FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures

Popov, Serguei; Buchanan, William J.

doi:10.1016/j.jpdc.2020.09.002

Cited by 45 publications

(68 citation statements)

References 28 publications

(46 reference statements)

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“…We continue the works of [11] and [2] and propose several adaptions, Section 8, of the fast probabilistic consensus protocol (FPC) that decreases the failure rate of at least one order of magnitude, e.g., see Fig. 6.…”

Section: Introductionmentioning

confidence: 92%

“…We present here only the key elements of the proposed protocol and refer the interested reader to [11] and [2] for more details. In order to define FPC we have to introduce some notation.…”

Section: Vanilla Fpcmentioning

confidence: 99%

“…As in [2] we consider a basic version of the FPC introduced in [11] in choosing some parameters by default. Specifically, we remove the cooling phase of FPC and the randomness of the initial threshold τ .…”

Section: Vanilla Fpcmentioning

confidence: 99%

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Fast Probabilistic Consensus with Weighted Votes

Müller

Penzkofer²,

Kuśmierz³

et al. 2020

Advances in Intelligent Systems and Computing

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The fast probabilistic consensus (FPC) is a voting consensus protocol that is robust and efficient in Byzantine infrastructure. We propose an adaption of the FPC to a setting where the voting power is proportional to the nodes reputations. We model the reputation using a Zipf law and show using simulations that the performance of the protocol in Byzantine infrastructure increases with the Zipf exponent. Moreover, we propose several improvements of the FPC that decrease the failure rates significantly and allow the protocol to withstand adversaries with higher weight. We distinguish between cautious and berserk strategies of the adversaries and propose an efficient method to detect the more harmful berserk strategies. Our study refers at several points to a specific implementation of the IOTA protocol, but the principal results hold for general implementations of reputation models.

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Section: Introductionmentioning

confidence: 92%

“…We present here only the key elements of the proposed protocol and refer the interested reader to [11] and [2] for more details. In order to define FPC we have to introduce some notation.…”

Section: Vanilla Fpcmentioning

confidence: 99%

See 1 more Smart Citation

Fast Probabilistic Consensus with Weighted Votes

Müller

Penzkofer²,

Kuśmierz³

et al. 2020

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The interested reader can refer to [19] for further details on this kind of consensus algorithm in DAGs. Alternatively, or indeed additionally, a voting algorithm such as [32], [33] can be used to achieve consensus in the presence of conflicting branches of the DAG.…”

Section: Writementioning

confidence: 99%

Access Control for Distributed Ledgers in the Internet of Things: A Networking Approach

Cullen

Ferraro

Sanders³

et al. 2022

IEEE Internet Things J.

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In the Internet of Things (IoT) domain, devices need a platform to transact seamlessly without a trusted intermediary. Although Distributed Ledger Technologies (DLTs) could provide such a platform, blockchains, such as Bitcoin, were not designed with IoT networks in mind, hence are often unsuitable for such applications: they offer poor transaction throughput and confirmation times, put stress on constrained computing and storage resources, and require high transaction fees. In this work, we consider a class of IoT-friendly DLTs based on directed acyclic graphs, rather than a blockchain, and with a reputation system in the place of Proof of Work (PoW). However, without PoW, implementation of these DLTs requires an access control algorithm to manage the rate at which nodes can add new transactions to the ledger. We model the access control problem and present an algorithm that is fair, efficient and secure. Our algorithm represents a new design paradigm for DLTs in which concepts from networking are applied to the DLT setting for the first time. For example, our algorithm uses distributed rate setting which is similar in nature to transmission control used in the Internet. However, our solution features novel adaptations to cope with the adversarial environment of DLTs in which no individual agent can be trusted. Our algorithm guarantees utilisation of resources, consistency, fairness, and resilience against attackers. All of this is achieved efficiently and with regard for the limitations of IoT devices. We perform extensive simulations to validate these claims.

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“…Hence, nodes query other nodes about their current opinion of the ledger and adjust their opinion based on the proportion of other opinions. Two voting mechanisms, fast probabilistic consensus (FPC) [ 18 ] and cellular automata (CA) [ 19 ] are used to allow nodes to communicate and decide on Txs status. There is a possibility of requiring a combination of both mechanisms to add flexibility to the voting process.…”

Section: Background and Distributed Ledger Technologiesmentioning

confidence: 99%

Computing Resource Allocation Scheme for DAG-Based IOTA Nodes

Hellani

Sliman

Samhat

et al. 2021

Sensors

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IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.

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FPC-BI: Fast Probabilistic Consensus within Byzantine Infrastructures

Cited by 45 publications

References 28 publications

Fast Probabilistic Consensus with Weighted Votes

Fast Probabilistic Consensus with Weighted Votes

Access Control for Distributed Ledgers in the Internet of Things: A Networking Approach

Computing Resource Allocation Scheme for DAG-Based IOTA Nodes

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