Research on Data Release and Location Monitoring Technology of Sensor Network Based on Internet of Things


  • Bin Lin School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China



Internet of things sensor network, data location monitoring technology, WSNs location algorithm, Huffman coding, industrial systems monitor the Internet of Things


The Internet of Things is another information technology revolution and industrial wave after computer, Internet and mobile communication. It is becoming a key foundation and an important engine for the green, intelligent and sustainable development of economic society. The new networked intelligent production mode characterized by the integration innovation of the Internet of Things is shaping the core competitiveness of the future manufacturing industry. The application of sensor network data positioning and monitoring technology based on the Internet of Things in industry, power and other industries is a hot field for the development of the Internet of Things. Sensor network processing and industrial applications are becoming increasingly complex, and new features have appeared in the sensor network scale and infrastructure in these fields. Therefore, the Internet of Things perception data processing has become a research hotspot in the deep integration process between industry and the Internet of Things. This paper deeply analyzes and summarizes the characteristics of sensor network perception data under the new trend of the Internet of Things as well as the research on location monitoring technology, and makes in-depth exploration from the release and location monitoring of sensor network perception data of the Internet of Things. Sensor network technology integrated sensor technology, micro-electromechanical system technology, wireless communication technology, embedded computing technology and distributed information processing technology in one, with easy layout, easy control, low power consumption, flexible communication, low cost and other characteristics. Therefore, based on the release and location monitoring technologies of sensor network data based on the Internet of Things in different applications, this paper studies the corresponding networking technologies, energy management, data management and fusion methods. Standardization system in wireless sensor network low cost, and convenient data management needs, design the iot oriented middleware, and develops the software and hardware system, the application demonstration, the results show that the design of wireless sensor network based on iot data monitoring and positioning technology is better meet the application requirements, fine convenient integration of software and hardware, and standardized requirements and suitable for promotion.


Download data is not yet available.

Author Biography

Bin Lin, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Bin Lin received his bachelor’s degree in energy and power engineering from University of Shanghai for Science and Technology in China. He is currently pursuing his master’s degree in computer science and engineering with the School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, China. His current research interests include Internet of Things, wireless sensor networks and big data analysis.


Y. Zhang, L. Sun, J. Liu, et al., “Design and implementation of intelligent art lighting systems based on wireless sensor network technology,” in 2011 Chinese Control and Decision Conference (CCDC), Mianyang, China, 2011, pp. 3765–3770.

B. Koo, H. Choi, and T. Shon, “WIVA: WSN monitoring framework based on 3D visualization and augmented reality in mobile devices,” in International Conference Smart Homes and Health Tlematics, Tours, France, 2009, pp. 158–165.

W. C. Chu, and K. F. Ssu, “Location-free boundary detection in mobile wireless sensor networks with a distributed approach,” Computer Networks, vol. 70, pp. 96–112, Sep. 2014.

W. Wei, Y. Zhang, F. Zhang, et al., “Research on Navigation Marks-Monitored Network System Based on WSN,” in Proceedings of the 2009 Asia-Pacific Conference on Information Processing, Shenzhen, China, 2009, pp. 521–524.

R. R. Swain, P. M. Khilar, and T. Dash, “Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 593–610, Feb. 2019.

Z. Zhang, and Y. Zhang, “Application of wireless sensor network in dynamic linkage video surveillance system based on Kalman filtering algorithm,” The Journal of Supercomputing, vol. 75, pp. 6055–6069, Sep. 2019.

M. Bouaziz, A. Rachedi, and A. Belghith, “EKF-MRPL: Advanced mobility support routing protocol for internet of mobile things: Movement prediction approach,” Future Generation Computer Systems, vol. 93, pp. 822–832, Apr. 2019.

H. Lin, X. Liu, X. Wang, et al., “A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks,” Sustainable Computing: Informatics and Systems, vol. 18, pp. 101–111, Jun. 2018.

R. K. Dwivedi, A. K. Rai, R. Kumar, “Outlier detection in wireless sensor networks using machine learning techniques: A survey,” in International Conference in Electrical and Electronics Engineering (ICE3), Gorakhpur, India, 2020, pp. 316–321.

K. Thangaramya, K. Kulothungan, R. Logambigai, et al., “Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Computer Networks, vol. 151, pp. 211–223, Mar. 2019.

S. K. Singh, and P. Kumar, “A comprehensive survey on trajectory schemes for data collection using mobile elements in WSNs,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 291–312, Jan. 2020.

M. Safaei, A. S. Ismail, H. Chizari, et al., “Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach,” Journal of Software: Practice and Experience, vol. 50, no. 4, pp. 428–446, Apr. 2020.

Z. Zhang, J. Zhou, S. Tang, et al., “Computing minimum k-connected m-fold dominating set in general graphs,” INFORMS Journal on Computing, vol. 30, no. 2, pp. 217–420, Mar. 2018.

M. Hussain, J. Ren, and A. Akram, “Classification of DoS attacks in wireless sensor network with artificial neural network,” International Journal of Network Security, vol. 22, no. 3, pp. 542–549, May 2020.

H. Radhappa, L. Pan, J. X. Zheng, et al., “Practical overview of security issues in wireless sensor network applications,” International Journal of Computers and Applications, vol. 40, no. 4, pp. 202–213, 2017.

J. Fan, T. Liang, T. Wang, et al., “Identification and localization of the jammer in wireless sensor networks,” The Computer Journal, vol. 62, no. 10, pp. 1515–1527, Oct. 2019.

S. Sivamani, J. Choi, K. Bae, et al., “A smart service model in greenhouse environment using event-based security based on wireless sensor network,” Concurrency and Computation Practice and Experience, vol. 30, no. 2, e4240, Jan. 2018.

V. Casares-Giner, T. I. Navas, D. S. Flórez, et al., “End to end delay and energy consumption in a two tier cluster hierarchical wireless sensor networks,” Information, vol. 10, no. 4, 135, 2019.

V. Savangsuk, “Attacks on wireless sensor networks: review,” International Journal of Computational Intelligence Research, vol. 14, no. 7, pp. 607–617, 2018.

S. Diwakaran, B. Perumal, and K. Vimala Devi, “A cluster prediction model-based data collection for energy efficient wireless sensor network,” The Journal of Supercomputing, vol. 75, pp. 3302–3316, Jun. 2019.

S. Kumar, N. Lal, V. K. and Chaurasiya, “An energy efficient IPv6 packet delivery scheme for industrial IoT over G.9959 protocol based Wireless Sensor Network (WSN),” Computer Networks, vol. 154, pp. 79–87, May 2019.

J. Cao, X. Zhang, C. Zhang, et al., “Improved convolutional neural network combined with rough set theory for data aggregation algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 647–654, Oct. 2018.

X. Yu, L. Zhou, and X. Li, “A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization,” Computer Networks, vol. 154, pp. 73–78, May 2019.

V. Mythili, A. Suresh, M. M. Devasagayam, et al., “Seat-dsr: spatial and energy aware trusted dynamic distance source routing algorithm for secure data communications in wireless sensor networks,” Cognitive Systems Research, vol. 58, pp. 143–155, Dec. 2019.

R. Suguna, V. Rathinasabapathy, “An SoC architecture for energy detection based spectrum sensing using Low Latency Column Bit Compressed (LLCBC) MAC in cognitive radio wireless sensor networks,” vol. 69, pp. 159–167, Sep. 2019.