Semantic-Aware Partial Replication with Reinforcement Learning for Real-Time Heterogeneous Data Management in Mobile Computing Networks

Real-time mobile computing environments, such as smart cities and disaster-response networks, generate heterogeneous and high-velocity data streams in highly dynamic settings. Traditional full replication incurs prohibitive bandwidth overhead, while naive partial replication risks missing life-critical alerts. This paper presents Semantic-Aware Partial Replication (SAPR), a unified and lightweight framework that resolves this critical bandwidth-reliability trade-off by intelligently selecting the most valuable information for dissemination. SAPR is an integrated solution to combine RDF/SPARQL-based semantic ranking, real-time priority handling, and efficiency-per-byte optimization with Deep Q-Network (DQN) adaptive control. The framework computes a semantic priority score based on data type, urgency, and spatial context, and then replicates the most utility-efficient triples per byte. Crucially, a DQN agent dynamically adapts the replication factor (k) by observing network conditions like mobility dynamics, neighbor count, and alert frequency, learning to prioritize bandwidth conservation in congested environments. Using a 26-node mobile network simulation validated with real CRAWDAD taxi traces and a 10,000-triple RDF dataset, SAPR achieves an exceptional balance. The proposed RL-SAPR scheme demonstrates 82% bandwidth reduction while successfully maintaining 99% alert delivery accuracy, significantly outperforming both semantic and priority-only baselines. This work confirms the feasibility of combining semantic intelligence with reinforcement learning to enable reliable, bandwidth-efficient knowledge sharing in mission-critical, bandwidth-constrained mobile networks.