A Novel Blockchain‐Integrated Deep Learning Framework for Securing Smart Healthcare Communication Networks

Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.

As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives.

Blockchain technology has attracted the curiosity of experts in a variety of sectors, including its potential for Smart Grid (SG) cybersecurity. The study investigates vulnerabilities in smart Direct Current-MicroGrid (DC-MG) systems, particularly community identity servers, which pose a threat to the grid due to the increasing sophistication of existing cybersecurity frameworks, causing delays in real-time activities. The research proposes a novel, grid-lock secure-chain consensus framework to address these issues and improve contemporary power systems’ capacity to defend themselves against cyberattacks. This design makes use of Proof of Vote (PoV), a consensus technique that enables decentralized voting across the network’s meter nodes to reach consensus. For safe identification and transaction validation, every meter node has public and private keys. All information is encrypted before being transmitted to other nodes. The information is kept on a distributed ledger, where the Secure Hash Algorithm (SHA-256) hash technique is used to cryptographically connect each block. Only legitimate blocks are added to the blockchain due to the PoV process, which also maintains separate voting and accounting rights for security. The proposed design increases encryption techniques and decentralizes permission to lessen the possibility of cyberattacks without compromising system performance. The proposed framework achieves a significant improvement, with throughput increased by 53% and latency reduced by 19% compared to conventional consensus mechanisms such as Practical Byzantine Fault Tolerance (PBFT) and Proof of Work (PoW). Specifically, the framework demonstrates a throughput of 150 Transactions per second (Tx/s) and a latency of 0.89 seconds, outperforming PBFT’s throughput of 98 Tx/s and latency of 11 seconds, and PoW’s throughput of 120 Tx/s and latency of 1 second. This method represents a major leap in employing blockchain technology for current power system security as it not only strengthens the grid against assaults but also maximizes its resilience and operational efficiency. In particular, results obtained from testing on 118-bus topology setups demonstrate high throughput and low latency, confirming the framework’s suitability for SG networks under high transaction volumes and potential cyber threats.

Introduction: Fault tolerance refers to a system's ability to continue functioning correctly even in the face of failures, ensuring that critical data is replicated and tasks are redistributed to maintain overall system operation. The widespread growth of distributed systems—such as cloud computing, the Internet of Things (IoT), and decentralized applications (dApps)—demands effective solutions to ensure system reliability, availability, and fault tolerance. Traditional fault tolerance methods often encounter issues related to scalability, security, and managing Byzantine failures in large-scale environments. Blockchain technology, known for its decentralized and immutable characteristics, presents innovative approaches to addressing these challenges, thereby strengthening fault tolerance and system integrity. This paper explores how blockchain technology can optimize fault tolerance in distributed systems, focusing on key concepts, benefits, challenges, and potential solutions. We examine how blockchain-based consensus mechanisms, such as Practical Byzantine Fault Tolerance (PBFT) and Proof of Work (PoW), can improve the reliability and resilience of distributed systems. Additionally, we explore several use cases—such as distributed databases, cloud services, and edge computing—where blockchain can significantly enhance fault tolerance. Objective: The objective of optimizing fault tolerance in distributed systems via consensus algorithms is to ensure that the system remains reliable, available, and consistent, even in the presence of faults or failures. These failures can range from benign issues like network delays to more serious ones such as node crashes or malicious behavior by participants. The optimization process focuses on minimizing the impact of such faults while maximizing the performance, scalability, and robustness of the system. Methods: Optimizing fault tolerance in distributed systems is essential for ensuring that the system remains functional and available despite the occurrence of failures, including node crashes, network partitions, or malicious behavior. Consensus algorithms play a pivotal role in this optimization by helping multiple distributed nodes agree on a common decision or state, even in the presence of faults.. Optimizing fault tolerance in distributed systems via consensus algorithms involves PBFT, PoW, PoS and PoA. Results: optimizing fault tolerance in distributed systems through consensus algorithms is a more resilient, reliable, and highly available system that can handle a wide range of failure scenarios without compromising data consistency, system availability, or performance. By applying the right consensus protocols, distributed systems can ensure that even in the face of node failures, network partitions, or malicious behavior, the system continues to function correctly. Conclusion: The optimization of fault tolerance in distributed systems through consensus algorithms is crucial for ensuring the reliability, availability, and consistency of these systems, especially as they scale and handle diverse failure scenarios. Distributed systems are inherently prone to various types of failures, including node crashes, network partitions, and even malicious behavior by participants. Consensus algorithms address these challenges by providing mechanisms that allow nodes to agree on a common decision or state, even when some nodes are faulty or unavailable.

