# Internet of Things Online Course Construction of Multimedia Technology

Authors: Jingbin Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The advancement of modern multimedia and internet education systems has enriched the presentation and dissemination methods of educational resources and changed people’s learning methods and behavior habits. Online learning has become a way of life and a hotspot of the times, and optimizing the quality of online education services has gradually become a common demand of society. IoT is the sense of relating all things to the Internet. It is another type of network that has been extended and expanded by accelerated improvements in network infrastructure. It has powerful communication, data storage, modification, and computing functions for related logic and data. From the perspective of online course construction, this paper proposes a research direction of IoT online courses based on multimedia technology. Firstly, a brief introduction to the theoretical overview of online courses is given, and then, an in-depth study of the IoT technology is carried out. Then, through the theoretical analysis of the online course mode, combined with the characteristics and functions of the IoT of multimedia technology, the design method of the online course system is proposed. Finally, a comparative experiment is conducted between the IoT online course of multimedia technology and the traditional online course. The experimental results show that the overall mean of the students’ two test scores under the IoT online course teaching of multimedia technology is 6.675 points higher than that under the traditional online course teaching. It shows that the teaching of IoT online courses based on multimedia technology is more conducive to improving students’ learning ability and level, thereby improving the quality of teaching.


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## Body

## 1. Introduction

In an era of high speed advancement in information and communication technology, people are increasingly close to each other and are communicating more and more closely. The pace of information sharing is accelerating, people rely more and more on the Internet to obtain more services, and education occupies a very important position in modern society. Even in such an environment, the education industry is still unable to fully realize the simultaneous development of educational information in various regions, and the problem of inequitable apportioning of good quality facilities in education has become a major obstacle to the transformation and upgrading of the education industry. The maturity of multimedia information technology has made online education appear in the public’s field of vision and has been rapidly popularized and promoted in various countries and regions around the world. Online courses have emerged as a new educational model. Its appearance has brought about new changes in the structure of the education and teaching system. With its unique advantages, online education effectively makes up for the defects of traditional education and teaching resources such as uneven distribution, lack of interaction, and dullness and meets the basic needs of modern teaching. Therefore, it has become one of the most rapidly developing educational forms in recent years, but at the same time, as a brand-new curriculum model, there are still many insurmountable factors in the construction of online courses. It poses significant demands on academic thinking, course experience assessment, managing course credits, and the teaching process at national and international levels; it poses higher requirements for teachers’ ideology; at the same time, it also brings new challenges to students’ self-knowledge, self-learning ability, and self-control.Internet of Things online courses with multimedia technology as the core can effectively avoid such problems. It can focus on students as primary stakeholders and give full play to the teacher’s leading role in the education system. It transforms the focus of education from the general direction to providing individualized learning guidance for its pupils on the basis of different individual characteristics, through two-way and multidirectional engagement and cooperation on the part of teachers and pupils, and collaborative exploration to solve difficulties and problems encountered in course learning; finally, students can achieve a more satisfactory learning effect. It can not only change the main body of course development, enhance the curriculum elements, and transform the course carrier and the means of execution but also help to enhance the professional knowledge and skills of teachers, improve the effectiveness of instruction in the classroom, and help to promote the capacity of students to learn actively and enrich their learning life. Based on the influence of online courses on college courses and teaching, the article has investigated the significance of multimedia Internet of Things technology on the worth and usefulness of online courses in colleges and universities, which has realistic guiding significance and practical significance in facilitating the reconstruction and upgrading of education and learning, increasing the teaching level, and providing the new thought for the discussion on the architecture of online courses.


## 2. Related Work

In recent years, many scholars have carried out research on multimedia Internet of Things technology. Kim et al. believes that multimedia IoT technologies can go beyond machine-to-machine communication (M2M) and can cover high-level interconnectivity of appliances, systems, and services across a wide range of protocols, domains, and apps. They try to cover automation in major engineering fields by connecting IoT embedded devices, including smart objects, to each other. Finally, it is experimentally demonstrated that the IoT in particular allows for the definition of the only technology that can interconnect with discernable integrated telecommunication facilities from within the available network architecture. At the same time, it also supports high-grade software applications such as intelligent power grids and intelligent monitoring, which verifies its feasibility and effectiveness [1]. Kong and Wang present a multimodal intelligent data analysis paradigm for IoT systems. The paradigm they proposed for data analysis based on multigame theory is the best combination of models, which are mainly used to construct effective and robust analytical models. In the framework of multiple games, they constructed Bayesian models, discount lines, recurrent trees, arbitrary woods, collogistic reversibility, and supportive ventures, proposed some new ideas to strengthen the conventional numerical equivalent, and finally verified the effectiveness of the suggested methods through demonstrations [2]. Karaadi et al. proposed a new concept of Acceptable Quality of Things (AQoT), which relates to IoT devices and their applications; AQoT can minimize bandwidth without affecting the quality of IoT devices. They finally verified this idea in experiments based on human detection and license plate detection use cases. Laboratory outcomes demonstrated that the AQoT concept based on IoT technology can significantly reduce bandwidth usage [3]. Zhou et al. conducted in-depth research on fifth-generation wireless communication networks. They believe that the Internet of Things of multimedia technology achieves significantly better amounts of mobile data, quintessential cellular subscriber data rates, quantity of simultaneously enabled units, and decreased port-to-port latency, which can support such multimedia information to compress, encode, transmit, process, synchronize, store, and mine, which has great application value for the development and design of the theory, technology and application of 5G multimedia communication [4]. Abdel-Basset et al. propose a new platform built on state-of-the-art PC supported diagnostics and the Internet of Things for the detection and monitoring of patients with infection in heart failure, for which the data are obtained from alternative sources, and they suggested medicare scheme seeks to obtain better diagnostic accuracy by blurring information. The system is able to deal with the imprecision and ambiguous uncertainty introduced by the symmetric priority scale of different disease symptoms, and users can know how dangerous the disease is in their body. Finally, the laboratory outcome demonstrated that the suggested system can work in a wide range and can effectively reduce the mortality as well as the clinical treatment cost associated with heart failure [5]. Kumari et al. study the peculiar properties and maturity of MMBD calculations in IoT environments and produced a sweeping methodology for MMBD, which they abstracted into a new process model for MMBD reflected on the IoT, and then provided a project example to simulate the product approach, and the final experimental results showed that the process model solved many research challenges related to MMBD, such as computational extensibility, availability, robustness, dissimilarity, and compliance with service requirements, to verify its feasibility [6]. To sum up, after several years of exploration, the IoT under multimedia has received in-depth research from many scholars, but there is not much research about the online course development of the IoT. Therefore, in order to promote the in-depth development of teaching, the practical research of online course construction based on multimedia IoT technology is urgent.


