Specializing Directed Acyclic Graph Federated Learning (SDAGFL) is a new federated learning framework with the advantages of decentralization, personalization, resisting a single point of failure, and poisoning attack. Instead of training a single global model, the clients in SDAGFL update their models asynchronously from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT), which is designed for IoT scenarios. Because of many the features inherited from DAG-DLT, SDAGFL is suitable for IoT scenarios in many aspects. However, the training process of SDAGFL is quite energy consuming, in which each client needs to compute the confidence and rating of the nodes selected by multiple random walks by traveling the ledger with 15–25 depth to obtain the “reference model” to judge whether or not to broadcast the newly trained model. As we know, the energy consumption is an important issue for IoT scenarios, as most devices are battery-powered with strict energy restrictions. To optimize SDAGFL for IoT, an energy-efficient SDAGFL based on an event-triggered communication mechanism, i.e., ESDAGFL, is proposed in this paper. In ESDAGFL, the new model is broadcasted only in the event that the new model is significantly different from the previous one, instead of traveling the ledger to search for the “reference model”. We evaluate the ESDAGFL on the FMNIST-clustered and Poets dataset. The simulation is performed on a platform with Intel®CoreTM i7-10700 CPU (CA, USA). The simulation results demonstrate that ESDAGFL can reach a balance between training accuracy and specialization as good as SDAGFL. What is more, ESDAGFL can reduce the energy consumption by 42.5% and 51.7% for the FMNIST-clustered and Poets datasets, respectively.
Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order$k$. Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.
Christian Berger, Sadok Ben Toumia, Hans P. Reiser
The novel blockchain generation of Byzantine fault-tolerant (BFT) state machine replication (SMR) protocols focuses on scalability and performance to meet the requirements of distributed ledger technology (DLT), e.g., decentralization and geographic dispersion. Validating scalability and performance of BFT protocol implementations requires careful evaluation. While experiments with real protocol deployments usually offer the best realism, they are costly and time-consuming. In this paper, we explore simulation of unmodified BFT protocol implementations as a method for cheap and rapid protocol evaluation: We can accurately forecast the performance of a BFT protocol while experimentally scaling its environment, i.e., by varying the number of nodes or geographic dispersion. Our approach is resource-friendly and preserves application-realism, since existing BFT frameworks can be simply plugged into the simulation engine without requiring code modifications or re-implementation.
Integrating dense small cell (DSC) networks with mobile edge computing is employed by 5G to tackle the contradiction between the computation limitations of user equipment (UE) and the stringent latency requirement of services. This paper investigates the service-oriented edge resource allocation problem in DSC networks, determining where to deploy the service entity, how many service entities should be deployed at each edge cloud, and how to assign the UEs to service entities. The problem is challenging for the following three aspects: 1) Service entity deployment and UE assignment are highly coupled. 2) Due to the overlap of coverage regions of densely deployed small cells, the allocation mechanism of different base stations has mutual effects on the overall service performance. 3) Considering the limited resources of edge clouds, it is a thorny problem to encourage edge clouds to cache and share service startup images. We devote the following efforts to tackle the problem under these challenges. First, we explore blockchain’s decentralized, traceable, and secure characteristics, and propose a scheme to encourage image sharing in mobile edge computing. Second, we formulate the service-oriented edge resource allocation as mixed integer non-linear programming. Third, towards the target of reducing the computational complexity, we decouple UE assignment from service entity deployment and solve it through Gibbs sampling. Moreover, the power of Lyapunov optimization and convex optimization is incorporated to reduce the long-term power consumption and budget. Experiment results demonstrate the superiority of our approach over current notable solutions.
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT). SDAGFL has the advantage of personalization, resisting single point of failure and poisoning attack in fully decentralized federated learning. Because of these advantages, the SDAGFL is suitable for the federated learning in IoT scenario where the device is usually battery-powered. To promote the application of SDAGFL in IoT, we propose an energy optimized SDAGFL based event-triggered communication mechanism, called ESDAGFL. In ESDAGFL, the new model is broadcasted only when it is significantly changed. We evaluate the ESDAGFL on a clustered synthetically FEMNIST dataset and a dataset from texts by Shakespeare and Goethe's works. The experiment results show that our approach can reduce energy consumption by 33\% compared with SDAGFL, and realize the same balance between training accuracy and specialization as SDAGFL.
