Joint Optimization of Energy and QoS for Smart Clothing With Multiposture Participation

Abstract

The rising aging population, inequality of medical resources, and severe COVID-19 infection rate raise inevitable individual and social contradictions. One of the representative developing technologies, smart wearables, is dedicated to offering accurate personal healthcare. Nevertheless, energy constraints as well as unpredictable data transmission are critical in the development of wearable devices. In this regard, we investigate the key concerns of energy life and quality of service (QoS) for smart clothing. Unlike general wireless sensing networks (WSNs), the wireless body area network (WBAN) embedded in smart clothing is highly affected by human postural changes. In this article, we formulate the smart clothing with multiposture participated from two perspectives: 1) for energy life, we address the energy consumption, the energy harvested by the nodes, and the battery discharge and 2) the QoS involves the path loss and time delay. Moreover, five typical daily activity states have been discussed to model the impact of posture changes. Under the influence of the posture state, the tradeoff between the collected tribological electrical energy and the consumed energy is also presented in the article. We parameterize the path loss, transmission delay, energy consumption, and collection in each posture and integrally formulate the energy problem and QoS to a joint optimization problem. Particle swarm optimization (PSO), sine cosine algorithm (SCA), and Q-learning algorithm are adopted to optimize the overall cost, time delay, and energy consumption. In addition, a comparison of the battery power of the nodes is conducted. Simulation results show that each algorithm achieves certain optimization effects, for example, PSO, SCA, and Q-learning reduce total costs by 14%, 22%, and 30%, respectively. Q-learning is also effectively decreasing latency and energy consumption and improving battery life.