Adaptive encryption and transmission in Lora networks using reinforcement learning for effective security of IOT devices in end-to-end transmission

A new RL-augmented encryption and transmission scheme applied to LoRa-based IoT communication can help alleviate the security-related problem and efficiency related weaknesses. This paper proposes an intelligent reinforcement learning-based framework with which data security and messaging efficiency can be improved within the IoT networks with LoRa architecture. At heart, the system employs a Q-learning algorithm that runs a continuous optimization on key transmission parameters-spreading factor, transmission power, and coding rate– in response to real-time feedback in the environment. To establish this secure communication, the framework also has a dynamic encryption scheme where key matrices are being generated based on timestamp and entropy values that will have the characteristic of extreme resistance to brute-force and replay attack. The encryption works serve as a trade-off with lightweight eigenvector-related transformations between security and computational cost. This RL agent runs in the Markov Decision Process (MDP), which means that its states correspond to the system entropy and channel, and actions are tuning adaptively to achieve optimum performance. Validated with a gas sensor implementation, the system obtained a decryption accuracy of 94.7% and save 32.5% of energy and increase the ratio of packet delivery by 27.8%. The framework employs reinforcement learning to adapt transmission parameters and uses standard AEAD encryption (e.g., AES-GCM) combined with eigenvector-based diffusion for enhanced data confidentiality. This integration achieves 94.7% decryption accuracy, 32.5% energy reduction, and a 27.8% increase in packet delivery ratio in low-power LoRa networks, underscoring its suitability for secure and energy-efficient IoT communication.