Multi-Objective Optimization of AoI and Rate for Secure Dual-Functional RIS-Assisted Networks: A Cross-Layer Design

• Published in: IEEE internet of things journal 2024-12, p.1-1
• Main Authors: Li, Jie, Li, Jing, Lv, Lu, Ju, Zhanyu, Gong, Fengkui
• Format: Article
• Language: English
• Subjects: age of information; Autonomous aerial vehicles; communication rate; Communication system security; Cross layer design; Cross-layer optimization; Internet of Things; Low latency communication; Optimal scheduling; Optimization; reconfigurable intelligent surface; Reconfigurable intelligent surfaces; resource allocation; Resource management; secure low-latency communication; Trajectory
• ISSN: 2327-4662
• DOI: 10.1109/JIOT.2024.3510560

We propose a cross-layer optimization algorithm to minimize the average age of information (AoI) and at the same time maximizing the communication sum rate in a dual-functional reconfigurable intelligent surface(DF-RIS)-assisted Internet of Things (IoT) network, where the DF-RIS performs simultaneous information reflection and jamming to defend against an eavesdropper. Specifically, the transmission scheduling and resource allocation are adaptively co-designed to achieve secure low-latency communication in a more practical and detailed channel environment. For the case with perfect channel state information (CSI) of eavesdropper and IoT devices, the transmission scheduling is transformed as a Markov decision process and optimized to find the optimal packet size by deep Q-network. The resource allocation algorithm is performed to optimize sub-carrier channels, transmit power, the scheduling matrix of the DF-RIS, and discrete phase shifts of the DF-RIS based on the alternating optimization method. For the imperfect CSI leads to an infinite number of inequality constraints, a robust resource allocation algorithm based on the transformed S-procedure is proposed, which can guarantee the security and low-latency of messages transmission. Simulation results show that the proposed algorithm can reduce the average AoI by at least 15.5% and increase communication sum rate by 9.2% than the existing baseline algorithms.