Yixin Li, Bin Cao, Liang Liang, Lei Zhang · 6 authors
Blockchain, a distributed ledger technology, has attracted many attentions to enable a decentralized and safe wireless networks for various applications. Considering the high density of nodes and the massive service requests in next-generation wireless network will result in a surge of blockchain forking, this paper proposes a Block Access Control (BAC) approach to address forking problem and transmit block effectively while improving transaction throughput and saving computational power. Then, using a Markov chain model, we analyse the performance of a wireless blockchain network by involving the effect of BAC approach. The results show that the BAC approach can help the network to achieve a high transaction throughput while addressing forking problem.
Recent advances in blockchain technology have led to a significant interest in developing blockchain-based applications. While data can be retained in a blockchain, the stored values can be deleted or updated. From a user viewpoint that searches for data, it is unclear whether the discovered data from the blockchain storage is relevant for real-time decision-making processes for block-chain-based applications. The data freshness issue serves as a critical factor, especially in dynamic networks handling real-time information. In general, transactions to renew data require additional processing time inside the blockchain network, which is called ledger-commitment latency. Due to this problem, some users may receive outdated data. As a result, it is important to investigate if the blockchain is suitable for providing real-time data services. In this article, we first describe block-chain-enabled (BCE) networks with Hyperledger Fabric (HLF). Then, we define age-of-information (AoI) of BCE networks and investigate influential factors on this AoI. Experiments are conducted to explore the impacts of the influential factors on data freshness in BCE networks. Lastly, we conclude by discussing future challenges.
In public distributed ledger technologies (DLTs), such as Blockchains, nodes can join and leave the network at any time. A major challenge occurs when a new node joining the network wants to retrieve the current state of the ledger. Indeed, that node may receive conflicting information from honest and Byzantine nodes, making it difficult to identify the current state. In this paper, we are interested in protocols that are stateless, i.e., a new joining node should be able to retrieve the current state of the ledger just using a fixed amount of data that characterizes the ledger (such as the genesis block in Bitcoin). We define three variants of stateless DLTs: weak, strong, and probabilistic. Then, we analyze this property for DLTs using different types of consensus.
This paper proposes a real-time chain and a novel embedded Markovian queueing model with variable bulk arrival (VBA) and variable bulk service (VBS) in order to establish and assure a theoretical foundation to design a blockchain-based real-time system with particular interest in Ethereum. Based on the proposed model, various performances are simulated in a numerical manner in order to validate the efficacy of the model by checking good agreements with the results against intuitive and typical expectations as a baseline. A demo of the proposed real-time chain is developed in this work by modifying the open source of Ethereum Geth 1.9.11. The work in this paper will provide both a theoretical foundation to design and optimize the performances of the proposed real-time chain, and ultimately address and resolve the performance bottleneck due to the conventional block-synchrony by employing an asynchrony by the real-time deadline to some extent.
Lam Duc Nguyen, Anders E. Kalør, Israel Leyva‐Mayorga, Petar Popovski
The data collected from Internet of Things (IoT) devices on various emissions or pollution, can have a significant economic value for the stakeholders. This makes it prone to abuse or tampering and brings forward the need to integrate IoT with a Distributed Ledger Technology (DLT) to collect, store, and protect the IoT data. However, DLT brings an additional overhead to the frugal IoT connectivity and symmetrizes the IoT traffic, thus changing the usual assumption that IoT is uplink-oriented. We have implemented a platform that integrates DLTs with a monitoring system based on narrowband IoT (NB-IoT). We evaluate the performance and discuss the tradeoffs in two use cases: data authorization and real-time monitoring.
