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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Energy-efficient packet size optimization for cognitive radio sensor networks(Ieee-Inst Electrical Electronics Engineers Inc, 2012) N/A; N/A; Department of Electrical and Electronics Engineering; Oto, Mert Can; Akan, Özgür Barış; Researcher; Faculty Member; Department of Electrical and Electronics Engineering; N/A; College of Engineering; N/A; 6647Cognitive Radio (CR) and its dynamic spectrum access capabilities can be exploited by many wireless network architectures including sensor networks. Thus, cognitive radio sensor networks (CRSN) has emerged as a promising solution to address the spectrum-related challenges of wireless sensor networks (WSN). Among others, determination of the optimal packet size is one of the most fundamental problems to be addressed for the practical realization of CRSN. The existing optimal packet size solutions devised for wireless, sensor, and CR networks are not applicable in CRSN regime. Hence, the objective of this paper is to determine the optimal packet size for CRSN that maximizes energy-efficiency while maintaining acceptable interference level for licensed primary users (PU) and achieving reliable event detection at the sink. The energy-efficient optimal packet size is analytically formulated and its variation with respect to different network parameters is observed. Results reveal that PU behavior and channel BER are the most critical parameters in determining the energy-efficient optimal packet size for CRSN.Publication Metadata only A traffic congestion avoidance algorithm with dynamic road pricing for smart cities(Institute of Electrical and Electronics Engineers (IEEE), 2013) Soylemezgiller, Fahri; N/A; N/A; Kuşcu, Murat; Kılınç, Deniz; Master Student; PhD Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 316349; N/AThe traffic congestion problem is a common issue for the residents of metropolises. Although expanding the capacity of transportation systems and stimulating the public transportation may decrease the traffic congestion, they cannot completely solve the traffic congestion problem. As a solution for the worsening traffic congestion problem in urban areas, road pricing systems have been employed. In this paper, we propose a radically different road pricing scheme to prevent and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in a city, we propose to employ a road pricing system over the entire city. Thus, our road pricing system can control the traffic flow in the entire traffic network of the city. Furthermore, the road prices are adjusted dynamically based on the instantaneous traffic densities of each road in the city in order to rapidly and efficiently control the traffic flow and to prevent the traffic congestion. Moreover, we propose to change the road prices according to the past usage statistics of the road by predicting a possible congestion. The simulation results of our road pricing algorithm show that traffic congestion is prevented over the entire traffic network and the traffic densities of the roads are homogenized.Publication Metadata only Uplink/downlink decoupled energy efficient user association in heterogeneous cloud radio access networks(Elsevier, 2020) N/A; N/A; Department of Electrical and Electronics Engineering; Saimler, Merve; Ergen, Sinem Çöleri; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 7211Heterogeneous Cloud Radio Access Networks (H - CRAN) is a network architecture that combines Macro Base Stations (MBS)s and Small Base Stations (SBS)s with cloud infrastructures. The dense deployment of SBSs in H-CRAN is needed to provide high data rates to User Equipments (UEs) but causes high energy consumption. Unrealistic power models lead to inefficient UE association schemes in terms of energy. In this paper, we study the joint optimization of Uplink (UL) and Downlink (DL) decoupled UE association and switching on/off the SBSs in H-CRAN by incorporating a realistic power model with the objective of minimizing the power consumption in H-CRAN. The power model encompasses static and the dynamic power consumption of MBS, the static power consumption of SBS, the power consumption of transmission links to cloud infrastructure and the power consumption of UEs. The problem is transformed into Single Source Capacitated Facility Location Problem (SSCFLP) which is NP-Hard. We then propose a heuristic algorithm based on the use of LP relaxation and solving many-to-one assignment problem with generalized assignment problem heuristics. Extensive simulations demonstrate that the proposed heuristic algorithm performs very close to optimal and achieves significant improvements in minimizing total power consumption compared to coupled UE association algorithm and algorithms utilizing the power consumption models that do not encompass MBS dynamic power consumption and the power consumption of transmission links to cloud infrastructure for various scenarios.Publication Metadata only Fair scheduling for spectrally efficient multi-service wireless data provisioning(Wiley, 2004) N/A; Department of Electrical and Electronics Engineering; N/A; Sunay, Mehmet Oğuz; Ekşim, Ali; Faculty Member; Master Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/AEfficient wireless packet data access is possible by exploiting multi-user diversity. This is done by using an opportunistic multiple access system that allocates system resources to one user at a time while using adaptive modulation and coding. Based on this outcome, recently the IS-856 system was developed. A scheduling algorithm provides resource allocation in the IS-856 system, and its proper design is perhaps one of the most crucial aspects ensuring good system performance. We present a number of such algorithms in this paper, two of which are new, and provide a comparative performance evaluation. The proposed algorithms appear to have the best overall performance of achieving high system throughputs without diverging much from the optimal latency performance. We then show that the IS-856 system can easily be adjusted to provide a multitude of services, each with different QoS requirements. Extensive performance evaluations show that good system performance can be maintained in the multi-service scenario. The paper also presents means of providing multicast service provisioning in the IS-856 system.Publication Metadata only Cognitive radio sensor networks in industrial applications(CRC Press, 2017) N/A; Department of Electrical and Electronics Engineering; Biçen, Ahmet Ozan; Akan, Özgür Barış; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647This