Development Of Wireless Network Technology Research Articles

Application of Improved HF-DBSCAN Algorithm in Analyzing Complex Trajectory Data of Wi-Fi Users in Smart Campus

ABSTRACT With the development of wireless network technology and the increasingly widespread application of mobile intelligent terminals, a large amount of wireless network data has been generated in smart campuses. By analyzing the trajectory data through clustering algorithms, more valuable user data information can be obtained. However, current clustering algorithms have high computational difficulty, long running time, and poor clustering accuracy for complex trajectories. Based on this background, the study proposes a spatial clustering of Application with Noise by Density-Based Spatial Clustering of Application with Noise combining Hausdorff and Frechet. The method is denoted as (Hausdorff and Frechet-Density Based Spatial Clustering of Application with Noise, HF-DBSCAN). DBSCAN denotes the Density Based Spatial Clustering of Application with Noise. In the experimental results, compared to the AH-DBSCAN, DBSCAN, and FD-DBSCAN algorithms, the running time of HF-DBSCAN algorithm in complex trajectory cluster analysis is reduced by 59.33%, 39.82%, and 35.12%, respectively, and the contour coefficient is closer to 1; the Davies-Boldin Index (DBI) values decreased by 59.24%, 64.42%, and 68.24%, respectively. The experiment shows that the optimized HF-DBSCAN algorithm has lower computational difficulty, better clustering performance, and higher clustering accuracy, which verifies the effectiveness of this study.

BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

DESIGN ANTENNA MIMO 2X2 PATCH RECTANGULAR FOR WIFI 6 APPLICATIONON 5GHZ BAND

The development of wireless network technology, particularly WiFi 6 at 5GHz, offers greater reliability and data transfer capacity. Antenna design is a crucial aspect in optimizing WiFi 6 networks in complex wireless environments. The study examined a rectangular patch 2x2 MIMO Antenna for WiFi 6 at 5GHz with a focus on improving network parameters such as return loss, bandwidth, VSWR, and gain. The use of various methods and references from Balanis (2005) and Webster (2021) supports the development of antenna technology, while previous research by Ni Putu Kartika Dewi (2019) strengthens the understanding of microstrip antennas for LTE applications. The main objectives of the study were to design and simulate a rectangular patch 2x2 MIMO Antenna for WiFi 6, as well as analyze simulation data using CST Studio Suite 2019 software. This research uses hardware in the form of Acer Aspire laptops and CST Studio Suite 2019 as simulation software and antenna design. The simulation results show that this antenna is able to work in the frequency range of 4.5-5.2 GHz with a bandwidth of 55.4 MHz and return loss of -20.677981 dB, VSWR 1.2038357, and gain 3.429 dBi with omnidirectional radiation patterns. However, the addition of antenna patches has an impact on decreasing gain. This study provides an in-depth understanding of MIMO antenna design to support WiFi 6 performance in complex wireless environments.

Linkage Selection Strategy Based on Attractor Profit Prediction in Heterogeneous Vehicle Network

With the continuous development of wireless network technology, heterogeneous wireless networks with multiple network fusions and overlapping coverage have been formed. In the process of vehicles passing through different types of networks with different coverage areas at high speed, there are frequent and inefficient network switching behaviors. Aiming at this situation, a linkage selection strategy based on attractor profit prediction is proposed in a heterogeneous vehicle network. By analyzing the correlation between the wireless network transmission rate and the vehicle position, the location of the attractor update was found to reduce the transmission loss caused by network switching delay. Considering the vehicle speed and the access time of the base station, the transmission amount that the vehicle network can provide is calculated, the attractor is updated, and the network selection is made according to the state of the attractor. Compared with multi-attribute decision making, the linkage selection strategy based on attractor profit prediction can effectively improve the transmission volume of the Internet of vehicles. Through the simulation analysis, the transmission volume of the Internet of vehicles network increases by 14.07%.

User QoS-Based Optimized Handover Algorithm for Wireless Networks

Due to the development of wireless network technology, various applications relying on good network quality are widely used on mobile devices. Taking the commonly used video streaming service as an example, a network with high throughput and low packet loss rate can meet the service requirements. When the moving distance of the mobile device is greater than the signal coverage of the AP, it will trigger the handover process to connect to another AP, and cause the network to disconnect and reconnect instantaneously. However, frequently triggering the handover procedure will cause a significant drop in network performance and affect the operation of application services. In order to solve this problem, this paper proposes the OHA and OHAQR. The OHA considers whether the signal quality is good or bad, and uses the corresponding HM method to solve the problem of frequent handover procedures. The OHAQR integrates the QoS requirements of the throughput and packet loss rate into the OHA with the Q-handover score, to provide high-performance handover services with QoS. Our experimental results show that the OHA and OHAQR have 13 and 15 handovers in a high-density scenario, respectively, and are better than the other two methods. The actual throughput and packet loss rate of the OHAQR are 123 Mbps and 5%, respectively, and the network performance is better than that of other methods. The proposed method shows excellent performance in ensuring the network QoS requirements and reducing the number of handover procedures.

