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Research ArticleDesign of Smart Classroom System Based on Internet of ThingsTechnology and Smart Classroom

Mingbao Zhang and Xiang Li

School of Digital Arts and Design Dalian Neusoft University of Information Dalian 116023 Liaoning China

Correspondence should be addressed to Mingbao Zhang zhangmingbaoneusofteducn

Received 18 May 2021 Revised 18 June 2021 Accepted 28 June 2021 Published 8 July 2021

Academic Editor Sang-Bing Tsai

Copyright copy 2021 Mingbao Zhang and Xiang Li is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

Smart classroom teaching is one of the new teaching methods With the support of technology teaching is carried out with thehelp of smart teaching tools to enhance teacher-student communication enhance studentsrsquo learning autonomy and provide newideas for the realization of studentsrsquo deep learning How to promote the overall intelligence of the teaching environment so that theteaching equipment can be used more efficiently and managed more effectively has become the main concern of schools isarticle mainly studies the smart classroom system based on the Internet of ings technology and smart classroom Fortemperature detection we mainly use the DS18B20 chip to detect the temperature in the classroom For the light intensity of theclassroom we use a photoresistor to collect the light data and after amplification by the amplifier the AD sampling process of thesingle-chipmicrocomputer is used to obtain the light intensity combined with the clockmodule to distinguish the influence of theclassroom light e data collection adopts the method of directly observing the source data and the data format has notundergone secondary conversion which ensures the accuracy of the source data is test uses the USR-TCP232 networkdebugging assistant to debug the data collection To optimize the safety and reliability of the system dual-computer backupswitching is adopted on the hardware and process monitoring and management strategies are adopted on the software At thesame time the amount of data interaction in the smart classroom is relatively large so it is necessary to build a highly availablecluster server so that the system not only has a certain degree of stability but also can quickly respond to usersrsquo access requestsWecan calculate that the average transmission time is about 10ms and 999 of the data transmission delay is less than 30ms eresults show that the Internet of ings and smart classroom provide great convenience for future smart campus constructiondaily teaching and campus management and can also provide reference for the construction of smart classrooms inother universities

1 Introduction

e continuous development of new media technologypromotes the development of modern education technologyand at the same time puts forward higher requirements formodern education technology workers e continuousupdating of educational technology coupled with the im-provement of the functionality and operability of multi-media classrooms has created favorable conditions for theapplication of modern educational technology in collegesand universities e use of multimedia equipment to de-velop and serve modern teaching has become a commonphenomenon and then developed into a new teaching mode

Based on the concepts of smart equipment managementsafety management and energy-saving management in theclassroom this paper designs and develops an IoT cloudplatform for smart classrooms and realizes real-time man-agement of classroom environment information combinedwith the development of smart campus

With the development of information technology andthe wave of intelligent technology colleges and universitiesare exploring new teaching models to meet the needs anddevelopment of educationrough thinking and improvingteaching studentsrsquo subjective initiatives can be mobilizedand their creativity cultivated e Internet of ingstechnology is developing rapidly e use of Internet of

HindawiMobile Information SystemsVolume 2021 Article ID 5438878 9 pageshttpsdoiorg10115520215438878

ings technology to build smart classrooms is helpful forinformation sharing and analysis and has a significant im-pact on the way of learning knowledge Students majoring inthe Internet of ings computer and other related majorscan use the platform to learn wireless sensing technologyand embedded development technology and can also use theprovided interface to carry out the secondary developmentof the project

e environment created by the smart classroomstimulates studentsrsquo interest in learning thereby improvingthe quality of education and teaching Li believes that as theMinistry of Education vigorously promotes the constructionof smart campuses the development concept of smartcampuses will have broad application prospects Howevercolleges and universities are still at the stage of digitalcampuses and there are still many problems He designedand realized a complete intelligent campus managementsystem by analyzing the design principles and design goals ofthe system His system is mainly divided into face recog-nition terminal hardware based on the Internet of ingsand smart campus software system Although the usersatisfaction of the system he studied is relatively high thereare still certain shortcomings [1] Kim believes that thedegree of student participation refers to the degree to whichstudents are immersed in learning when receiving educationin the classroom He proposed an environmental intelli-gence algorithm for smart classrooms e algorithm pro-vided information to teachers by measuring studentparticipation in real time He proposed an algorithm forevaluating student participation by measuring thermal in-frared images to evaluate studentsrsquo mental state He pro-posed a measurement model that uses thermal infraredimaging to characterize student participation e color ofthe teacherrsquos mobile application will change in real timeaccording to the immersion level of the students in theclassroom Although the algorithm he proposed is inno-vative it lacks algorithm simulation [2] Tissenbaum andSlotta believe that although K-12 media has had a significantimpact on many other aspects of life peoplersquos classroomenvironment has not yet incorporated ubiquitous com-puting augmented reality and other emerging technologieseven touch screens He has carried out a series of design-based research projects to investigate the smart classroominfrastructure which provides support for students andteachers in a new form of collaboration and inquiry in-cluding the substantial role of large projection displays andsmall touch surfaces as well as the dependence of thestudentrsquos physical location in the room His design includes(1) the role of a large-screen display to transmit aggregateinformation and environmental information (2) real-timecommunication between students (3) application of intel-ligent software agents to formulate real-time teaching logic(4) support cross-context learning and (5) investigate thecoordination of roles materials and environment Althoughhis research has a certain role in promoting the developmentof smart classrooms it lacks specific experimental data [3]Lin believes that in recent years many organizations haveannounced the importance of software development to thecountry society and individuals In the process of software