## 3. Internet of Things Online Course Construction of Multimedia Technology

The research on the construction of the Internet of Things multimedia technology online course mainly includes the theoretical overview of the online course, the Internet of Things technology, and the design content of the Internet of Things online course system based on multimedia technology.


### 3.1. Theoretical Overview of Online Courses

The main contents of the theoretical overview of online courses include the concept of online courses and the classification of online courses. This paper mainly studies the two categories of MOOCs and microcourses.


#### 3.1.1. Concept of Online Courses

In previous studies, many scholars define online courses as asynchronous self-learning courses developed under the guidance of teaching theories and curriculum theories, relying on information technology and network platforms, to achieve the teaching goals of a certain subject area [7]. As a new course teaching mode, online courses are also very different from traditional courses.


#### 3.1.2. Classification of Online Courses

Curriculum formats are divided into two main categories: MOOCs and microcourses, both of which are teaching videos with a teaching time of no more than 20 minutes and both need to be conducted online on the network platform. It is a simple, full-length teaching, and learning activity in a broadcast form based on the content of a specific subject area and requires practice tests and evaluations related to the subject of the course in the course of explaining the course [8]. Both are simple, full-scale teaching, and learning experiences that have been carefully designed with information technology and are presented in a broadcast form around a subject or teaching session.


#### 3.1.3. MOOC

The MOOC is a Massive Open Online Course, and the Chinese literal translation is a large-scale open online course. Among them, “large-scale” refers to the large scale of learners, which is not limited by the number, and a course can accommodate thousands of learners participating in it at the same time; “open” refers to the realization of real-time sharing of educational information on the whole platform, which is not limited by time and space, and anyone who is interested can participate without paying any cost [9]. At the same time, it also has many characteristics, as shown in Table 1.Table 1
Features of MOOCs.
SequenceFeaturesExplanation1MassiveThe load of online learning is huge2OpennessOpen learning objects, open learning forms, open resources, and open educational concepts3OnlineMainly through the network4PersonalizeCan fully realize the personalized learning of students


#### 3.1.4. Microlessons

The entire definition of microlesson is a minivideo course. It is an integrated educational procedure and educational assets designed and developed based on microteaching videos as the main presentation method, aiming at the knowledge and difficulties of a certain subject, example exercises, experimental operations, etc. It is generally recognized by teachers for its ability to support many learning styles [10]. It does not have the inherent flaws in the traditional online teaching model; on the contrary, it has many advantages, as shown in Table 2.Table 2
The advantages of microlectures.
SequenceAdvantage1The theme of the teaching content is prominent, and the direction is clear2Resource types are rich and diverse, and the situation is real3Teaching time is short and concise and easy to use4Resource composition is semistructured and easy to expand5Teacher-student communication is highly interactive and has a wide range of applications


### 3.2. IoT Technology

The innovation and development of scientific technology have promoted the speedy growth of IoT. In the process of perfecting and maturing the entire IoT technology, its main core technologies include the integration of informatization and industrialization, communication skills, information technology of electronic signature, and remote sensing technology, as shown in Figure1.Figure 1
Internet of Things key technology.The IoT industry chain structure is also built on the basis of core technologies, including sensors, communication terminals, M2M business management and service platforms, industry applications, and users, as shown in Figure2. It can be seen from this that the application fields and potential market scale of the IoT industry itself are very large, showing a good development momentum and gradually forming the trend of industrial clusters, which is an important factor in the rise of the Internet of Things industry itself [11].Figure 2
Internet of Things industry chain structure diagram.Since the outbreak of the first industrial revolution in 1874, science and technology have developed rapidly in various fields. So far, social development has entered the fourth stage of the process, as shown in Figure3.Figure 3
The development process of the industrial revolution.Following the growth of the IoT and the development of ICT innovations, the space and potential of the fourth stage of industrial development are huge. It will integrate resources on a global scale to create more value, as shown in Figure4.Figure 4
Internet of Things and Industry 4.0.


