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<p>Recently, wireless body area networks (WBANs) have attracted attention as promising technologies for developing the Internet of Things and medical-sensitive applications. However, one of the fundamental challenges in WBANs is transmitting medical data with minimal energy consumption, maximum throughput, and extended network lifetime. In this paper, we propose an Adaptive Particle Swarm Optimization-Learning Automata (APSOLA) routing protocol, which integrates Particle Swarm Optimization (PSO) and Learning Automata (LA). The protocol aims to increase network lifetime by providing a dynamic solution for clustering and routing, and it consists of three main phases (clustering, cluster head selection, and routing). Its main feature is adaptability to the physiological and dynamic conditions of the human body. The performance of protocol was assessed against two reference protocols, including Rahat Khan (RK) and energy-efficient dynamic cluster-head and routing-path selection (EEDCH). The finding demonstrated that APSOLA improves energy consumption (about 10% and 15%) as compared to RK and EEDCH, respectively, indicating efficient energy management. For network lifetime, the protocol achieved an 11.3% improvement over RK and a significant improvement over EEDCH. Additionally, the packet delivery ratio improved about 12.4% and 37.7% compared to RK and EEDCH, respectively, indicating higher reliability. The findings demonstrated that APSOLA could be an efficient, adaptive, and reliable approach for energy management and enhanced stability in WBANs used in smart healthcare systems.</p>

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A multi-objective adaptive PSO-learning automata protocol for clustering and routing in energy-efficient WBANs for smart healthcare

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Nasim Hosseini Larijani,
  • Ali Nodehi,
  • Hosein Mohamadi,
  • Mirsaeid Hosseini Shirvani

摘要

Recently, wireless body area networks (WBANs) have attracted attention as promising technologies for developing the Internet of Things and medical-sensitive applications. However, one of the fundamental challenges in WBANs is transmitting medical data with minimal energy consumption, maximum throughput, and extended network lifetime. In this paper, we propose an Adaptive Particle Swarm Optimization-Learning Automata (APSOLA) routing protocol, which integrates Particle Swarm Optimization (PSO) and Learning Automata (LA). The protocol aims to increase network lifetime by providing a dynamic solution for clustering and routing, and it consists of three main phases (clustering, cluster head selection, and routing). Its main feature is adaptability to the physiological and dynamic conditions of the human body. The performance of protocol was assessed against two reference protocols, including Rahat Khan (RK) and energy-efficient dynamic cluster-head and routing-path selection (EEDCH). The finding demonstrated that APSOLA improves energy consumption (about 10% and 15%) as compared to RK and EEDCH, respectively, indicating efficient energy management. For network lifetime, the protocol achieved an 11.3% improvement over RK and a significant improvement over EEDCH. Additionally, the packet delivery ratio improved about 12.4% and 37.7% compared to RK and EEDCH, respectively, indicating higher reliability. The findings demonstrated that APSOLA could be an efficient, adaptive, and reliable approach for energy management and enhanced stability in WBANs used in smart healthcare systems.