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. FEC and 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.