Optimizing Tourist Experience Through Energy Aware Scheduling Using a Hybrid RNN-DQN Model

Optimizing the trade-off between user satisfaction and device longevity remains a critical challenge in the deployment of smart tourism services on resource-constrained consumer electronics. This paper proposes a Hybrid RNN-DQN framework designed to achieve energy-aware scheduling through the integration of temporal behavior prediction and deep reinforcement learning. We formalize the problem as a Partially Observable Markov Decision Process (POMDP) where a Recurrent Neural Network (RNN) extracts latent tourist intent from sequential mobility traces, which is then utilized by a Deep Q-Network (DQN) to derive optimal sensing policies. Unlike traditional heuristic-based energy management, our model employs a multi-objective reward function that dynamically balances service quality with power conservation. To handle the spatial complexity of urban environments, we introduce a Spatial-Temporal Grid transformation that enables 2D Convolutional Neural Networks (CNN) to extract topological features from Point-of-Interest (POI) networks. Extensive experimental evaluations on real-world datasets (Gowalla and Brightkite) demonstrate that the proposed hybrid architecture achieves a 32% increase in battery life and an 18% improvement in recommendation precision compared to state-of-the-art baselines. Statistical validation through paired t-tests and comprehensive ablation studies further confirm the synergy between the recurrent and reinforcement learning components. Our findings provide a scalable and generalizable solution for pervasive computing environments, setting a new benchmark for energy-efficient context-awareness in the Internet of Things (IoT) era.