Christian Berger, Sadok Ben Toumia, Hans P. Reiser
Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Assessing that BFT protocol implementations meet their performance expectations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal & Tusk, and BullShark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6,742 operations per second (op/s) with 128 replicas, outperforming BullShark (2,318 op/s) and Tusk (1,952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.
Haibo Wang, Hongwei Gao, Teng Ma, Chong Li · 5 authors
Distributed Federated Learning (DFL) technology enables participants to cooperatively train a shared model while preserving the privacy of their local data sets, making it a desirable solution for decentralized and privacy-preserving Web3 scenarios. However, DFL faces incentive and security challenges in the decentralized framework. To address these issues, this paper presents a Hierarchical Blockchain-enabled DFL (HBDFL) system, which provides a generic solution framework for the DFL-related applications. The proposed system consists of four major components, including a model contribution-based reward mechanism, a Proof of Elapsed Time and Accuracy (PoETA) consensus algorithm, a Distributed Reputation-based Verification Mechanism (DRTM) and an Accuracy-Dependent Throughput Management (ADTM) mechanism. The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets, while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput. The DRTM improves the system efficiency in consensus, and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy. The performance of the proposed HBDFL system is evaluated by numerical simulations, which show that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.
Harun Jamil, Yang Jian, Faisal Jamil, Mohammad Hijjawi · 5 authors
This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy , including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization , task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization , enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers , and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.
In an era dominated by the Internet of Things, ensuring the longevity and sustainability of IoT devices has emerged as a pressing concern. This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices and suggests methods to improve their lifespan management. By examining factors such as security vulnerabilities, user awareness gaps, and the influence of fashion-driven technology trends, the paper underscores the need for legislative interventions, consumer education, and industry accountability. Additionally, it explores innovative approaches to improving IoT longevity, including the integration of sustainability considerations into architectural design through requirements engineering methodologies. Furthermore, the paper discusses the potential of distributed ledger technology, or blockchain, to promote transparent and decentralized processes for device provisioning and tracking. This study promotes a sustainable IoT ecosystem by integrating technology innovation, legal change, and social awareness to reduce environmental impact and enhance resilience for the digital future
LoRaWAN networks have become popular for enabling long-range, low-power connectivity in Internet of Things (IoT) applications. Traditional LoRa Wannetworks typically rely on a centralized architecture, which may pose limitations regarding scalability, reliability, and adaptability. In contrast, decentralized LoRaWAN networks offer a compelling alternative with several distinct features. This study explores the advantages of decentralized LoRaWAN networks over their centralized counterparts and presents DeLoRaN, a completely decentralized and fully compatible LoRaWAN network. Firstly, a decentralized network architecture enhances the availability of services by leveraging multiple copies of a LoRaWAN Network Server (NS), here called Network Controller, thereby eliminating the single points of failure. Secondly, the decentralized nature of the network improves data availability and integrity by utilizing shared and decentralized ledgers, such as blockchain technology. This ensures that data remains accessible and tamper-proof even in the presence of malicious actors or network failures. Thirdly, a decentralized network strengthens resilience by tolerating faulty or malicious nodes through the consensus mechanisms employed by the Network Controller. To prove our point, we present an implementation of our distributed approach and test it in different scenarios, to appreciate performance and scalability of DeLoRaN when compared to a centralized approach.
Aman Mishra, Yash Garg, Om Jee Pandey, Mahendra K. Shukla · 6 authors
At present, the centralized learning models, used for IoT applications generating large amount of data, face several challenges such as bandwidth scarcity, more energy consumption, increased uses of computing resources, poor connectivity, high computational complexity, reduced privacy, and large latency towards data transfer. In order to address the aforementioned challenges, Blockchain-Enabled Federated Learning Networks (BFLNs) emerged recently, which deal with trained model parameters only, rather than raw data. BFLNs provide enhanced security along with improved energy-efficiency and Quality-of-Service (QoS). However, BFLNs suffer with the challenges of exponential increased action space in deciding various parameter levels towards training and block generation. Motivated by aforementioned challenges of BFLNs, in this work, we are proposing an actor-critic Reinforcement Learning (RL) method to model the Machine Learning Model Owner (MLMO) in selecting the optimal set of parameter levels, addressing the challenges of exponential grow of action space in BFLNs. Further, due to the implicit entropy exploration, actor-critic RL method balances the exploration-exploitation trade-off and shows better performance than most off-policy methods, on large discrete action spaces. Therefore, in this work, considering the mobile scenario of the devices, MLMO decides the data and energy levels that the mobile devices use for the training and determine the block generation rate. This leads to minimized system latency and reduced overall cost, while achieving the target accuracy. Specifically, we have used Proximal Policy Optimization (PPO) as an on-policy actor-critic method with it's two variants, one based on Monte Carlo (MC) returns and another based on Generalized Advantage Estimate (GAE). We analyzed that PPO has better exploration and sample efficiency, lesser training time, and consistently higher cumulative rewards, when compared to off-policy Deep Q-Network (DQN).
