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International Journal of Educational Innovation and Science, 2020, 1(4); doi: 10.38007/IJEIS.2020.010405.

Cultivation of Creative Ability in the Teaching Reform of Industrial Design Wireless Sensor


Zhitong Yan

Corresponding Author:
Zhitong Yan

Harbin University, Harbin 150086, Heilongjiang, China


Wireless sensor network is a network with limited energy, which can collect network information in time, analyze the current network operation status, and feedback effective information to managers. It can be seen that the practical course of "Sensor Application Technology" has an important position in the course teaching. By introducing the wireless sensor network architecture and Zigbee related knowledge, secondly, the wireless sensor network topology control is studied, the LEACH algorithm and the HEED algorithm are deeply studied, and the load balancing topology control algorithm (LHTCA algorithm) is designed. The statistical calculation results show that the average private packet rate of 20 nodes reaches 26.9%. The algorithm balances the energy consumption of network nodes and helps to improve the network life cycle. Then, the wireless sensor network management system is designed, and the related software and hardware equipment for the system implementation is introduced. Finally, the load balancing topology algorithm designed by the application is applied to the deployment of the management system, and the teaching management system is tested and verified. The test results show that the system can collect and monitor the students' learning status and other related information in real time, so as to effectively realize the problem of combining theory and practice in teaching courses.Through the Internet teaching system, taking wireless data transmission and reception as the research object, after class through the teaching system to communicate with each other to discuss learning and practice experience, to stimulate students' desire to explore and innovate after class.


Wireless Sensor, Instructional Design, Network Topology, Network Management, LEACH Algorithm, HEED Algorithm

Cite This Paper

Zhitong Yan. Cultivation of Creative Ability in the Teaching Reform of Industrial Design Wireless Sensor. International Journal of Educational Innovation and Science (2020), Vol. 1, Issue 4: 47-67. https://doi.org/10.38007/IJEIS.2020.010405.


[1] Khan I, Belqasmi F, Glitho R, et al. Wireless Sensor Network Virtualization: A Survey. IEEE Communications Surveys & Tutorials. (2017) 18(1): 553-576. https://doi.org/10.1109/COMST.2015.2412971

[2] Sheng Z, Mahapatra C, Leung V, et al. Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks. IEEE Transactions on Cloud Computing. (2018) 6(99): 114-126. https://doi.org/10.1109/TCC.2015.2458272

[3] Jian S, Song L, Liu L, et al. Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine. Chinese Journal of Electronics. (2017) 26(2): 377-384. https://doi.org/10.1049/cje.2017.01.013

[4] Long J, Dong M, Ota K, et al. Achieving Source Location Privacy and Network Lifetime Maximization Through Tree-Based Diversionary Routing in Wireless Sensor Networks. IEEE Access. (2017) 2(2): 633-651. https://doi.org/10.1109/ACCESS.2014.2332817

[5] Shim K A. A Survey of Public-Key Cryptographic Primitives in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials. (2017) 18(1): 577-601. https://doi.org/10.1109/COMST.2015.2459691

[6] Lee J, Lim C, Kim H. Development of an instructional design model for flipped learning in higher education.  Educational Technology Research & Development. (2017) 65(2): 427-453. https://doi.org/10.1007/s11423-016-9502-1

[7] Nikhilesh S, Richa G, Mahalakshmi V N. Multistation exercises: a combination of problem-based learning and team-based learning instructional design for large-enrollment classes. AJP Advances in Physiology Education. (2018) 42(3): 424-428. https://doi.org/10.1152/advan.00023.2018

[8] Song W, Chen H, Zhang Q, et al. On-Chip Embedded Debugging System Based on Leach Algorithm Parameter on Detection of Wireless Sensor Networks. Mathematical Problems in Engineering. (2020) 2020(93): 1-7. https://doi.org/10.1155/2020/7249674

[9] Tang C. A clustering algorithm based on nonuniform partition for WSNs. Open Physics. (2020) 18(1): 1154-1160. https://doi.org/10.1515/phys-2020-0192