Blockchain-enabled Federated Learning (BFL) enables mobile devices to\ncollaboratively train neural network models required by a Machine Learning\nModel Owner (MLMO) while keeping data on the mobile devices. Then, the model\nupdates are stored in the blockchain in a decentralized and reliable manner.\nHowever, the issue of BFL is that the mobile devices have energy and CPU\nconstraints that may reduce the system lifetime and training efficiency. The\nother issue is that the training latency may increase due to the blockchain\nmining process. To address these issues, the MLMO needs to (i) decide how much\ndata and energy that the mobile devices use for the training and (ii) determine\nthe block generation rate to minimize the system latency, energy consumption,\nand incentive cost while achieving the target accuracy for the model. Under the\nuncertainty of the BFL environment, it is challenging for the MLMO to determine\nthe optimal decisions. We propose to use the Deep Reinforcement Learning (DRL)\nto derive the optimal decisions for the MLMO.\n
Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu · 6 authors
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively.
Vicent Cholvi, Antonio Fernández Anta, Chryssis Georgiou, Nicolas Nicolaou · 5 authors
A Distributed Ledger Object (DLO) is a concurrent object that maintains a totally ordered sequence of records, and supports two basic operations: append, which appends a record at the end of the sequence, and get, which returns the sequence of records. In this work we provide a proper formalization of a Byzantine-tolerant Distributed Ledger Object (BDLO), which is a DLO in a distributed system in which processes may deviate arbitrarily from their indented behavior, i.e. they may be Byzantine. Our formal definition is accompanied by algorithms to implement BDLOs by utilizing an underlying Byzantine Atomic Broadcast service. We then utilize the BDLO implementations to solve the Atomic Appends problem against Byzantine processes. The Atomic Appends problem emerges when several clients have records to append, the record of each client has to be appended to a different BDLO, and it must be guaranteed that either all records are appended or none. We present distributed algorithms implementing solutions for the Atomic Appends problem when the clients (which are involved in the appends) and the servers (which maintain the BDLOs) may be Byzantine.
Blockchain shows great potential to be applied in wireless IoT ecosystems for establishing the trust and consensus mechanisms without central authority's involvement. Based on RAFT consensus mechanism, this letter investigates the security performance of wireless blockchain networks in the presence of malicious jamming. We first map and model the blockchain transaction as a wireless network composed of uplink and downlink transmissions by assuming the follower nodes' position as a Poisson Point Process (PPP) with selected leader location. The probability of achieving successful blockchain transactions is derived and verified by extensive simulations. The results provide analytical guidance for the practical deployment of wireless blockchain networks.
For the modern world where data is becoming one of the most valuable assets,\nrobust data privacy policies rooted in the fundamental infrastructure of\nnetworks and applications are becoming an even bigger necessity to secure\nsensitive user data. In due course with the ever-evolving nature of newer\nstatistical techniques infringing user privacy, machine learning models with\nalgorithms built with respect for user privacy can offer a dynamically adaptive\nsolution to preserve user privacy against the exponentially increasing\nmultidimensional relationships that datasets create. Using these privacy aware\nML Models at the core of a Federated Learning Ecosystem can enable the entire\nnetwork to learn from data in a decentralized manner. By harnessing the\never-increasing computational power of mobile devices, increasing network\nreliability and IoT devices revolutionizing the smart devices industry, and\ncombining it with a secure and scalable, global learning session backed by a\nblockchain network with the ability to ensure on-device privacy, we allow any\nInternet enabled device to participate and contribute data to a global privacy\npreserving, data sharing network with blockchain technology even allowing the\nnetwork to reward quality work. This network architecture can also be built on\ntop of existing blockchain networks like Ethereum and Hyperledger, this lets\neven small startups build enterprise ready decentralized solutions allowing\nanyone to learn from data across different departments of a company, all the\nway to thousands of devices participating in a global synchronized learning\nnetwork.\n
We model the hardware and software architecture for generalized Internet of Things (IoT) by quantum cloud-computing and blockchain. To reduce the measurement error and increase the efficiency of quantum entanglement (i.e. the capability of fault tolerance) in the current quantum computers and communications, we design a quantum-computing chip by modelling it as a multi-input multi-output (MIMO) quantum channel and obtain its channel capacity via our recently derived mutual information formula. To capture the internal qubit data flow dynamics of the channel, we model it via a deep convolutional neural network (DCNN) with generalized stochastic pooling in terms of resource-competition among different quantum eigenmodes or users. The pooling is corresponding to a resource allocation policy with two levels of competitions as in cognitive radio: the first one is on users’ selection in a ‘win–lose’ manner; the second one is on resourcesharing among selected users in a ‘win–win’ manner. To wit, our scheduling policy is the one by mixing a saddle point to a zero-sum game problem and a Pareto optimal Nash equilibrium point to a nonzero- sum game problem. The effectiveness of our policy is proved by diffusion modelling with theory and numerical examples.
