Distributed Adaptive Coding Optimization for IoT Using Fulcrum Code and Model-Agnostic Meta-Learning (MAML) in Ultra-Low Latency Environments

The exponential growth of the Internet of Things (IoT) has amplified the demand for energy-conscious, low-latency, and efficient data transmission techniques. This study proposes an adaptive distributed coding scheme combining Fulcrum Code and Model-Agnostic Meta-Learning (MAML) to optimize IoT communications in ultralow-latency environments. Fulcrum Code improves packet resilience by adjusting redundancy to channel conditions, while MAML optimizes coding parameters in real-time to improve transmission efficiency. Forward Error Correction (FEC) and Hybrid Automatic Repeat Request (HARQ) further balance error correction and retransmission overhead. Simulations show significant reductions in transmission time and energy consumption, particularly in high-packet-loss scenarios. This approach ensures robust performance in resource-constrained IoT devices, preserving battery life while supporting critical applications such as smart cities, Industry 4.0, and the tactile Internet. By optimizing trade-offs between resilience and efficiency, this scheme advances scalable and energy-efficient communication for next-generation IoT systems.