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Publication Metadata only Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements (vol 82, pg 68, 2019)(ELSEVIER, 2020) Ayyildiz, Cem; Erdem, H. Emre; Dirikgil, Tamer; Dugenci, Oguz; Kocak, Taskin; Altun, Fatih; Gungor, V. Cagri; Bahcesehir University; Abdullah Gul University; Erciyes UniversityPublication Metadata only Physical layer authentication for extending battery life(ELSEVIER, 2021) Ayyildiz, Cem; Cetin, Ramazan; Khodzhaev, Zulfidin; Kocak, Taskin; Soyak, Ece Gelal; Gungor, V. Cagri; Kurt, Gunes Karabulut; Bahcesehir University; Oklahoma State University System; Oklahoma State University - Stillwater; University of Louisiana System; University of New Orleans; Abdullah Gul University; Universite de Montreal; Polytechnique MontrealIncreasing population density in cities, and the increasing demand for efficiency in resource usage call for architectures enabling smart cities, such as the Internet of Things (IoT). In most such scenarios, the data generated by IoT sensors is not confidential, but its integrity is critical. Data integrity can be achieved by establishing certification mechanisms that provide cryptographic message authentication protocols, however, this requires relatively expensive components for storing and processing the encryption key on the sensor and consumes more power while processing and transmitting data, which leads to the renunciation of security issues in cost sensitive deployments. In this paper, we propose a security solution that provides data integrity without draining the batteries of IoT sensors. Our solution consists of, (i) differentiating legitimate sensors by taking advantage of their impurities formed during the manufacturing process of the transceiver components, and (ii) eliminating the complex components that carry out cryptography as well as the redundant packet header fields, thereby yielding power savings. The testbed implementation of the proposed solution yields power measurement results providing an estimate of 2.52 times improvement in battery life without compromising the integrity of communications in the system, in addition to offering an increase in spectral efficiency and a decrease in the overall IoT device cost.Publication Metadata only A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things(WILEY, 2024) Zanbouri, Kouros; Darbandi, Mehdi; Nassr, Mohammad; Heidari, Arash; Navimipour, Nima Jafari; Yalcin, Senay; Islamic Azad University; Tartous University; Gulf University for Science & Technology (GUST); Istanbul Atlas University; Halic University; Kadir Has University; Ministry of Education of Azerbaijan Republic; Western Caspian University; National Yunlin University Science & Technology; Bahcesehir UniversityThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. We introduced a blockchain-based IIoT using a glowworm swarm optimization algorithm motivated by glowworms' behavior, movements' probability toward each other, and luciferin quantity. The proposed approach significantly improves four-way trade-offs such as scalability, decentralization, cost, and latency. imagePublication Metadata only A survey on task scheduling and optimization techniques for IoT-enabled UAV with Edge / Fog computing(SPRINGER, 2025) Satouf, Aram; Hamidoglu, Ali; Gul, Omer Melih; Kuusik, Alar; Kadry, Seifedine Nimer; Elghirani, Ali; Bahcesehir University; Bahcesehir University; University of Alberta; Istanbul Technical University; Tallinn University of Technology; Lebanese American UniversityThe Internet of Things (IoT) and cloud computing are two technologies that are rapidly growing and have the potential to change many different industries. IoT devices provide real-time data gathering and analysis, enabling organizations to make data-driven choices. Cloud computing offers a scalable and flexible platform for storing and processing the enormous amounts of data IoT devices create. However, several issues like excessive latency, network inefficiency, and security concerns have arisen due to the centralized architecture of cloud computing, particularly in IoT applications where strict real-time performance and operational reliability requirements are present including intelligent transportation systems (ITS), unmanned ground and aerial vehicles (UGVs, UAVs). Edge computing, a new architecture that tries to decentralize data processing from the cloud to the network edge, has been presented as a solution to these problems. In this survey, we investigate the role of cloud, fog, and edge computing in the smart environment containing wireless sensor networks (WSNs) and mobile IoT devices, especially UAVs. Integration of optimization algorithms and meta-heuristic techniques are provided in the context of IoT applications. Furthermore, we discuss the benefits of fog and edge computing, use cases, and the Quality of Service (QoS) used for task scheduling.Publication Metadata only An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach(ELSEVIER, 2023) Khan, Tayyab; Singh, Karan; Shariq, Mohd; Ahmad, Khaleel; Savita, K. S.; Ahmadian, Ali; Salahshour, Soheil; Conti, Mauro; Jawaharlal Nehru University, New Delhi; University of Padua; Universiti Teknologi Petronas; Universita Mediterranea di Reggio Calabria; Central Queensland University; Bahcesehir University; Piri Reis UniversityWireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.Publication Metadata only ARVA: An Augmented Reality-Based Visual Aid for Mobility Enhancement Through Real-Time Video Stream Transformation(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; Bahcesehir University; State University System of Florida; Florida Gulf Coast University; Daffodil International UniversityVisual field loss (VFL) is a persistent visual impairment