# The Application Trend and Advantages of Mobile Technology in Higher Education

Authors: Bo Li
Journal: Mobile Information Systems
 (2022)

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## Abstract

The emergence of mobile wireless technology has profoundly altered how people produce goods and live their lives, but even as we take advantage of its speed and convenience, we must also be aware of its drawbacks. A new technology supported by contemporary technology is mobile wireless Internet technology. It is computer-, network-, and new media-based. Education and teaching are becoming more important in society as a result of the social economy’s rapid growth and the reform and development policies’ gradual advancement. It can develop the skills required by society and make teaching more widely known. The semisupervised learning positioning algorithm used in this study lowers the training phase acquisition costs. The proposed algorithm significantly resolves the issue that the DV-Hop algorithm is not suitable for anisotropic network positioning by applying the concept of triple equidistant coverage to anisotropic wireless sensor networks and combining it with multiple mobile beacons. According to the results of the experimental analysis, the following conclusions can be made: information processing can be found to be essentially stable at a level of more than 40%, which will greatly strengthen the use of mobile wireless technology in higher education. The improvement in overall performance will reach 65.7 percent. Following the experiment, it was discovered that the average transmission efficiency for mobile wireless technology is 57.4%, which is extremely high.


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## Body

## 1. Introduction

Recent years have seen a significant increase in the use of powerful personal desktop computers, notebook computers, and even palmtop computers due to the rise in people’s living standards and the rapid development of personal data communication services [1]. In order for the majority of users to experience quick and stable data communication wherever they are, the computer network must evolve from the traditional wired network to the wireless network, from the fixed network to the mobile network, and from the single business function to the multimedia business function [2]. Building a campus network is essential to advancing quality education and adapting to the knowledge economy era. Countries all over the world are quickening the pace of educational modernization as part of the century-long educational reform. The degree of information technology has emerged as a key indicator of a nation’s overall strength in the international arena [3]. In order to realize the multilevel development and rationalisation of human and material resources in education, it is imperative that school education be integrated with family education, social education, and especially the implicit education of the mass media configuration [4]. Colleges and universities are positively responding to this impact by undertaking costly and extensive campus information technology construction [5]. It is a fundamental, ongoing, and consistent task, and the quality of its construction is a key indicator of the general level of school operation, resource integration and sharing capability, school image, and status of colleges and universities.Wireless access, wireless networks, wireless smart appliances, and other areas of daily life have all gradually incorporated wireless technology in recent years, which has increased its abnormally rapid and rapid development [6]. While this is happening, people are becoming more eager to use wireless technology in other fields of application [7] due to its ongoing innovation. The widespread use of wireless sensors in numerous fields, such as environmental monitoring, disaster relief, medical diagnosis, and military applications, has been facilitated by the development of hardware technology and wireless communication technology [8]. A new idea known as the “Internet of Things” has been proposed in recent years. It depicts a new interactive relationship between the physical and the human worlds and builds elaborate blueprints such as “Smart Earth” and “Perceive China” that indicate the course of future advancements in wireless sensor network technology [9]. Rapid deployment, straightforward setup, superior performance, easy upkeep, and low cost are the hallmarks of mobile wireless technology. Applications in unique environments are appropriate for it. The wireless sensor network is made up of numerous low-complexity microsensor nodes placed throughout the monitoring area that self-organize to create a multihop wireless network. Each sensor node includes a sensing module, a processing module, a communication module, and a power supply module to finish the data processing. Data transmission, reception, and forwarding are the three fundamental data processing operations. The goal is to collectively perceive, gather, and process information about the objects being observed within the network coverage area, and then transmit that perception information to the observer [10]. The ability of humans to know the world and gather information is greatly increased by the use of wireless sensor networks, which enable people to directly perceive the objective world.Mobile beacons are frequently used by current positioning algorithms to assist in the positioning of unknown nodes because they can decrease energy consumption and increase positioning accuracy [11]. The majority of node deployment techniques use random deployment, and the deployment areas primarily consist of two types of deployment shapes: regular deployment shapes and irregular deployment shapes, or communication blind spots [12]. This helps to make the experimental environment more realistic. The deployment of networks with regular shapes currently makes up the majority of static path planning applications. The current algorithms still have a few flaws, such as incomplete edge node coverage, low node positioning precision, and excessive path energy consumption [13]. Embedded devices with specific sensing, communication, and computing capabilities are known as wireless sensor network nodes [14]. As the main component of wireless sensor networks, they self-organize into a sensor network in a wireless multihop manner in the sensor distribution area, transmit the data about the physical world obtained by monitoring to the sink node, and communicate with the outside world through the Internet or satellite communication and gateway, allowing users to finally take advantage of the corresponding services [15]. The application trend and benefits of mobile wireless technology in higher education, however, have not been resolved or explained by the aforementioned research. The following research innovations are thus suggested in this paper:(1)
The traditional pattern matching localization algorithm needs to learn a large number of labeled data in the training stage, so as to establish a training model for predicting unlabeled data. However, in real life, collecting and marking data requires a lot of human and material resources, and the collection cost is relatively high. To solve this problem, a semisupervised learning localization algorithm based on mark-to-mark transfer is proposed, which reduces the acquisition cost in the training phase.(2)
A two-dimensional DV-Hop node localization algorithm assisted by multiple mobile beacons is proposed. In this part, based on the analysis of the node location calculation theory, the idea of triple equidistant coverage is applied to anisotropic wireless sensor networks, and combined with multiple mobile beacons, the proposed algorithm greatly improves the problem that the DV-Hop algorithm is not suitable for anisotropic network location.


