Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT

Abstract

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.