In this paper, we propose a scheme that implements a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) to generate, validate, and confirm the electricity transaction in Smart Grid. The convergence of the Smart Grid and distributed ledger concept has recently been introduced. Since Smart Grids require a distributed network architecture for power distribution and trading, the Distributed Ledger-based Smart Grid design is a spotlighted research domain. However, only the Blockchain-based methods, which are a type of the distributed ledger scheme, are currently either being considered or adopted in the Smart Grid. Due to computation-intensive consensus schemes such as Proof-of-Work and discrete block generation, Blockchain-based distributed ledger systems suffer from efficiency and latency issues. We propose a DAG-based distributed ledger for Smart Grids, called PowerGraph, to resolve this problem. Since a DAG-based distributed ledger does not need to generate blocks for confirmation, each transaction of the PowerGraph undergoes the validation and confirmation process individually. In addition, transactions in PowerGraph are used to keep track of the energy trade and include various types of transactions so that they can fully encompass the events in the Smart Grid network. Finally, to ensure that PowerGraph maintains a high performance, we modeled the PowerGraph performance and proposed a novel consensus algorithm that would result in the rapid confirmation of transactions. We use numerical evaluations to show that PowerGraph can accelerate the transaction processing speed by over 5 times compared to existing DAG-based DLT system.
Mohammad M. Jalalzai, Costas Busch, Golden G. Richard
There have been numerous solutions to improve the message complexity of Byzantine Fault Tolerant (BFT) protocols. Unfortunately, these solutions do not guarantee consistent performance and fall back to quadratic message complexity if a certain threshold of node failures is encountered in the network. Furthermore, reliance on a single primary to forward a proposed blockchain block to all replicas in the network can provide a potential attack vector, in which the primary can create discrepancies among histories of honest replicas. This results in increased latency during the view change (denial of service). Therefore, we propose a BFT-based protocol that guarantees consistent performance and shifts the reliance from a single primary to broadcast a candidate block to a sub-committee of replicas.
Lachlan J. Gunn, Jian Liu, Bruno Vavala, N. Asokan
Consensus mechanisms used by popular distributed ledgers are highly scalable but notoriously inefficient. Byzantine fault tolerance (BFT) protocols are efficient but far less scalable. Speculative BFT protocols such as Zyzzyva and Zyzzyva5 are efficient and scalable but require a trade-off: Zyzzyva requires only $3f + 1$ replicas to tolerate $f$ faults, but even a single slow replica will make Zyzzyva fall back to more expensive non-speculative operation. Zyzzyva5 does not require a non-speculative fallback, but requires $5f + 1$ replicas in order to tolerate $f$ faults. BFT variants using hardware-assisted trusted components can tolerate a greater proportion of faults, but require that every replica have this hardware. We present SACZyzzyva, addressing these concerns: resilience to slow replicas and requiring only $3f + 1$ replicas, with only one replica needing an active monotonic counter at any given time. We experimentally evaluate our protocols, demonstrating low latency and high scalability. We prove that SACZyzzyva is optimally robust and that trusted components cannot increase fault tolerance unless they are present in greater than two-thirds of replicas.