## 2. Related Work

Li and Chen believe that the exploration and development of communication technology has gone through a long period of time, and it has also crossed different stages and achieved different levels of leaps and improvements, from the simplest text communication to the current electronic device transmission; every progress provides a steady stream of impetus for the development of society [16]. Lv et al. put forward that the remote mobile Internet technology is the product of the times and the most direct embodiment of technology changing life. Education is a form of talent cultivation in China and an effective way to realize people’s self-appreciation. With the emergence of distance education, the form of education has been updated and the content has been enriched. The application of mobile wireless Internet technology makes the forms of distance education richer, the operation time more flexible, and the technology more beneficial to students’ autonomous learning [17]. The research of Ma and Li showed that mobile wireless technology is not a certain technology but the integrated application of multiple technologies, mainly including wireless access technology, mobile computing, and Internet technology. If we want to give full play to the advantages of technology in distance education, we must clarify the relevant connotation and lay a foundation for the society to cultivate sound talents [18]. Guo et al. believe that a very critical value orientation of subjects when choosing media is the tendency to choose media that can bring more spatiotemporal flexibility for information processing. This value orientation is directly related to the fact that the learners of distance education are mainly adults, especially incumbents, because incumbents expect more choices in the time and place of learning due to the double burden of life and work [19]. Yang and Chan think that the sharing of students’ high-quality teaching resources can be realized through video courseware in schools. This kind of distance education mode of real-time interactive teaching breaks through the traditional time and space limitation, and students can study reasonably according to their actual situation. Distance education has greatly alleviated the contradiction between the study, work, and life of on-the-job staff, and solved the difficult problem of face-to-face counseling [20]. The research of Rubini and Mangayarkarasi showed that various technologies in the field of wireless communication pay more and more attention to complementarity. This is mainly reflected in different access technologies with different coverage, different application areas, different functional characteristics, and different access rates. Considering the popular wireless technologies 3G, WLAN, and UWB as an example, they can achieve a complementary effect [21]. Takahashi’s research found that digital campuses have incomparable advantages over traditional campuses. First of all, the digital campus breaks the constraints of space, and the concept of classroom is no longer limited to the traditional classroom but refers to the network space, information space, and virtual space where the groups of learners who are studying at the same time in the network environment are located. It reflects the openness of space [22]. Luis et al. put forward that from the perspective of the characteristics of future talents, talents in the information age must have a strong ability to obtain and select information, a strong network ability, a strong knowledge self-construction ability, and an active desire and ability to learn for life. Faced with this situation, our traditional education and teaching will face an unprecedented impact and will also produce revolutionary and essential changes [23]. Kim et al. believed that reasonably planning and designing the campus network environment, adopting the popular three-tier network architecture, and using technology to reasonably divide the network area can effectively isolate the network broadcast storm, block network attacks, facilitate the daily maintenance and management of the network, and improve the utilization of network bandwidth. Using the routing technology of layer 3 switch, we can reasonably manage the shared network resources and maximize the rational utilization of network resources [24]. Basavaiah et al. found that UASP designs static paths based on the unit area so that the path planning does not need to consider the collinearity problem of virtual beacon nodes. To ensure the positioning coverage, the angle between the beacons in the unit area is 60 degrees, which ensures the positioning accuracy; in order to reduce unnecessary path overhead, the trend of the entire path is in the form of a Scan curve [25]. Patzold believed that although the existing localization algorithms have been implemented and applied in large-scale wireless sensor networks, with the further expansion of the network scale, the competition of wireless channels will lead to an increase in the propagation delay and energy consumption, and the message may be lost due to unreliable wireless transmission; especially, the ranging error will accumulate with the increase in hop number, which will affect the localization accuracy and convergence speed of the node localization algorithm in large-scale sensor networks [26]. Ding et al. proposed that the location of wireless sensor networks includes the location of unknown nodes and the location of external targets. The former is the basis of the latter. Node location accuracy plays an important role in the accuracy of node location information, and accurate node location information plays a vital role in the whole life cycle of wireless sensor networks [27].Based on the research of the abovementioned related work, this paper determines the application trend and positive role of mobile wireless technology in higher education, and builds an algorithm-optimized mobile wireless technology application trend and advantage analysis model in higher education. Algorithm analysis is used to conduct in-depth analysis and research on the acquired and collected data, make more effective use of data, and mine valuable information hidden behind the data.