Introduction: Fault tolerance refers to a system's ability to continue functioning correctly even in the face of failures, ensuring that critical data is replicated and tasks are redistributed to maintain overall system operation. The widespread growth of distributed systems—such as cloud computing, the Internet of Things (IoT), and decentralized applications (dApps)—demands effective solutions to ensure system reliability, availability, and fault tolerance. Traditional fault tolerance methods often encounter issues related to scalability, security, and managing Byzantine failures in large-scale environments. Blockchain technology, known for its decentralized and immutable characteristics, presents innovative approaches to addressing these challenges, thereby strengthening fault tolerance and system integrity. This paper explores how blockchain technology can optimize fault tolerance in distributed systems, focusing on key concepts, benefits, challenges, and potential solutions. We examine how blockchain-based consensus mechanisms, such as Practical Byzantine Fault Tolerance (PBFT) and Proof of Work (PoW), can improve the reliability and resilience of distributed systems. Additionally, we explore several use cases—such as distributed databases, cloud services, and edge computing—where blockchain can significantly enhance fault tolerance. Objective: The objective of optimizing fault tolerance in distributed systems via consensus algorithms is to ensure that the system remains reliable, available, and consistent, even in the presence of faults or failures. These failures can range from benign issues like network delays to more serious ones such as node crashes or malicious behavior by participants. The optimization process focuses on minimizing the impact of such faults while maximizing the performance, scalability, and robustness of the system. Methods: Optimizing fault tolerance in distributed systems is essential for ensuring that the system remains functional and available despite the occurrence of failures, including node crashes, network partitions, or malicious behavior. Consensus algorithms play a pivotal role in this optimization by helping multiple distributed nodes agree on a common decision or state, even in the presence of faults.. Optimizing fault tolerance in distributed systems via consensus algorithms involves PBFT, PoW, PoS and PoA. Results: optimizing fault tolerance in distributed systems through consensus algorithms is a more resilient, reliable, and highly available system that can handle a wide range of failure scenarios without compromising data consistency, system availability, or performance. By applying the right consensus protocols, distributed systems can ensure that even in the face of node failures, network partitions, or malicious behavior, the system continues to function correctly. Conclusion: The optimization of fault tolerance in distributed systems through consensus algorithms is crucial for ensuring the reliability, availability, and consistency of these systems, especially as they scale and handle diverse failure scenarios. Distributed systems are inherently prone to various types of failures, including node crashes, network partitions, and even malicious behavior by participants. Consensus algorithms address these challenges by providing mechanisms that allow nodes to agree on a common decision or state, even when some nodes are faulty or unavailable.

As the foundation of a blockchain, consensus algorithm significantly affects the blockchain system’s performance. To a consortium blockchain, Practical Byzantine Fault Tolerance (PBFT) has been widely believed as a good candidate consensus due to its many advantages. However, PBFT is not particularly designed for a consortium blockchain. Thus, there is still a large improvement space to implement the PBFT algorithm in a sharded blockchain. Based on network sharding, we aim to address the problems incurred by the traditional PBFT algorithm. Because when there are large number of nodes in a P2P network, PBFT can lead to a significant performance degradation. Even worse, Byzantine nodes cannot be found timely in a large-scale blockchain network where the PBFT algorithm is adopted. In this paper, we propose an adapted version of BFT consensus for the sharded blockchain. The proposed cross-shard BFT consensus mainly consists of a two-phase consensus mechanism after performing network sharding. In the first phase, Raft consensus is first adopted within each shard, in which a leader is elected. In the second phase, those leaders from all shards form a committee and perform a committee-wise PBFT consensus. Through introducing anchor nodes within each shard, the security of the proposed two-phase consensus is guaranteed. We analyze the security of the cross-shard BFT consensus based on a committee-wise monitoring framework. Through simulations, we find out that the proposed cross-shard BFT consensus yields a higher throughput, lower latency than the original PBFT. The fault-tolerance ability of the proposed consensus is around 1.5\(\times \) to 2\(\times \) of PBFT.KeywordsBlockchainConsensus algorithmPractical byzantine fault toleranceNetwork sharding

Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of consensus mechanisms; two key consensus mechanisms are, Proof of Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). While the PoW based mechanism is computationally intensive, due to the puzzle solving, the PBFT consensus mechanism is communication intensive due to the all-to-all messages; thereby, both may result in high energy consumption and, hence, there is a trade-off between the computation and the communication energy costs. In this paper, we propose a hybrid-blockchain (H-chain) framework appropriate for scenarios where multiple organizations exist and where the framework enables private transaction verification and public transaction sharing and audit, according to application needs. In particular, we study the energy consumption of the hybrid consensus mechanisms in H-chain. Moreover, this paper proposes a reward plan to incentivize the blockchain agents so that they make contributions to the H-chain while also considering the energy consumption. While the work is generally applicable to IoT applications, the paper illustrates the framework in a scenario which secures an IoT application connected using a software defined network (SDN). The evaluation results first provide a method to balance the public and private parts of the H-chain deployment according to network conditions, computation capability, verification complexity, among other parameters. The simulation results demonstrate that the reward plan can incentivize the blockchain agents to contribute to the H-chain considering the energy consumption of the hybrid consensus mechanism, this enables the proposed H-chain to achieve optimal social welfare.

Blockchain technology has become a significant paradigm which has been utilized to transform various industries and applications. Its decentralized, transparent, and secure nature has led to widespread adoption in diverse fields such as finance, healthcare, supply chain management, and the Internet of Things (IoT). This paper presents a comprehensive survey of blockchain technology, focusing on three key aspects: consensus algorithms, data storage mechanisms, and blockchain architectures. We provide a detailed overview of various consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authentication (PoAh), and Practical Byzantine Fault Tolerance (PBFT), discussing their mechanisms, advantages, limitations, and challenges. Furthermore, we explore different data storage mechanisms, such as on-chain, off-chain, and hybrid storage, analyzing their implications for scalability, security, and efficiency. We also delve into various blockchain architectures, including single, dual, and multi-blockchain architectures, examining their suitability for different applications. This survey provides a holistic understanding of blockchain technology, highlighting its potential, challenges, and future directions. It serves as a valuable resource for researchers, developers, and practitioners interested in exploring and leveraging the capabilities of blockchain.

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.

Blockchain, one of the modern technologies, has received significant attention recently. The blockchain is an immutable ledger which records the transaction in a decentralized manner that ensures high security. Blockchain-based applications are popular in numerous fields like financial services, cryptocurrencies, cybersecurity, supply chain, health care, E-Governance, asset management, Internet of Things (IoT), and so on. In order to validate a transaction within a ledger, blockchain uses the concept of consensus. Consensus guarantees fault tolerance, reliability and high security of the blockchain systems. The most widely used consensus algorithms in the modern blockchains are the Proof of Work (PoW), the Proof of Stake (PoS), the Proof of Activity(PoA) and the Practical Byzantine Fault Tolerance(PBFT). In this work, we conduct a performance evaluation of these four consensus algorithms using Naive implementation of a blockchain network. We also conduct experiments with two popular consensus mechanisms in Ethereum platform. We study the characteristics of transaction and query latencies by varying the number of complexity and transactions. Finally, we present the conclusion on the performance consensus algorithms.

ULS-PBFT: An ultra-low storage overhead PBFT consensus for blockchain

PurposeThe study reported here aimed to evaluate both biological and psychosocial factors as predictors for quality of life as well as to examine the associations between the factors and quality of life in individuals with schizophrenia.MethodsEighty individuals with schizophrenia were recruited to the study. The Thai version of the World Health Organization Quality of Life-BREF was utilized to measure the quality of life. The five Marder subscales of the Positive and Negative Syndrome Scale were applied. Other tools for measurement included the Calgary Depression Scale for Schizophrenia and six social support deficits (SSDs). Pearson/Spearman correlation coefficients and the independent t-test were used for the statistical analysis to determine the associations of variables and the overall quality of life and the four domain scores. A multiple linear regression analysis of the overall quality of life and four domain scores was applied to determine their predictors.ResultsThe Positive and Negative Syndrome Scale total score, positive symptoms, negative symptoms, disorganized thought, and anxiety/depression showed a significant correlation with the overall quality of life and most of the four domain scores. Depression, SSDs, and adverse drug events showed a significant correlation with a poorer overall quality of life. The multiple linear regression model revealed that negative symptoms, depression, and seeing a relative less often than once per week were predictors for the overall quality of life (adjusted R2=0.472). Negative symptoms were also found to be the main factors predicting a decrease in the four domains of quality of life – physical health, psychological, social relationships, and environment.ConclusionNegative symptoms, depression, and poor contact with relatives were the foremost predictors of poor quality of life in individuals with schizophrenia. Positive symptoms, negative symptoms, disorganized thought, anxiety/depression, SSDs, and adverse events were also found to be correlated with quality of life.