characterized by limited vision spots (scotoma) within the normal visual field, significantly impacting daily activities for affected individuals. Current Virtual Reality (VR) and Augmented Reality (AR)-based visual aids suffer from low video quality, content loss, high levels of contradiction, and limited mobility assessment. To address these issues, we propose an innovative vision aid utilizing AR headset and integrating advanced video processing techniques to elevate the visual perception of individuals with moderate to severe VFL to levels comparable to those with unimpaired vision. Our approach introduces a pioneering optimal video remapping function tailored to the characteristics of AR glasses. This function strategically maps the content of live video captures to the largest intact region of the visual field map, preserving quality while minimizing blurriness and content distortion. To evaluate the performance of our proposed method, a comprehensive empirical user study is conducted including object counting and multi-tasking walking track tests and involving 15 subjects with artificially induced scotomas in their normal visual fields. The proposed vision aid achieves 41.56% enhancement (from 57.31% to 98.87%) in the mean value of the average object recognition rates for all subjects in object counting test. In walking track test, the average mean scores for obstacle avoidance, detected signs, recognized signs, and grasped objects are significantly enhanced after applying the remapping function, with improvements of 7.56% (91.10% to 98.66%), 51.81% (44.85% to 96.66%), 49.31% (43.18% to 92.49%), and 77.77% (13.33% to 91.10%), respectively. Statistical analysis of data before and after applying the remapping function demonstrates the promising performance of our method in enhancing visual awareness and mobility for individuals with VFL.Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Rahman, Afzal; Ali, Haider; Badshah, Noor; Rada, Lavdie; Khan, Ayaz Ali; Hussain, Hameed; Zakarya, Muhammad; Ahmed, Aftab; Rahman, Izaz Ur; Raza, Mushtaq; Haleem, Muhammad; University of Peshawar; Bahcesehir UniversitySelective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.Publication Metadata only Scaling Laws for Age of Information in Wireless Networks(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Buyukates, Baturalp; Soysal, Alkan; Ulukus, Sennur; University System of Maryland; University of Maryland College Park; Bahcesehir University; Virginia Polytechnic Institute & State UniversityWe study age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks. There are n nodes uniformly and independently distributed on a fixed area that are randomly paired with each other to form n source-destination (S-D) pairs. Each source node wants to keep its destination node as up-to-date as possible. To accommodate successful communication between all n S-D pairs, we first propose a three-phase transmission scheme which utilizes local cooperation between the nodes along with what we call mega update packets to serve multiple S-D pairs at once. We show that under the proposed scheme average age of an S-D pair scales as O(n(1/4) log n) as the number of users, n, in the network grows. Next, we observe that communications that take place in Phases I and III of the proposed scheme are scaled-down versions of network-level communications. With this along with scale-invariance of the system, we introduce hierarchy to improve this scaling result and show that when hierarchical cooperation between users is utilized, an average age scaling of O(n(alpha(h)) log n) per-user is achievable, where h denotes the number of hierarchy levels and alpha(h) = 1/3 center dot 2(h)+1. We note that alpha(h) tends to 0 as h increases, and asymptotically, the average age scaling of the proposed hierarchical scheme is O(log n). To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.Publication Metadata only A survey on integrated computing, caching, and communication in the cloud-to-edge continuum(ELSEVIER, 2024) Maia, Adyson; Boutouchent, Akram; Kardjadja, Youcef; Gherari, Manel; Soyak, Ece Gelal; Saqib, Muhammad; Boussekar, Kacem; Cilbir, Idil; Habibi, Sama; Ali, Soukaina Ouledsidi; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Bahcesehir University; Sabanci University; Concordia University - CanadaCloud and edge computing have proposed different functionalities to enable multiple applications requiring different communication, computing, and caching (3C) resources. The upcoming futuristic applications (e.g., metaverse, holographic, and haptic communication) impose further stringent requirements (e.g., ultra-low latency, ultra -high reliability) on the infrastructure. These requirements call for a paradigm shift in the infrastructure architecture where all resource components and owners collaborate from the cloud up to the edge, creating a cloud-to-edge continuum of integrated resources. Furthermore, we argue that artificial intelligence (AI) and collaborative-based decisions are promising techniques to efficiently manage the highly complex architecture that jointly leverages 3C in the continuum. This article presents a comprehensive survey of existing research, including AI and collaborative-based studies, targeting the effective and seamless provision of 3C resources and services in the cloud-to-edge continuum. Through an extensive analysis of driving use cases, the synergy between these three main services is scrutinized to highlight its crucial role in the nextgeneration network infrastructures (NGNI). Finally, a discussion on the opportunities and challenges brought by integrating 3C in NGNI from different perspectives, including architectural design as well as the regulatory and business aspects, are presented.