### 3.3. System Design of Internet of Things Online Course Based on Multimedia Technology

Through the theoretical analysis of the online course mode, combined with the characteristics and functions of the IoT of multimedia technology, the process of online course system design is summarized, as shown in Figure5.Figure 5
Online course system design process.The functional modules of the multimedia technology Internet of Things online course system are mainly composed of Internet correspondence block, material gathering block, bus communication block, and application loading block [12]. Among them, the internet correspondence block is mainly be in charge of the teaching information transmission and the communication function between the tutor and the pupils and between the pupils and the pupils; the data acquisition module is mainly responsible for finding, analyzing, and organizing teaching resources in the wave database. Teaching resources are systematically divided into units, and the collection module divides the distribution nodes of these units into three categories according to their location and importance. Then, it uploads teaching information to the learning platform and then uses radio frequency technology to strengthen the real-time sharing and intercommunication of online teaching information [13].The energy consumption model of the online course system is the same as most traditional energy consumption models. The receiver consumes energy to operate the receiving circuit, and the transmitter consumes energy to operate the transmitting amplifier and the transmitting circuit. According to the different distances between the receiver and the transmitter, the energy control module works in the multipath fading channel model or the free space channel model, as shown in Figure6.Figure 6
Energy consumption model.The transmitter sends the bit data through the distanced, and the energy that the node needs to consume is
(1)ETxl,d=Eelecl+Eampl,d=l×Eelec+l×εfs×d2,d≤d0,l×Eelec+l×εmp×d4,d>d0.The parameter interpretation of formula (1) is shown in Table 3.Table 3
Explanation of the parameters of the formula.
ParameterMeaningUnitETxThe energy consumed by the node sending moduleJEelecThe energy consumed by the transmitter (receiver) circuit to send and receive each bit of datanJ/bitεfsThe power consumption parameters of the sensor node transmit amplifier under the free space channel modelPJ/bitεmpPower consumption parameters of transmit amplifiers under the multipath fading channel modelPJ/bitd0Distance thresholdmAmong them,(2)d0=εfsεmp.When a node receives bits of data, the energy that the node needs to consume is [14]
(3)ERxl=l×Eelec.When selecting clusters and cluster heads, fully consider the relationship between the state of the network, the remaining energy of nodes, and the distance, and the sink node (base station) is located inside the sensing area. As the network model defined in this paper, it is assumed thatk cluster heads are selected in the rth round of election, and each cluster contains N/k−1 member nodes and one cluster head node. According to the energy consumption formula, we can get the energy consumed by the cluster head node in one round as [15]
(4)ECH=nk−1l∙Eelec+nkl∙EDA+l∙Eelec+l∙εfsdtoBS,2where k is the number of cluster head nodes, EDA is the energy consumed by the cluster head node for data fusion, dtoBS is the average distance between the cluster head node and the base station, and [16]
(5)dtoBS=∫Ax2+y21AdA=0.765M2.The energy consumed by a noncluster head node in a round is [17]
(6)EnonCH=l∙Eelec+l∙εfs∙dtoCH2.Among them,dtoCH is the average distance from the nodes in the cluster to the cluster head. Assuming that the nodes in the cluster are uniformly distributed ρx,y and the distribution density is, then there are [18]
(7)dtoCH2=∫x=0x=xmax∫y=0y=ymaxx2+y2ρx,ydxdy=M22πk.So the energy consumption within a single cluster is about [19]
(8)Ecluster=ECH+nkEnonCH.In one run, the energy consumption of the entire network is [20]
(9)Er=l2nEelec+nEDA+εfskdtoBS2+ndtoCH2.Take the derivative ofE, with respect to k, and let the result be 0 [21]:
(10)Er‘=lεfsdtoBS2−N2πM2k2=0.Get(11)k0=n2πMdtoBS.Substitute into formula (11) to get
(12)k0=n2π20.765.It can be seen that in the set scenario, the optimal number of cluster heads is only related to the number of nodes in the cluster. With the network running, it is necessary to dynamically adjust the number of clusters in the monitoring area to prolong the network lifetime. In addition, the information of the nodes in the cluster can be sent to the aggregation point in a piggyback way, so as to obtain accurate information without increasing the overhead of the system.In the cluster-based topology control method, the size and number of clusters have a great impact on the system performance. Whether the number of clusters is too large or too small, it will cause the imbalance of energy consumption in the network and the increase of system energy consumption [22]. Considering that the wireless sensor energy is limited, the optimal number of cluster heads in each round of operation should minimize the total energy consumption of the network in this round, which is defined as
(13)fk=k−k0×Er′.It can be known that(14)k−k02×k+k0≥0.It can also be transformed into(15)k−N2πMdtoBS2×lεfsdtoBS2−N2πM2k2≥0.Assumingk≠k0, so fk>0, it satisfies the sufficient condition for the existence of the minimum value of the continuous function, and k0 is the optimal number of cluster heads.After the network nodes are divided into several clusters, the cluster head needs to consider tasks such as collecting data in the cluster and communicating with the sink node, which requires the cluster head to have sufficient energy, and the probability function for constructing the cluster head is [23]
(16)fi=αf1i+βf2i+γf3i,f1i=Ei1/m∑k=1mEi,f2i=1/N∑k=1Ndi,k1/m∑k=1mdi,k,f3i=1/N∑k=1ndtoBSidtoBSi,α+β+γ=1,0≤α,β,γ≤1.Among them,f1i is the ratio of the surplus power of the core unit to the median surplus power of the nodes in the cluster. The larger the value is, the more energy the node is, and the more competent it is to act as the leader knot. f2i is simply the comparison of the median differential frequency between the nodes in the sensing area and the cluster knot to the median differential frequency between the nodes in the family and the cluster knot. The larger the value is, the smaller the Euclidean distance between nodes in the cluster, the smaller the energy consumed during intracluster communication, and the more suitable this stage is to act as the cluster head node. f3i is the comparison of the median diameter of the knot in the sensing area to the aggregation node and the diameter of the knot to the aggregation node. The larger the value, the closer the knot is to the aggregation node, and the more suitable it is to act as a continuation node [24].In each round, we select the node with the largest value of the cluster head probability function in the virtual partition as the cluster head node. It can be seen thatf1i is for the consideration of the remaining energy of the node, so that more nodes with remaining nodes act as cluster heads; f2i optimizes the selection of the distance between nodes in the cluster, so that the nodes with closer distances between the nodes in the cluster act as the cluster head; f3i optimizes the distance between clusters, so that after the node completes data aggregation and fusion, it can be sent to the sink node with lower energy consumption [25–27]. Each component is adjusted by α,β,γ coefficients. For example, in the early stage of network operation, the remaining energy of nodes is sufficient, and the influence of distance factors is given priority.


