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Cengiz, Korhan; Lipsa, Swati; Dash, Ranjan Kumar; Ivkovic, Nikola; Konecki, Mario
    Unmanned aerial vehicles (UAVs) are increasingly being deployed in crucial missions for the armed forces, law enforcement, industrial control monitoring, and other sectors. However, these hostile operating circumstances, along with the UAVs' dependence on wireless protocols, pose substantial security threats, limiting their mainstream application. With network security being such a major issue for UAV networks, the machine learning-based intrusion detection system (IDS) has been determined to be an effective strategy for protecting them. Additionally, though the existing methods offer effective strategies for detecting and categorizing abnormalities in the system, they are limited by their inability to adjust to various attack patterns. The dataset used as well as the memory and computational requirement of existing models, poses new challenges. One of the main concerns pertains to the reduced computational and memory demands of these models. So, the work carried out in this paper addresses this challenge. A new dimensional reduction technique based on correlation coefficient, information gain, and principal component analysis (PCA) is introduced to reduce the dimensionality of the UAV Attack Dataset. A novel intrusion detection system based on an artificial neural network (ANN) and genetic algorithm (GA) is then proposed. The genetic algorithm is used to generate the optimal weights of the artificial neural network. A comparison is made between the proposed model and the backpropagation network and its variant in terms of its convergence and prediction accuracy. Furthermore, the performance of the proposed model is compared with that of other classifiers. This comparison reveals that the proposed model is time efficient with an increased prediction accuracy of at least 6% more than other classifiers.
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    SOHCL-RDT: A self-organized hybrid cross-layer design for reliable data transmission in wireless network
    (Elsevier, 2023) Cengiz, Korhan; Kumari, Rani; Sah, Dinesh Kumar; Ivkovic, Nikola; Salah, Bashir
    In this paper, we propose SOHCL-RDT'' which stands for a self-organized hybrid cross-layer design for reliable data transmission in wireless network. The communication paradigm is changing and new approach related to machine learning or other optimization algorithms are being introduce rapidly. The TCP/IP or OSI model is not at all equipped to accommodate such a vast changes in its established protocol stacks. Considering this, we have proposed the hybrid cross layer design where the communication or transmission will be handle using two set of protocol stack. One set for the established classical network, and another using cross layer approach. Our design leverages the strengths of both the physical and MAC layers to optimize packet transmission and minimize energy consumption. An optimization algorithm based on gradient descent is also developed to adjust transmission parameters in real-time. The objective is to invoke the classical model only when it needed; it means until unless gradient descent is able to make classification regarding the node scheduling and achieve the acknowledgment, the TCP/IP protocol stack will be in deactivation. Using this method, we have performed our experiments mainly on two parameters named as packet delivery ratio (PDR), end-to-end delay (E2ED); because these are important aspect of reliability. In addition to that, the energy consumption of network is also compared with the existing algorithms. The results show that the proposed hybrid cross-layer design outperforms the existing algorithms. The performance gain can be attributed to the cross-layer approach and the use of the optimization algorithm. Overall, the proposed hybrid cross-layer design is a promising solution for reliable data transmission in wireless sensor networks, with the potential to improve network performance and prolong network lifetime by reducing energy consumption.& COPY; 2023 Published by Elsevier B.V.
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    Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Lipsa, Swati; Dash, Ranjan Kumar; Ivkovic, Nikola; Cengiz, Korhan
    In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.
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    TDMA policy to optimize resource utilization in Wireless Sensor Networks using reinforcement learning for ambient environment
    (Elsevier B.V., 2022) Sah, Dinesh Kumar; Amgoth, Tarachand; Cengiz, Korhan; Alshehri, Yasser; Alnazzawi, Noha
    Data packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor.