Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 2Citation - Scopus: 5Physical Layer Authentication for Extending Battery Life(Elsevier, 2021-12) Ayyildiz, Cem; Cetin, Ramazan; Khodzhaev, Zulfidin; Kocak, Taskin; Soyak, Ece Gelal; Gungor, V. Cagri; Kurt, Gunes KarabulutIncreasing 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.Article Citation - WoS: 30Citation - Scopus: 60HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing(IEEE-Inst Electrical Electronics Engineers Inc, 2023-11-01) Golec, Muhammed; Gill, Sukhpal Singh; Parlikad, Ajith Kumar; Uhlig, SteveHeart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall, F-Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.