development various unpredictable problems are oftenencountered especially when developing large and complexsoftware To reduce the possibility of these problems it isessential for students to apply software engineering tech-niques to scientifically define the standards models andprocesses required in the software development process Hisgoal is to apply an innovative teachingmethod called flippedclassroom to implement a learner-centered learning envi-ronment in software engineering courses In addition healso developed an intelligent learning diagnosis system tosupport the teaching of this course He conducted experi-ments on a software engineering course at a university inTaiwan to explore the effectiveness of the proposed methode experimental group adopts the flipped classroomteaching method and the control group adopts the tradi-tional classroom teaching method Although his research ismore accurate it is not innovative enough [4]

rough the Internet of ings application platform forteachers and students to build a learning experience plat-form with strong credibility teachers and student users canintegrate into all aspects of the Internet ofings applicationplatform through actual operation and management whichfacilitates the high-speed flow and sharing of informationresources To make full use of the Internet of ings ap-plication technology to enhance the information manage-ment of the university classrooms based on the professionalcharacteristics to promote the integration of theory andapplication practice of diversified universities through theInternet of ings application platform to provide teachersand students with a strong credibility of learning experienceteachers and students can integrate into all aspects of theInternet of ings application platform through the actualoperation and management

2 Smart Classroom System Design

21 Internet of ings With the rapid development of theInternet of ings a large amount of unstructured data willbe generatedese data are growing exponentiallye dataare complex and polymorphic and there is no obviouscorrelation between them However the traditional dataacquisition analysis storage and processing technologiescan not meet the needs of the rapid development of society[5 6] erefore if we can focus on the following keytechnologies in data processing of the Internet of ingsaccording to the characteristics of data massive data storagedata fusion data query search and mining intelligent de-cision making etc it will play a technical support andpromotion role in information construction smart cityindustrial manufacturing smart agriculture commerce fi-nance transportation and other fields [7 8]

Assuming that in the regression of the function curvethe weight coefficient A the standard deviation S the mobileapplication behavior variable x and the linear change var-iable y ensure a high degree of fit between the function curveand the sample point data it is necessary to establish analternative relationship expression between x and y based onthe regression equation formula e calculated residual SScan be used to determine whether the fitting is a composite

2 Mobile Information Systems

data mining analysis requirement and the expression is asfollows [9]

A

1 minus1113936

ni1 xi minus yi( 1113857

1113936ni1 yi minus yiminus1( 1113857

1113971

(1)

S

1113936

ni1 yi minus yiminus1( 1113857

n minusn

radic

1113971

(2)

SS

1113936

ni1 Ai minus Si

11138681113868111386811138681113868111386811138681113868

n minusn

radic

1113971

(3)

e relationship between the conversion delay of thecurve regression sampling point and the perception accuracyis as follows

DT 1113944

n

i1|d|

2δ(ω minus ϖ) (4)

Among them DT represents the conversion delay and drepresents the perception accuracy [10]

e errors of linear data mining are as follows

dif 1113936

nmij xijP ximinus1jminus11113872 1113873

1113936nmij xij

(5)

Among them i represents the user scale j represents thenumber of data mining polls and the function P is used forcurve fitting [11]

e similarity between the user demand conclusion oflinear data mining and the actual demand is as follows

SL 1113936

nmij xijP xij1113872 1113873 minus dif

1113936nmij

P xij1113872 1113873

1113969 (6)

e characteristics of the Internet of ings technologyare to give objects to perceive the environment and tocommunicate with each other and have simple intelligenceso for the architecture design the Internet ofings needs tosupport the above three main features Generally speakingthe technical architecture of the intelligent Internet ofingscan be divided into three levels We can call it the controllayer or perception layer the transmission layer or networklayer and the application layer or service layer [12 13] Afterthe IoT terminal device perceives the surrounding envi-ronment data these data need to be transmitted ereforethe support of the network layer is required rough theconnection of the network layer objects can be connected inseries to form a mesh structure In addition to transmissionthe confidentiality and correctness of data transmissionmust be ensured while stability and continuity are requirede higher-level requirement is to occupy less bandwidthand the transmission process needs to occupy less energyconsumption [14 15]

Zigbee wireless network channel distribution is shown inTable 1 Different types of network can be used to transmitdifferent types of data to achieve the optimal allocation ofnetwork resources and the perfect integration of sensor

network and current network and to realize fast stableaccurate safe and reliable transmission of data On theterminal various functional requirements can be realizedaccording to data characteristics and user needs which canbe realized through application design and development[16]