## 3. Methodology

### 3.1. Research on Related Theories

#### 3.1.1. Analysis on the Development Trend of Mobile Wireless Technology in Education

The use of the Internet and network technology has largely taken over people’s jobs and personal lives, affecting almost every aspect of social production. As a result, information sharing between work and personal life is crucial, and information sharing between units and families is becoming more and more common. The technology used for line mobile interconnection is a hybrid of wireless access, mobile computing, and Internet technologies. The following three types of media systems can be constructed using a combination of the abovementioned three types of technologies. Mobile terminal equipment, a communication network, and a teaching server make up the system’s hardware. These media systems have complete two-way communication capabilities, as well as synchronous and asynchronous communication abilities in terms of interactive characteristics.The abovementioned three media systems are significantly more flexible in terms of space-time information processing than the previous media systems, based on the characteristics of wireless access technology that enable learners to obtain information access entrance easily, portability and operability of mobile devices, and potential massive resources on the Internet. In essence, they have the ability to process information whenever and wherever they choose, and the system’s mobile terminal device interface is user-friendly and straightforward. The media system’s level of accessibility is described in terms of mobile wireless Internet technology. These media systems are fully capable of synchronous and asynchronous communication as well as bidirectional communication in terms of interactive characteristics. This essay examines some of the accessible features of these media systems. This paper contends that these media systems have the potential to be chosen by the primary body of higher education if they are capable of outperforming the previous media systems in this regard.Many times, people cannot predict the time, location, and communication capacity demand when they communicate. The advantages of mobile wireless communication technology are not limited by time and space. Flexible methods can be applied to ensure the smooth communication of voice, image, and data. The network can undoubtedly be used as the core technology in the switching network of the transition circuit, and Jubai is also the most suitable choice at present. This can improve the ability of the communication network to process data, ensure the smooth flow of radio communication signals, and solve the problem of signal transmission in the process.


#### 3.1.2. Overview of Mobile Wireless Technology

With the ongoing reshaping of the national consciousness and the accelerating development of society, wireless communication technology is advancing. It is not difficult to find that the involvement of wireless communication technology is becoming more and more integral to people’s daily work, daily life, ideological exchange, medical security, and consumer entertainment. As people’s needs continue to evolve and change, wireless communication has also changed from a fixed to a mobile one. People are becoming more and more aware of the practical value of this technology as a result of the development of numerous applications based on wireless communication technology. Since its successful development, wireless transmission technology has started to integrate with other fields in addition to mobile communication. Wireless technology is becoming more and more ingrained in our daily lives and jobs, as evidenced by wireless monitoring, wireless smart appliances, and wireless detection. Data transmission has a strong direction when data are at the center. Wireless sensor network applications are task-oriented networks, and their main goal is to deliver useful data to users. Users are more interested in the data itself than they are in where it came from. As a result, the wireless sensor network must focus on data, and data transmission must have a clear direction. Figure1 shows the positioning performance of the current wireless positioning system.Figure 1
Analysis of the positioning performance of the current wireless positioning system.Currently, the most successful commercial positioning system is GPS. To find the user nodes, it makes use of the precise synchronous satellite clock’s timing and ranging capabilities. High real-time, real-time accuracy, and high robustness are its benefits. It is frequently used in open spaces such as the outdoors and for vehicle navigation. However, the GPS signal is blocked in environments that are enclosed, submerged, underwater, or obstructed, making accurate positioning challenging. For this article, the application to the field of higher education necessitates the use of other tools, but it is primarily necessary to analyse the superposition state and virtual positioning of mobile wireless technology, so the research on wireless sensors typically includes trilateration, triangulation measurement, and maximum-likelihood estimation.For the trilateration method, the distance between one unknown node and the other three known nodes is used to calculate the position of the unknown node. The specific calculation principle is shown in Figure2. It is known that A, B, and C are three beacons, and the coordinates are x1,y1,x2,y2,x3,y3. The distances from the three beacons to the unknown node D are d1,d2,d3, respectively. Assuming that the coordinates of D are x,y, the corresponding distances can be obtained using the following formula:(1)x−x12+y−y12=d1,x−x22+y−y22=d2,x−x32+y−y32=d3.Figure 2
Principle of trilateration.The coordinates of noded at the position are calculated by the following formula:(2)xy=2x1−x32y1−y32x2−x32y2−y3−1x12−x32+y12−y32+d32−d12x22−x32+y22−y32+d32−d22.For the triangulation method, the arc formed by the nodesA, D, and C needs to be in the triangle ABC; then, a circle with the center of O1x∘1,y∘1 can be determined, and the circle is unique. Let the radius of the circle be r1, then α=∠AO1C=2π−2∠ADC can be obtained, and there is the following formula:(3)xo1−x12+yo1−y12=r1,xo1−x32+yo1−y32=r1,x1−x32+y1−y32=2r12−2r12cosα.Therefore, the coordinates and radiusO1 of the circle center r1 corresponding to the arc ADC can be obtained, and the distance between nodes A and D can be calculated. Similarly, the distance between D, B, D, and C can also be calculated, and then, the coordinate value of D can be obtained using the trilateration method.The maximum-likelihood estimation is similar to the trilateral method, except that the coordinate information of at least three nodes needs to be known. As shown in Figure3, the coordinates of n known beacon nodes are x1,y1,x2,y2,…,xn,yn, and the distance from unknown node d to n beacons is d1,d2,…,dn, which is generally expressed as(4)x−xi2+y−yi2=di2.Figure 3
Maximum-likelihood estimation construction diagram.Among them,i=1,2,…,n; at this time, the coordinate D^=ATA−1ATb of the unknown node D can be obtained using the minimum mean square error estimation, where(5)A=x1−x2y1−y2……x1−xny1−yn,b=x12−x22+y12−y22+d12−d22…x12−xn2+y12−yn2+d12−dn2.


