# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 2 Related Work
### Consensus Protocol Scalability  
Although the PBFT algorithm is a satisfactory consensus solution in distributed systems, its scalability is low and the number of nodes involved in consensus has a direct impact on the communication complexity of the PBFT consensus algorithm, so researchers have tried to streamline the number of nodes involved in consensus by forming a consensus committee from all the nodes of the system and then releasing the consensus results to other nodes, thus improving the scalability of the system.  
The Permacoin [15] algorithm then elects the leader based on the participant's free disk size. And the strategy for electing leaders can be used as a strategy for electing committees with slight modifications. The T-PBFT algorithm [16] combines the feature trust model with the consensus algorithm to evaluate the trust level of nodes, select some nodes with higher credit to participate in the consensus, and use the nodes with higher quality in the network to establish the consensus group instead of a single master node. PoPT [17] proposes an algorithm to measure the participation of nodes in a public chain system and elects a committee based on the participation ranking. The dBFT [18] algorithm elects the committee based on POS and associates nodes with the margin of node access to solve the disinterested problem of native POS. The Tendermint [19] algorithms elect committees based on PoS, which associate nodes with margins to solve non-benefit problems of native PoS. The Algorand algorithm [20] uses VRF to randomly select a subset of network nodes as a committee to ensure the unpredictability of committee members and improve the security and the scalability of the system. Due to the high communication complexity of the PBFT consensus algorithm in practical applications, many scholars have improved the PBFT algorithm from consensus structure. [PERSON] et al. [21] proposed a scalable multi-layer consensus mechanism based on PBFT. By layer-grouping nodes into different layers and limiting communication within groups, it was analyzed that the complexity of communication was minimal when the network was evenly distributed in subgroups in the second layer. [PERSON] et al. [22] constructed a multi-level network model based on blockchain for security monitoring to ensure identity information security and enhance system reliability, but increased system management overhead. [PERSON] et al. [23] proposed a grouping algorithm based on reputation integral to improve the possibility of malicious nodes making errors, but did not consider the problem of reduced system communication performance when the number of nodes increased. [PERSON] et al. [24] constructed a multi-layer blockchain to group users according to certain security levels and stored data of different levels into the chain of corresponding levels, to achieve secure data access control. [PERSON] et al. [25] used a hashing algorithm to ensure that consistently nodes are grouped, which can avoid a large amount of communication between nodes, reduce the communication complexity of the network, and improve the scalability of the network, but cannot identify Byzantine nodes. [PERSON] et al. [26] proposed a bottom-up two-tier RSU chain consensus protocol (TLPBFT) based on blockchain and trust values, and the algorithm latency rises significantly when the number of regional nodes is high. [PERSON] et al. [27] proposed a quality-of-service-based PBFT algorithm that reduces the inter-node consensus latency but does not consider the presence of malicious nodes in the group.