22 Smart Classroom System e system structure of thesmart classroom system is shown in Figure 1 Pad mobileplatform allows users to register and log in and realize thecollection and upload of basic face information throughreceiving the instructions from PC workstation the collectionand preprocessing of face attendance information are com-pleted the submission of face attendance information iscompleted the online examination teaching evaluationelectronic whiteboard display and other functions are real-ized and the results of teaching evaluation information andperformance information can be queried in real time [17]

e equipment control system in the classroom uses PIDcontrol related technology to set the target temperaturevalue realize automatic temperature control and adjust theset value of stable operation in the target area e rela-tionship between the three PID algorithms is [18]

PIDout Pout + Iout + Dout (7)

In the formula PIDout represents the total output valuePout represents the current deviation value Iout representsthe historical deviation value and Dout represents the latestdeviation value [19]

Suppose that N sensors are used to perform featuredetection with a certain performance index of the targetobject independently of each other and the obtained data issorted in ascending order to obtain a set of detection se-quences T1 T2 Tnminus1 Tn where T1 is the lower limit of thedetection sequence and Tn is the upper limit of the detectionsequence [20] Define the median TM as

TM T(n2) + T(n2)+1

2 (8)

According to the extreme value theory of multivariatefunctions the minimum condition of the total mean squareerror σ2 is obtained e weight corresponding to eachsensor is Wi(i 1 2 n) When the variance is smaller thecorresponding weight evaluation factor is larger [21] eweighting factor Wi of each sensor corresponding to theminimum total mean square error σ2min is [22]

σ2min 1

1113936ni1 1σ2i1113872 1113873

(9)

Wi 1

σ2i 1113936nk1 1σ2k1113872 1113873

(i 1 2 n) (10)

23 Smart Classroom Any education ecosystem is insepa-rable from the social environmente material flow energyflow and information flow in the education ecosystem will

Mobile Information Systems 3

eventually be transformed into social benefits and serve thesociety Whether it is the cultivation of talent or techno-logical inventions and creations they will eventually appearin the society e society has been developed and pro-gressed and the economic benefits produced are invested ineducation to promote the redevelopment of education andform a virtuous circle of sustainable development [23]Education bears the responsibility of historical and culturalheritage Any education ecosystem not only presents thefunction of serving people and society but also containsmore or less cultural details For example in a universitycultural heritage is more important than teaching and ed-ucating people to a certain extent and it is the spiritual forceto support its successful long-term development [24]

Before the class learn to inspire students to learn in-dependently e class is based on cloud technology greatwisdom and other information technologies and the tabletis used to combine the classroom with resources to providestudents with rich learning resources and to use big data andother technologies for students e learning process andlearning feedback are tracked and evaluated in real time [25]ldquoSmart classroomrdquo integrates self-learning before classanswering questions and key explanations in the class andfeedback services after class through self-learning plansmicrovideos teaching resources homework assessmentsetc ldquoSmart classroomrdquo makes teaching more and moreintelligent makes the education environment more andmore networked and makes studentsrsquo learning more andmore personalized so that students can adapt to theinformationized social environment and promote the de-velopment of studentsrsquo wisdom [26]

e basic teaching process based on the smart classroomis shown in Figure 2 Before class the teacher analyzes thestudentsrsquo homework makes clear the studentsrsquo existingknowledge and experience and combines the characteristics

of the studentsrsquo age and physical and mental development topreliminarily determine the teaching objectives Accordingto the learning situation textbook analysis teaching diffi-culties and problems encountered by students in the pre-view teachers determine the teaching design scheme [27]Students show their own preview results and put forwardtheir own confusion In the process of inquiry teachersshould give corresponding guidance e teacher sends thehomework to the student client and the students submit it tothe teacher after completing the homework e teachercorrects the homework in time gives feedback to the stu-dents and answers questions online through the tabletStudents summarize what they have learned and uploadtheir feelings or questions after class to the learning plat-form and teachers organize students to discuss and ex-change as an expanding supplement Teachers push networkresources to enable students to expand their learningaccording to their own situation [28 29]

3 Smart Classroom SystemSimulation Experiment

31 Test Environment of the System e test environment ofthe smart classroom teaching system mainly includes thehardware environment and related operating system and thedatabase of the Internet of ings engineering experimentplatform constructed with the optical wireless switch and itsdistributed wireless system as the core supplemented by theintelligent access gateway and its distributed wired systeme software environment is composed of two parts and thespecific test environment configuration is shown in Table 2

32 Environmental Intelligence Perception For temperaturedetection we mainly use the DS18B20 chip to detect the

Table 1 Zigbee wireless network channel distribution

Number of frequencybands

Channel frequencyspacing

Transfer speed(kbps)

Modulationmode

Maximumfrequency

Minimumfrequency

1 0 20 B0SK 8682MHz 868MHz10 2 40 HPSK 928MHz 902MHz16 5 250 QPSK 24835MHz 2400MHz

