The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.

The development of the healthcare industry is closely tied to the history of human development. The integration of sensing, communication, computing, and control technologies, along with cloud-based solutions, enables the realization of the Internet of Things concept and forms a series of Internet of Healthcare Things (IoHT) applications. However, providing realtime health services is one of the most important challenges for IoHT systems. To address this issue, a fog computing architecture (FC) is proposed as an additional computing layer to support the cloud computing layer, aiming to reduce service response time, computing costs, and energy consumption. In this study, we conduct a comprehensive evaluation to optimize resource allocation for hospitals under the constraints of scale and patient volume, as well as SLA thresholds. Then, we provide recommendations to optimize investment costs for computing infrastructure supporting real-time health services. Finally, we discuss challenges and open issues.

In this paper, we propose an innovative integration of Quantum Key Distribution (QKD) and Blockchain-based Self-Sovereign Identity (SSI) within the Open RAN (O-RAN) framework for 6G networks to address the critical need for enhanced security and robust identity management. We first present a general architecture that takes a multi-layered approach and is carefully designed to leverage the different capabilities of quantum security and blockchain technology. The architecture ensures seamless and secure operation across different layers of the O-RAN, focusing on the Distributed Identity Management (DIM) and Management & Orchestration layers, and explains the interactions between these layers to improve the security and operational efficiency of the network. We also investigate detailed case studies and applications that demonstrate the practicality and transformative potential of integrating QKD-secured blockchain identity management systems in real-world 6G scenarios. We also address the inherent challenges and limitations of such integration and propose viable solutions to overcome them. Finally, we provide insights into future research and implementation directions and highlight the critical role of quantum-secured blockchain systems in the evolution of telecommunication networks toward a more secure, decentralized, and user-centric paradigm.

The guesswork refers to the distribution of the minimum number of trials needed to guess a realization of a random variable accurately. In this study, a non-trivial generalization of the guesswork called guessing cost (also referred to as cost of guessing) is introduced, and an optimal strategy for finding the$\rho $-th moment of guessing cost is provided for a random variable defined on a finite set whereby each choice is associated with a positive finite cost value (unit cost corresponds to the original guesswork). Moreover, we drive asymptotically tight upper and lower bounds on the logarithm of guessing cost moments. Similar to previous studies on the guesswork, established bounds on the moments of guessing cost quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of Rényi’s entropy. Moreover, new random variables are introduced to establish connections between the guessing cost and the guesswork, leading to induced strategies. Establishing this implicit connection helped us obtain improved bounds for the non-asymptotic region. As a consequence, we establish the guessing cost exponent in terms of Rényi entropy rate on the moments of the guessing cost using the optimal strategy by considering a sequence of independent random variables with different cost distributions. Finally, with slight modifications to the original problem, these results are shown to be applicable for bounding the overall repair bandwidth for distributed data storage systems backed up by base stations and protected by bipartite graph codes.

The increasing demand for mobile data services has led to a need for efficient and cost-effective network sharing solutions. Blockchain technology has emerged as a promising solution for addressing the challenges associated with network sharing, such as interoperability, trust, and accountability. This paper presents a comprehensive classification and categorization of blockchain-based network sharing scenarios, highlighting their advantages and limitations. We have identified seven network sharing scenarios, ranging from centralized network sharing to fully decentralized spectrum sharing. For each scenario, the suitability of some of the selected blockchain architectures, from public, private, sidechain, and hybrid, is evaluated through extensive evaluations. We also identify gaps and opportunities of blockchain-based network sharing solution and present future research directions at the end of paper. Our analysis and results reveal that a single blockchain architecture is not suitable for all network sharing scenarios but careful analysis should be performed when selecting the suitable blockchain network in network sharing.

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. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.

There has been a rapid increase in the number of connected vehicles with a huge amount of data exchange between these vehicles that needs to be communicated, processed and analyzed reliably and efficiently. For secure and decentralized authentication, self-sovereign identity (SSI) management in vehicular networks have attracted attention in recent years. Hierarchical deployment frameworks, on the other hand, can provide secure and efficient knowledge sharing for vehicular networks with heterogeneous and geographically distributed vehicles and infrastructure in 6G networks. In this paper, we explore the joint use of hierarchical federated learning, as a collaborative machine learning framework, and hierarchical SSI management in vehicular networks, highlighting its advantages, limitations. At the end of the paper, we also provide two illustrative use cases.

Guessing a random variable with finite or countably infinite support in which each selection leads to a positive cost value has recently been studied within the context of "guessing cost". In those studies, similar to standard guesswork, upper and lower bounds for the ρ-th moment of guessing cost are described in terms of the known measure Rényi’s entropy. In this study, we non-trivially improve the known bounds using previous techniques along with new notions such as balancing cost. We have demonstrated that the novel lower bound proposed in this work, achieves 5.84%, 18.47% higher values than that of the known lower bound for ρ = 1 and ρ = 5, respectively. As for the upper bound, the novel expression provides 10.93%, 5.54% lower values than that of the previously presented bounds for ρ = 1 and ρ = 5, respectively.