## 3.1. Theoretical Overview of Online Courses

The main contents of the theoretical overview of online courses include the concept of online courses and the classification of online courses. This paper mainly studies the two categories of MOOCs and microcourses.


### 3.1.1. Concept of Online Courses

In previous studies, many scholars define online courses as asynchronous self-learning courses developed under the guidance of teaching theories and curriculum theories, relying on information technology and network platforms, to achieve the teaching goals of a certain subject area [7]. As a new course teaching mode, online courses are also very different from traditional courses.


### 3.1.2. Classification of Online Courses

Curriculum formats are divided into two main categories: MOOCs and microcourses, both of which are teaching videos with a teaching time of no more than 20 minutes and both need to be conducted online on the network platform. It is a simple, full-length teaching, and learning activity in a broadcast form based on the content of a specific subject area and requires practice tests and evaluations related to the subject of the course in the course of explaining the course [8]. Both are simple, full-scale teaching, and learning experiences that have been carefully designed with information technology and are presented in a broadcast form around a subject or teaching session.


### 3.1.3. MOOC

The MOOC is a Massive Open Online Course, and the Chinese literal translation is a large-scale open online course. Among them, “large-scale” refers to the large scale of learners, which is not limited by the number, and a course can accommodate thousands of learners participating in it at the same time; “open” refers to the realization of real-time sharing of educational information on the whole platform, which is not limited by time and space, and anyone who is interested can participate without paying any cost [9]. At the same time, it also has many characteristics, as shown in Table 1.Table 1
Features of MOOCs.
SequenceFeaturesExplanation1MassiveThe load of online learning is huge2OpennessOpen learning objects, open learning forms, open resources, and open educational concepts3OnlineMainly through the network4PersonalizeCan fully realize the personalized learning of students


### 3.1.4. Microlessons

The entire definition of microlesson is a minivideo course. It is an integrated educational procedure and educational assets designed and developed based on microteaching videos as the main presentation method, aiming at the knowledge and difficulties of a certain subject, example exercises, experimental operations, etc. It is generally recognized by teachers for its ability to support many learning styles [10]. It does not have the inherent flaws in the traditional online teaching model; on the contrary, it has many advantages, as shown in Table 2.Table 2
The advantages of microlectures.
SequenceAdvantage1The theme of the teaching content is prominent, and the direction is clear2Resource types are rich and diverse, and the situation is real3Teaching time is short and concise and easy to use4Resource composition is semistructured and easy to expand5Teacher-student communication is highly interactive and has a wide range of applications


## 3.1.1. Concept of Online Courses

In previous studies, many scholars define online courses as asynchronous self-learning courses developed under the guidance of teaching theories and curriculum theories, relying on information technology and network platforms, to achieve the teaching goals of a certain subject area [7]. As a new course teaching mode, online courses are also very different from traditional courses.


## 3.1.2. Classification of Online Courses

Curriculum formats are divided into two main categories: MOOCs and microcourses, both of which are teaching videos with a teaching time of no more than 20 minutes and both need to be conducted online on the network platform. It is a simple, full-length teaching, and learning activity in a broadcast form based on the content of a specific subject area and requires practice tests and evaluations related to the subject of the course in the course of explaining the course [8]. Both are simple, full-scale teaching, and learning experiences that have been carefully designed with information technology and are presented in a broadcast form around a subject or teaching session.


## 3.1.3. MOOC

The MOOC is a Massive Open Online Course, and the Chinese literal translation is a large-scale open online course. Among them, “large-scale” refers to the large scale of learners, which is not limited by the number, and a course can accommodate thousands of learners participating in it at the same time; “open” refers to the realization of real-time sharing of educational information on the whole platform, which is not limited by time and space, and anyone who is interested can participate without paying any cost [9]. At the same time, it also has many characteristics, as shown in Table 1.Table 1
Features of MOOCs.
SequenceFeaturesExplanation1MassiveThe load of online learning is huge2OpennessOpen learning objects, open learning forms, open resources, and open educational concepts3OnlineMainly through the network4PersonalizeCan fully realize the personalized learning of students


## 3.1.4. Microlessons

The entire definition of microlesson is a minivideo course. It is an integrated educational procedure and educational assets designed and developed based on microteaching videos as the main presentation method, aiming at the knowledge and difficulties of a certain subject, example exercises, experimental operations, etc. It is generally recognized by teachers for its ability to support many learning styles [10]. It does not have the inherent flaws in the traditional online teaching model; on the contrary, it has many advantages, as shown in Table 2.Table 2
The advantages of microlectures.
SequenceAdvantage1The theme of the teaching content is prominent, and the direction is clear2Resource types are rich and diverse, and the situation is real3Teaching time is short and concise and easy to use4Resource composition is semistructured and easy to expand5Teacher-student communication is highly interactive and has a wide range of applications


