Deep Reinforcement Learning for Real-Time Compression-Decompression Optimization in Edge Devices

With the proliferation of IoT and edge devices, massive amounts of heterogeneous data are generated in real time. Efficient compression?decompression mechanisms are essential to reduce storage overhead, minimize transmission latency, and optimize energy consumption. However, traditional compression algorithms lack adaptability to dynamic environments where resource constraints and workload patterns vary rapidly. This research proposes a Deep Reinforcement Learning (DRL)-based adaptive framework for real-time compression?decompression optimization in edge devices. The agent dynamically selects compression levels, algorithms, and bit allocation strategies based on current device constraints (CPU, memory, bandwidth, and energy) and application-specific quality requirements. Experimental evaluations show that the DRL-based approach achieves up to 40% reduction in latency and 30% improvement in energy efficiency, while maintaining near-lossless reconstruction accuracy compared to state-of-the-art baselines.