Pad

Pad Wirelessrouter

Wirelessrouter

PC workstation Pad

Pad

PadWifi Wifi

Figure 1 e system structure of the smart classroom system


temperature in the classroom For the light intensity of theclassroom we use a photoresistor to collect the light dataand after amplification by the amplifier the AD samplingprocess of the single-chip microcomputer is used to ob-tain the light intensity combined with the clock module todistinguish the influence of the classroom light In theclassrooms of colleges and universities most of theparticipants in the activities are adults who emit heat andhumidity to the outside world e effective monitoringrange is set to 0ndash100RH and the resolution is 25RH Atthe same time the fault self-check and alarm are carriedout according to the electrical and physical characteristicsof the equipment In order not to affect the indoor ac-tivities or the teaching process the alarm only occurs onthe server side or the userrsquos mobile terminal and does notsupport the on-site whistle alarm to facilitate themaintenance and safety of personnel after failure andsupport human body static electricity or leakageprotection

33 Data Acquisition Test e test adopts the method ofobserving the source data directly and the data format is notconverted twice which ensures the accuracy of the sourcedata e data in the data frame is decoded by relevantsoftware and the decoded data is printed on the PC screenby means of printf reset is test uses a USR-TCP232network debugging assistant to debug the data acquisitionrough the control function of the collected data we cansee that the prototype system of the smart classroom has

realized the closed-loop functions such as data acquisitionoutput and data processing and equipment control andrealized the automatic control e tester can control theoperation of the equipment in real time and the testfunction can completely realize the management of thesmart classroom save labor cost and improve safety indexand equipment utilization

34 Equipment Out-of-Control Alarm Test After receivingthe control instruction the control system node starts theregulation of the device When the device starts to work itreturns information to the host computer managementsystememain page device control module will receive thereturn value of the control device indicating that the deviceis in accordance with the configuration if you do not receivethe returned task signal and the system defaults to the devicefailure or communication failure then the system will sendthe control command again If the execution controlcommand is sent 10 times in a row the system still has noreturn signal en the system will send the data reported tothe mobile phone information reserved by the administratorto report the loss of control of the equipment in theclassroom is enables the administrator to grasp thecontrol and operating status of the equipment in the smartclassroom for the first time and to grasp and troubleshootthe faults in time

35 Performance Test To optimize the safety and reliabilityof the system dual-computer backup switching is adoptedon the hardware and process monitoring and managementstrategies are adopted on the software e smart classroomteaching system has relatively high requirements for thestable operation of the system and the data in the systemrequires long-term storage characteristics and no accidentsare allowed Once data is lost or the system is paralyzed itwill bring immeasurable consequences to the school At thesame time the amount of data interaction in the smartclassroom is relatively large so it is necessary to build ahighly available cluster server so that the system not only hasa certain degree of stability but also can quickly respond tousersrsquo access requests

Study guide

Specific task

Perplexity

Microvideo

Supportingresources

Before class

Answeringquestions

Typicaltasks

Individualguidance

Organizingdiscussion

Independentinquiry

Individualcollective guidance

Cooperativelearning

Sorting out theachievements

Publish share

In class After class

Study task list Supportingresources

Learningfeedback

Achievementdisplay

Feedbackevaluation

Figure 2 e basic teaching process is based on a smart classroom

Table 2 Test environment configuration

Name System and versionWeb application server Cent OS 60Web application language JavaWeb server middleware Apache + Jboss + Jdk15Database server My SQLFront communication server UNIXFlow media services Windows Media Services 9Workstation Windowsreg7 Professional 32-bitOptical carrier wireless switch WCS2410C


36 Wisdom Evaluation is study uses the intelligentevaluation method to evaluate the quality of teaching andlearning In the intelligent evaluation the intelligentteaching tool rain classroom collects the data of studentsrsquolearning process or learning results to realize the compre-hensive evaluation of students e big data provides animportant support for the evaluation of smart classroomteaching According to various data sources modularanalysis technology is used to form visual analysis resultse former teaching evaluation is a single evaluation byteachers In the intelligent evaluation teachers are the mainbody of studentsrsquo evaluation students are the objects beingevaluated and the main body of evaluation and the evalu-ation of teaching and learning quality

4 Results and Discussion

e actual space layout of the smart classroom is shown inFigure 3 With the support of cloud desktop mobile In-ternet and other technologies it has formed a smartclassroom cloud desktop software a smart classroom clouddesktop management system a smart classroom clouddesktop computing module a smart classroom clouddesktop storage module and a live lesson anchor system (theteaching environment background system (teaching plat-form) and the smart classroom cloud desktop terminal) Onthe equipment learners and teachers clearly stated that thecurrent hardware equipment needs to be upgraded andreformed and they believe that the number location sizeand clarity of the display screens need to be reconsidered themutual interaction of the screens and the support forhandheld devices and audio systems should be ldquosilentrdquo andcan support the interaction between teachers and studentse quality of the network continues to be strengthened andthe diversity of the physical environment needs to bestrengthened In addition it is hoped that a lighter or greenversion of learning tools can be provided and the type andquantity of various terminal equipment consider it

e benefits of the smart classroommodel to students in allaspects are shown in Table 3 It can be seen from the table that769 of the students think this teachingmethod is very helpfulto improve their interest in learning and 231 of the studentsthink it is helpful which fully shows that everyone has fullyrecognized that this teachingmethod can improve their interestin learning In terms of improving intercultural communica-tion ability 615 of the students think it is very helpful 231think it is helpful and 154hold a neutral attitudeMost of thestudents think this teaching method is helpful for them toimprove their intercultural communication ability

e smart classroom teaching system uses more teachersand students so when the maximum number of users isonline the average response time of system transactions isrequired to be less than 5 seconds the maximum responsetime is less than 10 seconds and the response transactionsuccess rate is higher than 98 and the CPU is occupiede rate should be less than 80 and the memory usageshould be less than 80 e specific test data is shown inTable 4When the load of the number of online users reaches500 the average response time of the system is 365 seconds

and the maximum response time is 511 seconds which is inline with the expected value e CPU and memory occu-pancy rate can also ensure the stability of the system

