University Campus Network Research Articles

تحليل وتصميم شبكة محلية لاسلكية آمنة للحرم الجامعي في الجامعات الليبية

This research presents the wireless university campus network and discusses about the strengths and weaknesses of Design a secure wireless campus networks for ABC University in Libya, and about the costs and advantages of different vendor solutions. Wireless offers mobility and is available in locations where a wired outlet doesn't exist. The disadvantages of wireless include some minor interference problems and a quickly evolving and competitive group of standards. The purpose of the research is designing and implementing a secure campus network for Libyan university based on Cisco equipments. Moreover, the research can provide practical experience in network design, configuration, and security for helping organizations and institutions protect their network resources and data. Two end products or deliverables will result from the research: the first is a new architectural design for an efficient and advanced WLAN-friendly network to ensure the high performance of the data transaction process and ensure high availability of data transmissions. The second product is a report for network administrators or network architecture to guide users towards the achievement of WLAN-friendly by utilising advanced networking tools and technologies. Keywords: - Campus network; Wireless Mesh Network; wireless network; Encryption; Cisco equipment; security.

A Framework for Deploying IPV6 at Higher Education Institutions

Despite the fact that IPv6 was developed 30 years ago, its deployment within higher education institutions worldwide is still slow. While many have started transitioning from IPv4 to IPv6, a large number of them have deferred the transition due to the time needed to complete the transition, budget implications, and security issues. The main problem is how to deploy IPv6 without disrupting the teaching activities of academic departments. This paper introduces a phased framework for deploying IPv6 at the university campus networks of higher education institutions. The framework consists of three phases: preparation and assessment, perimeter network, and internal network. In addition to an independent phase to manage the security issues, a Venn diagram has been created to compare the proposed framework with a state-of-art framework. The results show that the proposed framework retains high value as a comprehensive IPv6 deployment framework at higher education institutions.

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

The ''metaverse'', wherein users can immerse in virtual worlds through their VR headsets to work, study, play, shop, socialize, and entertain, is fast becoming a reality. However, little is known about the network dynamics of metaverse VR applications, which are needed to make telecommunications network infrastructure ''metaverse ready'' to support superlative user experience. This work is an empirical measurement study of metaverse VR network behavior. By analyzing metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, characterizing network traffic per state, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Our second contribution develops a real-time method MetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implements MetaVRadar in a large university campus network to demonstrate its usability.

University campus network planning and design based on GPON technology

With the continuous development of Internet applications, in order to meet the increasing demands of teachers and students for the campus network in teaching, scientific research, office work and daily life, it is necessary to build a network environment that can meet the needs of smart campuses and future new applications. This paper makes an in-depth analysis of the current situation of campus networks, uses Gigabit Passive Optical Network (GPON) technology to upgrade and transform the existing network, reduces the cost of switching equipment and maintenance difficulty, improves the network transmission rate, increases the upper limit of campus network user access, and effectively solves the problem of separation between wired and wireless networks.

A novel energy-saving method for campus wired and dense WiFi network applying machine learning and idle cycling techniques

University campus networks need wired (ethernet) and dense wireless fidelity networks that have devices like access points, switches, and routers that are always turned on. Consequently, they generate two important problems: the energy bill and the influence of carbon dioxide into the atmosphere. Energy savings are the solution to those problems. There are several proposals to augment the energy savings separately in ethernet and wireless fidelity, but there is no integrated method to simultaneously reduce them in both parts of the networks. Our novel method combines idle cycling and machine learning techniques to efficiently obtain energy savings in both parts of the network simultaneously. We categorize network devices into two groups: (a) those that are always turned on and (b) those that can be dynamically turned on or off based on network performance. We formulated two algorithms that decide when to turn on and off access points. We use Ward's machine learning hierarchical clustering technique to optimize the energy savings of our model in the network of the Unidades Tecnológicas de Santander (Bucaramanga, Colombia). We showed that energy savings of 21.5 kWh per day are possible. The success of the model in this context highlights its potential to achieve substantial energy savings.

