# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 1 Introduction
### Literature Review
Based on the trust rating received from the vehicles, each RSU calculates the trust values of its target vehicles, packs these data into a block, then solves the Proof-of-Work (PoW) and Proof-of-Stack (PoS) consensus to add the block into the blockchain. In the proposed scheme, the trust evaluation relies only on the geographic position of the vehicle, if it is near to the event location then it is trustworthy. Nevertheless, the location is not sufficient to decide on the credibility of the received data and the trustworthiness of the originator vehicle because the message may be generated by a malicious vehicle located near the event. In [17] [PERSON] et al. proposed a Blockchain-based Anonymous Reputation System (BARS). To estimate the reputation of a vehicle both direct historical interactions and indirect opinions about the target vehicle are considered. In their entity-centric framework, three different blockchains are used. The first one is used to store valid certificates, the second one is used to store the revoked certificates and the third one is used to store the messages disseminated in the network. To authenticate the vehicles, their certificates are searched in the first and second blockchains. If the certificate is present in the former blockchain and absent in the latter one, the vehicle is authentic. Further, RSUs will act as validators of this framework using Proof of Work consensus. BARS is based also on a Law enforcement authority (LEA) responsible for recording the pairs of public keys and real identities of the vehicles. LEA is a centralized authority which leads to a lack of scalability. [PERSON] et al. [18] proposed a distributed trust management scheme for VANETs named DTCMV based on blockchain. The scheme consists of three steps: the transmission of messages, the creation of blocks, and the validation of the block. In their scheme, the RSUs will serve as miners performing a PoW consensus. However, the authors did not explain the trust metrics calculation process and which factors are considered to assess the trustworthiness of the vehicle. In [19] the reputation score is calculated using an indirect reciprocity principle, where each vehicle calculates its reputation score according to its cooperation with other peers. The limitation of this approach is the fact that each vehicle updates its reputation value. [PERSON] et al. [20], propose a blockchain-based protocol for computing trust management on the Internet of Vehicles using smart contracts, certificates, and a dynamic proof-of-work consensus algorithm. The proposed entity-centric protocol consists of two phases: a setup phase and a data transfer phase. In the first phase, the vehicles register to become trusted users and get a blockchain account and unique ID. In the second phase, secure communication links between trusted vehicles are established. However, the proposed protocol revokes malicious vehicles definitively from the network, also it does not assess the credibility of the message itself, so a trust vehicle can broadcast forged messages without being detected. A Blockchain-based collaborative intrusion detection networks (CIDNs) framework is proposed in [21]. It is a data-centric framework that enables the vehicles to form a consortium chain by verifying the received data messages and the alarm rankings, which uses blockchains. The RSUs serve as a validator, responsible for detecting advanced malicious vehicles. The RSUs compute the trust values by comparing the received messages with the expected content. Unfortunately, this framework does not detect malicious vehicles in real-time. In [22], the author proposed a trust management system using a multi-criteria decision-making model, where each vehicle assesses the reliability of the messages and computes the trust value of the sender. Afterward, each vehicle transmits the computed trust values to the nearest RSU, which in turn calculates the trust values using a multi-criteria decision-making model.