### 3.2. Advantage and Trend Analyses for Mobile Wireless Technology

The basic setting of semisupervised learning is that given a marked data setL=x1,y1,…,xL,yL and an unlabeled data set U=x1,…,xU from an unknown distribution, it is expected that the function f:X⟶Y can accurately predict the mark x of the data y. Here, xi,xi′ is the vector of dimension d, yi is the marker of xi, and L and U are the sizes of L and U, respectively, indicating the number of data contained.Supposex1,y1…xl,yl is marked data, and YL=y1…yl is a marked set of marked data. S=sjj=1C represents the set of markers for all data. The size of C is known, and all markers have appeared in the marker set with marker 1. xl+1,yl+1…xl+u,yl+u is unmarked data, and YU=yl+1…yl+u is unknown. Generally, l≪u. n=l+u represents the number of all data. All data can be expressed as X=x1…xl+u. Therefore, the problem can be described as estimating YL based on the similarity between the marked information in X and the data in xhxh∈YU.Therefore, when the distance between two data pointsi,j is closer and closer, the weight of the connecting edge between them will be greater. The weight of the edge is defined as(6)wij=exp−dij2σ2=exp−∑d=lDxid−xjd2σ2,where 1≤i=j≤n; then, the weight of the edge between them is 0. σ is a hyperparameter, which is used to control the width of the weight wij so that the weight matrix of the edges of the entire data set can be constructed.The DV-Hop localization algorithm is a distributed, hop-by-hop nonranging node localization algorithm. Under the network topology shown in Figure4, the actual distance and routing path between beacon nodes are shown in Figure 4. Among them, nodes N1, N2, and N3 are beacon nodes. Nodes N1, N2, and N3 exchange location information in the form of flooding, and unknown node K obtains the minimum hop information with each beacon node and saves it.Figure 4
Network topology diagram of the DV-Hop algorithm.In order to calculate the estimated average per-hop distance between the three nodes, this paper adopts the following scheme for calculation:(7)Cj=∑Xi−Xj2+Yi−Yj2∑hi.Then, the minimum number of hops and average per hop obtained in the first two stages are obtained using the unknown nodeK, and the estimated distance from itself to the three beacon nodes is obtained. As shown in Tables 1 and 2, with the increase in the unknown node density, the average traffic of the DV-Hop algorithm increases sharply with the increase in the node density, and the evaluation traffic of the other two algorithms is relatively stable. The average traffic of the TMB-DV-Hop algorithm is always lower than that of the MB-DV-Hop algorithm.Table 1
Average traffic of isotropic sensor networks.
Node density30405060DV-Hop35477254MB-DV-Hop23.4523.4723.1123.34TMB-DV-Hop2422.324.523.5Table 2
Average traffic of anisotropic sensor networks.
Node density30405060DV-Hop52679775MB-DV-Hop26.5525.4324.5524.51TMB-DV-Hop25.324.1223.7521.45Assuming that the distance error between the estimated coordinates of nodei and the real coordinates is di, the ratio of the average distance error of N unknown nodes to the communication radius R in the two-dimensional environment is recorded as the average positioning error:(8)di=x^−x2+y^−y2,Δd¯=1NR∑i=1Ndi.In the above formula,x,y are the actual coordinates of the unknown node and x^,y^ are the estimated coordinates of the unknown node. The average traffic is expressed as the average of the packets sent and received by the node. Assuming that pi is the total number of packets sent and received by node i, and N is the number of nodes, the average traffic is expressed as(9)traffic=1N∑i=1Npi2.


