The original design of standard digital fountain codes assumes that the coded information symbols are equally important. In many applications, some source symbols are more important than others, and they must be recovered prior to the rest. Unequal error protection (UEP) designs are attractive solutions for such source transmissions. In this paper, we introduce a more generalized design for the first universal fountain code design, Luby transform (LT) codes, that make it particularly suited for progressive bit stream transmissions. We apply the generalized LT codes to a progressive source and show that it has better UEP properties than other published results in the literature. For example, using the proposed generalization, we obtained up to 1.7-dB peak signal to noise ratio gain in a progressive image transmission scenario over the two major UEP fountain code designs.

The main objective of this paper is to highlight the research directions and explain the main roles of current Artificial Intelligence (AI)/Machine Learning (ML) frameworks and available cloud infrastructures in building end-to-end ML lifecycle management for healthcare systems and sensitive biomedical data. We identify and explore the versatility of many genuine techniques from distributed computing and current state-of-the-art ML research, such as building cognition-inspired learning pipelines and federated learning (FL) ecosystem. Additionally, we outline the advantages and highlight the main obstacles of our methodology utilizing contemporary distributed secure ML techniques, such as FL, and tools designed for managing data throughout its lifecycle. For a robust system design, we present key architectural decisions essential for optimal healthcare data management, focusing on security, privacy and interoperability. Finally, we discuss ongoing efforts and future research directions to overcome existing challenges and improve the effectiveness of AI/ML applications in the healthcare domain.

Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.

A robust coded scheme for progressive multimedia transmission is proposed for additive white Gaussian noise, flat Rayleigh fading channels, and frequency-selective channels using different unequal error protection methods in combination. Hierarchical modulation is coupled with a packetization/combining strategy and an efficient channel encoder consisting of a cyclic redundancy check outer coder concatenated with an inner rate-compatible punctured convolutional coder. Distortion-optimal hierarchical parameters are jointly chosen with the set of channel coding parameters on a packet-switched wireless network with fixed length packets. A lower bound for the performance improvement of the proposed system is derived and shown to give significant gains at lower packet sizes and higher transmission rates. The proposed system is also shown to outperform several existing schemes for realistic wireless channels.

Blockchain Network (BCN)-based Self-Sovereign Identity (SSI) has recently surfaced as an identity and access management framework that allows users and organizations to control over their data. On the other hand, Open Radio Access Networks (O-RANs) propose a framework for facilitating the exchange of infrastructure-related data. This study presents the critical role of BCN-SSI in the O-RAN system architecture and promises an authentication process to provide insights into the robust security measures used to seamlessly establish the identification of users and entities within the O-RAN ecosystem in the post-quantum era. We show that the integration of BCN-SSI enhances security protocols and ensure the integrity of communication networks in the evolving technological landscape. As an illustrative use case, spectrum sharing, is also presented to demonstrate the practicality and the impact of the proposed framework. Furthermore, we address the potential threats posed by quantum attacks on BCN-based SSI systems by investigating Post-Quantum Cryptography (PQC). Simulation results confirm the effectiveness of the proposed BCN-based DIM and emphasize its role in securing identity management processes within the O-RAN context. Finally, we address the use of efficient computational techniques in the context of PQC, which holds significant potential in terms of reducing energy consumption overall.