Browsing by Author "Ozdag, Recep"

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3d Location Optimization of Multiple Unmanned Aerial Vehicle Base Stations in Next-Generation Cellular Networks and a New Meta-Heuristic Approach
(Gazi Univ, Fac Engineering Architecture, 2021) Ozdag, Recep
The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication is emerging as an effective technique that should be used for the services planned in the next-generation cellular networks or in disaster situations. Currently, effective 3D placement of BS mounted on UAVs or Drones (Drone-BS), also called Low-altitude Air Platforms, in the defined area significantly increases the Quality of Service (QoS) of wireless communication. This study has been aimed to be performed dynamic deployments (location optimization) of Drone-BSs randomly distributed in the 3D plane in order to optimally cover the users in the urban environment according to the Air-to-Ground (ATG) model. Using new approaches based on the Electromagnetism-Like (EML) Algorithm and the Whale Optimization Algorithm (WOA), which are widely used in the literature and are meta-heuristic, it was planned to be covered the maximum number of users on the ground by multiple Drone-BSs. In addition, Extensive-interval Optimal Fitness Search Algorithm (EOFSA-EML) and Discrete-interval Optimal Fitness Search Algorithm (DOFSA-EML) approaches were developed based on the EML algorithm. Based on comparison metrics for multiple Drone-BSs distributed by EOFSA-EML, DOFSA-EML, and Pure-WOA, it has been found that EML-based algorithms achieve optimal results compared to Pure-WOA.
Coverage Analysis and a New Metaheuristic Approach Using the Elfes Probabilistic Detection Model in Wireless Sensor Networks
(Elsevier Sci Ltd, 2022) Ali, Nawzad Hasan; Ozdag, Recep
The dynamic deployment of sensors in AoI (Area of Interest) plays an important role in ensuring the quality of services (QoS) of Wireless Sensor Networks (WSNs) by optimising the coverage and lifetime of the network. In this paper, a new dynamic deployment approach using metaheuristics based on Electromagnetism-Like (EM-L) algorithm and Elfes Probabilistic Detection Model (EPDM) was proposed to optimise the coverage of WSNs based on the sensor coverage problem, and coverage analysis of AoI was performed. A variable threshold detection probability (TDP) was defined instead of defining a fixed TDP as in the literature. Thus, a more realistic modelling environment was created by considering the signal-to-noise ratio (SNR) in the coverage calculation. The simulation results show that the sensors are always effectively deployed in different scenarios with variable TDPs by the proposed approach compared to a random distribution.
Covid-19 Diagnosis on Ct Images With Bayes Optimization-Based Deep Neural Networks and Machine Learning Algorithms
(Springer London Ltd, 2022) Canayaz, Murat; Sehribanoglu, Sanem; Ozdag, Recep; Demir, Murat
Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.
Multi-Metric Optimization With a New Metaheuristic Approach Developed for 3d Deployment of Multiple Drone-Bss
(Springer, 2022) Ozdag, Recep
The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication has been claimed to be an effective technique for services planned in next generation cellular networks (5G and 5G+). Effective 3D deployments of BS mounted on drone (drone-BS), in the Area of Interest (AoI) may increase the Quality-of-Service (QoS) of wireless communication in the Internet of Things (IoT). In this article, we solve a dynamic deployment problem (location optimization) of multiple drone-BSs that is NP-hard according to the Air-to-Ground (ATG) model using optimization algorithms with the aim of ensuring optimal rates of coverage of the ground users (user equipments) assumed to be located at certain distance intervals in a defined urban environment. In this regard, we have developed Optimal Fitness-value Search Approaches at Continuous-data range (OFSAC-PSO and OFSAC-EML) and Optimal Fitness-value Search Approaches at Discrete-data range (OFSAD-PSO and OFSAD-EML), which are both based on Particle Swarm Optimization (PSO) and Electromagnetism-Like (EML) algorithm. In the study where Monte-Carlo simulations are run, we have created different scenarios for uniform and non-uniform distributions of user equipments (UEs). We have made comparisons according to the following metrics for the approaches that we have developed: the fitness function values of drone-BSs, AoI coverage rates, the number of iteration of simulations, drone-BS altitudes, optimal 2D coverage map, and the 3D optimal locations of drone-BSs. Our simulation results show that, based on the compared metrics, OFSAC developed with PSO-based is optimal compared to other approaches.