Exploration of Psychological Health of Vocational College Students and Campus Network Informatization Management

Exploration of Psychological Health of Vocational College Students and Campus Network Informatization Management

Performance Evaluation of VoIP Analysis and Simulation

The use of technology has impacted on communication in so many ways. The advent of voice over internet protocols (VoIP) has made the world a global village where one can reach out to any part of the universe. But a challenge exists as to how to make communication and data transmission faster, the volume of traffic, bandwidth and latency in networks, that has to be transmitted between the sender and the receiver. The overall customer experience can be improved by the use of technology, which also makes it simpler to collect client information. Data packets are addressed and routed by the Internet Protocol (IP). This research aimed at deploying jitter, throughput, network traffic delay and bandwidth (JiTTraB) as a performance metrics to measure voice over internet protocols (VoIP) to measure the Quality of Service (QoS) of networks. This method prioritizes network traffic going through a router and providing acceptable service to most users in a quest to address VoIP concerns. In comparison to an existing university campus network (UCN), JiTTraB has a higher throughput, more data transmission at a given time with a minimal delay of about 0.001 seconds. However, UCN queues data with a high rate of delay before transmission. Data seem to crash considerably on the UCN due to network overload above its throughput margin and growth capacity. Thus, the proposed network design based on JiTTraB outperforms UCN in terms of transmission rate, throughput, bandwidth, delay and jitter. In addition, the JiTTraB communication network enhances QoS by performing better in close-quarters conversations than in far-quarters conversations. With an expansion reserve of 30%, the JiTTraB can handle up to 350 conversations securely while maintaining VoIP QoS standards without degrading the functionality of other network services or applications.

MA-STS-Based Social Intimacy Analysis Algorithm Using Real Campus Network Data

In recent years, the widespread availability of Wi-Fi in various settings, including universities, enterprises, and large shopping centers, has become increasingly prevalent. The user’s time and location information embedded in wireless network systems can reveal individual and group social relationships, which indirectly reflect each person’s psychological well-being. However, due to challenges in obtaining complete data, the high complexity of related data, and the absence of suitable data analysis models, few studies have analyzed student social behavior using data from university campus networks. This paper employs real-world data from a renowned Chinese university’s wireless campus network for in-depth analysis and introduces a novel multiangle semantic trajectory similarity (MA-STS) algorithm to infer the intimacy and relationship types (such as teacher-student, friends, classmates, or romantic partners) between users. The experiments demonstrate that the proposed algorithm achieves an accuracy of over 95%.

Lightweight Authentication Billing Enhancement Mechanism Based on the Fourier Fast Transform Algorithm

In recent years, with the development and extensive application of Internet technology and computer technology, and under the role of the continuous expansion and enrollment expansion of colleges and universities, how to effectively solve the Internet problems faced by teachers and students in colleges and universities has gradually become an important problem that the university information management department should solve in time. In order to continuously improve the efficiency of Internet access in universities, the Internet authentication and billing system for universities proposed in this paper take J2EE as the development platform and combines RADIUS protocol and PPPoE protocol to provide users with diversified Internet billing methods and strategies. At the same time, based on the Fourier fast transform algorithm, the development environment for the Internet authentication and billing system for colleges and universities is deployed, and after the Internet authentication and billing system is implemented, from the functional point of view of the system, the main functional modules are designed and implemented, including the authentication management function. module, billing management function module. The experimental results have shown that the throughput of PPPoE access server in the campus network using the Fourier rapid transform algorithm for traffic load balancing delay is small and stable between 200 ms and 300 ms after the optimization, which provides a feasible solution to promote the healthy and harmonious development of the university campus network.

Class Image Processing Application of XSS Intrusion Intelligent Detection for Big Data in Campus Network Construction

In order to improve the effect of XSS intrusion intelligent detection, this paper proposes an application of big data-oriented XSS intrusion intelligent detection based on class image processing in the construction of university campus network. In this application, image like processing method is used for data acquisition, data cleaning, data sampling, feature extraction, and other data preprocessing; design a word vector quantization algorithm based on neural network to realize word vector quantization and get word vector big data; through theoretical analysis and derivation, a variety of deep neural network intelligent detection algorithms with different depths are realized; through experiments, it is found that the average recognition rate of each deep DNN for the class I big data set is about 99.44%, the variance is about 0.000002, and the standard deviation is about 0.001589; the average recognition rate of class II big data set is about 99.77%, the variance is about 0.000006, and the standard deviation is about 0.002427. The experimental results show that this method has the characteristics of high recognition rate, good stability, and excellent overall performance.

College Campus Network

The project is Entitled as “COLLEGE CAMPUS NETWORK”. This project is gave the solution for college campus, to serves placement opportunities is one of the important modes of communication in India. The main aim of the project is student can’t be missing the placement opportunity then placement cell officer check information. This project implementation is the computerized system (online) of placing the students in advanced. Student must login into system; they can write and posted any message on various topic of their choice. Can register upload image with their post all student registration will be approved or rejected by the admin. As well as the post will kept bending and it higher authority approves or rejects each and every post. Placement officer can create a list of a student by specifying the department and criteria this will includes basic details marks. Placement officer can create a list of a student by specifying the department and criteria this will includes basic details marks placement officer can send verification regarding placements. The project titled “College campus network” is developed with: XML AND JAVA as front end and SQLITEDATABASE as back end.

