Mobile crowdsourcing (MCS) takes advantage of widely distributed mobile devices to complete some temporal-spatial tasks. Edge computing is integrated into MSC to reduce the service delay from a remote cloud, and enhance the quality of services (QoS) through preprocessing data. However, the profit-driven edge nodes (workers) may provide fake or low-quality answers for lowering their data processing cost, which results in serious QoS challenges. We propose a reputation-based accountability mechanism, in which workers are accountable for their answer provision through reputation values, and edge nodes take on different responsibilities of blockchain management based on reputation values and some key factors. Specifically, the combination of the data-centric method and entity-centric method is utilized for precisely evaluating the quality of answers and managing the worker’s reputation. Storage nodes, mining candidate nodes, relay nodes, and verification nodes are mainly responsible for transaction retrieval, block generation, block forwarding and block verification, respectively. It reduces the latency of blockchain maintenance in a large-scale network. Finally, the experimental results show that our scheme achieves high-level reliability and reasonable efficiency for large-scale services in MCS.