We consider a novel distributed data storage/caching scenario in a cellular network, where multiple nodes may fail/depart simultaneously To meet reliability, we allow cooperative regeneration of lost nodes with the help of base stations allocated in a set of hierarchical layers1. Due to this layered structure, a symbol download from each base station has a different cost, while the link capacities between the nodes of the cellular system and the base stations are also constrained. Under such a setting, we formulate the fundamental trade-off with closed form expressions between repair bandwidth cost and the storage space per node. Particularly, the minimum storage as well as bandwidth cost points are formulated. Finally, we provide an explicit optimal code construction for the minimum storage regeneration point for a special set of system parameters.

The base station-mobile device communication traffic has dramatically increased recently due to mobile data, which in turn heavily overloaded the underlying infrastructure. To decrease Base Station (BS) interaction, intra-cell communication between local devices, known as Device-to-Device, is utilized for distributed data caching. Nevertheless, due to the continuous departure of existing nodes and the arrival of newcomers, the missing cached data may lead to permanent data loss. In this study, we propose and analyze a class of Low-Density Parity Check (LDPC) codes for distributed data caching in cellular networks. Contrary to traditional distributed storage, a novel repair algorithm for LDPC codes is proposed which is designed to exploit the minimal direct BS communication. To assess the versatility of LDPC codes and establish performance comparisons to classic coding techniques, novel theoretical and experimental evaluations are derived. Essentially, the theoretical/numerical results for repair bandwidth cost in presence of BS are presented in a distributed caching setting. Accordingly, when the gap between the cost of downloading a symbol from BS and from other local network nodes is not dramatically high, we demonstrate that LDPC codes can be considered as a viable fault-tolerance alternative in cellular systems with caching capabilities for both low and high code rates.

A novel fault-tolerant computation technique based on array Belief Propagation (BP)-decodable XOR (BP-XOR) codes is proposed for distributed matrix-matrix multiplication. The proposed scheme is shown to be configurable and suited for modern hierarchical compute architectures such as Graphical Processing Units (GPUs) equipped with multiple nodes, whereby each has many small independent processing units with increased core-to-core communications. The proposed scheme is shown to outperform a few of the well–known earlier strategies in terms of total end-to-end execution time while in presence of slow nodes, calledstragglers. This performance advantage is due to the careful design of array codes which distributes the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. An interesting trade-off between end-to-end latency and total communication cost is precisely described. In addition, to be able to address an identified problem of scaling stragglers, an asymptotic version of array BP-XOR codes based on projection geometry is proposed at the expense of some computation overhead. A thorough latency analysis is conducted for all schemes to demonstrate that the proposed scheme achieves order-optimal computation in both the sublinear as well as the linear regimes in the size of the computed product from an end-to-end delay perspective.

It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box-Cox transformation up to appropriate scaling and shifting operations.

In this paper, we introduce the notion of cost of guessing and provide an optimal strategy for guessing a random variable taking values on a finite set whereby each choice may be associated with a positive finite cost value. Moreover, we drive asymptotically tight upper and lower bounds on the moments of cost of guessing problem. Similar to previous studies on the standard guesswork, established bounds on moments quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of the Rényi's entropy. A new random variable is introduced to bridge between cost of guessing and the standard guesswork and establish the guessing cost exponent on the moments of the optimal guessing. Furthermore, these bounds are shown to serve quite useful for finding repair latency cost for distributed data storage in which sparse graph codes may be utilized.

This study presents a novel coded computation technique for distributed matrix-matrix product computation at a massive scale that outperforms well known previous strategies in terms of total execution time. Our method achieves this performance by distributing the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. The product computation is performed using MDS array Belief Propagation (BP)-decodable codes based on pure XOR operations. In addition, our scheme is configurable and suited for modern compute node architectures equipped with multiple processing units organized in a hierarchical manner. Assuming the number of backup nodes being sublinear in the size of the product, we shall demonstrate that the proposed scheme achieves order-optimal computation from an end-to-end latency perspective while ensuring acceptable communication requirements that can be addressed by today's high speed network link infrastructures.

Archiving and systematic backup of large digital data generates a quick demand for multi-petabyte scale storage systems. As drive capacities continue to grow beyond the few terabytes range to address the demands of today's cloud, the likelihood of having multiple/simultaneous disk failures became a reality. Among the main factors causing catastrophic system failures, correlated disk failures and the network bandwidth are reported to be the two common source of performance degradation. The emerging trend is to use efficient/sophisticated erasure codes (EC) equipped with multiple parities and efficient repairs in order to meet the reliability/bandwidth requirements. It is known that mean time to failure and repair rates reported by the disk manufacturers cannot capture life-cycle patterns of distributed storage systems. In this study, we develop failure models based on generalized Markov chains that can accurately capture correlated performance degradations with multiparity protection schemes based on modern maximum distance separable EC. Furthermore, we use the proposed model in a distributed storage scenario to quantify two example use cases: Primarily, the common sense that adding more parity disks are only meaningful if we have a decent decorrelation between the failure domains of storage systems and the reliability of generic multiple single-dimensional EC protected storage systems.