Many researchers are studying the applications that ensemble blockchain and IoT. In such applications, a blockchain is normally deployed on top of the infrastructure-based underlying IoT network (e.g., Wi-Fi), which incurs the single point of failure issue. This work considers using infrastructure-less IoT for blockchain (i.e., ad-hoc IoT-blockchain network) to avoid the issue. Like in other networks, latency is one of the essential parameters to assess the network quality in the ad-hoc IoT -blockchain system. The latency is inherently related to the synchronization of transactions and blocks messages in the blockchain. Therefore, latency evaluation in an ad-hoc IoT-blockchain network is necessary. This research evaluates two latency metrics named Transaction-Oriented Latency (TOL) and Block-Oriented Latency (BOL) in an ad-hoc IoT-Blockchain system with Ethereum and two well-known consensus mechanisms: Proof of Work (PoW) and Proof of Authority (PoA). We create a smart contract that the different number of transactions can trigger; we then adopt the measurement method of the latency values. The evaluation results reveal the TOL and BOL following the transaction number in both the ad-hoc IoT-blockchain with PoA and PoW.
Although Metaverse has recently been widely stud-ied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for computing and communication on local devices, resulting in the inability to access the Metaverse services. To address this issue, this paper proposes a practical blockchain-based mobile edge computing (MEC) platform for resource sharing and optimal utilization to complete the requested offloading tasks, given the heterogeneity of servers' available resources and that of users' task requests. To be specific, we first elaborate the design of our proposed system and then dive into the task allocation mechanism to assign offloading tasks to proper servers. To solve the multiple task allocation (MTA) problem in polynomial time, we devise a learning-based algorithm. Since the objective function and constraints of MTA are significantly affected by the servers uploading the tasks, we reformulate it as a reinforcement learning problem and calculate the rewards for each state and action considering the influences of servers. Finally, numerous experiments are conducted to demonstrate the effectiveness and efficiency of our proposed system and algorithms.
Gordon Owusu Boateng, Guolin Sun, Daniel Ayepah-Mensah, Daniel Mawunyo Doe · 6 authors
Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.
Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao · 9 authors
The emerging industrial metaverses realize the map-ping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby en-hancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.
Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.
Wenjun Wu, Dehao Sun, Kaiqi Jin, Yang Sun · 5 authors
To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles (IoV) and Industrial Internet of Things (IIoT), the blockchain-enabled Mobile Edge Computing (MEC) system has received extensive attention. However, blockchain is a computing and communication intensive technology due to the complex consensus mechanisms. To facilitate the implementation of blockchain in the MEC system, this paper adopts the committee-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and focuses on the committee selection problem. Vehicles and IIoT devices generate the transactions which are records of the application tasks. Base Stations (BSs) with MEC servers, which serve the transactions according to the wireless channel quality and the available computing resources, are blockchain nodes and candidates for committee members. The income of transaction service fees, the penalty of service delay, the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index. The committee selection problem is modeled as a Markov decision process, and the Proximal Policy Optimization (PPO) algorithm is adopted in the solution. Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods.
It is witnessed that blockchain technology has been widely studied in Internet of Things (IoT) applications due to its decentralized tamper-resistance. Meanwhile, satellite-based IoT (S-IoT) becomes popular and has been regarded as a potential solution of the scalability due to its ubiquitous coverage inherited from satellites. Nevertheless, the large-scale blockchain network enabled S-IoT (BNS-IoT) would be limited by timely performing consensus. In this paper, we propose an age-critical blockchain sharding (ABS) scheme with the metric of information timeliness, i.e., age of information (AoI) to realize timely consensus in BNS-IoT. Specifically, we propose a forking-waiting-retransmission (FR) mechanism for the ABS scheme to deal with forking events, and realize a secure consensus. Then, we derive the closed-form expressions of average AoI (AAoI), throughput and security performance of the FR mechanism in ABS scheme, respectively, and compare with the$n$-block confirmation and select the longest-chain ($n$-LC) mechanism. Simulation results show that our ABS scheme can realize the linear expansion of throughput with the increasing number of shards, and our FR mechanism can greatly improve the security by sacrificing minor AAoI compared with the$n$-LC mechanism. Furthermore, our ABS scheme can outperform the conventional random sharding (RS) scheme in terms of AAoI and throughout.
Nguyen Quang Hieu, The Anh Tran, Cong Luong Nguyen, Dusit Niyato · 6 authors
Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO.
Byzantine consensus is a critical component in many permissioned Blockchains and distributed ledgers. We propose a new paradigm for designing BFT protocols called DQBFT that addresses three major performance and scalability challenges that plague past protocols: (i) high communication costs to reach geo-distributed agreement, (ii) uneven resource utilization hampering performance, and (iii) performance degradation under varying node and network conditions and high-contention workloads. Specifically, DQBFT divides consensus into two parts: 1) durable command replication without a global order, and 2) consistent global ordering of commands across all replicas. DQBFT achieves this by decentralizing the heavy task of replicating commands while centralizing the ordering process. Under the new paradigm, we develop a new protocol, Destiny that uses a combination of three techniques to achieve high performance and scalability: using a trusted subsystem to decrease consensus's quorum size, using threshold signatures to attain linear communication costs, reducing client communication. Our evaluations on 300-replica geo-distributed deployment reveal that DQBFT protocols achieve significant performance gains over prior art: $\approx$3x better throughput and $\approx$50\% better latency.
