Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT
2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
Multi-mode power internet of things (PIoT) provides spatio-temporal coverage for low-carbon operation in smart park through combining various communication media. Heterogeneous resources are dynamically and intelligently managed to improve resource utilization and achieve anti-eavesdropping. However, resource management in multi-mode power IoT confronts challenges such as the mutual contradiction in joint communication and security quality of service (QoS) guarantee and the inadaptability to low-carbon services. In this paper, we propose an Adaptive learning-based secure and energy-aware resource management algorithm (ANGEL) to optimize multi-mode channel selection and power splitting for artificial noise (AN)-based anti-eavesdropping. Based on deep actor-critic (DAC) and 'win or learn fast (WoLF)' mechanism, ANGEL can realize multi-attribute QoS guarantee, adaptive resource management, and security enhancement. Simulation results demonstrate its superior performance in energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.© 2022 IEEE.
Carbon, Energy utilization, Natural resources management, Parks, Power management, Quality of service, Resource allocation, Security systems
H. Liao et al., "Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5171-5176, doi: 10.1109/GLOBECOM48099.2022.10001389.
IR conditions: non-described