WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 58Citation - Scopus: 68Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements(Elsevier Science Bv, 2019-01) Ayyildiz, Cem; Erdem, H. Emre; Dirikgil, Tamer; Dugenci, Oguz; Kocak, Taskin; Altun, Fatih; Gungor, V. CagriThis paper presents a system that monitors the health of structural elements in Reinforced Concrete (RC), concrete elements and/or masonry buildings and warn the authorities in case of physical damage formation. Such rapid and reliable detection of impairments enables the development of better risk management strategies to prevent casualties in case of earthquake and floods. Piezoelectric (PZT) sensors with lead zirconate titanate material are the preferred sensor type for fracture detection. The developed sensor mote hardware triggers the PZT sensors and collects the responses they gather from the structural elements. It also sends the collected data to a data center for further processing and analysis in an energy-efficient manner utilizing low-power wireless communication technologies. The access and the analysis of the collected data can be remotely performed via a web interface. Performance results show that the fractures serious enough to cause structural problems can be successfully detected with the developed system. (C) 2018 Elsevier B.V. All rights reserved.Article Citation - WoS: 52Citation - Scopus: 69Spectrum-Aware Bio-Inspired Routing in Cognitive Radio Sensor Networks for Smart Grid Applications(Elsevier Science Bv, 2017-03) Fadel, E.; Faheem, M.; Gungor, V. C.; Nassef, L.; Akkari, N.; Malik, M. G. A.; Akyildiz, I. F.Cognitive radio sensor networks (CRSNs) have been proposed to serve as a reliable, robust, and efficient communications infrastructure that can address both the existing and future energy management requirements of the smart grid. The existing and envisioned applications of CRSN-based smart grid include substation automation, overhead transmission line monitoring, home energy management, advanced metering infrastructure, wide-area situational awareness, demand response, outage management, distribution automation, asset management. To realize these applications, in this paper, honey bee mating optimization-based routing and cooperative channel assignment algorithms have been proposed. The developed framework significantly decreases the probability of packet loss and preserves high link quality among sensor nodes in harsh smart grid spectrum environments. The proposed approach performance has been evaluated in terms of packet delivery ratio, delay, and energy consumption demonstrating that it has successfully addressed the QoS requirements of most of the SG applications presented. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 52Citation - Scopus: 62MGRP: Mobile Sinks-Based QoS-Aware Data Gathering Protocol for Wireless Sensor Networks-Based Smart Grid Applications in the Context of Industry 4.0-Based on Internet of Things(Elsevier Science Bv, 2018-05) Faheem, Muhammad; Gungor, V. C.The recent advances in internet of things (IoT) and industrial wireless sensor networks (IWSNs) paradigm provide a promising opportunity for upgrading todays elderly electricity industrial systems and even allow the fourth stage of the industrial revolution, referred to as smart grid industry (SGI) 4.0. In SGI 4.0 paradigm, the WSNs are considered as promising solutions due to their advantages, such as cable replacement, ease of deployment, flexibility, and cost reduction. However, harsh and complex smart grid (SG) environments pose great challenges to guarantee reliable communication for WSNs-based SG applications due to equipment noise, electromagnetic interference, multipath effects and fading in SG environments. This results in deteriorating the quality-of-service (QoS) requirements as well as the network lifetime of multi-hop communication-based WSNs for SG applications. Thus, for SGI 4.0 paradigm to come true, a WSN-based highly reliable communication infrastructure is crucial that will wirelessly connect and integrate power system components for more efficient, reliable, and intelligent operations of the next-generation electricity power grids. To address these challenges, in this paper a novel multi-mobile sinks-based QoS-aware data gathering protocol (called MQRP) for WSNs-based SG applications has been proposed to empower SGI 4.0. The extensive simulations study is carried through a network simulation tool called EstiNet9.0. The obtained experimental facts show that the proposed scheme has not only improved the QoS performance metrics, such as packet delivery ratio, memory utilization, control message overhead, residual energy, network lifetime, and throughput, but also reduced packet error rate and end-to-end delay compared to existing data collection schemes. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 12Analyzing Lifetime of Energy Harvesting Wireless Multimedia Sensor Nodes in Industrial Environments(Elsevier Science Bv, 2018-05) Tekin, Nazli; Erdem, H. Emre; Gungor, V. CagriRecently, there has been a great demand for multimedia communication using Wireless Multimedia Sensor Networks (WMSNs) in industrial environments thanks to their low cost, flexibility, and rapid deployment. However, WMSNs face a major challenge of limited lifetime due to their limited battery capacity. Compared to regular data transmission, multimedia data transmission causes higher energy consumption because of larger data sizes leading to faster depletion of sensor node's batteries. The objective of this paper is to analytically quantify the impact of different energy harvesting methods based on vibration, indoor solar, and temperature difference as well as Fast-Zonal DCT and BinDCT based image compression methods on the lifetime of Telos and Mica2 sensor nodes deployed in indoor industrial environment. Performance results show that energy harvesting and image compression techniques improve lifetime of Mica2 and Telos motes by 51.8% and 25.8%, respectively when used with proper power management methods. (C) 2017 Published by Elsevier B.V.