The Internet of Things (IoT) is a technology that allows every object or item to become part of the Internet and interact with each other. One of the technologies based on the IoT is Long Range (LoRa). Apart from the increasing number of IoT services, security aspects become a separate issue in the development of the IoT. One of the solutions is to utilize blockchain technology in the IoT topology to secure the data and transactions that occur in the IoT network. The blockchain can take minutes to compute a cryptographic chain. It also needs sufficient computing resources. This problem gave rise to the idea of establishing a lightweight blockchain platform with low latency that could run on devices with low computing resources as well as IoT devices. We offered a technology called Lightweight Multi-Fog (LMF) in our previous publication that is implemented using the Lightweight Scalable Blockchain (LSB) algorithm and the fog network on the IoT to solve the problem of integrating a blockchain with the IoT. In this paper, we simulate how the broadcast domain on LMF works and verify the results in lower latency and energy transmission compared to the standard blockchain model. The results showed that the average increase of the total delivery time (Taverage) on the LMF platform was smaller than the average increase of the total delivery time (Taverage), which was 0.53% for the variations in the number of nodes and 0.27% for the variations in the number of brokers/miners. Regarding the average increase of the total energy delivery (Eaverage), the Proof of Work (PoW) platform has a smaller increase of the total energy delivery (Eaverage), which is 1.68% during the variations in the number of nodes. In contrast, the LMF platform has a smaller average increase of the total shipping energy (Eaverage), which is 0.28% for the variations in the number of brokers/miners.

The integration between fog computing and the Internet of Things (IoT) creates plenty of new opportunities. Fog computing nodes run complex tasks on behalf of IoT devices, and the topological proximity of fog computing to the IoT enables several advantages (e.g., low latency). However, some IoT devices are mobile, and mobility may compromise the fog advantages. When a device moves, the communication path to the corresponding fog service may increase, with an impact on the fog advantages (which are a consequence of fog proximity) and overall performance. To overcome this issue, the fog service may be migrated across the fog computing infrastructure and maintained close enough to the served IoT device(s). It is worth noting, though, that service migration comes at a cost and may affect application Quality of Service (QoS). In this paper, we consider a fog service to be implemented as multiple containers, having one of them encapsulating an MQTT broker. Our contribution is the evaluation of the impact of container migration, which is considered in various flavours, on application QoS as perceived by mobile things. To this purpose, we consider an augmented reality application based on the MQTT protocol and conduct a set of experiments over a real fog computing testbed. Results show how migrating the fog service gives some benefits on the experienced QoS with respect to a case where no migration is performed.

Queueing theory employs mathematical analysis to establish effectiveness metrics. Optimization models are then formulated using significant and efficient measures, such as data, to ascertain system efficiency and requirements. Each queuing system represents a discrete event system problem, and simulating these systems aids in addressing challenges and conducting practical performance analysis. Blockchain offers various benefits, including redistribution, accessibility, durability, reliability, constancy, anonymity, auditability, and data security. Its applications span across cryptocurrencies, financial services, reputation management, the 'Internet of Things’, the sharing economy, and social and community services. Notably, foundational theory is increasingly pertinent in the blockchain field. For instance, performance analysis and optimization of blockchain systems rely on mathematical models like Markov processes and queueing theory. In smart healthcare, blockchain technology enhances disease diagnosis, patient care, and overall quality of life. Due to the substantial patient data stored on blockchain in smart healthcare architectures, queueing models are indispensable for efficient data processing. This paper leverages Markov chains to establish queueing theory for blockchain systems and assess the performance of smart healthcare architecture. A "Markovian-batch-service" queueing framework is devised for this purpose, modeling input and processing parameters essential for reliable queuing network simulations.