To evaluate the effectiveness of the smart classroommodel this article selects 100 high school students in parallelclasses to ensure that they have no obvious differences in allaspects except for the different teaching models ecomparison result of classroom teaching structure is shownin Figure 4 In this experimental study the interactive be-havior of the experimental class with students as the mainbody accounted for 50 which was higher than the pro-portion of the interactive behavior with the teacher as themain body (467) the interactive behavior of the controlclass with the student as the main body accounted for 406lower than the proportion of interactive behavior withteachers as the main body (579) Compared with the twoclasses the proportion of interactive behavior with studentsas the main body in the experimental class is about 10higher than that in the control class and the proportion ofinteractive behavior with teachers as the main body is about11 lower than that in the control class

e experimental environment chosen in this paper is anindoor test environment with a distance of five metersbetween the terminal node and the coordinator and there isno obstruction in the line of sight Operate according to theabove process and observe the final result After power-onwe use an oscilloscope to observe the data transmissionwaveform diagram of the data transceiver interface efigure shows the level signal of the chip data pin during thedata transmission and reception process e high levelrepresents the number 1 the low level represents the number0 and the waveform diagram shows the chip can receive thedata sent e waveform diagram is shown in Figure 5According to the test results the coordinator successfullyobtained the data packets transmitted from the device ter-minal e content of the data packet was verified to provethat the content of the data packet was consistent before andafter the data transmission and no data transmission errorswere found According to the comparison of the differencebetween the sent data timestamp and the received datatimestamp in the data packet header we can calculate thatthe average transmission time is about 10ms and 999 ofthe data transmission delay is less than 30ms

Figure 3 e actual layout of the smart classroom (picture fromhttpalturlcomdxjfb)


e system write rate results are shown in Figure 6 Asthe distance increases the read and write rates are main-tained above 90 and the trend is generally stable In thetraditional teaching model teachers impart knowledge to

students through books and blackboards while students testtheir own learning effects through classrooms and examsbut this traditional model has a single and relatively boringway of imparting knowledge and skills in the classroometeaching effect is not good enough and the teacher cannotgrasp the studentsrsquo learning of knowledge and skills in timee smart classroom is fully adapted to the development ofthe Internet of ings era integrating network electronicsand practical teaching enriching teaching methods andrecording the various situations of teachers and students atschool through data and it is also convenient for teachers tounderstand the studentsrsquo mastery in a timely manner

5 Conclusions

Educational reforms rely increasing on educational equip-ment Only with the help of existing technology can nolonger meet the needs of teachers and students promptingresearchers in the field of education to actively and activelydevelop new technologies and research new educationalequipment New technologies and new equipment account

Table 4 System test data

Usernumber CPU() RAM() Average response time

(seconds)Maximum response time

(seconds)Transaction response success rate

()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate

Control groupTest group

Figure 4 Comparison results of classroom teaching structure

T

1

Figure 5 Waveform diagram (picture from httpalturlcomet6y6)

9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2

Group 3Write rate ()

Figure 6 System write rate results

Table 3 e benefits of the smart classroom model to students in all aspects

Survey item Very helpful Helpful General Not much help No help

Increase interest in learning 10 3 0 0 0769 231 00 00 00

Cultivate learning ability 6 4 2 1 0461 308 154 76 00

Improve classroom learning enthusiasm 8 4 1 0 0615 308 76 00 00

Improve cross-cultural communication skills 8 3 2 0 0Survey item 615 231 154 00 00


for an increasing proportion of smart classrooms whichhave become a powerful driving force for the development ofsmart classrooms is article tries to find the commonproblems in the application of teaching informatization byinvestigating the current status of education informatizationconstruction to start from the realistic requirements of theoperation of the teaching business process and the futureinnovative teaching business process from the perspective ofdevelopment needs the design and implementation of thesmart classroom teaching system are discussed and studiedBased on the prototype architecture implementationstrategy and software platform of the Internet of ingsapplication system this paper presents the implementationplan of the college smart classroom Internet of ingssystem from the aspects of system realization main tech-nology system function realization and system installationand debugging e combination of networking applicationtechnology and data mining technology through the ap-plication of software platforms hardware platform andintegrated technologies solves many information technol-ogy problems in classroom management student atten-dance equipment management and teaching activitymanagement in college education and teachingmanagement

Data Availability

No data were used to support this study

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

is work was supported by General program of Humanitiesand social sciences of Ministry of Education (No18YJCZH084) Key Projects of Dalian Academy of SocialSciences (No 2020dlsky193)

References

[1] W Li ldquoDesign of smart campusmanagement system based oninternet of things technologyrdquo Journal of Intelligent amp FuzzySystems vol 40 no 2 pp 3159ndash3168 2021