## 3.1. Research on Related Theories

### 3.1.1. Analysis on the Development Trend of Mobile Wireless Technology in Education

The use of the Internet and network technology has largely taken over people’s jobs and personal lives, affecting almost every aspect of social production. As a result, information sharing between work and personal life is crucial, and information sharing between units and families is becoming more and more common. The technology used for line mobile interconnection is a hybrid of wireless access, mobile computing, and Internet technologies. The following three types of media systems can be constructed using a combination of the abovementioned three types of technologies. Mobile terminal equipment, a communication network, and a teaching server make up the system’s hardware. These media systems have complete two-way communication capabilities, as well as synchronous and asynchronous communication abilities in terms of interactive characteristics.The abovementioned three media systems are significantly more flexible in terms of space-time information processing than the previous media systems, based on the characteristics of wireless access technology that enable learners to obtain information access entrance easily, portability and operability of mobile devices, and potential massive resources on the Internet. In essence, they have the ability to process information whenever and wherever they choose, and the system’s mobile terminal device interface is user-friendly and straightforward. The media system’s level of accessibility is described in terms of mobile wireless Internet technology. These media systems are fully capable of synchronous and asynchronous communication as well as bidirectional communication in terms of interactive characteristics. This essay examines some of the accessible features of these media systems. This paper contends that these media systems have the potential to be chosen by the primary body of higher education if they are capable of outperforming the previous media systems in this regard.Many times, people cannot predict the time, location, and communication capacity demand when they communicate. The advantages of mobile wireless communication technology are not limited by time and space. Flexible methods can be applied to ensure the smooth communication of voice, image, and data. The network can undoubtedly be used as the core technology in the switching network of the transition circuit, and Jubai is also the most suitable choice at present. This can improve the ability of the communication network to process data, ensure the smooth flow of radio communication signals, and solve the problem of signal transmission in the process.


### 3.1.2. Overview of Mobile Wireless Technology

With the ongoing reshaping of the national consciousness and the accelerating development of society, wireless communication technology is advancing. It is not difficult to find that the involvement of wireless communication technology is becoming more and more integral to people’s daily work, daily life, ideological exchange, medical security, and consumer entertainment. As people’s needs continue to evolve and change, wireless communication has also changed from a fixed to a mobile one. People are becoming more and more aware of the practical value of this technology as a result of the development of numerous applications based on wireless communication technology. Since its successful development, wireless transmission technology has started to integrate with other fields in addition to mobile communication. Wireless technology is becoming more and more ingrained in our daily lives and jobs, as evidenced by wireless monitoring, wireless smart appliances, and wireless detection. Data transmission has a strong direction when data are at the center. Wireless sensor network applications are task-oriented networks, and their main goal is to deliver useful data to users. Users are more interested in the data itself than they are in where it came from. As a result, the wireless sensor network must focus on data, and data transmission must have a clear direction. Figure1 shows the positioning performance of the current wireless positioning system.Figure 1
Analysis of the positioning performance of the current wireless positioning system.Currently, the most successful commercial positioning system is GPS. To find the user nodes, it makes use of the precise synchronous satellite clock’s timing and ranging capabilities. High real-time, real-time accuracy, and high robustness are its benefits. It is frequently used in open spaces such as the outdoors and for vehicle navigation. However, the GPS signal is blocked in environments that are enclosed, submerged, underwater, or obstructed, making accurate positioning challenging. For this article, the application to the field of higher education necessitates the use of other tools, but it is primarily necessary to analyse the superposition state and virtual positioning of mobile wireless technology, so the research on wireless sensors typically includes trilateration, triangulation measurement, and maximum-likelihood estimation.For the trilateration method, the distance between one unknown node and the other three known nodes is used to calculate the position of the unknown node. The specific calculation principle is shown in Figure2. It is known that A, B, and C are three beacons, and the coordinates are x1,y1,x2,y2,x3,y3. The distances from the three beacons to the unknown node D are d1,d2,d3, respectively. Assuming that the coordinates of D are x,y, the corresponding distances can be obtained using the following formula:(1)x−x12+y−y12=d1,x−x22+y−y22=d2,x−x32+y−y32=d3.Figure 2
Principle of trilateration.The coordinates of noded at the position are calculated by the following formula:(2)xy=2x1−x32y1−y32x2−x32y2−y3−1x12−x32+y12−y32+d32−d12x22−x32+y22−y32+d32−d22.For the triangulation method, the arc formed by the nodesA, D, and C needs to be in the triangle ABC; then, a circle with the center of O1x∘1,y∘1 can be determined, and the circle is unique. Let the radius of the circle be r1, then α=∠AO1C=2π−2∠ADC can be obtained, and there is the following formula:(3)xo1−x12+yo1−y12=r1,xo1−x32+yo1−y32=r1,x1−x32+y1−y32=2r12−2r12cosα.Therefore, the coordinates and radiusO1 of the circle center r1 corresponding to the arc ADC can be obtained, and the distance between nodes A and D can be calculated. Similarly, the distance between D, B, D, and C can also be calculated, and then, the coordinate value of D can be obtained using the trilateration method.The maximum-likelihood estimation is similar to the trilateral method, except that the coordinate information of at least three nodes needs to be known. As shown in Figure3, the coordinates of n known beacon nodes are x1,y1,x2,y2,…,xn,yn, and the distance from unknown node d to n beacons is d1,d2,…,dn, which is generally expressed as(4)x−xi2+y−yi2=di2.Figure 3
Maximum-likelihood estimation construction diagram.Among them,i=1,2,…,n; at this time, the coordinate D^=ATA−1ATb of the unknown node D can be obtained using the minimum mean square error estimation, where(5)A=x1−x2y1−y2……x1−xny1−yn,b=x12−x22+y12−y22+d12−d22…x12−xn2+y12−yn2+d12−dn2.


