Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.

LoRaWAN is a Low Power Wide Area Network (LPWAN) technology which enables low cost and low power IoT device communication even in dense urban areas. LoRa modulation is capable of extracting data from a weak signal in noisy environments. This modulation technique can be useful for the delivery of critical data in the noisy environment. In this study, we investigate LoRaWAN technology in the context of data transmission for health care systems (or critical health data in disaster environments). In the study, we explore the standard and evaluate data frame transmission in LoRaWAN with a custom test-bed system.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

Digital Twin-as-a-Service (DTaaS) has emerged as a transformative paradigm to deliver modular, scalable, and intelligent digital twin (DT) functionalities. Regarding this, several studies have investigated DT platforms and their enabling technologies. However, their focus mostly remains domain-specific, thereby limiting the potential for generalisation across diverse contexts. Furthermore, these studies lack a systematic framework to address both architectural elements and their performance evaluation required for an end-to-end DTaaS platform. To bridge this gap, in this survey, we present a comprehensive component-based taxonomy for DTaaS platforms, categorised into core and supportive components. As the core components, we review Data, Model, Service, and Communication, whereas the supportive components include Infrastructure, Orchestration, and Security elements. For each component, we analyse the state-of-the-art methods, enabling technologies, and associated design trade-offs. We further introduce a metric-driven perspective to design measurable and context-aware DTaaS platforms. In this regard, we present several DT performance metrics, including twinning rate, twin fidelity, and age-of-twin and map them to DTaaS components. To demonstrate the applicability of our framework, we present a case study based on our own DTaaS platform. We perform two metric-driven implementations, (i) Radio Access Network (RAN) management and (ii) Distributed Denial of Service (DDoS) attack detection by exemplifying how DT-metrics can be integrated with the DTaaS components. Also, we outline key open issues for context-aware, interoperable, and secure DTaaS platforms. By jointly addressing an end-to-end architectural design and performance metrics, this survey demonstrates a foundational reference for the structured development, evaluation, and operation of DTaaS platforms.

İstanbul Üniversitesi Yayın Projesi