[2] P W Kim ldquoAmbient intelligence in a smart classroom forassessing studentsrsquo engagement levelsrdquo Journal of AmbientIntelligence and Humanized Computing vol 10 no 10pp 3847ndash3852 2019

[3] M Tissenbaum and J D Slotta ldquoDeveloping a smart class-room infrastructure to support real-time student collabora-tion and inquiry a 4-year design studyrdquo Instructional Sciencevol 47 no 1 pp 1ndash40 2019

[4] Y-T Lin ldquoImpacts of a flipped classroom with a smartlearning diagnosis system on studentsrsquo learning performanceperception and problem solving ability in a software engi-neering courserdquo Computers in Human Behavior vol 95pp 187ndash196 2019

[5] N Gnotthivongsa A K N HuangdongjunA Huangdongjun and K Non Alinsavath ldquoReal-time cor-responding and safety system to monitor home appliances

based on the internet of things technologyrdquo InternationalJournal of Modern Education and Computer Science vol 12no 2 pp 1ndash9 2020

[6] Z Lv X Li H Lv and W Xiu ldquoBIM big data storage inWebVRGISrdquo IEEE Transactions on Industrial Informaticsvol 16 no 4 pp 2566ndash2573 2019

[7] H Tao W Zhao R Liu and M Kadoch ldquoSpace-air-groundiot network and related key technologiesrdquo IEEE WirelessCommunications vol 27 no 2 2019

[8] R Atiqur ldquoAutomated smart car parking system for smartcities demand employs internet of things technologyrdquo In-ternational Journal of Informatics and CommunicationTechnology (IJ-ICT) vol 10 no 1 pp 46ndash53 2021

[9] X Bai Q Wang and S Cao ldquoApplication of infusion controlsystem based on internet of things technology in joint or-thopedics nursing workrdquo Journal of Healthcare Engineeringvol 2021 no 4 pp 1ndash11 2021

[10] L Chamba-Eras A Gomez and J Aguilar ldquoMulti-agentsystems for the management of resources and activities in asmart classroomrdquo IEEE Latin America Transactions vol 19no 9 pp 1511ndash1519 2021

[11] J Aguilar J Altamiranda and F Diaz ldquoSpecification of amanaging agent of emergent serious games for a smartclassroomrdquo IEEE Latin America Transactions vol 18 no 1pp 51ndash58 2020

[12] Y Huda and D Faiza ldquoDesain sistem pembelajaran jarak jauhberbasis smart classroom menggunakan layanan live videowebcastingrdquo Jurnal Teknologi Informasi Dan Pendidikanvol 12 no 1 pp 25ndash32 2019

[13] Z Lv and H Song ldquoMobile internet of things under dataphysical fusion technologyrdquo IEEE Internet of ings Journalvol 7 no 5 2019

[14] J Shu Z Min M Zhi and Q Hu ldquoResearch of the universityteaching interaction behavior characteristics in the smartclassroomrdquo International Journal of Information and Edu-cation Technology vol 8 no 11 pp 773ndash778 2018

[15] M Miraoui ldquoA context-aware smart classroom for enhancedlearning environmentrdquo International Journal on SmartSensing and Intelligent Systems vol 11 no 1 pp 1ndash8 2018

[16] S K Gupta T S Ashwin and R M R Guddeti ldquoStudentsrsquoaffective content analysis in smart classroom environmentusing deep learning techniquesrdquo Multimedia Tools and Ap-plications vol 78 no 18 pp 25321ndash25348 2019

[17] K A Dweikat and H A Hasan ldquoAttitudes of EFL teacherstowards using smartphones in the classroom during COVID-19 Pandemicrdquo Universal Journal of Educational Researchvol 9 no 1 pp 116ndash128 2021

[18] T Mailewa P Chandrasiri and D Chandrasena ldquoe impactof smart classrooms on the academic success of Sri Lankangovernment school studentsrdquo International Journal of Sci-entific amp Technology Research vol 9 no 12 pp 323ndash3332020

[19] M Djordjevic B Jovicic S Markovic V Paunovic andD Dankovic ldquoA smart data logger system based on sensor andInternet of ings technology as part of the smart facultyrdquoJournal of Ambient Intelligence and Smart Environmentsvol 12 no 1 pp 1ndash15 2020

[20] G P Revathi and S Supreetha ldquoSmart farmmonitoring usinginternet of things and lora technology based on wirelesssensor networksrdquo Journal of Computational and eoreticalNanoscience vol 17 no 9 pp 4136ndash4140 2020

[21] A Mohamed Q Peng and M M Abid ldquoIntegrated main-tenance logistics monitoring system for high speed rail based


on internet of things technologyrdquo European Trans-portTrasporti Europei vol 2020 no 75-6 pp 1ndash10 2020

[22] N Sun T Li G Song and H Xia ldquoNetwork securitytechnology of intelligent information terminal based onmobile internet of thingsrdquo Mobile Information Systemsvol 2021 no 8 pp 1ndash9 2021

