AI-Based Joint Optimization of QoS and Security for 6G Energy Harvesting Internet of Things

The data privacy and confidentiality in Internet-of-Things (IoT) networks have been one of the most concerned problems due to increasing threats. The commonly utilized IoT chips adopt a fixed authentication and encryption scheme in the link layer even though multiple options are usually supported. As different authentication and encryption operations mean dissimilar protections and various energy consumption, the fixed security strategy neglects the remaining energy, dynamic threats, and diverse service requirements, leading to low energy efficiency. Moreover, fixed high-level security protections consume too much energy even though the security requirement may be low, which results in a short working time. To address this problem, we propose an artificial intelligence (AI)-based adaptive security specification method for 6G IoT networks where the IoT devices are connected to cellular networks via different frequency bands, including terahertz (THz) and millimeter wave (mmWave). The IoT sensing devices are assumed to support the energy harvesting technique which is expected to be widely adopted in 6G. In our proposal, the extended Kalman filtering (EKF) method is first adopted to predict future harvesting power. Then, in each energy-aware cycle, we design a mathematical model to calculate the required energy of different security strategies and choose the supported highest level protection which can meet service requirement and avoid energy exhaustion. The simulation results illustrate that the proposal can not only provide satisfied security protection for different services but also adjust the security protection to avoid the energy exhaustion, leading to a significant improvement of throughput and working time.