Mohammad M. Jalalzai, Costas Busch, Golden G. Richard
Byzantine Fault Tolerant (BFT) consensus exhibits higher throughput in comparison to Proof of Work (PoW) in blockchains. But BFT-based protocols suffer from scalability problems with respect to the number of replicas in the network. The main reason for this limitation is the quadratic message complexity of BFT protocols. Previously, proposed solutions improve BFT performance for normal operation, but will fall back to quadratic message complexity once the protocol observes a certain number of failures. This makes the protocol performance unpredictable as it is not guaranteed that the network will face a a certain number of failures. As a result, such protocols are only scalable when conditions are favorable (i.e., the number of failures are less than a given threshold). To address this issue we propose Proteus, a new BFT-based consensus protocol which elects a subset of nodes $c$ as a root committee. Proteus guarantees stable performance, regardless of the number of failures in the network and it improves on the quadratic message complexity of typical BFT-based protocols to $O(cn)$, where $c<<n$, for large $n$. Thus, message complexity remains small and less than quadratic when $c$ is asymptotically smaller than $n$, and this helps the protocol to provide stable performance even during the view change process (change of root committee). Our view change process is different than typical BFT protocols as it replaces the whole root committee compared to replacing a single primary in other protocols. We deployed and tested our protocol on $200$ Amazon $EC2$ instances, with two different baseline BFT protocols (PBFT and Bchain) for comparison. In these tests, our protocol outperformed the baselines by more than $2\times$ in terms of throughput as well as latency.
Increasingly, connected cars are becoming a decentralized data platform. With greater autonomy, they have growing needs for computation and perceiving the world around them through sensors. While todays generation of vehicles carry all the necessary sensor data and computation on board, we envision a future where vehicles can cooperate to increase their perception of the world beyond their immediate view, resulting in greater safety, coordination and more comfortable experience for their human occupants. In order for vehicles to obtain data, compute and other services from other vehicles or road side infrastructure, it is important to be able to make micro payments for those services and for the services to run seamlessly despite the challenges posed by mobility and ephemeral interactions with a dynamic set of neighboring devices. We present MOTIVE, a trusted and decentralized framework that allows vehicles to make peer to peer micropayments for data, compute and other services obtained from other vehicles or road side infrastructure within radio range. The framework utilizes distributed ledger technologies including smart contracts to enable autonomous operation and trusted interactions between vehicles and nearby entities.
Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu
Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
Distributed ledgers provide many advantages over centralized solutions in IoT projects including but not limited to improved security, transparency and fault tolerance. However, in order to leverage them at scale, their well-known limitations, i.e., scalability and performance, should be adequately addressed. DAG-based distributed ledgers have been proposed to tackle the performance and scalability issues by design. The first among them, IOTA, has shown promising signs in terms of scalability and performability. In this thesis, we first conduct a comprehensive literature review on both distributed ledger technology applications in IoT and the performance evaluation of such decentralized systems. Then we present a detailed technical overview of IOTA, following a contractive review of different DAG-based distributed ledger technologies. Next, we propose a scalable transactive smart homes infrastructure by leveraging IOTA protocol and following the separation of concerns (SOC) design principle. Based on the proposed solution, an experiment with 40 home nodes is conducted to prove the concept at large scale in a cloud environment. The results show that our solution provides a high transaction speed and scalability, as well as good performance on micropayment which is important in IoT initiatives. We conduct an analysis and discuss how the new system breaks out the Blockchain Trilemma, which claims that it is almost impossible for a blockchain platform to simultaneously reach decentralization, scalability and security. Based on our findings on scalability and performance, we conclude that the proposed DAG-based distributed ledger is an effective solution for building an IoT infrastructure for smart communities, in which local residents can freely and securely transfer values. Finally, we rigorously study the performance of the ledger to examine its applicability for IoT projects in which a high throughput is required. More specifically, we investigate the IOTA system to answer two key research questions: 1) what is the confirmation rate in the system given the design parameters and 2) what will be the optimal waiting time for a user to resend its previously submitted but not yet confirmed transaction to the ledger? In order to answer these vital questions, we perform real experimentation, simulation and analytical modeling. Our findings reveal the impact of arrival rate of transactions, consensus algorithm, randomness of the weighted random walk for tip selection and network delay on the confirmation rate. By decomposing the transaction confirmations in each graph layer, we build an analytical layered model. Thanks to the analytical modeling, we shed some light on the distribution of confirmation process, which is leveraged to calculate the optimal time for resending the unconfirmed transaction to the distributed ledger. Our performance model can be used by IoT project designers to perform what-if analysis and capacity planning in advance of the real deployments, with high level of accuracy.
