Fei Hongmei, Jia Dingyi, Zhang Baitao, Li Chaoqun, Zhang Yao, Luo Tao, Zhou Jie
College of Information Science and Technology, Shihezi University, Shihezi, 832000, China.
College of Computer Science and Technology, Shandong University, Qingdao, China.
Sci Rep. 2024 Oct 29;14(1):25969. doi: 10.1038/s41598-024-77686-y.
Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network's nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.
服务质量(QoS)路由协议是无线传感器网络(WSN)研究领域的一个热门话题。然而,识别一条同时满足多个QoS约束的最优路径的任务被公认为是一个NP难问题,其复杂度随着网络节点数量的增加而成比例加剧。因此,提出了一种新颖的启发式多目标信任路由方法,即基于莱维混沌自适应蛇优化的多信任路由方法(LCASO-MTRM),旨在提高链路带宽,同时减少延迟、数据包丢失和能量消耗。该方法在LCASO框架内融入了创新的混沌和自适应算子。混沌算子增强了种群多样性,扩大了求解空间,并加速了搜索过程。同时,自适应算子提高了收敛性,增强了鲁棒性,并有效防止了停滞。此外,本文还引入了一种新颖的多目标QoS路由模型,该模型集成了链路信任机制,能够更准确地评估链路信任级别,并精确反映当前链路状态。通过与改进粒子群优化算法(IPSO)、改进人工蜂群算法(IABC)和克隆鲸鱼优化算法(CWOA)的仿真比较,验证了LCASO-MTRM的有效性。仿真结果表明,LCASO-MTRM显著降低了49.53%的能量消耗、22.56%的延迟和40.21%的数据包丢失,同时提高了6.13%的带宽,优于其他算法。