Privacy-Preserving Computation Offloading Using Deep Reinforcement Learning in Internet of Medical Things

초록

The Internet of Medical Things (IoMT) plays a crucial role in smart healthcare by leveraging medical devices that generate vast amounts of data for monitoring and diagnosis. IoMT networks are prone to challenges like dynamic topology, computational limitations, and power constraints that make it difficult to process data. Offloading computationally intensive operations to high-capacity computational nodes is necessary. However, medical devices provide nonindependent and identically distributed (non-i.i.d.) data in different formats, making it more complex. Conventional rule-based offloading schemes are inefficient in enhancing network performance and hence have high latency and energy consumption. This article introduces a secure computational offloading framework using deep reinforcement learning. With deep deterministic policy gradient (DDPG) and federated learning as the basis, the framework: 1) observes the network; 2) learns offloading decisions; and 3) safeguards data privacy. It reduces energy and latency with a security guarantee. A similar to cloud-fog architecture is implemented with a DDPG-based local model, which is executed at the fog broker, and a global model that is executed at the cloud server. We use advance encryption standard (AES) + encryption for secure transmission of data (ECC). MATLAB simulations are conducted for encrypted and unencrypted offloading and exhibit up to 12% improvement in encrypted cases and 13% in unencrypted cases compared to existing solutions.

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