Embedded Wids Kismet Sebagai Perangkat Deteksi Serangan Data Link Layer Wi-Fi Access Point

The rapid development of wireless network technology has an impact with a significant increase of users. Wi-Fi is one of the wireless technologies that is currently used widely by humans as a medium of transmission to exchange information. The increasing in the number of users is directly proportional to the increase the risk of crime occurring. In this case an attack on a Wi-Fi network aimed at taking certain advantage of the user or it’s victim was done by threatening aspects of information security. Wireless Intrusion Detection System (WIDS) is a tool used to detect intrusions that occur against wireless network technology, one of it is Wi-Fi into the form of logs. Kismet is one of the wids that is free and opensource with complete WiFi attack detection capability. The implementation of WIDS Kismet applied to Raspberry Pi 4 was shown as an alternative wids device that has a low cost for use in small Wi-Fi network environments such as home networks or small enterprise. The analysis is done by calculating the performance of accuracy, precision, recall, and f-measure values from the implementation results using confusion matrix method. The result of the calculation obtained by the highest value for each calculated performance with 99.83% accuracy, 97.96% precision, 100% recall, and 98.9% fmeasure. The calculation aims to provide information that can be utilized for users who want to do a similar implementation and use it to detect Wi-Fi networks.

Impact of Reducing Data Plan Costs on the Digital Divide in China: A Digital Lifestyles Perspective

The digital divide first emerged as an important social and human rights issue in the 1990s. With the rapid development of wireless network technology (e.g., Wi-Fi and cellular networks) and mobile terminal devices (e.g., mobile phones, laptops, and tablets), this issue has shifted to concern mobile Internet access. The three main Chinese telecom operators (China Mobile Communications Corporation, China Unicom Communications Corporation, and China Telecom Communications Corporation) have recently promoted unlimited data plans (UDPs) to reduce information usage costs and increase mobile data use. However, assessments of the impact of these policy changes on the mobile digital divide are limited. This study therefore offers insights into how a reduction in information costs can eliminate this divide. Using a difference-in-differences (DID) approach based on a monthly panel of system-generated mobile app data for subscribers, we find that the UDPs have various effects on promoting the use of mobile Internet and on the digital divide, depending on whether subscribers are of high or low socioeconomic status (i.e., urban versus rural and rich versus poor). We show that the relative differences are alleviated, but the absolute difference further increases after the implementation of a UDP subscription. In addition, a UDP subscription has heterogeneous effects on lifestyles. For the poor or those in rural areas, only promoting UDP does not effectively narrow the mobile digital divide because of insufficient cognition about and persistent weaknesses in the use of information technology.

Online Education Satisfaction Assessment Based on Machine Learning Model in Wireless Network Environment.

With the development of wireless network technology, the transformation of educational concepts, the upgrading of users' educational needs, and the transformation of lifestyles, online education has made great strides forward. However, due to the rapid growth of online education in my country, many regulatory systems have not kept pace with the development of online education, resulting in low user experience and satisfaction with online education. The establishment of a user satisfaction model is beneficial for attracting attention and thinking about research in the field of online education service quality, assisting enterprises in recognizing the specific impact of various factors in services, accelerating service quality improvement, and assisting in the formulation of industry norms and improving enterprise competitiveness, all of which help students acquire knowledge more easily. In the era of big data, traditional satisfaction evaluation methods have many drawbacks, so more and more machine learning methods are applied to satisfaction evaluation models. This paper takes the research of machine learning algorithm as the core to carry out the research work, uses the cost-sensitive idea to improve the decision tree, considers the cost of different types of classification errors, and uses the random forest principle to integrate the generated decision tree, thereby improving the accuracy of the model. The model has better stability, and the validity of the model is verified by experiments. For a follow-up in-depth investigation of online education satisfaction rating technology, the linked work of this paper has certain reference and reference value.

