Adaptive learning-based delay-sensitive and secure edge-end collaboration for multi-mode low-carbon power IoT

Abstract

Multi-mode power internet of things (PIoT) combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park. Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping. However, edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service (QoS) guarantee, inadaptability of resource management, and multi-mode access conflict. We propose an Adaptive learning based delAy-sensitive and seCure Edge-End Collaboration algorithm (ACE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to optimize multi-mode channel selection and split device power into artificial noise (AN) transmission and data transmission for secure data delivery. ACE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can achieve multi-attribute QoS guarantee, adaptive resource management and security enhancement, and access conflict elimination with the combined power of deep actor-critic (DAC), "win or learn fast (WoLF)" mechanism, and edge-end collaboration. Simulations demonstrate its superior performance in queuing delay, energy consumption, secrecy capacity, and adaptability to differentiated low-carbon services.