MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme resist attack without considering channel state information. scheme, MEC sensor is chosen send artificial jamming signals improve secrecy rate of system. Power control technique utilized optimize transmission power both source save energy. The remaining energy concerned ensure routine data signal transmission. Additionally, DMEC-JAE integrates with transfer for higher rate. performance bounds concerning rate, consumption, utility evaluated. Simulation results show that can approach high speed, which outperforms benchmark schemes.