## 3.1.1. Analysis on the Development Trend of Mobile Wireless Technology in Education

The use of the Internet and network technology has largely taken over people’s jobs and personal lives, affecting almost every aspect of social production. As a result, information sharing between work and personal life is crucial, and information sharing between units and families is becoming more and more common. The technology used for line mobile interconnection is a hybrid of wireless access, mobile computing, and Internet technologies. The following three types of media systems can be constructed using a combination of the abovementioned three types of technologies. Mobile terminal equipment, a communication network, and a teaching server make up the system’s hardware. These media systems have complete two-way communication capabilities, as well as synchronous and asynchronous communication abilities in terms of interactive characteristics.The abovementioned three media systems are significantly more flexible in terms of space-time information processing than the previous media systems, based on the characteristics of wireless access technology that enable learners to obtain information access entrance easily, portability and operability of mobile devices, and potential massive resources on the Internet. In essence, they have the ability to process information whenever and wherever they choose, and the system’s mobile terminal device interface is user-friendly and straightforward. The media system’s level of accessibility is described in terms of mobile wireless Internet technology. These media systems are fully capable of synchronous and asynchronous communication as well as bidirectional communication in terms of interactive characteristics. This essay examines some of the accessible features of these media systems. This paper contends that these media systems have the potential to be chosen by the primary body of higher education if they are capable of outperforming the previous media systems in this regard.Many times, people cannot predict the time, location, and communication capacity demand when they communicate. The advantages of mobile wireless communication technology are not limited by time and space. Flexible methods can be applied to ensure the smooth communication of voice, image, and data. The network can undoubtedly be used as the core technology in the switching network of the transition circuit, and Jubai is also the most suitable choice at present. This can improve the ability of the communication network to process data, ensure the smooth flow of radio communication signals, and solve the problem of signal transmission in the process.


## 3.1.2. Overview of Mobile Wireless Technology

With the ongoing reshaping of the national consciousness and the accelerating development of society, wireless communication technology is advancing. It is not difficult to find that the involvement of wireless communication technology is becoming more and more integral to people’s daily work, daily life, ideological exchange, medical security, and consumer entertainment. As people’s needs continue to evolve and change, wireless communication has also changed from a fixed to a mobile one. People are becoming more and more aware of the practical value of this technology as a result of the development of numerous applications based on wireless communication technology. Since its successful development, wireless transmission technology has started to integrate with other fields in addition to mobile communication. Wireless technology is becoming more and more ingrained in our daily lives and jobs, as evidenced by wireless monitoring, wireless smart appliances, and wireless detection. Data transmission has a strong direction when data are at the center. Wireless sensor network applications are task-oriented networks, and their main goal is to deliver useful data to users. Users are more interested in the data itself than they are in where it came from. As a result, the wireless sensor network must focus on data, and data transmission must have a clear direction. Figure1 shows the positioning performance of the current wireless positioning system.Figure 1
Analysis of the positioning performance of the current wireless positioning system.Currently, the most successful commercial positioning system is GPS. To find the user nodes, it makes use of the precise synchronous satellite clock’s timing and ranging capabilities. High real-time, real-time accuracy, and high robustness are its benefits. It is frequently used in open spaces such as the outdoors and for vehicle navigation. However, the GPS signal is blocked in environments that are enclosed, submerged, underwater, or obstructed, making accurate positioning challenging. For this article, the application to the field of higher education necessitates the use of other tools, but it is primarily necessary to analyse the superposition state and virtual positioning of mobile wireless technology, so the research on wireless sensors typically includes trilateration, triangulation measurement, and maximum-likelihood estimation.For the trilateration method, the distance between one unknown node and the other three known nodes is used to calculate the position of the unknown node. The specific calculation principle is shown in Figure2. It is known that A, B, and C are three beacons, and the coordinates are x1,y1,x2,y2,x3,y3. The distances from the three beacons to the unknown node D are d1,d2,d3, respectively. Assuming that the coordinates of D are x,y, the corresponding distances can be obtained using the following formula:(1)x−x12+y−y12=d1,x−x22+y−y22=d2,x−x32+y−y32=d3.Figure 2
Principle of trilateration.The coordinates of noded at the position are calculated by the following formula:(2)xy=2x1−x32y1−y32x2−x32y2−y3−1x12−x32+y12−y32+d32−d12x22−x32+y22−y32+d32−d22.For the triangulation method, the arc formed by the nodesA, D, and C needs to be in the triangle ABC; then, a circle with the center of O1x∘1,y∘1 can be determined, and the circle is unique. Let the radius of the circle be r1, then α=∠AO1C=2π−2∠ADC can be obtained, and there is the following formula:(3)xo1−x12+yo1−y12=r1,xo1−x32+yo1−y32=r1,x1−x32+y1−y32=2r12−2r12cosα.Therefore, the coordinates and radiusO1 of the circle center r1 corresponding to the arc ADC can be obtained, and the distance between nodes A and D can be calculated. Similarly, the distance between D, B, D, and C can also be calculated, and then, the coordinate value of D can be obtained using the trilateration method.The maximum-likelihood estimation is similar to the trilateral method, except that the coordinate information of at least three nodes needs to be known. As shown in Figure3, the coordinates of n known beacon nodes are x1,y1,x2,y2,…,xn,yn, and the distance from unknown node d to n beacons is d1,d2,…,dn, which is generally expressed as(4)x−xi2+y−yi2=di2.Figure 3
Maximum-likelihood estimation construction diagram.Among them,i=1,2,…,n; at this time, the coordinate D^=ATA−1ATb of the unknown node D can be obtained using the minimum mean square error estimation, where(5)A=x1−x2y1−y2……x1−xny1−yn,b=x12−x22+y12−y22+d12−d22…x12−xn2+y12−yn2+d12−dn2.