[23] Z Ark ldquoe effect of ldquointernet of thingsrdquo technology in thedigital transformation process of businesses letmelerin dijitaldnuum surecinde ldquonesnelerin internetirdquo teknolojisinin etkisirdquoTurkish Studies - Economics Finance Politics vol 15 no 3pp 1247ndash1266 2020

[24] C Civelek ldquoEvaluation of internet of things (IoT) technologyto be used as a precision agriculture solution for Turkeyrsquosagriculturerdquo Fresenius Environmental Bulletin vol 29 no 7App 5689ndash5695 2020

[25] A Mohammadian J H Dahooie and A R QorbanildquoIdentifying and categorizing data analytics applications ofinternet of things (IoT) technology in the field of smart ag-riculture by using meta synthesis method (in Persian)rdquo SRELSJournal of Information Management vol 6 no 1 pp 1ndash232020

[26] S Xu J Chen M Wu and C Zhao ldquoE-commerce supplychain process optimization based on whole-process sharing ofinternet of things identification technologyrdquo ComputerModeling in Engineering amp Sciences vol 126 no 2pp 843ndash854 2021

[27] X Kong Y Xu Z Jiao D Dong X Yuan and S Li ldquoFaultlocation technology for power system based on informationabout the power internet of thingsrdquo IEEE Transactions onIndustrial Informatics vol 16 no 10 pp 6682ndash6692 2020

[28] L Zhang H Yuan and S H Chang ldquoResearch on the overallarchitecture of internet of things middleware for intelligentindustrial parksrdquo e International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1081ndash10892020

[29] A Ghosh S Liaquat and S Ahmed ldquoHealthcare-internet ofthings (H-IoT) can assist and address emerging challenges inhealthcarerdquo International Journal of Innovative Science ampTechnology vol 1 no 2 pp 12ndash18 2021


ings technology to build smart classrooms is helpful forinformation sharing and analysis and has a significant im-pact on the way of learning knowledge Students majoring inthe Internet of ings computer and other related majorscan use the platform to learn wireless sensing technologyand embedded development technology and can also use theprovided interface to carry out the secondary developmentof the project

e environment created by the smart classroomstimulates studentsrsquo interest in learning thereby improvingthe quality of education and teaching Li believes that as theMinistry of Education vigorously promotes the constructionof smart campuses the development concept of smartcampuses will have broad application prospects Howevercolleges and universities are still at the stage of digitalcampuses and there are still many problems He designedand realized a complete intelligent campus managementsystem by analyzing the design principles and design goals ofthe system His system is mainly divided into face recog-nition terminal hardware based on the Internet of ingsand smart campus software system Although the usersatisfaction of the system he studied is relatively high thereare still certain shortcomings [1] Kim believes that thedegree of student participation refers to the degree to whichstudents are immersed in learning when receiving educationin the classroom He proposed an environmental intelli-gence algorithm for smart classrooms e algorithm pro-vided information to teachers by measuring studentparticipation in real time He proposed an algorithm forevaluating student participation by measuring thermal in-frared images to evaluate studentsrsquo mental state He pro-posed a measurement model that uses thermal infraredimaging to characterize student participation e color ofthe teacherrsquos mobile application will change in real timeaccording to the immersion level of the students in theclassroom Although the algorithm he proposed is inno-vative it lacks algorithm simulation [2] Tissenbaum andSlotta believe that although K-12 media has had a significantimpact on many other aspects of life peoplersquos classroomenvironment has not yet incorporated ubiquitous com-puting augmented reality and other emerging technologieseven touch screens He has carried out a series of design-based research projects to investigate the smart classroominfrastructure which provides support for students andteachers in a new form of collaboration and inquiry in-cluding the substantial role of large projection displays andsmall touch surfaces as well as the dependence of thestudentrsquos physical location in the room His design includes(1) the role of a large-screen display to transmit aggregateinformation and environmental information (2) real-timecommunication between students (3) application of intel-ligent software agents to formulate real-time teaching logic(4) support cross-context learning and (5) investigate thecoordination of roles materials and environment Althoughhis research has a certain role in promoting the developmentof smart classrooms it lacks specific experimental data [3]Lin believes that in recent years many organizations haveannounced the importance of software development to thecountry society and individuals In the process of software

development various unpredictable problems are oftenencountered especially when developing large and complexsoftware To reduce the possibility of these problems it isessential for students to apply software engineering tech-niques to scientifically define the standards models andprocesses required in the software development process Hisgoal is to apply an innovative teachingmethod called flippedclassroom to implement a learner-centered learning envi-ronment in software engineering courses In addition healso developed an intelligent learning diagnosis system tosupport the teaching of this course He conducted experi-ments on a software engineering course at a university inTaiwan to explore the effectiveness of the proposed methode experimental group adopts the flipped classroomteaching method and the control group adopts the tradi-tional classroom teaching method Although his research ismore accurate it is not innovative enough [4]

rough the Internet of ings application platform forteachers and students to build a learning experience plat-form with strong credibility teachers and student users canintegrate into all aspects of the Internet ofings applicationplatform through actual operation and management whichfacilitates the high-speed flow and sharing of informationresources To make full use of the Internet of ings ap-plication technology to enhance the information manage-ment of the university classrooms based on the professionalcharacteristics to promote the integration of theory andapplication practice of diversified universities through theInternet of ings application platform to provide teachersand students with a strong credibility of learning experienceteachers and students can integrate into all aspects of theInternet of ings application platform through the actualoperation and management

