2022 Vol.5 Dec.No4

• Title: Research on experimental teaching information method of multimedia sensor network in colleges and universities based on cloud co
• Name: Yuxin Ge1，Cequan Fu2
• Company: 1.Nanyang Technopreneurship Center , Nanyang Technological University , Singapore, Singapore; (2.China Economics and Management Academy, Central University of Finance and Economics, Beijing, China
• Abstract:

  Aiming at the problem that the information acquisition time of the multimedia sensor network experimental teaching information method in colleges and universities is long, the pheromone is not limited, and the timeliness of the sensor network learning monitoring is low, a multimedia sensor network monitoring experimental teaching method based on cloud computing is proposed. According to the pheromone and heuristic factor in cloud computing, calculate the probability function of the node transition path of the multimedia sensor network, get the parameters of the cloud computing ant colony to adjust the heuristic factor and the importance of the residual information in the path, use this parameter to adjust the global information of the sensor network to the optimal state, update the pheromone in the multimedia network, get the volatilization factor, and calculate the number of pheromones accordingly, The data packet loss rate and network energy consumption weight factor in the multimedia sensor network are obtained, and the multimedia sensor network learning monitoring experimental teaching is realized. The experimental results show that the multimedia sensor network learning monitoring method proposed in this paper has a high timeliness, and the network energy consumption is low, which improves the performance of the sensor network.

  
  

• Keyword: Multi-Media; Sensor network; Learning monitoring; Experimental teaching; Cloud computing
• DOI: 10.12250/jpciams2022090511
• Citation form: Yuxin Ge.Research on experimental teaching information method of multimedia sensor network in colleges and universities based on cloud computing [J]. Computer Informatization and Mechanical System,2022,Vol.5,pp.48-52

[1]LI Fei,LIU Min,JIANG Hao. A Quantum Ant Colony Multi-Objective Routing Algorithm for Industrial Equipment Monitoring Network[J]. Chinese Journal of Sensors and Actuators, 2019:1366-1373.

[2]CHEN Zhiyong. Real Time Feedback Simulation for Learning Monitoring of Multimedia Sensor Networks[J]. Science and Technology Bulletin, 2019, v.35;No.253(09):118-121+126.

[3]HE Jun. Ship Multimedia Sensor Network Compression Technology Based On Sparse Matrix[J]. Ship Science and Technology, 2020, v.42(04):164-166.

[4]ZHENG Wei. Simulation of Data Multi-Host Transmission Method in Multimedia Sensor Networks[J]. Computer Simulation, 2020, 037(001):165-169.

[5]ZHOU Yuanlin,TAO Yang,LI Zhengyang,et al. Energy-Efficient Clustering Routing Algorithm Based on Evolutionary Game Theory for Wireless Sensor Networks[J]. Chinese Journal of Sensors and Actuators, 2020, 033(003):436-442..

[6]WANG Ting,SUI Jianghua. Optimization of sensor network coverage distribution improved particle swarm optimization[J]. Journal of Liaoning University of Engineering and Technology (Natural Science Edition), 2020, v.39;No.247(03):88-94.

[7]YU Yuanqin,YU Xiuwu,CHEN Haiwen,et al. Design of regional alarm monitoring system based on optical fiber sensing network[J]. Laser Journal, 2019, v.40;No.259(04):106-111.

[8]ZHOU Feng,ZHU Lijun,WANG Xuan,et al. Data Transmission Strategy Based on Automatic Learning Machine in Wireless Sensor Network[J]. Chinese Journal of Sensors and Actuators, 2020, v.33(01):134-137+156.

[9]Ji Shanshan. Path Planning for Mobile Sink Based on Enhanced Ant Colony Optimization Algorithm in Wireless Sensor Networks[J]. Journal of System Simulation, 2019(11).

[10]WANG Chao. Large Scale Heterogeneous Sensor Communication Large Data Intelligent Scheduling Simulation[J]. Computer Simulation, 2019, 036(004):445-448,473.

[11]HANG Chao,LI Gang,XIE Yuzhuo,et al. WSN Data Fusion Algorithm Based on Non-Uniform Clustering and Ant Colony Neural Network[J]. Chinese Journal of Sensors and Actuators, 2020, v.33(10):109-114.

[12]DING Hua. Data aggregation algorithm of WSN based on ant colony optimization[J]. Journal of Shenyang University of Technology, 2020, v.42;No.210(02):90-94.

[13]Wang Hongyun,Shu Yong’an. Dynamic multipath load balancing based on ant colony algorithm in DCN[J]. Application Research of Computers, 2020(7).

[14]DAI Tianhong,ZHAO Yongzheng,ZHANG Jiawei,et al. Signal propagation characteristics of wireless sensor networks in forestry environmental monitoring[J]. Journal of Fujian Agriculture and Forestry University (Natural Science Edition), 2020, 049(002):199-205.

[15]WU Jiawen,LI Guanghui. KM-FNN Algorithm for Missing Data Reconstruction in Wireless sensor Networks[J]. Chinese Journal of Sensors and Actuators, 2019, v.32(08):127-134.