Abstract
Objective: To solve the problems of low computational efficiency and poor energy-saving effect of routing algorithm in wireless sensor networks, a new routing protocol for wireless sensor networks is established. Methods: Based on the architecture of grain storage environment monitoring system, a node scheduling method based on node residual energy clustering was proposed for wireless sensor network for grain storage environment monitoring. The number of active nodes in the cluster was calculated from two aspects: network coverage and monitoring accuracy. The working nodes were determined according to the energy and distribution of nodes, and the performance and superiority of the energy-saving strategy were verified. Results: Compared with the routing protocol of traditional wireless network, the energy consumption of this protocol was significantly reduced, and the energy consumption of each node in the network was relatively uniform and effectively balanced. Conclusion: This method improves the calculation efficiency and reduces the energy consumption, and has a good scheduling effect.
Publication Date
10-28-2021
First Page
133
Last Page
137,166
DOI
10.13652/j.issn.1003-5788.2021.10.023
Recommended Citation
Xian, HUANG; Jia, CHEN; and Ke-xin, CHEN
(2021)
"Application of wireless sensor technology in grain storage environment monitoring,"
Food and Machinery: Vol. 37:
Iss.
10, Article 23.
DOI: 10.13652/j.issn.1003-5788.2021.10.023
Available at:
https://www.ifoodmm.cn/journal/vol37/iss10/23
References
[1] 罗剑, 毕晓东. 基于改进萤火虫聚类的异构WSNs能耗优化路由算法[J]. 传感技术学报, 2018, 31（10）: 1 584-1 591.
[2] 项辉宇, 薛真, 冷崇杰, 等. 基于Halcon的苹果品质视觉检测试验研究[J]. 食品与机械, 2016, 32（10）: 123-126.
[3] 纪辛然. 无线传感器网络自适应动态路由算法仿真[J]. 计算机仿真, 2021, 38（6）: 259-262, 310.
[4] 曹迪, 谢敏杰, 黄霆豪, 等. 改进蚁群算法的环境能源采集型WSN多目标路由研究[J]. 小型微型计算机系统, 2021, 42（5）: 1 115-1 120.
[5] 李伟, 张凤梅. 基于K-Means的无线传感网络节能算法研究[J]. 传感器与微系统, 2021, 40（4）: 41-44.
[6] 张华南, 金红, 王峰. 无线传感器网络节能探索与研究[J]. 计算机工程与科学, 2021, 43（2）: 295-303.
[7] 刘浩然, 赵翠香, 李轩, 等. 一种基于改进遗传算法的神经网络优化算法研究[J]. 仪器仪表学报, 2016, 37（7）: 1 573-1 580.
[8] 高亮, 张新燕, 张家军, 等. 基于拟合剔除的优化小波神经网络太阳辐射量预测[J]. 电测与仪表, 2019, 40（4）: 1-5.
[9] GAUTAM J V, PRAJAPATI H B, DABHI V K, et al. Empirical study of job scheduling algorithms in hadoop map reduce[J]. Cybernetics and Information Technologies, 2017, 21（1）: 146-163.
[10] 杨双艳, 杨紫刚, 张四伟, 等. 基于近红外光谱和PSO-SVM算法的烟叶自动分级方法[J]. 贵州农业科学, 2018, 46（12）: 141-144.
[11] 王阳阳, 黄勋, 陈浩, 等. 基于同态滤波和改进K-means的苹果分级算法研究[J]. 食品与机械, 2019, 35（12）: 47-51, 112.
[12] CAETANO C E F, LIMA A B, PAULINO J O S, et al. A conductor arrangement that overcomes the effective length issue in transmission line grounding[J]. Electric Power Systems Research, 2018, 46（5）: 159-162.
[13] JIA Zhi-wei, WANG Li-jun, ZHANG Jin-chuan, et al. High efficiency, low power-consumption DFB quantum cascade lasers without lateral regrowth[J]. Nanoscale Research Letters, 2017, 12（1）: 88-95.
[14] 钟潇柔. 基于动态遗传算法的云计算任务节能调度策略研究[D]. 哈尔滨: 哈尔滨工业大学, 2015: 40-45.
[15] 王利平, 庞晓艳, 朱雨, 等. 基于物联网和移动互联的二次设备运维技术研究与应用[J]. 中国电力, 2019, 52（3）: 177-184.
[16] 杨阳, 汪玉成, 吕玉祥, 等. 基于软件定义的电力通信网络业务感知流量调度机制研究[J]. 自动化与仪器仪表, 2017（8）: 146-149.
[17] 徐彬泰, 周洁, 于秋生, 等. 基于协作智能与子梯度优化算法的电力业务差异化QoS路由策略[J]. 电测与仪表, 2020, 57（10）: 50-57.
[18] 翟学明, 王佳, 李金泽. 基于蚁群算法和BP神经网络的信道分配策略的研究[J]. 传感技术学报, 2016, 29（3）: 445-450.
[19] 李祝红, 赵灿明, 闫龙, 等. 智能电网中电力线通信网络负载均衡的机会路由协议[J]. 计算机应用, 2019, 39（3）: 812-816.
[20] 叶波. 基于负载均衡度的云计算任务调度算法[J]. 东北电力大学学报, 2019, 39（1）: 88-95.