chapter explains the benefits of cognitive radio sensor networks (CRSN) in industrial applications and discusses the open research directions for this promising research area. It also discusses the CRSN architecture configurations in industrial applications. The chapter presents the algorithm needs of CRSN for spectrum management in industrial applications. It provides the open research issues on communication protocol development for CRSN in industrial applications. Minimization of environmental effects, adaptation to varying spectrum conditions, and overlay deployment of multiple sensor networks are some of the promising advantages of CRSN in industrial applications. However, the realization of CRSN in industrial applications mainly requires efficient spectrum management functionalities to dynamically manage the spectrum access of sensor nodes in challenging industrial communication environments. Requirements and research challenges for main three spectrum management functionalities of cognitive radio, that is, spectrum sensing, spectrum decision, and spectrum mobility, are explored below for CRSN from the perspective of industrial applications.Publication Metadata only Role allocation through haptics in physical human-robot interaction(Institute of Electrical and Electronics Engineers (IEEE), 2013) N/A; N/A; Department of Computer Engineering; Department of Mechanical Engineering; Küçükyılmaz, Ayşe; Sezgin, Tevfik Metin; Başdoğan, Çağatay; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 18632; 125489This paper presents a summary of our efforts to enable dynamic role allocation between humans and robots in physical collaboration tasks. A major goal in physical human-robot interaction research is to develop tacit and natural communication between partners. In previous work, we suggested that the communication between a human and a robot would benefit from a decision making process in which the robot can dynamically adjust its control level during the task based on the intentions of the human. In order to do this, we define leader and follower roles for the partners, and using a role exchange mechanism, we enable the partners to negotiate solely through force information to exchange roles. We show that when compared to an “equal control” condition, the role exchange mechanism improves task performance and the joint efficiency of the partners.Publication Metadata only Distributed deep reinforcement learning with wideband sensing for dynamic spectrum access(Ieee, 2020) Ucar, Seyhan; N/A; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Kaytaz, Umuralp; Akgün, Barış; Ergen, Sinem Çöleri; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 258784; 7211Wideband spectrum sensing (WBS) has been a critical issue for communication system designers and specialists to monitor and regulate the wireless spectrum. Detecting and identifying the existing signals in a continuous manner enable orchestrating signals through all controllable dimensions and enhancing resource usage efficiency. This paper presents an investigation on the application of deep learning (DL)-based algorithms within the WBS problem while also providing comparisons to the conventional recursive thresholding-based solution. For this purpose, two prominent object detectors, You Only Learn One Representation (YOLOR) and Detectron2, are implemented and fine-tuned to complete these tasks for WBS. The power spectral densities (PSDs) belonging to over-the-air (OTA) collected signals within the wide frequency range are recorded as images that constitute the signal signatures (i.e., the objects of interest) and are fed through the input of the above-mentioned learning and evaluation processes. The main signal types of interest are determined as the cellular and broadcast types (i.e., GSM, UMTS, LTE and Analogue TV) and the single-tone. With a limited amount of captured OTA data, the DL-based approaches YOLOR and Detectron2 are seen to achieve a classification rate of 100% and detection rates of 85% and 69%, respectively, for a nonzero intersection over union threshold. The preliminary results of our investigation clearly show that both object detectors are promising to take on the task of wideband signal detection and identification, especially after an extended data collection campaign.Publication Metadata only Minimum length scheduling for multi-cell wireless powered communication networks(IEEE, 2020) N/A; N/A; Department of Electrical and Electronics Engineering; Salık, Elif Dilek; Önalan, Aysun Gurur; Ergen, Sinem Çöleri; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 7211Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron–Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.Publication Metadata only Resource allocation for ultra-reliable low-latency vehicular networks in finite blocklength regime(Institute of Electrical and Electronics Engineers Inc., 2022) Department of Electrical and Electronics Engineering; N/A; Ergen, Sinem Çöleri; Khan, Nasir; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/AEnsuring ultra-reliable low-latency communication (URLLC) is crucial in the timely delivery of safety-critical messages in vehicle-to-vehicle (V2V) communications. The stringent latency requirement in URLLC requires the usage of finite block length information theory. Previously proposed resource allocation schemes for V2V communication rely on Shannon rate and do not incorporate spectrum allocation into the blocklength and power optimization while relying solely on slow-varying large-scale channel statistics. This paper investigates the combined spectrum, blocklength, and power allocation to minimize the worst-case decoding-error probability in the finite blocklength (FBL) regime for a URLLC-based V2V communication scenario. We first formulate the problem as a non-convex mixed-integer nonlinear programming problem (MINLP). To solve this challenging problem, we decompose the original problem into two interrelated subproblems. First, the spectrum allocation is performed by clustering vehicles into distinct zones. Second, an iterative block coordinate descent (BCD) based algorithm is developed for the blocklength and transmit power optimization. Via extensive simulations, we demonstrate that the proposed scheme outperforms the benchmark scheme based on a path-following iterative strategy and yields substantially higher network reliability for different network parameters.Publication Metadata only Vehicular social sensor networks(CRC Press, 2017) N/A; N/A; Department of Electrical and Electronics Engineering; Çepni, Kardelen; Özger, Mustafa; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647N/A