2 Smart Classroom System Design

21 Internet of ings With the rapid development of theInternet of ings a large amount of unstructured data willbe generatedese data are growing exponentiallye dataare complex and polymorphic and there is no obviouscorrelation between them However the traditional dataacquisition analysis storage and processing technologiescan not meet the needs of the rapid development of society[5 6] erefore if we can focus on the following keytechnologies in data processing of the Internet of ingsaccording to the characteristics of data massive data storagedata fusion data query search and mining intelligent de-cision making etc it will play a technical support andpromotion role in information construction smart cityindustrial manufacturing smart agriculture commerce fi-nance transportation and other fields [7 8]

Assuming that in the regression of the function curvethe weight coefficient A the standard deviation S the mobileapplication behavior variable x and the linear change var-iable y ensure a high degree of fit between the function curveand the sample point data it is necessary to establish analternative relationship expression between x and y based onthe regression equation formula e calculated residual SScan be used to determine whether the fitting is a composite



A

1 minus1113936



1113971

(1)

S

1113936


n minusn

radic

1113971

(2)

SS

1113936

ni1 Ai minus Si

11138681113868111386811138681113868111386811138681113868

n minusn

radic

1113971

(3)


DT 1113944

n

i1|d|




dif 1113936


1113936nmij xij

(5)



SL 1113936


1113936nmij

P xij1113872 1113873

1113969 (6)









TM T(n2) + T(n2)+1

2 (8)


σ2min 1

1113936ni1 1σ2i1113872 1113873

(9)

Wi 1

σ2i 1113936nk1 1σ2k1113872 1113873

(i 1 2 n) (10)














Modulationmode

Maximumfrequency

Minimumfrequency


Pad

Pad Wirelessrouter

Wirelessrouter

PC workstation Pad

Pad

PadWifi Wifi








Study guide

Specific task

Perplexity

Microvideo

Supportingresources

Before class

Answeringquestions

Typicaltasks

Individualguidance


Independentinquiry


Cooperativelearning


Publish share



Learningfeedback

Achievementdisplay

Feedbackevaluation

















5 Conclusions






()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate



T

1


9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2











Data Availability




Acknowledgments


References



































A

1 minus1113936



1113971

(1)

S

1113936


n minusn

radic

1113971

(2)

SS

1113936

ni1 Ai minus Si

11138681113868111386811138681113868111386811138681113868

n minusn

radic

1113971

(3)


DT 1113944

n

i1|d|




dif 1113936


1113936nmij xij

(5)



SL 1113936


1113936nmij

P xij1113872 1113873

1113969 (6)









TM T(n2) + T(n2)+1

2 (8)


σ2min 1

1113936ni1 1σ2i1113872 1113873

(9)

Wi 1

σ2i 1113936nk1 1σ2k1113872 1113873

(i 1 2 n) (10)














Modulationmode

Maximumfrequency

Minimumfrequency


Pad

Pad Wirelessrouter

Wirelessrouter

PC workstation Pad

Pad

PadWifi Wifi








Study guide

Specific task

Perplexity

Microvideo

Supportingresources

Before class

Answeringquestions

Typicaltasks

Individualguidance


Independentinquiry


Cooperativelearning


Publish share



Learningfeedback

Achievementdisplay

Feedbackevaluation

















5 Conclusions






()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate



T

1


9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2











Data Availability




Acknowledgments


References













































Modulationmode

Maximumfrequency

Minimumfrequency


Pad

Pad Wirelessrouter

Wirelessrouter

PC workstation Pad

Pad

PadWifi Wifi








Study guide

Specific task

Perplexity

Microvideo

Supportingresources

Before class

Answeringquestions

Typicaltasks

Individualguidance


Independentinquiry


Cooperativelearning


Publish share



Learningfeedback

Achievementdisplay

Feedbackevaluation

















5 Conclusions






()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate



T

1


9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2











Data Availability




Acknowledgments


References







































Study guide

Specific task

Perplexity

Microvideo

Supportingresources

Before class

Answeringquestions

Typicaltasks

Individualguidance


Independentinquiry


Cooperativelearning


Publish share



Learningfeedback

Achievementdisplay

Feedbackevaluation

















5 Conclusions






()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate



T

1


9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2











Data Availability




Acknowledgments


References














































5 Conclusions






()100 10 8 085 177 100200 195 16 205 294 100300 352 25 265 382 100400 479 37 315 473 100500 704 48 365 511 100

2615

467

501

579

406

Silence or chaos

Teacher interaction

Student interaction

Inte

activ

e beh

avio

r

10 20 30 40 50 60 700Rate



T

1


9658

9812

9744

9866

9799

9577

94

95

96

97

98

99

100

101

Val

ue

100 150 200 250 30050Distance

94009450950095509600965097009750980098509900

Writ

e rat

e (

)

Group 1Group 2











Data Availability




Acknowledgments


References



































Data Availability




Acknowledgments


References
































