A Blockchain Consensus Optimization-Based Algorithm for Food Traceability

Abstract

At present, blockchain technology is more and more widely used in the field of food traceability, and good results have been achieved. However, many of the current blockchain technologies and algorithms are not developed for the specific situation of food traceability, resulting in resource waste and low computational efficiency. In view of these problems, this paper analyzes and summarizes the classic distributed consensus mechanism in blockchain technology, focusing on the PBFT (practical Byzantine fault tolerance) consensus mechanism and the existing problems related to the improvement scheme. In order to solve the problem of low efficiency of a consensus algorithm in a food traceability scenario, this paper proposes a blockchain consensus algorithm suitable for the food traceability scenario based on clustering and food credit. In addition, the differences between the improved algorithm and the classical Byzantine consensus algorithm in consensus algorithm time and communication times are analyzed through experiments and simulations. The consensus efficiency of the improved algorithm in this paper is significantly improved, which can greatly reduce the application difficulty of blockchain in food traceability.

A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles

“…In low-speed scenarios, the consensus mechanism proposed in this study performs slightly better than other approaches. Especially in the high-speed scenario, the relay nodes using the PoMS algorithm perform better than PoET in literature [27] and TIA in literature [30], although the packet loss rate still inevitably increases. This is attributed to the fact that the algorithm takes into account the speed fluctuations and effectively avoids the problem that the distance between nodes exceeds the communication range due to the speed difference, thereby performing well in terms of transmission efficiency.…”

“…This is due to the algorithm's prediction of speed fluctuations, which effectively resists the effects of driving state changes. The effect of speed fluctuations of mobile nodes is not considered in any of the schemes [27,30], where the consensus delay increases rapidly with higher node mobility. On the other hand, in the literature [29], a probabilistic model is used in the PoQF to simulate the speed change while combining the vehicle location information for consensus between nodes, which effectively reduces the consensus time of the vehicle in the mobile state, but the algorithm's effectiveness decreases, when the speed reaches a higher threshold.…”

“…As the probability of malicious nodes increases, the increase of invalid messages in the network has a significant impact on the speed of consensus execution. In networks with less malicious nodes, the time cost in [27] is mainly the baseline waiting time, while [29] and PoMS perform similarly; in networks containing more malicious nodes, [30] it spends more rounds to check credible nodes for the election period and the waiting time increases; The PoQF determines the normality of behavior based on the node's votes. The more malicious nodes there are, the longer it takes to get enough votes, the larger the number of malicious nodes, which increases the time consumption.…”

“…In this framework, the vehicles communicate exclusively with other nodes and vehicles in the federated blockchain. Despite these precautions, potential threats [25][26][27] that can disrupt the blockchain network still exist. The next section explains how the PoMS algorithm counteracts such attacks.…”

An Optimization of Blockchain Parameters for Improving Consensus and Security in eHealthChain

“…A blockchain consensus optimization scheme for food traceability [21] used clustering and food credit to select upper consensus nodes, but it resulted in low throughput. Optimization of the PBFT consensus algorithm [22] was designed to improve efficiency, but the user-perceived latency remained high.…”