Decentralized data integrity in mixed-mobility sensor networks: a multiagent reinforcement learning approach

Abstract

In megacities utilizing mixed-mobility transport networks, Back-Office Systems (BOS) are inundated with heterogeneous sensor data—ranging from NFFFC bus validators to GPS telemetry on ferries. Traditional centralized architectures struggle to reconcile this data when faced with stochastic hardware failures, connectivity loss, or "missed taps," which are prevalent in developing urban environments like Ho Chi Minh City (HCMC). The core scientific contribution of this paper is a fairness-aware decentralized integrity mechanism designed to ensure equitable revenue apportionment. To operationalize this, we employ a custom Multi-Agent Reinforcement Learning (MARL) framework, utilizing a Deep Reinforcement Learning (DRL) module as the primary decision engine. The Java Agent DEvelopment Framework (JADE) and a GraphTheoretic Spatiotemporal Transformer (GTST) are utilized strictly as architectural and methodological enablers. These agents negotiate the validity of incomplete sensor logs, learning to probabilistically impute missing data points rather than discarding them. By integrating a GTST for ground-truth estimation, our approach achieves a "fairness-aware" consensus on revenue allocation. Experimental results on a synthetic dataset of 10,000 mixed-mobility transactions demonstrate that this agent-based approach achieves 100.0% revenue recovery with a manageable processing latency of 560 ms/batch. Furthermore, our decentralized MARL framework yields an imputation accuracy of 91.2% and a spatial Root Mean Square Error (RMSE) of 1.12 km—a 54% reduction compared to standard centralized K-Nearest Neighbors (KNN) baselines— while improving algorithmic fairness from 0.54 (discriminatory) to 1.00 (equitable) across peripheral urban zones.