Wireless network security technology and application in colleges and universities

The rapid development of computer technology has penetrated into all areas of people's work and life. At the same time, with the rapid development of wireless network technology, colleges and universities have become the biggest beneficiaries of the later development of wireless network technology. At the same time, the problem of wireless network security technology and application in colleges and universities is an unavoidable problem in the current Internet era, and the development of network security technology is also changing with each passing day. The purpose of this article is to study wireless network security technology and applications in colleges and universities. This research summarizes and analyzes the current status of wireless network security and the development trends and key technologies in this field. Through case investigation and case analysis, the current wireless network security characteristics and network security technology applications in universities are investigated and analyzed, and the university is summarized Wireless network security features and applications. Combining the current mainstream wireless network security protection technology and security protection strategy, research and design a multi-level, multi-measure university wireless network security protection program to ensure the network security of colleges and universities. Taking a university as an example, the research and application of the wireless network security event detection method and application scheme using data fusion technology designed and proposed in this study for the wireless network security technology of universities and colleges have been verified through actual security technology application and effect testing the effectiveness and applicability of the proposed algorithm and scheme. The survey results show that for a sensor network with 1250 nodes, the efficiency of the algorithm proposed in this study is 2.1%, while the efficiency of this protocol of the MCTA algorithm is 0.9%, and that of the Gur Game algorithm is 0.27%. Compared with the other two methods, the algorithm and scheme proposed in the article are more suiTable for intensive dynamic wireless sensor network security technology applications.

An Efficient Energy Aware Routing Mechanism for Wireless Body Area Networks

The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network (WBAN), which is a technology commonly used in the medical field. WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician, to monitor the patients remotely. These nodes typically have limited battery energy that led to a shortage of network lifetime. Therefore, energy efficiency is considered one of the most demanding challenges in routing design for WBAN. Many proposed routing mechanisms in WBAN did not cover the source node energy and energy harvesting techniques. Therefore, this study proposes an Efficient Energy Aware Routing (EEAR) mechanism. This paper constructs a path cost function that considers three parameters: residual energy, number of hops to the sink, and the distance between the nodes. Besides, data aggregation with filtration and hybrid energy harvesting technique are used to extend the network lifetime, reduce the network traffic load, and maintain the source node energy. Extensive simulations using MATLAB have been performed to evaluate the performance of the proposed mechanism. EEAR is contrasted with the two latest schemes, called Priority-based Congestion-avoidance Routing Protocol (PCRP) and Energy Efficient Routing Protocol (EERP). The results show the significant performance of the EEAR mechanism in terms of network lifetime, residual energy, network stability, and throughput.

Rolling bearing fault diagnosis based on wireless sensor network data fusion

With the continuous development of intelligent manufacturing, mechanical equipment is developing in the direction of large-scale, integration, precision and intelligence. The coupling between different equipment in the operation of the system also makes the mechanical equipment increasingly become a whole, which puts forward higher requirements for the fault diagnosis of mechanical equipment, especially rolling bearings. With the rapid development of wireless network technology, rolling bearing fault diagnosis has become a reality. In this paper, the wireless sensor network will be innovatively used to collect the data of key parts of mechanical equipment, so as to improve the problem of insufficient and accurate collected data in the traditional convolution neural network fault detection algorithm, convert the corresponding time-domain signal into image signal, and combine the global average pool layer on the basis of the combination of nonlinear convolution layers, So as to optimize the corresponding wireless network structure, and finally realize the adaptive extraction and fault diagnosis of rolling bearing fault features. The experimental results show that the fault detection accuracy of the optimized convolution neural network algorithm is about 99.8%, and has good robustness and practical value.

Unmanned aerial vehicle‐enabled layered architecture based solution for disaster management

AbstractUnmanned aerial vehicle (UAV) has become a priority in the industrial and academic research community for the last few years. With the fast development of wireless networking technologies, it is widely used in many applications such as surveillance, search and rescue (SAR) operation, disaster management, remote monitoring, and so forth. Emergency communication for victims and first responders is one of the critical requirements during natural and human‐made disasters. Airborne Base Stations (BS) or UAV‐mounted BS has been used to provide high‐speed internet services and rescue responses in natural disasters and emergencies in recent years. Most of the previous research on UAV‐mounted BS (UAVmBS) only considers outdoor users. It assumes their coordinate position in a two‐dimensional (2D) plane, taking only Euclidean pathloss‐based distance into account. Though, in man‐made or urban natural disaster situations such as floods, cyclones, building fires, and so forth, users are stuck in single or multistory high‐rise buildings situated in a three‐dimensional (3D) plane. In such cases, it is possible to interact and rescue reliably if pathloss features are known a priori, both for UAVmBS to users (downlink) and vice versa (uplink). As pathloss in the above situations is a mixture of outdoor and indoor elements, adopting a compatible model can be challenging for communications. This article proposes a layered architecture and studies the path loss models best suited for the disaster management system (DMS), which considers both indoor and outdoor parameters. Further, the main goal is to effectively implement a layered architecture employing UAVmBS in urban disasters for better capacity and coverage improvement. Simulation outputs display the progress in transmission delays, throughput, and effective packet delivery for disaster management.