As the number of IoT devices increases, sustainability is becoming a bottleneck of the production process in industrial systems. As a matter of fact, inefficient management and scarce resources significantly impeded the development of sustainability. In recent years, it has been observed that the digital twin (DT) technology plays a promising role in facilitating the interaction between the Internet of Things (IoT) assets and digital services. However, high-fidelity models of DTs raise the requirement of efficient data flows, which is limited by realistic constraints, such as data collection strategy and energy supply. We propose a sustainable data collection and management approach to construct DTs for physical assets. With this approach, data packets are uploaded to the data brokers, namely, agents, by a large number of IoT devices. The challenge lies in the balance between enduring data collection and the information loss associated with the stale data. In this article, we aim to optimize the metrics of data fidelity and reveal delay while guaranteeing both sustainable energy and sustainable information. Additionally, a shareable and sustainable blockchain-based DT management architecture is proposed, which does not rely on data exchanges with a single centralized server. Our analytical and simulation results demonstrate the applicability of our proposed architecture.
Lei Zhang, Long Cheng, Fahad Alsokhiry, Mohamed A. Mohamed
This paper proposes a secure management framework for optimal scheduling and operation of energy systems in smart cities, which will bring balance in modern power systems. The expanded infiltration of energy systems along with stochastic performance and high data transfer rate can bring new challenges that may severely affect precise energy management. This research work investigates the optimal and secure operation of energy systems in the presence of smart transportation systems in a correlated energy system. To achieve the maximum efficiency from the transportation system, the ideas of vehicle-to-subway (V2S) and vehicle-to-grid (V2G) are utilized to develop a bilateral power flow strategy (charging/discharging or idle mode) to provide the chief charging requirements of energy systems. In this regard, the proposed model suggests a novel stochastic architecture based on unscented transformation (UT) to handle the operational uncertainties of the transportation systems when considering the correlated power generation of wind turbines (WTs). Moreover, blockchain technology is deployed to guarantee the security of data transfer within the smart city. Hence, there is a secure collaboration within all sub-systems in the smart cities, which has triggered the integrated management in this structure. The simulation results are discussed on a typical smart city test system to show the quality of the proposed model.
We present a new stochastic model for the evolution of Directed Acyclic Graphs (DAG)-based distributed ledgers (DL), under the presence of heterogeneous delay. This model is used to analyse the performance metrics of the DL, showing in particular that the number of unapproved messages does not diverge to infinity, even under the presence of delay. We propose an analysis based on conveniently defined sets, as well as an alternative drift-based analysis. The former allows to get a bound on the number of unapproved messages, while the latter, through a simpler analysis, allows to probe the existence of such bound. For particular scenarios, we are able to derive the expected value of the drift of unapproved messages, through a Markov process-based approach. State-of-the-art mathematical models trying to capture the impact of delays on the performance of such DLs rely on some particular simplifications. In contrast, through our model, we are able to analytically derive similar performance guarantees, in a more realistic setup. In particular, we focus on IOTA foundation's tangle, while our results can be extended to other DAG-based distributed ledgers. We compare our results to results obtained in a real testbed, showing good accordance between them.
This chapter covers blockchain age protocols. Some novel and some variants of classical blockchain consensus protocols were discussed in Chapter 7 . We start with Ethereum and finish this chapter at Solana. Along the way, we will cover in detail the characteristics, strengths, weaknesses, properties, and inner workings of major consensus protocols used in platforms such as Cosmos, Ethereum 2.0, and Polkadot.
Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients’ home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients’ information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
With the rapid development of satellite technology and the high transmission efficiency of LEO satellites, LEO satellite communication has received increasing attention. However, the frequent switching of satellite-earth links imposes a great challenge in LEO communication authentication. To tackle this challenge, this paper proposes a Blockchain-based Authentication Protocol Using Cryptocurrency Technology (BAPC), which solves the problem of a long pause time of satellite services caused by user access authentication in a scenario of frequent switching between satellites and ground users. First, we design three stages of the authentication process and introduce the cryptocurrency technology. Using currency transactions as the certificate of authentication improves not only the security of authentication, but also the efficiency of switching authentication. Next, in the network topology, the satellite cluster is divided into multiple regions to improve the efficiency of block consensus. Finally, the protocol is tested through extensive NS2-based simulations, and the results verify that BAPC can greatly shorten the response time of switching authentication and significantly reduce the time of block generation and the network throughput. As the number of users increases, the block generation time and network throughput can be further reduced.