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Abstract <p>With the rapid development of the Internet of Things (IoT) and intelligent sensing technologies, high-density wireless sensor networks (HDWSNs) are widely applied in various domains, including environmental monitoring, intelligent manufacturing, healthcare, and intelligent transportation. However, due to their inherent resource constraints, achieving high-performance quality of service (QoS) routing remains a long-standing challenge in HDWSNs. To achieve low energy consumption and great communication performance, this study designs a novel QoS routing model that comprehensively considers key performance metrics such as energy consumption, latency, and packet loss rate, accurately characterizing the multi-constrained optimization requirements in real-world HDWSNs deployment environments. Accordingly, an immune clonal adaptive shuffled frog-Leaping algorithm-based QoS-aware routing protocol (ICASFLA-QRP) is proposed. ICASFLA-QRP significantly extends the network lifetime of HDWSNs while effectively reducing transmission latency and packet loss rate. To ensure consistency between the continuous search process of ICASFLA and the discrete next-hop routing decisions, a discretization mapping mechanism is further introduced to project continuously updated candidate solutions onto feasible integer-encoded routing paths. In addition, an innovative immune clonal strategy is introduced to dynamically maintain population diversity, effectively preventing premature convergence and enabling thorough exploration of the global search space. Furthermore, an adaptive operator is designed to automatically adjust algorithmic parameters based on the evolutionary state of the population, thereby greatly enhancing the search efficiency and flexibility of the algorithm. Experimental results on benchmark functions and HDWSN routing scenarios demonstrate that the proposed method consistently outperforms representative traditional routing protocols and heuristic routing protocols, including AODV, DSDV, GA-AOMDV, QoSR-PSO, and EHRP-WSN. Specifically, network energy consumption and communication latency decrease by at least 44.34% and 9.92%, respectively, while network lifetime, packet delivery ratio, and system throughput increase by at least 18.09%, 2.09%, and 10.18%, respectively. These results verify the effectiveness and robustness of the proposed algorithm in HDWSNs environments.</p> Graphical abstract <p></p>

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An intelligent heuristic-based QoS-aware routing protocol for energy-efficient and low-latency internet of things

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Xin Liu,
  • Cheng Liu,
  • Lei Yu,
  • Yi Zhou,
  • Bo Jin

摘要

Abstract

With the rapid development of the Internet of Things (IoT) and intelligent sensing technologies, high-density wireless sensor networks (HDWSNs) are widely applied in various domains, including environmental monitoring, intelligent manufacturing, healthcare, and intelligent transportation. However, due to their inherent resource constraints, achieving high-performance quality of service (QoS) routing remains a long-standing challenge in HDWSNs. To achieve low energy consumption and great communication performance, this study designs a novel QoS routing model that comprehensively considers key performance metrics such as energy consumption, latency, and packet loss rate, accurately characterizing the multi-constrained optimization requirements in real-world HDWSNs deployment environments. Accordingly, an immune clonal adaptive shuffled frog-Leaping algorithm-based QoS-aware routing protocol (ICASFLA-QRP) is proposed. ICASFLA-QRP significantly extends the network lifetime of HDWSNs while effectively reducing transmission latency and packet loss rate. To ensure consistency between the continuous search process of ICASFLA and the discrete next-hop routing decisions, a discretization mapping mechanism is further introduced to project continuously updated candidate solutions onto feasible integer-encoded routing paths. In addition, an innovative immune clonal strategy is introduced to dynamically maintain population diversity, effectively preventing premature convergence and enabling thorough exploration of the global search space. Furthermore, an adaptive operator is designed to automatically adjust algorithmic parameters based on the evolutionary state of the population, thereby greatly enhancing the search efficiency and flexibility of the algorithm. Experimental results on benchmark functions and HDWSN routing scenarios demonstrate that the proposed method consistently outperforms representative traditional routing protocols and heuristic routing protocols, including AODV, DSDV, GA-AOMDV, QoSR-PSO, and EHRP-WSN. Specifically, network energy consumption and communication latency decrease by at least 44.34% and 9.92%, respectively, while network lifetime, packet delivery ratio, and system throughput increase by at least 18.09%, 2.09%, and 10.18%, respectively. These results verify the effectiveness and robustness of the proposed algorithm in HDWSNs environments.

Graphical abstract