Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new transactions via modeling blockchain decisions as a distributed machine-learning task. Wbr identifies network nodes that are willing to cooperate toward securing ground truth, showing robustness to adversarial subnetworks that are greater than 50% and reducing collaboration error by 50% compared to other similar schemes. This two-step approach of reputation plus transaction classification for generating blockchain data is treated as a novel method of preventing fraud and double-spending attacks in blockchain networks. To capture adversary influence, a bayesian game is formulated and implemented to show superior performance to the state-of-the-art along with resource consumption metrics.