## 3.2. IoT Technology

The innovation and development of scientific technology have promoted the speedy growth of IoT. In the process of perfecting and maturing the entire IoT technology, its main core technologies include the integration of informatization and industrialization, communication skills, information technology of electronic signature, and remote sensing technology, as shown in Figure1.Figure 1
Internet of Things key technology.The IoT industry chain structure is also built on the basis of core technologies, including sensors, communication terminals, M2M business management and service platforms, industry applications, and users, as shown in Figure2. It can be seen from this that the application fields and potential market scale of the IoT industry itself are very large, showing a good development momentum and gradually forming the trend of industrial clusters, which is an important factor in the rise of the Internet of Things industry itself [11].Figure 2
Internet of Things industry chain structure diagram.Since the outbreak of the first industrial revolution in 1874, science and technology have developed rapidly in various fields. So far, social development has entered the fourth stage of the process, as shown in Figure3.Figure 3
The development process of the industrial revolution.Following the growth of the IoT and the development of ICT innovations, the space and potential of the fourth stage of industrial development are huge. It will integrate resources on a global scale to create more value, as shown in Figure4.Figure 4
Internet of Things and Industry 4.0.


## 3.3. System Design of Internet of Things Online Course Based on Multimedia Technology

Through the theoretical analysis of the online course mode, combined with the characteristics and functions of the IoT of multimedia technology, the process of online course system design is summarized, as shown in Figure5.Figure 5
Online course system design process.The functional modules of the multimedia technology Internet of Things online course system are mainly composed of Internet correspondence block, material gathering block, bus communication block, and application loading block [12]. Among them, the internet correspondence block is mainly be in charge of the teaching information transmission and the communication function between the tutor and the pupils and between the pupils and the pupils; the data acquisition module is mainly responsible for finding, analyzing, and organizing teaching resources in the wave database. Teaching resources are systematically divided into units, and the collection module divides the distribution nodes of these units into three categories according to their location and importance. Then, it uploads teaching information to the learning platform and then uses radio frequency technology to strengthen the real-time sharing and intercommunication of online teaching information [13].The energy consumption model of the online course system is the same as most traditional energy consumption models. The receiver consumes energy to operate the receiving circuit, and the transmitter consumes energy to operate the transmitting amplifier and the transmitting circuit. According to the different distances between the receiver and the transmitter, the energy control module works in the multipath fading channel model or the free space channel model, as shown in Figure6.Figure 6
Energy consumption model.The transmitter sends the bit data through the distanced, and the energy that the node needs to consume is
(1)ETxl,d=Eelecl+Eampl,d=l×Eelec+l×εfs×d2,d≤d0,l×Eelec+l×εmp×d4,d>d0.The parameter interpretation of formula (1) is shown in Table 3.Table 3
Explanation of the parameters of the formula.
ParameterMeaningUnitETxThe energy consumed by the node sending moduleJEelecThe energy consumed by the transmitter (receiver) circuit to send and receive each bit of datanJ/bitεfsThe power consumption parameters of the sensor node transmit amplifier under the free space channel modelPJ/bitεmpPower consumption parameters of transmit amplifiers under the multipath fading channel modelPJ/bitd0Distance thresholdmAmong them,(2)d0=εfsεmp.When a node receives bits of data, the energy that the node needs to consume is [14]
(3)ERxl=l×Eelec.When selecting clusters and cluster heads, fully consider the relationship between the state of the network, the remaining energy of nodes, and the distance, and the sink node (base station) is located inside the sensing area. As the network model defined in this paper, it is assumed thatk cluster heads are selected in the rth round of election, and each cluster contains N/k−1 member nodes and one cluster head node. According to the energy consumption formula, we can get the energy consumed by the cluster head node in one round as [15]
(4)ECH=nk−1l∙Eelec+nkl∙EDA+l∙Eelec+l∙εfsdtoBS,2where k is the number of cluster head nodes, EDA is the energy consumed by the cluster head node for data fusion, dtoBS is the average distance between the cluster head node and the base station, and [16]
(5)dtoBS=∫Ax2+y21AdA=0.765M2.The energy consumed by a noncluster head node in a round is [17]
(6)EnonCH=l∙Eelec+l∙εfs∙dtoCH2.Among them,dtoCH is the average distance from the nodes in the cluster to the cluster head. Assuming that the nodes in the cluster are uniformly distributed ρx,y and the distribution density is, then there are [18]
(7)dtoCH2=∫x=0x=xmax∫y=0y=ymaxx2+y2ρx,ydxdy=M22πk.So the energy consumption within a single cluster is about [19]
(8)Ecluster=ECH+nkEnonCH.In one run, the energy consumption of the entire network is [20]
(9)Er=l2nEelec+nEDA+εfskdtoBS2+ndtoCH2.Take the derivative ofE, with respect to k, and let the result be 0 [21]:
(10)Er‘=lεfsdtoBS2−N2πM2k2=0.Get(11)k0=n2πMdtoBS.Substitute into formula (11) to get
(12)k0=n2π20.765.It can be seen that in the set scenario, the optimal number of cluster heads is only related to the number of nodes in the cluster. With the network running, it is necessary to dynamically adjust the number of clusters in the monitoring area to prolong the network lifetime. In addition, the information of the nodes in the cluster can be sent to the aggregation point in a piggyback way, so as to obtain accurate information without increasing the overhead of the system.In the cluster-based topology control method, the size and number of clusters have a great impact on the system performance. Whether the number of clusters is too large or too small, it will cause the imbalance of energy consumption in the network and the increase of system energy consumption [22]. Considering that the wireless sensor energy is limited, the optimal number of cluster heads in each round of operation should minimize the total energy consumption of the network in this round, which is defined as
(13)fk=k−k0×Er′.It can be known that(14)k−k02×k+k0≥0.It can also be transformed into(15)k−N2πMdtoBS2×lεfsdtoBS2−N2πM2k2≥0.Assumingk≠k0, so fk>0, it satisfies the sufficient condition for the existence of the minimum value of the continuous function, and k0 is the optimal number of cluster heads.After the network nodes are divided into several clusters, the cluster head needs to consider tasks such as collecting data in the cluster and communicating with the sink node, which requires the cluster head to have sufficient energy, and the probability function for constructing the cluster head is [23]
(16)fi=αf1i+βf2i+γf3i,f1i=Ei1/m∑k=1mEi,f2i=1/N∑k=1Ndi,k1/m∑k=1mdi,k,f3i=1/N∑k=1ndtoBSidtoBSi,α+β+γ=1,0≤α,β,γ≤1.Among them,f1i is the ratio of the surplus power of the core unit to the median surplus power of the nodes in the cluster. The larger the value is, the more energy the node is, and the more competent it is to act as the leader knot. f2i is simply the comparison of the median differential frequency between the nodes in the sensing area and the cluster knot to the median differential frequency between the nodes in the family and the cluster knot. The larger the value is, the smaller the Euclidean distance between nodes in the cluster, the smaller the energy consumed during intracluster communication, and the more suitable this stage is to act as the cluster head node. f3i is the comparison of the median diameter of the knot in the sensing area to the aggregation node and the diameter of the knot to the aggregation node. The larger the value, the closer the knot is to the aggregation node, and the more suitable it is to act as a continuation node [24].In each round, we select the node with the largest value of the cluster head probability function in the virtual partition as the cluster head node. It can be seen thatf1i is for the consideration of the remaining energy of the node, so that more nodes with remaining nodes act as cluster heads; f2i optimizes the selection of the distance between nodes in the cluster, so that the nodes with closer distances between the nodes in the cluster act as the cluster head; f3i optimizes the distance between clusters, so that after the node completes data aggregation and fusion, it can be sent to the sink node with lower energy consumption [25–27]. Each component is adjusted by α,β,γ coefficients. For example, in the early stage of network operation, the remaining energy of nodes is sufficient, and the influence of distance factors is given priority.