## 3.2. Advantage and Trend Analyses for Mobile Wireless Technology

The basic setting of semisupervised learning is that given a marked data setL=x1,y1,…,xL,yL and an unlabeled data set U=x1,…,xU from an unknown distribution, it is expected that the function f:X⟶Y can accurately predict the mark x of the data y. Here, xi,xi′ is the vector of dimension d, yi is the marker of xi, and L and U are the sizes of L and U, respectively, indicating the number of data contained.Supposex1,y1…xl,yl is marked data, and YL=y1…yl is a marked set of marked data. S=sjj=1C represents the set of markers for all data. The size of C is known, and all markers have appeared in the marker set with marker 1. xl+1,yl+1…xl+u,yl+u is unmarked data, and YU=yl+1…yl+u is unknown. Generally, l≪u. n=l+u represents the number of all data. All data can be expressed as X=x1…xl+u. Therefore, the problem can be described as estimating YL based on the similarity between the marked information in X and the data in xhxh∈YU.Therefore, when the distance between two data pointsi,j is closer and closer, the weight of the connecting edge between them will be greater. The weight of the edge is defined as(6)wij=exp−dij2σ2=exp−∑d=lDxid−xjd2σ2,where 1≤i=j≤n; then, the weight of the edge between them is 0. σ is a hyperparameter, which is used to control the width of the weight wij so that the weight matrix of the edges of the entire data set can be constructed.The DV-Hop localization algorithm is a distributed, hop-by-hop nonranging node localization algorithm. Under the network topology shown in Figure4, the actual distance and routing path between beacon nodes are shown in Figure 4. Among them, nodes N1, N2, and N3 are beacon nodes. Nodes N1, N2, and N3 exchange location information in the form of flooding, and unknown node K obtains the minimum hop information with each beacon node and saves it.Figure 4
Network topology diagram of the DV-Hop algorithm.In order to calculate the estimated average per-hop distance between the three nodes, this paper adopts the following scheme for calculation:(7)Cj=∑Xi−Xj2+Yi−Yj2∑hi.Then, the minimum number of hops and average per hop obtained in the first two stages are obtained using the unknown nodeK, and the estimated distance from itself to the three beacon nodes is obtained. As shown in Tables 1 and 2, with the increase in the unknown node density, the average traffic of the DV-Hop algorithm increases sharply with the increase in the node density, and the evaluation traffic of the other two algorithms is relatively stable. The average traffic of the TMB-DV-Hop algorithm is always lower than that of the MB-DV-Hop algorithm.Table 1
Average traffic of isotropic sensor networks.
Node density30405060DV-Hop35477254MB-DV-Hop23.4523.4723.1123.34TMB-DV-Hop2422.324.523.5Table 2
Average traffic of anisotropic sensor networks.
Node density30405060DV-Hop52679775MB-DV-Hop26.5525.4324.5524.51TMB-DV-Hop25.324.1223.7521.45Assuming that the distance error between the estimated coordinates of nodei and the real coordinates is di, the ratio of the average distance error of N unknown nodes to the communication radius R in the two-dimensional environment is recorded as the average positioning error:(8)di=x^−x2+y^−y2,Δd¯=1NR∑i=1Ndi.In the above formula,x,y are the actual coordinates of the unknown node and x^,y^ are the estimated coordinates of the unknown node. The average traffic is expressed as the average of the packets sent and received by the node. Assuming that pi is the total number of packets sent and received by node i, and N is the number of nodes, the average traffic is expressed as(9)traffic=1N∑i=1Npi2.


