三维隧道WSN多跳路由的建模与仿真研究

doi:10.16182/j.issn1004731x.joss.25-1100

摘要/Abstract

摘要：

针对三维隧道无线传感器网络拓扑复杂、能量耦合强导致能耗不均衡和生命周期短的问题，从系统建模与仿真视角构建融合空间拓扑、能量演化与路由决策的系统级运行模型，提出基于部落竞争与成员合作算法(competition of tribes and cooperation of members,CTCM)的能量均衡多跳路由算法(energy management for balanced and efficient routing,EMBER)。通过隧道空间建模推导最优簇头数量，采用CTCM优化高斯混合模型分簇，结合数据包络分析实现高效簇头选取。引入基于能量阈值的重成簇机制以提升能量利用率，构建多目标路由优化模型完成簇间多跳传输。仿真结果表明：该模型与路由机制可有效均衡网络能量消耗并延长系统生命周期。

关键词: 隧道无线传感器网络, 系统建模, 分簇多跳路由, 能量均衡, 部落竞争与成员合作算法, 网络仿真

Abstract:

To address the unbalanced energy consumption and short network lifetime caused by the complex topology and strong energy coupling of three-dimensional tunnel wireless sensor networks, this study constructs a system-level operating model integrating spatial topology, energy evolution, and routing decision-making, and proposes an energy management for balanced and efficient routing (EMBER) algorithm based on the competition of tribes and cooperation of members (CTCM) algorithm. The optimal number of cluster heads is derived through tunnel spatial modeling. Clustering is performed using a Gaussian mixture model optimized by CTCM, and efficient cluster-head selection is achieved by combining data envelopment analysis. An energy-threshold-based re-clustering mechanism is introduced to enhance energy utilization, and a multi-objective routing optimization model is developed to enable inter-cluster multi-hop transmission. Simulation results demonstrate that the proposed model and routing mechanism effectively balance network energy consumption and extend the system lifetime.

Key words: tunnel wireless sensor network, system modeling, clustered multi-hop routing, energy balance, competition of tribes and cooperation of members algorithm, network simulation

中图分类号:

TP391.9

曹芳,杨继广,火久元 . 三维隧道WSN多跳路由的建模与仿真研究[J]. 系统仿真学报, 2026, 38(9): 2739-2754.

Cao Fang,Yang Jiguang,Huo Jiuyuan . Modeling and Simulation of Multi-hop Routing in 3D Tunnel Wireless Sensor Networks[J]. Journal of System Simulation, 2026, 38(9): 2739-2754.

图/表 13

图1

图2

图3

表1

实验参数

分类	参数	值
网络参数	隧道半径 $R$ /m	5
	BS位置/m	(-30, 10, 10)
	初始能量/J	0.5
	数据包大小/bit	4 000
	$E e l e c$ /(nJ/bit)	50
	$ε f s$ /(pJ/bit/m²)	10
	$ε m p$ /(pJ/bit/m²)	0.001 3
	$E D A$ /(nJ/bit)	5
CTCM	部落数量 $n$	40
	部落成员 $m$	$ℵ$
	解空间维度 $d$	3
	自我学习因子 $c 1$	2
	全局学习因子 $c 2$	1
	部落逃逸系数 $c 3$	0.1

表1

表2

实验场景设置参数

场景	区域( $L ⋅ W ⋅ H$ )/m	节点个数	CH个数
Ⅰ	1 000×20×20	400	13
Ⅱ	500×20×20	200	7

表2

图4

图5

图6

图7

图8

表3

不同算法的BEED

算法	场景Ⅰ			场景Ⅱ
算法	$F N D$	$L N D$	$β e$	$F N D$	$L N D$	$β e$
TCSAA	50	540	0.907	143	719	0.801
IFCRA	132	653	0.798	356	684	0.480
POFCA	239	757	0.684	389	768	0.493
EMBER	569	832	0.316	572	779	0.266

表3

图9

图10

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