# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
### Related Work  
Recently, there has been a popular trend in blockchain structure formulation in edge-based applications, e.g., Vehicular Networks [11], Drone Networks [12] and NFTs [13]. By abstracting the terminal as a virtual node, vehicles and drones serve as edge nodes and participate in blockchain computing as miners to achieve edge computing resource coordination and system performance optimization. In the current literature, the use of distributed crowdsourcing schemes for task solving has received significant interest. To guarantee privacy, [PERSON] et al. [14] established a blockchain-based decentralized framework for crowdsourcing system, by allowing users to store encrypted solutions in the distributed storage, and implemented a software prototype on the Ethereum public test network with a real-world dataset. To mitigate the biased evaluation of malicious requesters, [PERSON] et al. [15] proposed a privacy-aware verification protocol leveraging the threshold Paillier cryptosystem, which dives into the concrete crowdsourcing modules and key functionalities against curious and even malicious participants, and subsequently a confident-aware TD algorithm to estimate the task truths was formulated. To address some of the existing limitations in the crowdsourcing system, [PERSON] et al. [16] leveraged blockchain technology to study the security and privacy requirements of blockchain-based crowdsourcing systems, and proposed a concrete solution with a prototype implementation. Through inserting known result subtasks into crowdsourced computing tasks, [PERSON] et al. [17] suggested a novel consensus mechanism (Proof of Capturing-Work) and developed the blockchain to conveniently manage trust for crowdsourcing networks. [PERSON] et al. [18] proposed to divide the crowdsourcing service process into nine stages, and a novel Proof of Capturing-Work (PoCW) to achieve consensus, which can be managed by smart contracts during the execution of each step, to conveniently manage trust for crowdsourcing networks. Further, some researchers have adopted the blockchain-based reputation approach to address the incentive problem of crowdsourcing participants. To handle issues concerning efficiency and safety, [PERSON] et al. [19] introduced reputation as a metric to measure reliability and trustworthiness of the nodes, and then designed a reputation-based worker selection scheme for reliable federated learning via the use of a multi-weight subjective logic model. Moreover, they proposed an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning, in order to achieve secure reputation management for miners with non-repudiation and tamper-resistant properties. To support communication between peers, [PERSON] and [PERSON] [20] formulated a novel self-organized shard formation algorithm to maximize both its payoff and the coalition reputation, via the implementation of a new reputation-based voting scheme, establishing a novel self-organized shard formation algorithm that converges to the reputation-based stable shard structure. To encourage honest behavior in IoT, a credit-based incentive approach is proposed for consensus protocol to provide a new module of reputation, so that each cooperative or non-cooperative behavior can be rewarded or punished, respectively. Miners can then share a global view of reputation [21], so as to encourage nodes to join in network collaboration. Similarly, [PERSON] et al. [22] proposed a novel fine-grained FL concept to decentralize the shared ML models on the edge servers, and then suggested the core requirements of fine-grained federated learning systems and the concept of the blockchain-based fine-grained reputation-aware approach in edge computing to maintain trustworthy collaborative training. [PERSON] et al. [23] investigated the design, implementation, and evaluation of a reputation scheme for the Blockchain Signaling System (BloSS).