[10] L Séguin-Charbonneau, Walter J, LD Théroux, et al. Automated Defect Detection for Ultrasonic Inspection of CFRP Aircraft Components. NDT & E International. (2021) 1: 102478. https://doi.org/10.1016/j.ndteint.2021.102478

[11] Boselin P, Sakkthi V, Babu A, et al. Mobility Assisted Dynamic Routing for Mobile Wireless Sensor Networks. Social Science Electronic Publishing. (2017) 3(1): 9-19. https://doi.org/10.5121/ijait.2013.3102

[12] Limin, Shen, Jianfeng, et al. A Secure and Efficient ID-Based Aggregate Signature Scheme for Wireless Sensor Networks. IEEE Internet of Things Journal. (2017) 4(2): 546-554. https://doi.org/10.1109/JIOT.2016.2557487

[13] Kurt S, Tavli B. Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations. IEEE Antennas & Propagation Magazine. (2017) 59(1): 18-37. https://doi.org/10.1109/MAP.2016.2630035

[14] Wang J, Cao J, Ji S, et al. Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks. Journal of Supercomputing. (2017) 73(7): 1-14. https://doi.org/10.1007/s11227-016-1947-9

[15] Nurellari E, Mclernon D, Ghogho M. Distributed Two-Step Quantized Fusion Rules Via Consensus Algorithm for Distributed Detection in Wireless Sensor Networks. IEEE Transactions on Signal & Information Processing Over Networks. (2017) 2(3): 321-335. https://doi.org/10.1109/TSIPN.2016.2549743

[16] Sharma K P, Sharma T P. rDFD: reactive distributed fault detection in wireless sensor networks. Wireless Networks. (2017) 23(4): 1145-1160. https://doi.org/10.1007/s11276-016-1207-1

[17] Tomic I, Mccann J A. A Survey of Potential Security Issues in Existing Wireless Sensor Network Protocols. IEEE Internet of Things Journal. (2017) 4(6): 1910-1923. https://doi.org/10.1109/JIOT.2017.2749883

[18] Zhao J, Yagan O, Gligor V D. Topological Properties of Wireless Sensor Networks Under the Q-Composite Key Predistribution Scheme With Unreliable Links (CMU-CyLab-14-002). IEEE/ACM Transactions on Networking. (2017) 25(3): 1789-1802. https://doi.org/10.1109/TNET.2017.2653109

[19] Debattista M. A comprehensive rubric for instructional design in e-learning. Campus-Wide Information Systems. (2018) 35(2): 93-104. https://doi.org/10.1108/IJILT-09-2017-0092

[20] Baten E, Praet M, Desoete A. The relevance and efficacy of metacognition for instructional design in the domain of mathematics. ZDM. (2017) 49(4): 613-623. https://doi.org/10.1007/s11858-017-0851-y

[21] Costley J, Lange C. The mediating effects of germane cognitive load on the relationship between instructional design and students' future behavioral intention. Electronic Journal of e-Learning. (2017) 15(2): 174-187.

[22] Rezaei E, Zavaraki E Z, Hatami J, et al. The effect of MOOCs instructional design model-based on students' learning and motivation. Man in India. (2017) 97(11): 115-126.

[23] Varaprasad V. Improving the Network Life Time of Wireless Sensor Network using MAODV Protocol with LEACH Algorithm. International Journal of Computer Sciences and Engineering. (2018) 6(16): 534-538. https://doi.org/10.26438/ijcse/v6i6.534538

[24] Mounika K, Rambabu C, Prasad V. Improving the Network Life Time of Wireless Sensor Network using MAODV Protocol with LEACH Algorithm. International Journal of Computer Sciences and Engineering. (2018) 6(6): 534-538. https://doi.org/10.26438/ijcse/v6i6.534538

[25] Mascaraque N, Bauchy M, Smedskjaer M M. Correlating the Network Topology of Oxide Glasses with their Chemical Durability. Journal of Physical Chemistry B. (2017) 121(5): 1139-1147. https://doi.org/10.1021/acs.jpcb.6b11371