Circuit system based on cloud edge fusion and outdoor intelligent light networking

SummaryWith the continuous improvement of the national design standards of urban road lighting system, the intelligent, automatic, and remote management of urban street lighting system by using modern computer technology and the rapid development of Internet of things technology have become one of the important symbols of modern city. This paper mainly studies the circuit system based on cloud edge fusion outdoor intelligent light networking. Based on the development of wireless networking technology of new light source and sensor and data communication and processing technology of Internet of things, this paper analyzes the development trend and support status of intelligent lighting system, summarizes the overall demand for intelligent lighting system, and proposes a complete set of communication interface software, data‐processing software, operation management software, and street lamp controller group system architecture. The street lamp controller realizes the short distance wireless network through ZigBee technology and realizes the communication interaction of control and state acquisition data by GPRS wireless transmission technology. This paper tests the real‐time performance, packet loss rate, and stability of sensor data service provided by sensor nodes in the system. Finally, the monitoring experiments of the lighting circuit system are carried out, and the status of all street lamps in the centralized controller of street lamps is inquired every 5 min for 20 times. In these 20 times, the success rate of the first return of the switch state of the lamp is 100%, while that of the current and voltage is 98.6%. The experimental results show that the design of the lighting circuit system is feasible and the system runs stable and realizes the intelligent monitoring of the lighting system.

Multipath TCP-Based IoT Communication Evaluation: From the Perspective of Multipath Management with Machine Learning.

With the development of wireless networking technology, current Internet-of-Things (IoT) devices are equipped with multiple network access interfaces. Multipath TCP (MPTCP) technology can improve the throughput of data transmission. However, traditional MPTCP path management may cause problems such as data confusion and even buffer blockage, which severely reduces transmission performance. This research introduces machine learning algorithms into MPTCP path management, and proposes an automatic learning selection path mechanism based on MPTCP (ALPS-MPTCP), which can adaptively select some high-quality paths and transmit data at the same time. This paper designs a simulation experiment that compares the performance of four machine learning algorithms in judging path quality. The experimental results show that, considering the running time and accuracy, the random forest algorithm has the best performance in judging path quality.

PERCEIVED BENEFIT OF MOBILE COMMERCE TO THE CUSTOMERS IN HO CHI MINH CITY: THE MULTI-DIMENSION CONCEPT APPROACH

The increase in the number of mobile device users, along with the development of wireless network technology, that have created a boom in mobile commerce since 2015 in Vietnam in general, and in Ho Chi Minh City in particular. Despite being a potential transaction type, the growth of mobile commerce has been declining in recent years, and customers are less interested in this type of transaction.Therefore, the businesses have to provide many benefits to the customers to not only attract the newcustomer, but retain the existing customers. The study explores and assesses the perceived benefits ofmobile commerce toward the customers in the Vietnamese market generally, and Ho Chi Minh City particularly. The results of qualitative and quantitative research have shown four main benefits of mobilecommerce, including convenience, selection, hedonic shopping, and ease of access. At the same time, theresearch proposes implications for businesses that are trading through applications in mobile devices.

Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments.

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.

Modeling and Understanding the Localization Performance With Network Signatures

With the continuous development of wireless network technology, more and more mobile devices are connected to the network, and the location information of these devices is becoming one of the important basis for analyzing other geographic data in the net. Location is an important problem in spatial information sensing, and the theoretical basis of location is the free space fading feature of wireless signals. In this paper, we proposes WiLocWare, which is a middle ware to understand and modeling the performance of the state-of-the-art and to be proposed algorithms. We evaluated and characterized the correlation between localization accuracy and networks parameters such as signal propagation model, the coverage of wireless radios, the distributions of wireless devices and the density of the anchor nodes. The experimental results show the localization accuracies under different wireless signatures, WiLocWare is also scalable for the performance evaluation for the to be proposed algorithms.

PDR-Net: Perception-Inspired Single Image Dehazing Network With Refinement

During recent years, we have witnessed a rapid development of wireless network technologies which have revolutionized the way people take and share multimedia content. However, images captured in the outdoor scenes usually suffer from limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite the recent progress of image dehazing methods, the visual quality of dehazed results still needs further improvement. In this paper, we propose a deep convolutional neural network (CNN) for single image dehazing called PDR-Net, which includes a perception-inspired haze removal subnetwork that reconstructs the latent dehazed image and a refinement subnetwork that further enhances the contrast and color properties of the dehazed result by joint multi-term loss optimization. Compared to the previous methods, our method combines the advantages of existing indoor and outdoor image dehazing training data, which makes the proposed PDR-Net generalized to various hazy images and effective for improving the visual quality of the dehazed results. Extensive experiments demonstrate that the proposed method achieves comparable and even better performance on both real and synthetic images in qualitative and quantitative metrics. Additionally, the potential usage of our method in high-level vision tasks is discussed.

A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information.

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.