K. Suresh Kumar, Jafar A. Alzubi, Nadia Sarhan, E. M. Awwad · 6 authors
This paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user's experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network's bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency are also minimized by executing the optimal resource management scheme.
In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.
I Evelyn Ezhilarasi, J. Christopher Clement, Joseph M. Arul
Abstract Cognitive radio network is a promising technology to enhance the spectrum utilization and to resolve the spectrum scarcity issues. But the malicious users play havoc with the network during spectrum sensing and demean the network performance. It is mandatory to identify such malicious attacks and address it. There have been many traditional methods to mitigate the cognitive radio network attacks. In this paper, we have surveyed advanced attack mitigation techniques like machine learning, deep learning and blockchain. Thus, by detecting and addressing the malicious activities, the throughput and overall network performance can be improved.
Christian Berger, Sadok Ben Toumia, Hans P. Reiser
Recent Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols increasingly focus on scalability to meet the requirements of distributed ledger technology (DLT). Validating the performance of scalable BFT protocol implementations requires careful evaluation. Our solution uses network simulations to forecast the performance of BFT protocols while experimentally scaling the environment. Our method seamlessly plug-and-plays existing BFT implementations into the simulation without requiring code modification or re-implementation, which is often time-consuming and error-prone. Furthermore, our approach is also significantly cheaper than experiments with real large-scale cloud deployments. In this paper, we first explain our simulation architecture, which enables scalable performance evaluations of BFT systems through high-performance network simulations. We validate the accuracy of these simulations for predicting the performance of BFT systems by comparing simulation results with measurements of real systems deployed on cloud infrastructures. We found that simulation results display a reasonable approximation at a larger system scale, because the network eventually becomes the dominating factor limiting system performance. In the second part of our paper, we use our simulation method to evaluate the performance of PBFT and BFT protocols from the "blockchain generation", such as HotStuff and Kauri, in large-scale and realistic wide-area network scenarios, as well as under induced faults.
Wenxuan Ye, Chendi Qian, Xueli An, Xueqiang Yan · 5 authors
Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the co-ordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.
Motivated by proof-of-stake (PoS) blockchains such as Ethereum, two key desiderata have recently been studied for Byzantine-fault tolerant (BFT) state-machine replication (SMR) consensus protocols: Finality means that the protocol retains consistency, as long as less than a certain fraction of validators are malicious, even in partially-synchronous environments that allow for temporary violations of assumed network delay bounds. Accountable safety means that in any case of inconsistency, a certain fraction of validators can be identified to have provably violated the protocol. Earlier works have developed impossibility results and protocol constructions for these properties separately. We show that accountable safety implies finality, thereby unifying earlier results.
Jiawen Kang, Jinbo Wen, Dongdong Ye, Bingkun Lai · 10 authors
Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a user-centric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.
Zi-hao Wang, Hang Wang, Zhuowen Li, Xinghua Li · 9 authors
The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two phases: leader election and block consensus. In the leader election phase, we integrate multi-criteria decision-making and link prediction algorithms to design an efficient stable-leader election method. In the block consensus phase, we propose a dynamic block verification algorithm based on historical verification information to achieve efficient block consensus. Our theoretical analysis demonstrates that the proposed consensus protocol is safe and live, effectively ensuring the consistency of message sharing in FANET. Experiment results show that our scheme outperforms traditional Raft schemes, with 35% increase in consensus success rate and 25% improvement in consensus efficiency.
Nowadays, the freshness of status update data is critical for emergent tasks in wireless sensor networks (WSNs). This paper analyzes the peak age of information (PAoI) in the transaction-confirmation process of blockchain technology in the context of unmanned aerial vehicle (UAV) aided wireless sensor networks. Each access node transmits the latest packets to the associated access node, which processes the packets relying on blockchain technology. We investigate the transaction-confirmation process based on a finite-buffer batch-service${\bf GI/M^{[b]}/1/N}$queueing system. Additionally, the embedded Markov chain techniques are utilized to obtain the state probabilities of the system. Moreover, we deduce the closed-form expressions of average PAoI, which regards as the worse case of the update packet's freshness. Numerical results verify that reducing the block size and the queue length leads to a lower PAoI. Besides, fixed interval network traffic and Markov property network traffic based situations display much better aging behavior than other situations.