A New Metaheuristic Approach Based on Orbit in the Multi-Objective Optimization of Wireless Sensor Networks
(Springer, 2021) Ozdag, Recep; Canayaz, Murat
Wireless sensor networks (WSNs) is a research area which has been used in various applications and has continuously developed up to now. WSNs are used in many applications, especially in military and civilian applications, with the aim of monitoring the environment and tracking objects. For this purpose, increasing the coverage rate of WSNs is one of the important criteria that determine the effective monitoring of the network. Since the sensors that make up the WSNs have a limited capacity in terms of energy, process and memory, various algorithmic solutions have been developed to optimize this criterion. The effective dynamic deployment of sensor nodes, which is the primary goal of these solutions, has a critical role in determining the performance of the network. A new orbit-based dynamic deployment approach based on metaheuristic Whale Optimization Algorithm has been proposed in this study. The goal is to optimize the convergence speed of the nodes, the coverage rate of the network, the total displacement (movement) distances of sensors and the degree ofk-coverage of each target (Grid) point in the area by effectively performing the dynamic deployments of sensors after their random distribution. This approach is compared with MADA-WOA and MADA-EM in the literature. Simulation results indicated that the approach developed in rapidly converging sensors to each other, increasing the network's coverage rate, and in minimizing the total movement distances of the sensors in the area and the degrees ofk-coverage of Grid points covered by the sensors could be proposed.
A Novel Hybrid Path Planning Method for Sweep Coverage of Multiple Uavs
(Springer, 2025) Ozdag, Recep
Sweep coverage (SC) is an NP-hard problem that needs to be solved with a small number of mobile sensors (sensor nodes) to monitor or observe targets in wireless sensor networks (WSNs) over a period of time. One of the proposed solutions to optimize the lifetime of WSNs is to monitor targets in the area of interest (AoI) by path planning of the sensor nodes. Considering the applications of unmanned aerial vehicles (UAVs) in military and civilian fields, in this study, we model multiple UAVs to solve the SC problem based on non-uniformly distributed targets in the AoI. In this modeling, we conduct effective coverage path planning for UAVs by performing an SC task for targets in the AoI with multiple asynchronously flying UAVs. To this end, we propose a new approach, which we call Weighted Targets Sweep Coverage, based on the metaheuristic Crow Search Algorithm and the heuristic Greedy algorithm. In our proposed approach, we define a new objective function by considering the UAV task completion time and the AoI coverage rate as metrics. In the Monte Carlo simulations that we perform to measure the performance, we run 5 different scenarios considering the number of targets defined for AoI and the alpha parameters defined for the objective function. The results of the scenario studies show that the proposed approach outperforms the other algorithms by up to 21% and 50% in terms of UAV task completion time and AoI coverage rate, respectively.
Probabilistic Dynamic Distribution of Wireless Sensor Networks With Improved Distribution Method Based on Electromagnetism-Like Algorithm
(Elsevier Sci Ltd, 2016) Ozdag, Recep; Karci, Ali
Performance of the Wireless Sensor Networks (WSNs) depends significantly on coverage area which is determined via the effective dynamic distribution of sensors. Making mobile sensors' dynamic distributions, which determines their positions within the network effectively, improves performances of WSNs by enabling sensors to form the coverage area more efficiently. In this paper, we initially propose the electromagnetism-like (EM) algorithm as the sensor distribution strategy to increase the coverage area of network after random distribution of sensors. Forming more effective coverage area by using mobile and stationary sensors and probabilistic detection model has been aimed by developing the Optimal Sensor Detection Algorithm that is based on the proposed EM algorithm (OSDA-EM). For this purpose, it has been thought that we would attain to more realistic results, with probabilistic detection model by forming the coverage area more effectively. Additionally, performance of the developed OSDA-EM algorithm has been compared with the Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms which was previously used in the dynamic distribution of WSNs. Simulation results have shown that the developed OSDA-EM can be preferred in dynamic distribution of WSNs that performed with probabilistic detection model. (C) 2015 Elsevier Ltd. All rights reserved.
Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks
(Sage Publications inc, 2015) Ozdag, Recep; Karci, Ali
Thedynamic deployment of sensors in wireless networks significantly affects the performance of the network. However, the efficient application of dynamic deployments which determines the positions of the sensors within the network increases the coverage area of the network. As a result of this, dynamic deployment increases the efficiency of the wireless sensor networks (WSNs). In this paper, dynamic deployment was applied to WSNs which consist of mobile sensors by aiming at increasing the coverage area of the network with electromagnetism-like (EM) algorithm which is a population-based optimization algorithm. A new approach has been improved in calculating the coverage rate of the sensors by using binary detection model so as to carry out the dynamic deployments of sensors and it has been thought to reach realistic results efficiently. Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.