Secrecy Performance Analysis of Cooperative Nonorthogonal Multiple Access in IoT Networks

Different system models utilizing Non-orthogonal multiple access (NOMA) have been successfully studied to meet the growing capacity demands of the Internet of Things (IoT) devices for the next-generation networks. However, analyzing the anti-eavesdropping for NOMA systems under different scenarios and settings still needs further exploration before it can be practically deployed. Therefore, in this paper, we study the secrecy performance of a cooperative NOMA system in IoT networks where two source nodes communicate with their respective destination nodes via a common relay in the presence of an eavesdropper. Specifically, two source node sends their data in parallel over the same frequency band to the common relay node using uplink NOMA. Then, the relay node forwards the decoded symbols to the respective destination nodes using downlink NOMA in the presence of an eavesdropper. To enhance the security performance of the considered system, we study and propose an artificial noise (AN)-aided scheme in which the two destination nodes emit a jamming signal to confuse the eavesdropper while receiving the signal from the common relay node. We also study the effect of NOMA power allocation, perfect successive interference cancellation (pSIC), and imperfect SIC (ipSIC) on the considered system. Analytical expressions for the Ergodic capacity, Ergodic secrecy sum rate (ESSR), and secrecy outage probability (SOP) are mathematically derived and verified with the simulation results. Our results demonstrate that a significantly higher ESSR and lower SOP of the system can be attained compared to a conventional NOMA system without a destination-assisted jamming signal scheme.acceptedVersio

NEMO: Real-Time Noise and Exhaust Emissions Monitoring for Sustainable and Intelligent Transportation Systems

Research and development efforts on sustainable and intelligent transportation systems are accelerating globally as the transportation sector contributes significantly to environmental pollution and produces a variety of noise and emissions that impact the climate. With the emergence of ubiquitous sensors and Internet of Things (IoT) applications, finding innovative transport solutions, including adequate climate change mitigation, will all be vital components of a sustainable transport future. Thus, it is essential to continuously monitor noise and exhaust emissions from road vehicles, trains, and ships. As a contribution to addressing this as part of an effort of the European Union project called “NEMO: Noise and Emissions Monitoring and Radical Mitigation", in this paper, we propose the design and development of a real-time noise and exhaust emissions monitoring for sustainable and intelligent transportation systems. We report real-world field testing in some European cities where vehicle noise and exhaust emissions data are gathered in the cloud-enabled Nautilus platform and evaluated using artificial intelligence (AI) algorithms to determine their categorization into different classes of emitters and thereby enabling the infrastructure managers to define logic and actions to be taken by high emitters in near real-time. We outline the creation of a complete NEMO solution to monitor and reduce noise and emissions in real time for sustainable and intelligent transportation systems.acceptedVersio

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.Comment: Accepted at IEEE Internet of Things Journa