Belief propagation (BP) on binary erasure channels (BEC) is a low complexity decoding algorithm that allows the recovery of message symbols based on bipartite graph pruning process. Recently, array XOR codes have attracted attention for storage systems due to their burst error recovery performance and easy arithmetic based on Exclusive OR (XOR)-only logic operations. Array BP-XOR codes are a subclass of array XOR codes that can be decoded using BP under BEC. Requiring the capability of BP-decodability in addition to Maximum Distance Separability (MDS) constraint on the code construction process is observed to put an upper bound on the achievable code block-length, which leads to the code construction process to become a hard problem. In this study, we introduce asymptotically MDS array BP-XOR codes that are alternative to exact MDS array BP-X OR codes to allow for easier code constructions while keeping the decoding complexity low with an asymptotically vanishing coding overhead. We finally provide a code construction method that is based on discrete geometry to fulfill the requirements of the class of asymptotically MDS array BP-XOR codes.

An erasure-coded archival file storage system is presented using a chunk-based deduplication mechanism and fountain codes for space/time efficient operation. Unlike traditional archival storage, this proposal considers the deduplication operation together with correction coding in order to provide a reliable storage solution. The building blocks of deduplication and fountain coding processes are judiciously interleaved to present two novel ideas, reducing memory footprint with weaker hashing and dealing with the increased collisions using correction coding, and applying unequal error protection to deduplicated chunks for increased availability. The combination of these two novel ideas made the performance of the proposed system stand out. For example, it is shown to outperform one of the replication-based as well as RAID data protection schemes. The proposed system also addresses some of the fundamental challenges of today's low-cost deduplicated data storage systems such as hash collisions, disk bottleneck and RAM overflow problems, securing savings up to 90% regular RAM use.

Rateless codes allow a user to incrementally send additional redundancy, so they can be useful for heterogeneous and time-varying networks for which the choice of redundancy level in advance is difficult. Rateless codes are an attractive application layer forward error correction solution due to their flexibility and capacity-approaching performance. The original rateless codes were developed for the delivery of equally important information. In many multimedia applications, some data symbols are more important than others. Unequal error protection (UEP) designs are attractive solutions for such transmissions. However previous UEP rateless code designs were aimed for coarsely layered sources and might exhibit poor performance for fine-grained progressive coding. The main objective of this paper is to introduce a more generalized coding scheme, parameters of which can be tailored for progressive multimedia transmission. We present the optimization of a generalized rateless code using two different progressive source transmission protocols. Proposed coding scheme is shown to exhibit better unequal protection and recovery time properties than other published results.

A state-of-the-art progressive source encoder is combined with a concatenated block coding mechanism to produce a robust source transmission system for embedded bit streams. The proposed scheme efficiently trades off the available total bit budget between information bits and parity bits through efficient information block size adjustment, concatenated block coding, and random block interleavers. The objective is to create embedded codewords such that, for a particular information block, the necessary protection is obtained via multiple channel encodings, contrary to the conventional methods that use a single code rate per information block. This way, a more flexible protection scheme is obtained. The information block size and concatenated coding rates are judiciously chosen to maximize system performance, subject to a total bit budget. The set of codes is usually created by puncturing a low-rate mother code so that a single encoder-decoder pair is used. The proposed scheme is shown to effectively enlarge this code set by providing more protection levels than is possible using the code rate set directly. At the expense of complexity, average system performance is shown to be significantly better than that of several known comparison systems, particularly at higher channel bit error rates.

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 study, we introduce a more generalized design for the first universal fountain code design, LT codes, that makes 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 1.7dB PSNR gain in a progressive image transmission scenario over the two major UEP fountain code designs.

One of the challenges in radiotherapy of moving tumors is to determine the location of the tumor accurately. Existing solutions to the problem are either invasive or inaccurate. We introduce a non-invasive solution to the problem by tracking the tumor in 3D using bi-plane ultrasound image sequences. We present CrossTrack, a novel tracking algorithm in this framework. We pose the problem as recursive inference of 3D location and tumor boundary segmentation in the two ultrasound views using the tumor 3D model as a prior. For the segmentation task, a robust graph-based approach is deployed as follows: First, robust segmentation priors are obtained through the tumor 3D model. Second, a unified graph combining information across time and multiple views is constructed with a robust weighting function. For the tracking task, an effective mechanism for recovery from respiration-induced occlusion is introduced. Our experiments show the robustness of CrossTrack in handling challenging tumor shapes and disappearance scenarios, with sub-voxel accuracy, and almost 100% precision and recall, significantly outperforming baseline solutions.

With Unequal Error Protection (UEP), more important symbols are given greater protection against channel errors than are less important symbols. The protection can be accomplished by various methods, including joint source-channel coding (JSCC) and hierarchical modulation. In this paper, a robust progressive source transmission system using forward error correction (FEC) and a bits-to-symbol assignment methodology that provides UEP is proposed. UEP is not only provided by hierarchical modulation but also by the packetization methodology combined with channel coding. It is demonstrated by simulation that our system improves performance compared to an Equal Error Protection (EEP) technique and to a baseline JSCC-only mechanism.