## 4. Internet of Things Online Course Experiment of Multimedia Technology

In this experiment, college students were taken as the experimental objects, and the sample size was 100. Divide it into two classes, in which there are 22 boys and 28 girls in class A and 27 boys and 23 girls in class B. The students in the two classes have basically the same level of theoretical knowledge and practical skills in the online course subjects they want to participate in, and they are at the same starting point. Through a semester of teaching practice, the students of A and B are compared and analyzed through statistical online course platform data [28–30].The experimental model of the Internet of Things multimedia technology online course is shown in Figure7. The main body is divided into the course leader, the experimental substitute teacher and the students. The course leader is mainly responsible for the general situation of the course and checking the assessment of students, and the substitute teacher is responsible for the preview and testing of the students. Students can mainly choose courses and complete tests and assessments.Figure 7
Experiment model of Internet of Things multimedia technology online course.


### 4.1. Preexperimental Student Situation Test

In order to improve the accuracy of this experiment and reduce the error, before the start of the experiment, they use correlation analysis to study the correlation between gender, learning stage, school type, and major category and whether they have participated in online courses. The statistical results are shown in Figure7 and Tables 4 and 5.Table 4
Correlation analysis of class A students.
Pearson correlation coefficientWhat is your gender?Correlation coefficient-0.052P value0.217What is your current stage of study?Correlation coefficient-0.007P value0.814What type of school did you attend?Correlation coefficient-0.019P value0.068What is your major?Correlation coefficient-0.041P value0.362Table 5
Correlation analysis of class B students.
Pearson correlation coefficientWhat is your gender?Correlation coefficient-0.063P value0.194What is your current stage of study?Correlation coefficient-0.021P value0.625What type of school did you attend?Correlation coefficient-0.014P value0.096What is your major?Correlation coefficient-0.032P value0.225It can be seen from Figure8 and Table 4 that the correlation coefficient between the gender of students in class A and whether they have participated in online courses is -0.052, which is close to 0. And the P value is 0.217, which is greater than 0.05, and there is no significant correlation between the two, indicating that there is no correlation between student gender and whether they have attended online courses. The correlation coefficient value between the learning stage of class A students and whether they have participated in online courses is -0.007, which is close to 0. And the p value is 0.814, which is greater than 0.05, and there is no significant correlation between the two. This shows that there is no correlation between students’ learning stage and whether they have participated in online courses. The correlation coefficient between the school type of class A students and whether they have participated in online courses is -0.019, and the P value is 0.068, which is greater than 0.05, and there is no significant correlation between the two. This suggests that there is no correlation between a student’s school type and whether they have taken online courses. The correlation coefficient between the major category of class A students and whether they have participated in online courses is -0.041, which is close to 0, and the P value is 0.362, which is greater than 0.05. There is no significant correlation between the two. This shows that there is no correlation between the student’s major category and whether they have attended online courses.Figure 8
Participation of male and female college students in online courses. (a) shows whether students in class A participate in online courses. (b) shows whether students in class B participate in online courses.
(a)(b)


