Introduces two carbon-aware DRL-based intrusion detection systems for IoT edge gateways, reporting 94% accuracy for a supervised LSTM-DRL model and 98% for a label-free Autoencoder-DRL hybrid.
Lyapunov-guided deep reinforcement learning for stable online computation offloading in mobile-edge computing networks,
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Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.
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Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
Introduces two carbon-aware DRL-based intrusion detection systems for IoT edge gateways, reporting 94% accuracy for a supervised LSTM-DRL model and 98% for a label-free Autoencoder-DRL hybrid.
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Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.