## 4. Result Analysis and Discussion

In order to obtain a reasonable, scientific, and feasible experimental model and accurate and reliable experimental results, the experiment in this paper will follow the basic experimental criteria. It is expected that the advantages of the experimental model will be verified on the basis of the original experimental data, which will make the model have practical significance for analysing the application trend and advantages of mobile wireless technology in higher education. This paper will analyse and experiment from four important aspects: the proportion of beacon nodes, information processing performance, actual processing error, and wireless transmission efficiency. Let K1 and K2 be two data experimental sets of random data sets in terms of beacon node ratio and information processing performance. The experimental diagrams are shown in Figures5 and 6.Figure 5
Analysis of the proportion of beacon nodes.Figure 6
Information processing performance analysis.As can be seen from Figure5, whether in an isotropic network or in an anisotropic website, the accuracy of the positioning algorithm based on the kernel function partly depends on the number of beacon nodes. The more the number of beacon nodes, the better the positioning accuracy. More realistic environmental factors are considered. Due to multipath channel, interference, and other environmental factors, an irregular wireless propagation model is used to simulate the disturbance of communication range, so it needs good stability in beacon nodes. However, in the range of 1–2, it can be seen that the sample set K1 fluctuates greatly, which is caused by the occasional interference of the signal source in the early stage. Because it cannot be eliminated in time, it shows a strong fluctuation range in a certain period of time. In terms of information processing, it can be found that it can be basically stabilized at a level of more than 40%, which will greatly strengthen the application of mobile wireless technology in the field of higher education, and the cumulative performance has improved to 65.7%. For example, the experiment of textbook-free education or cross-time teaching in the classroom enriches the teaching methods and is also a convenient teaching form. It has opened up a new road for the development of higher education and achieved good results in information teaching.Based on the basis and conclusion of the above experiment, this paper will experiment. Let s and n be the experimental sets of actual processing error and wireless transmission efficiency, respectively, and the analysis results are shown in Figures7 and 8.Figure 7
Actual processing error analysis.Figure 8
Analysis of wireless transmission efficiency.It serves as a crucial parameter index for the experiment’s error analysis during actual operation. In general, the experimental requirements can be met as long as the error is within a reasonable range, but in theory, more accurate conclusions are always sought, so the lower the error, the better the performance. In general, the experimental error is below 50%, which is a reasonable error interval, but at the same time, it can be seen that there are very few peak data in the image, and most of them are below 40%, which also reflects the superiority of mobile wireless technology. The data processing is also very compatible, which will provide a strong guarantee for the development. When it comes to data transmission efficiency, the conventional limited transmission is impacted by a number of factors, including line wear and interface ageing, which can result in data transmission errors or even transmission failure. Nevertheless, the results of the experiment show that the average transmission efficiency for mobile wireless technology is 57.4%. Furthermore, as long as the mobile device is in good condition, data transfer can be accomplished effectively regardless of the time or circumstances.


## 5. Conclusions

Domestic mobile wireless technology has ever-increasing demands for flexibility, signal stability, and content confidentiality, and innovative technology of methods will receive increasing attention. That’s what I think, given how mobile wireless technology is constantly evolving and developing, it is important to note, though, that there are still a lot of contentious issues with the advancement of modern technology, such as the accuracy of information transmission, the security of information protection, and the loss and preservation of information, which need to be further improved in the coming years. Because of its advantages of quick deployment, simple installation, reliable performance, convenient maintenance, flexible networking, and low cost, mobile wireless technology is favoured by the industry. With the importance of modern education growing, higher education development is crucial to raising the standard of instruction across the entire nation. As a novel technical tool, mobile wireless Internet technology aids in the resolution of the issue of educational resource sharing, the implementation of network teaching across time, space, and geography, the realization of reciprocal interaction and communication, the disruption of the traditional teaching paradigm, and the provision of technical support for new education. The results of the experimental analysis lead to the following conclusions: the information processing can be found to be essentially stable at more than 40%, which will strengthen the use of mobile wireless technology in higher education, and the cumulative performance will rise to 65.7 percent. The results of the experiment reveal that mobile wireless technology has an average efficiency of 57.4 percent, which is incredibly high.



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*Source: 3874673-2022-08-10.xml*