Haytham Qushtom, Jelena Mišić, Vojislav B. Mišić, Xiaolin Chang
The consensus algorithm is an essential ingredient of any blockchain system. Many different consensus mechanisms, such as practical Byzantine fault tolerance (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and their many derivatives, have been proposed over the years, but the complementary problems of performance and resilience to malicious behavior of the nodes have yet to be resolved in a satisfactory manner. In this work, we propose a consensus mechanism that integrates PoS with PBFT, which can effectively deal with dishonest nodes, both individual validators and leaders, while maintaining high performance. Our model incentivized truthful behavior by using trust score and reward mechanisms as crucial components of the block validation and ordering processes. The performance of the proposed scheme is evaluated using an analytical model that employs a semi-Markov process, defined by an ergodic multidimensional Markov chain with a finite number of states. The results show the efficiency of the proposed model in consensus-based decision making, even under a high likelihood of dishonest node behavior.
With the improvement of people's awareness of environmental protection, electric vehicles (EVs) are becoming more and more popular, and the issue of vehicle to grid (V2G) energy trading is also put on the agenda. To protect the security and privacy of EVs when they trade energy with the grid, many scholars have introduced the emerging blockchain technology. However, there are few studies on the blockchain consensus algorithm for the EVs charging scenario, while the consensus is exactly the core technology in blockchain for reaching agreement in distributed systems, which to some extent determines the efficiency of V2G. Therefore, aiming at the above scenario, this paper proposes two low-complexity consensus algorithms, namely (PBFT-enabled RAFT) PRAFT and (RAFT-enable PBFT) RPBFT, which are combined the typical blockchain consensus PBFT and RAFT, and can be respectively applied to two EVs charging scenarios. In our V2G model, charging piles (CPs) and charging stations (CSs) will participate in the blockchain consensus as nodes. Through theoretical analysis and simulation, and compared with other methods, these two consensus algorithms have high scalability, low communication complexity, low storage overhead, high throughput, and low latency. Meanwhile they can also avoid the risk of Byzantine leader in RAFT. Finally, we demonstrate the two consensus algorithms in a real charging scenario, which show their transaction latency and energy consumption can well adapt to the EVs charging scenario in V2G networks.
Carlos Núñez‐Gómez, Martijn de Vos, Jérémie Decouchant, Johan Pouwelse · 6 authors
With the proliferation of Internet of Things (IoT) ecosystems, traditional resource orchestration mechanisms, executed on fog devices, encounter significant scalability, reliability and security challenges. To tackle these challenges, recent decentralized algorithms in Fog-IoT use Distributed Ledger Technologies to orchestrate resources and payments between peers. However, while distributed ledgers provide many desirable properties, their consensus mechanism introduces a performance bottleneck. This paper introduces Light-HIDRA, a consensus-less and decentralized resource orchestration system for Fog-IoT environments. At its core, Light-HIDRA uses Byzantine Reliable Broadcast (BRB) to coordinate actions without centralized control, therefore drastically reducing communication overhead and latency compared to consensus-based solutions. Light-HIDRA coordinates the scheduling and execution of workloads, and securely manages the payments that peers receive for dedicating resources to workloads. Light-HIDRA further increases performance and reduces overhead by grouping peers into distinct domains. We conduct an in-depth analysis of the protocol’s security properties, investigating its efficiency and robustness in diverse situations. We evaluate the performance of Light-HIDRA, highlighting its performance over HIDRA, a state-of-the-art baseline that uses smart contracts. Our experiments demonstrate that Light-HIDRA reduces the bandwidth usage by up to 57x, the latency of workload offloading by up to 142x, and shows superior throughput compared to HIDRA.
Chryssis Georgiou, Michel Raynal, Elad M. Schiller
Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques.
As smart cities become more decentralized, the need for reliable and secure cyber-physical systems (CPS) that guarantee safe interactions and secure data storage without loss of privacy is continuously increasing. Blockchain is a rapidly emerging technology in this domain. It demonstrated effectiveness thanks to the cryptographic mechanisms it utilizes and to its immutability. Private blockchains are the most suited to applications that require privacy and confidentiality when data is very sensitive. In this case, the most commonly used consensus protocol is Practical Byzantine Fault Tolerance (PBFT). However, PBFT requires the participation of all nodes in the consensus process, which increases bandwidth consumption and consensus delay significantly. In this paper, we propose an adaptive PBFT protocol called APBFT that optimizes the number of nodes participating in the consensus based on their response time and credibility. Therefore, we reduce the amount of communication and the response delays. We maintain the asynchrony of the algorithm so that it remains resilient to DoS attacks. The simulation results show that our algorithm outperforms the original PBFT in terms of delays and message traffic.