The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog and Cloud infrastructure. Although there are a number of real-world frameworks attempt to support such integration, they have many limitations from various perspectives including platform independence, security, resource management and multi-application assistance. To address these limitations, we propose a simplified but effective framework, named FogBus for facilitating end-to-end IoT-Fog(Edge)-Cloud integration. FogBus offers a platform independent interface to IoT applications and computing instances for execution and interaction. It not only assists developers in building applications but also helps users in running multiple applications at a time and service providers to manage their resources. In addition, FogBus applies Blockchain, authentication and encryption techniques to secure operations on sensitive data. Because of its lightweight and cross platform software systems, it is easy to deploy, scalable and cost e_cient. We demonstrate the effectiveness of our framework by creating a computing environment with it that integrates finger pulse oximeter as IoT devices with Smartphone-based gateway and Raspberry Pi-based Fog nodes for Sleep Apnea analysis. We also run several experiments on this computing environment varying FogBus settings. The experimental results show that different FogBus settings can improve latency, energy, network and CPU usage of the computing infrastructure.
Nguyen Cong Luong, Tran The Anh, Huỳnh Thị Thanh Bình, Dusit Niyato · 6 authors
To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.
Fedor Muratov, Andrei Lebedev, Nikolai Iushkevich, Bulat Nasrulin · 5 authors
Consensus in decentralized systems that asynchronously receive events and which are subject to Byzantine faults is a common problem with many real-life applications. Advances in decentralized systems, such as distributed ledger (i.e., blockchain) technology, has only increased the importance of finding performant and secure solutions to consensus of state machine replication in decentralized systems. YAC is a practical decentralized consensus algorithm, that solves the problems of inefficient message passing and strong leaders that occur in classical Byzantine fault tolerant consensus algorithms. The algorithm is open source and currently is used to provide Byzantine fault tolerant consensus for the Hyperledger Iroha blockchain project. We provide proofs of safety and liveness, as well as empirical results showing that our algorithm can scale to dozens of validating peers.
This paper proposes Consensus-Before-Talk (CBT), a spectrum etiquette architecture leveraged by distributed ledger technology (DLT). In CBT, secondary users' spectrum access requests reach a consensus in a distributed way, thereby enabling collision-free distributed dynamic spectrum access. To achieve this consensus, the secondary users need to pay for the extra request exchanging delays. Incorporating the consensus delay, the end-to-end latency under CBT is investigated. Both the latency analysis and numerical evaluation validate that the proposed CBT achieves the lower end-to-end latency particularly under severe secondary user traffic, compared to the Listen-Before-Talk (LBT) benchmark scheme.
Consensus is one of the key problems in blockchains. There are many articles analyzing the performance of threat models for blockchains. But the network stability seems lack of attention, which in fact affects the blockchain performance. This paper studies the performance of a well adopted consensus algorithm, Raft, in networks with non-negligible packet loss rate. In particular, we propose a simple but accurate analytical model to analyze the distributed network split probability. At a given time, we explicitly present the network split probability as a function of the network size, the packet loss rate, and the election timeout period. To validate our analysis, we implement a Raft simulator and the simulation results coincide with the analytical results. With the proposed model, one can predict the network split time and probability in theory and optimize the parameters in Raft consensus algorithm.