A Joint Optimization With Dynamic Strategy for Hub Position in IoB Over Human Body Channel

The 6th generation wireless systems (6G) bring blazing speeds, ultralow latency, and increased intelligence for various human-centric services (HCSs). As promising technologies in 6G, the Internet of Bodies (IoB) and intelligent fabric promote the development of wearable devices and health monitoring applications. However, IoB is facing three challenges: 1) limited energy; 2) information confidentiality; and 3) equipment miniaturization. In this article, the performance of the IoB on intelligent fabrics is influenced by human posture and the position of the hub. Specifically, the proposed system model has three major concerns: 1) we adopt multifunctional fabrics that integrate sensor and hub functions to miniaturize the IoB equipment; 2) the conventional wireless channel has been replaced by the human body channel with greater privacy and energy efficiency; and 3) we focus on energy consumption, latency, path loss, and network lifespan in the IoB under four typical daily activity states. Finally, we minimize the total cost of the joint optimization model for energy and Quality of Service (QoS) in the cases of dynamically activating different hub areas. Heuristic algorithms [particle swarm optimization (PSO) and sine cosine algorithm (SCA)], Q-learning, REINFORCE-baseline, Rainbow, and proximal policy optimization (PPO) are applied for optimization and comparisons with the original algorithm. The simulation results demonstrate the effectiveness of all six algorithms, which, respectively, reduced the total cost by 17.46%, 14.13%, 23.73%, 28.46%, 30.30%, and 35%, the delay by 19.16%, 12.48%, 43.26%, 32.06%, 41.38%, and 53.15%, and the energy consumption by 21.49%, 23.94%, 9.48%, 18.99%, 13.77%, and 24.16%. In comparison to the unoptimized scenario, the energy efficiency improved by up to 31.86%, and the remaining battery energy increased by up to 113.61%, thereby significantly extending the network lifetime. The results demonstrate the superiority of the PPO algorithm in solving the joint optimization problem in IoB. This study can promote the further integration of health monitoring and smart fabrics, advancing the development of embodied intelligence.