University Library, Jilin Agricultural University, No.2888 Xincheng Street, Changchun 130118, Jilin, China
With the rapid development of microelectronic technology and communication technology, the traditional computer-based computing model is gradually transformed into a human-centered universal computing model. This led to the idea of the Internet of things, which allows people to access information about their surroundings on demand through different terminals. Library is an indispensable part of human life. How to provide a more comfortable library environment to better meet people's family requirements is a place where the Internet of things plays its role. The purpose of this paper is to solve the difference between the data fusion of library environment and the data fusion of other environments by the method of data fusion oriented to library, objectively reflect the change of library environment, and consider the influence of people in the environment, which also puts forward some new problems for the data fusion. In this paper, the process of data fusion middleware from obtaining data to informing the upper module of the changed family environment after deploying the smart library system in a library includes data collection and processing, how to judge whether events are triggered, how the system reacts, and the acquisition and update of user preferences. In the end, this paper presents a general technical framework of situational awareness for smart library system. This paper presents an effective data fusion model and algorithm for library.
Smart Library, Internet of Things, Situational Awareness, Information Fusion, Data Fusion
Hui Wang. Situational Perception Information Fusion Technology of Internet of Things for Smart Library. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 9: 16-29. https://doi.org/10.25236/IJFET.2021.030904.
 Ruslan Aleksandrovich Baryshev, Sergey Vladimirovich Verkhovets, Olga Ivanovna Babina. The smart library project: Development of information and library services for educational and scientific activity [J]. Electronic Library, 2018, 36(10). 81-94.
 Nícolas A.R.L. Netto, Carmen L.T. Borges. Enhancing the situational awareness of voltage security region via probabilistic reliability evaluation [J]. International Transactions on Electrical Energy Systems, 2019(5):663-674.
 Todd Martin, K.-C. Chang, Xin Tian. A probabilistic situational awareness and reasoning methodology for satellite communications resource management [J]. IEEE Aerospace Conference Proceedings, 2015, 2015(55):33-36.
 Alicia Kissinger-Knox, Patrick Aragon, Moti Mizrahi. Does Non-Moral Ignorance Exculpate? Situational Awareness and Attributions of Blame and Forgiveness [J]. Acta Analytica, 2018, 33(1):161-179.
 Pogrebnyakov N, Maldonado E. Didn’t roger that: Social media message complexity and situational awareness of emergency responders [J]. 2018, 40(23):166-174.
 Campbell A A, Harlan T, Campbell M. Using Real-Time Data to Warn Nurses of Medication Administration Errors Using a Nurse Situational Awareness Dashboard [J]. 2018, 250(48):140.
 Gaohui Cao, Mengli Liang, Xuguang Li. How to make the library smart? The conceptualization of the smart library [J]. The Electronic Library, 2018, 36(8):210-234.
 Wang Jialing. Readers’ Privacy Protection Technology under the Smart Library Mode [J]. Libraly Journal, 2017, 36(9). 90-112.
 Jongil Lim, Christopher J. Palmer, Michael A. Busa. Additional helmet and pack loading reduce situational awareness during the establishment of marksmanship posture [J]. Ergonomics, 2017, 60(6): 824-836.
 Yan Li, Guang-qiu Huang, Chun-zi Wang. Analysis framework of network security situational awareness and comparison of implementation methods [J]. EURASIP Journal on Wireless Communications and Networking, 2019, 2019(1):78-82.
 Elizabeth D. Rosenman, Aurora J. Dixon, Jessica M. Webb. A Simulation-based Approach to Measuring Team Situational Awareness in Emergency Medicine: A Multicenter, Observational Study [J]. Academic Emergency Medicine Official Journal of the Society for Academic Emergency Medicine, 2017, 25(2):61-75.
 Russell L, Goubran R, Kwamena F, et al. Agile IoT for Critical Infrastructure Resilience: Cross-modal Sensing as Part of a Situational Awareness Approach[J]. 2018, PP (99):1-1.
 Thays R. Gonçalves, Larissa N. Rosa, Alex S. Torquato. Assessment of Brazilian Monovarietal Olive Oil in Two Different Package Systems by Using Data Fusion and Chemometrics [J]. Food Analytical Methods, 2019, 90(683): 771-780.
 Zheng WANG, Zhihua MAO, Junshi XIA. Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea [J]. Frontiers of Earth Science, 2018, 12(2):156.
 Mohammad Al-Sharman, Bara J. Emran, Mohammad A. Jaradat. Precision Landing Using an Adaptive Fuzzy Multi-Sensor Data Fusion Architecture [J]. Applied Soft Computing, 2018, 69(45):412-436.
 LuMing Qi, Jieqing Li, Honggao Liu. An additional data fusion strategy for the discrimination of porcini mushrooms from different species and origins in combination with four mathematical algorithms [J]. Food & Function, 2018, 9(11):78.
 Xi D, Li L, Zhang J, et al. Improvement of Mammographic Mass Classification Performance Using an Intelligent Data Fusion Method[J]. 2018, 8(2):275-283.
 Zhao T, Fu J, Wang Y, et al. An underwater measurement and control network centralized data fusion localization algorithm based on Chan-algorithm method [J]. 2018, 13(14):142.
 Ambra R. Di Rosa, Francesco Leone, Carmelo Scattareggia. Botanical origin identification of Sicilian honeys based on artificial senses and multi-sensor data fusion [J]. European Food Research & Technology, 2018, 244(2): 1-9.
 Aida Makni, Alain Y. Kibangou, Hassen Fourati. Data Fusion-Based Descriptor Approach for Attitude Estimation underaccelerated maneuvers [J]. Asian Journal of Control, 2019, 7(4):45.