### 4.2. Comparison Test of Students’ Learning Ability

Under the condition that the teaching process of the two classes A and B are kept the same, two unit tests were conducted, and the difficulty of the two test questions was from shallow to deep, step by step. Two classes of students take the test at the same time, and the test time is 90 minutes. The distribution of the two test scores is shown in Figure9.Figure 9
Statistics of two test scores. (a) shows the first test scores of students in two classes. (b) shows the second test scores of students in two classes.
(a)(b)As can be seen from Figure9, in the first assessment test, according to the statistics of students in class A, there are 8 students with excellent grades, 22 students with good grades, 15 students with passing grades, and 5 students with failed grades; judging from the statistics of the students’ grades in class B, there are 6 students with excellent grades, 12 students with good grades, 24 students with passing grades, and 8 students with failing grades; in the second assessment test, according to the statistics of class A students, there are 6 students with excellent grades, 18 students with good grades, 17 students with passing grades, and 9 students with failed grades; judging from the statistics of the students’ grades in class B, there are 5 students with excellent grades, 11 students with good grades, 18 students with passing grades, and 16 students with failing grades.As can be seen from Figure10, in the distribution of scores in the first test, the outstanding scores of class A accounted for 16% of the class, the good scores accounted for 44% of the class, the passing scores accounted for 30% of the class, and the failing scores accounted for 30%, and failing grades account for 10% of the class; class B’s outstanding grades account for 12% of the class, good grades account for 24% of the class, passing grades account for 48% of the class, and failing grades account for 16% of the class; in the distribution of scores in the second test, the outstanding scores of class A accounted for 12% of the class, the good scores accounted for 36% of the class, the passing scores accounted for 34%, and the failing scores accounted for 34%, with failing grades accounting for 18% of the class; class B has excellent grades for 10% of the class, good grades for 22% of the class, passing grades for 36%, and failing grades for 32% of the class.Figure 10
Two-test score distribution. (a) shows the distribution of scores in the first test. (b) shows the distribution of scores in the second test.
(a)(b)


### 4.3. Online Course Learning Satisfaction Comparison Test

After the whole teaching practice, the two online courses are designed in six aspects: page design, teaching mode, improving students’ problem-solving ability, improving students’ learning ability, improving students’ communication ability, and attracting students, a questionnaire survey was conducted to the students participating in the experiment, and the survey results are shown in Figure11.Figure 11
Statistical results of students’ evaluation of online courses. (a) shows students’ evaluation of the Internet of Things online course. (b) shows students’ evaluation of traditional online courses.
(a)(b)As can be seen from Figure11, in the evaluation of the Internet of Things online course of multimedia technology, the average score of page design is 82.34 points, the average score of teaching mode is 86.79 points, the average score of improving students’ problem-solving ability is 86.83 points, the average score of improving students’ learning ability is 89.61 points, and the average score of improving students’ communication ability is 85.42 points; the average score for attracting students is 87.16; in the evaluation of traditional online courses, the average score of page design is 82.75 points, the average score of teaching mode is 81.29 points, the average score of improving students’ problem-solving ability is 82.47 points, the average score of improving students’ learning ability is 79.82 points, and the average score of improving students’ communication ability is 79.82 points; the average score of improving students’ communication ability is 80.38 points, and the average score of attracting students is 82.46 points.


## 4.1. Preexperimental Student Situation Test

In order to improve the accuracy of this experiment and reduce the error, before the start of the experiment, they use correlation analysis to study the correlation between gender, learning stage, school type, and major category and whether they have participated in online courses. The statistical results are shown in Figure7 and Tables 4 and 5.Table 4
Correlation analysis of class A students.
Pearson correlation coefficientWhat is your gender?Correlation coefficient-0.052P value0.217What is your current stage of study?Correlation coefficient-0.007P value0.814What type of school did you attend?Correlation coefficient-0.019P value0.068What is your major?Correlation coefficient-0.041P value0.362Table 5
Correlation analysis of class B students.
Pearson correlation coefficientWhat is your gender?Correlation coefficient-0.063P value0.194What is your current stage of study?Correlation coefficient-0.021P value0.625What type of school did you attend?Correlation coefficient-0.014P value0.096What is your major?Correlation coefficient-0.032P value0.225It can be seen from Figure8 and Table 4 that the correlation coefficient between the gender of students in class A and whether they have participated in online courses is -0.052, which is close to 0. And the P value is 0.217, which is greater than 0.05, and there is no significant correlation between the two, indicating that there is no correlation between student gender and whether they have attended online courses. The correlation coefficient value between the learning stage of class A students and whether they have participated in online courses is -0.007, which is close to 0. And the p value is 0.814, which is greater than 0.05, and there is no significant correlation between the two. This shows that there is no correlation between students’ learning stage and whether they have participated in online courses. The correlation coefficient between the school type of class A students and whether they have participated in online courses is -0.019, and the P value is 0.068, which is greater than 0.05, and there is no significant correlation between the two. This suggests that there is no correlation between a student’s school type and whether they have taken online courses. The correlation coefficient between the major category of class A students and whether they have participated in online courses is -0.041, which is close to 0, and the P value is 0.362, which is greater than 0.05. There is no significant correlation between the two. This shows that there is no correlation between the student’s major category and whether they have attended online courses.Figure 8
Participation of male and female college students in online courses. (a) shows whether students in class A participate in online courses. (b) shows whether students in class B participate in online courses.
(a)(b)


