一个新的基于群优化的动态路径规划和能源优化模型,用于移动水槽的无线传感器网络,使用混合整数线性编程
Fangyan Chen1, Xiangcheng Wu1, Zhiming Wang1
1School of Artificial Intelligence, Jianghan University, Wuhan 430056, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
本研究介绍了一种灵感来自群优化 (ACO) 的模型,使用混合整数线性编程 (MILP) 来优化无线传感器网络 (WSN). 该模型平衡了能源消耗,延迟和网络寿命,以提高性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 优化理论 优化理论
背景情况:
- 无线传感器网络 (WSN) 对于环境监测和工业控制至关重要.
- 传感器节点的能量限制需要平衡能源消耗,传输延迟和网络寿命,以防止能量漏洞.
研究的目的:
- 开发一种新的优化模型,其灵感来源于WSN的生物食策略 (食草动物和).
- 解决WSN多目标优化的挑战,特别是能源效率,延迟和网络寿命.
主要方法:
- 使用混合整数线性编程 (MILP) 构建一个群优化 (ACO) 概念化的模型.
- 集成离散路径选择和移动水槽的持续停留时间优化.
- 将激素强度和蒸发映射到MILP能量约束和成本函数,用于动态路径规划.
主要成果:
- 通过可调节的权重系数,实现了数据传输延迟和能源消耗之间的灵活权衡.
- 将复杂的WSN路径规划和调度转化为具有全球最佳性保证的确定性优化模型.
- 通过实验验证,在能源效率,实时性能和延长网络寿命方面取得了显著的改进.
结论:
- 拟议的ACO概念化的MILP模型有效地优化了WSN性能.
- 该模型为在能源有限的网络中平衡竞争目标提供了一个强大的框架.
- 这种方法提高了整体网络效率,并延长了运营寿命.
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