由于海底环境并不具备连续稳定的电力供应,使得传感器节点通常依赖于有限的内部能量,而船舶在海底的动态位置会令节点密度布局变得更加复杂,从而导致WSN节点能量消耗不均衡。为此,提出考虑船舶动态位置的海底WSN节点能耗控制算法。建立海底WSN结构模型,利用多项式卡尔曼滤波器预测船舶轨迹;划分海底WSN簇结构,动态调整各簇的节点数量,控制海底WSN节点密度,建立船舶位置动态变化能耗模型,估算各簇节点在不同时间段的能耗需求,使其均衡消耗WSN节点能量,从而实现节点能耗控制。实验结果表明,本文算法应用后,有效平衡了节点能耗,保持连通性和覆盖性,提高海底WSN整体性能,稳定运行时间更长且失效节点增长较稳定。
Due to the lack of continuous and stable power supply in the seabed environment, sensor nodes usually rely on limited internal energy, and the dynamic location of ships on the seabed makes the node density layout more complex, resulting in unbalanced energy consumption of WSN nodes. Therefore, an energy consumption control algorithm for submarine WSN nodes considering ship dynamic position is proposed. The submarine WSN structure model is established, and the ship trajectory is predicted by polynomial Kalman filter. The submarine WSN cluster structure is divided, the number of nodes in each cluster is dynamically adjusted, the density of submarine WSN nodes is controlled, the energy consumption model of dynamic change of ship position is established, the energy consumption demand of each cluster node in different time periods is estimated, and the energy consumption of WSN nodes is balanced, so as to achieve node energy consumption control. Experimental results show that the proposed algorithm effectively balances node energy consumption, maintains connectivity and coverage, improves the overall performance of subsea WSN, and has a longer stable running time and a stable growth of failed nodes.
2024,46(16): 148-152 收稿日期:2024-04-12
DOI:10.3404/j.issn.1672-7649.2024.16.024
分类号:TP393
基金项目:山东省船舶控制工程与智能系统工程技术研究中心科研专项资金项目(SSCC20230004)
作者简介:张传聪(1988 – ),男,硕士,讲师,研究方向为无线传感器网络、信息系统项目管理
参考文献:
[1] 余修武, 张可, 刘永, 等. 基于反向学习的群居蜘蛛优化WSN节点定位算法[J]. 控制与决策, 2021, 36(10): 2459-2466.
YU Xiuwu, ZHANG Ke, LIU Yong, et al. WSN node localization based on social spider optimization and opposition based learning[J]. Control and Decision, 2021, 36(10): 2459-2466.
[2] 张欢. 基于节点交易密度的WSN信任计算方法仿真[J]. 计算机仿真, 2022, 39(1): 370-374+464.
ZHANG Huan. Simulation of WSN trust calculation method based on node transaction density[J]. Computer Simulation, 2022, 39(1): 370-374+464.
[3] 何少尉. 一种改进的无线传感器网络密度自适应冗余节点调度算法[J]. 电视技术, 2023, 47(8): 32-36.
HE Shaowei. An improved density-adaptive redundant node scheduling algorithm for wireless sensor networks[J]. Video Engineering, 2023, 47(8): 32-36.
[4] 常宇飞, 李重阳, 张爱军, 等. 差分进化花朵授粉算法的WSN节点部署策略[J]. 陆军工程大学学报, 2023, 2(1): 86-92.
CHANG Yufei, LI Chongyang, ZHANG Aijun, et al. Strategy of WSN node deployment based on differential-evolution flower-pollination algorithm[J]. Journal of Army Engineering University of PLA, 2023, 2(1): 86-92.
[5] XIAO Jie , LI Chaoqun , ZHOU Jie . Minimization of energy consumption for routing in high-density wireless sensor networks based on adaptive elite ant colony optimization[J]. Journal of Sensors, 2021, 2021(2), 5590951.1–5590951.12.
[6] ADHi L R, PAVALARAJAN S. Sling-shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things-based networks[J].International Journal of Communication Systems 2023, 36(4), 5406.1–5406.18.
[7] 王均刚, 丁惠倩, 胡柏青. 基于滑动窗口PSO-LSSVR的船舶轨迹预测模型[J]. 武汉理工大学学报, 2022, 44(12): 35-43,59.
WANG Jungang, DING Huiqian, HU Baiqing. Ship trajectory prediction model based on sliding window PSO-LSSVR[J]. Journal of Wuhan University of Technology, 2022, 44(12): 35-43,59.
[8] 徐瑞龙, 祁云嵩, 石琳. 基于Transformer模型和Kalman滤波预测船舶航迹[J]. 计算机应用与软件, 2021, 38(5): 106-111.
XU Ruilong, QI Yunsong, SHI Lin. Prediction of ship track based on transformer model and Kalman filter[J]. Computer Applications and Software, 2021, 38(5): 106-111.