## 4.2. Comparison Test of Students’ Learning Ability

Under the condition that the teaching process of the two classes A and B are kept the same, two unit tests were conducted, and the difficulty of the two test questions was from shallow to deep, step by step. Two classes of students take the test at the same time, and the test time is 90 minutes. The distribution of the two test scores is shown in Figure9.Figure 9
Statistics of two test scores. (a) shows the first test scores of students in two classes. (b) shows the second test scores of students in two classes.
(a)(b)As can be seen from Figure9, in the first assessment test, according to the statistics of students in class A, there are 8 students with excellent grades, 22 students with good grades, 15 students with passing grades, and 5 students with failed grades; judging from the statistics of the students’ grades in class B, there are 6 students with excellent grades, 12 students with good grades, 24 students with passing grades, and 8 students with failing grades; in the second assessment test, according to the statistics of class A students, there are 6 students with excellent grades, 18 students with good grades, 17 students with passing grades, and 9 students with failed grades; judging from the statistics of the students’ grades in class B, there are 5 students with excellent grades, 11 students with good grades, 18 students with passing grades, and 16 students with failing grades.As can be seen from Figure10, in the distribution of scores in the first test, the outstanding scores of class A accounted for 16% of the class, the good scores accounted for 44% of the class, the passing scores accounted for 30% of the class, and the failing scores accounted for 30%, and failing grades account for 10% of the class; class B’s outstanding grades account for 12% of the class, good grades account for 24% of the class, passing grades account for 48% of the class, and failing grades account for 16% of the class; in the distribution of scores in the second test, the outstanding scores of class A accounted for 12% of the class, the good scores accounted for 36% of the class, the passing scores accounted for 34%, and the failing scores accounted for 34%, with failing grades accounting for 18% of the class; class B has excellent grades for 10% of the class, good grades for 22% of the class, passing grades for 36%, and failing grades for 32% of the class.Figure 10
Two-test score distribution. (a) shows the distribution of scores in the first test. (b) shows the distribution of scores in the second test.
(a)(b)


## 4.3. Online Course Learning Satisfaction Comparison Test

After the whole teaching practice, the two online courses are designed in six aspects: page design, teaching mode, improving students’ problem-solving ability, improving students’ learning ability, improving students’ communication ability, and attracting students, a questionnaire survey was conducted to the students participating in the experiment, and the survey results are shown in Figure11.Figure 11
Statistical results of students’ evaluation of online courses. (a) shows students’ evaluation of the Internet of Things online course. (b) shows students’ evaluation of traditional online courses.
(a)(b)As can be seen from Figure11, in the evaluation of the Internet of Things online course of multimedia technology, the average score of page design is 82.34 points, the average score of teaching mode is 86.79 points, the average score of improving students’ problem-solving ability is 86.83 points, the average score of improving students’ learning ability is 89.61 points, and the average score of improving students’ communication ability is 85.42 points; the average score for attracting students is 87.16; in the evaluation of traditional online courses, the average score of page design is 82.75 points, the average score of teaching mode is 81.29 points, the average score of improving students’ problem-solving ability is 82.47 points, the average score of improving students’ learning ability is 79.82 points, and the average score of improving students’ communication ability is 79.82 points; the average score of improving students’ communication ability is 80.38 points, and the average score of attracting students is 82.46 points.


## 5. Conclusion

Through the comparative experimental data of the Internet of Things online courses of multimedia technology and traditional online courses, the following conclusions can be drawn:(1)
In the comparison test of students’ learning ability, the overall mean of the students’ first test scores under the multimedia technology Internet of Things online course teaching is 7.49 points higher than the overall mean of the traditional online courses. In the second test comparison, the overall average test score of the Internet of Things online course of multimedia technology is also higher than that of the traditional online course by an advantage of 5.86 points(2)
In the course learning satisfaction comparison test, the overall average evaluation of the Internet of Things online courses of multimedia technology is 4.83 points higher than the overall evaluation of traditional online courses(3)
The whole comparative experimental data shows that under the condition of keeping the same experimental conditions of students in classes A and B, after different modes of online course teaching practice, both in terms of students’ learning ability and students’ course learning satisfaction, all the students of class A performed better(4)
The continuous update and expansion of Internet applications have promoted the process of education modernization, and a new round of changes in education and teaching has begun. Multimedia Internet of Things technology is implemented in a variety of industry applications and has exerted its greatest value. Combining it with the education industry will not only help its own diversified development but also help the education industry to improve the level of intelligent teaching. For students, the teaching of online courses based on multimedia IoT can not only enhance their interest in learning but also improve their ability to solve problems and communicate with people. For teachers, the personalized teaching developed by multimedia Internet of Things technology for students can effectively reduce the teaching burden of teachers, improve teaching efficiency, and realize the development of teaching informatization. It is believed that with the continuous maturity and improvement of multimedia technology, college teaching can be further optimized(5)
Although this paper has carried out a profound research on the construction of online courses using computer technology and multimedia Internet of Things technology, there are still many shortcomings. The depth and breadth of the research in this paper are not enough, and the research on my academic level is also limited, and the research on online courses is still in the preliminary stage. In the future work, we will study appropriate teaching methods and means from more perspectives based on the existing technology and level and continuously improve the teaching quality



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*Source: 4052994-2022-04-13.xml*