The Analysis of Public Opinion in Colleges and Universities Oriented to Wireless Networks under the Application of Intelligent Data Mining

In recent years, the incidence of public opinion in colleges and universities has been high. Monitoring, forecasting, and responding to public opinion of students in colleges and universities have increasingly become the work that education management departments at all levels attach great importance to. For each university, how to understand the sensation of teachers and students in real time in the era of informationization entering the intelligent campus has become an urgent problem. How to collect college campus network information, analyze and manage this information, and find hot topics from it has a profound impact on the reform of colleges and universities. Hence, in this paper, we propose a public opinion analysis framework based on intelligent data mining technique. Its advantage lies in the fact that it can withdraw the needed and unknown knowledge and regularities from the massive network data and host log data. It is a new attempt to use data mining in achieving public opinion. At present, data mining algorithm applied to public opinion analysis mainly has four basic patterns: association, sequence, classification, and clustering. Data mining technology is advanced for: it can process large amount of data. It does not need the users’ subjective evaluation and is more likely to discover the ignored and hidden information. Here, initially, the dataset is collected, which is preprocessed and divided into a training set and test set. Feature extraction of the text is done using Linear Discriminant Analysis (LDA). After that, text cosine similarity calculation is performed to compute the similarity between text vectors obtained from the LDA. Convolutional neural network (CNN) is used for classification purpose. We proposed Krill Herd Harmony Search Optimization Algorithm (KHHSOA) for optimizing the CNN and classifying the text into positive and negative opinion. The proposed system is simulated using MATLAB simulation tool, and the performance is analyzed in terms of metrics like accuracy, precision, recall, F -measure, kappa coefficient, and error rate. The proposed method is proved to be better when compared with the existing techniques.

An integrated flexibility optimizer for economic gains of local energy communities — A case study for a University campus

With a capacity-based network tariff structure, consumers are encouraged to reduce their connection capacity to avoid higher costs. However, overloading beyond the administrative grid connection capacity limit would result in an increased connection capacity, thus prosumers have to pay the increased electricity bill for the rest of the year. Therefore, it is important to optimize the energy generation and consumption profiles of local energy communities (LECs) considering the comfort level of occupants. This work aims to reduce the overloading of the grid connection and increase the utilization of local renewable energy resources (RES) thus avoids being penalized throughout the year due to casual intermittent overloading in peak hours, even once in a year. The present work proposes a novel data-driven flexibility optimizer model for day-ahead scheduling of energy profiles for LECs, considering photovoltaic (PV) generation, heat pump (HPs), and cooling loads. The proposed methodology has been developed to explore the flexibility potentials from a university campus network which includes both electrical and heating/cooling systems in an integrated way. A two-layer optimization strategy is developed, to guard the occupant’s comfort level. Simulation has been performed for complete two months, considering winter and summer scenarios. A peak demand reduction of 16% has been observed with negligible energy usage differences between the proposed and the baseline case. Two types of flexibility indicators are estimated to give a deeper insight into the performance. Economical gains of 9 % and 16 % are estimated depending on the type and voltage level of the connection.

Local 5G services on campus premises: scenarios for a make 5G or buy 5G decision

Purpose The purpose of this paper is to build possible future scenarios for indoor connectivity in a venue such as a university campus and build alternative value network configurations (VNCs) defining different local network deployment options, focused on the Finnish telecom market. Design/methodology/approach In this paper, Schoemaker’s scenario planning method (Schoemaker, 1995) is used to construct future scenarios and the VNC method of Casey et al. (2010) is used to build alternative VNCs. The paper studies the Aalto University campus network for current end-user data usage demand and the existing technology used in meeting the end-user needs and forecasts the demand for the next five years to understand the need for 5G. Findings This research concludes that with the provision of local spectrum licenses, there is an opportunity for venue owners to take the role of 5G local operator on the venue premises. Furthermore, it enables venue owners to collaborate with the incumbent mobile network operators (MNOs) in a neutral host model and provide venue-specific connectivity services. Research limitations/implications A detailed economic assessment for the network deployment in the campus is considered for future study. Originality/value Considering the provision for local spectrum licenses, this paper has taken a unique attempt in identifying the future scenarios for local 5G network operations. It provides a strategic direction for the venue owners in adopting 5G technology and whether to make 5G or buy 5G from MNOs.

Carpooling as an Immediate Strategy to Post-Lockdown Mobility: A Case Study in University Campuses

Carpooling is a mobility concept that has been showing promising results in reducing single occupancy use of private cars, which prompted many institutions, namely universities, to implement carpooling platforms to improve their networks sustainability. Nowadays, currently under a pandemic crisis, public transportation must be used with limitations regarding the number of occupants to prevent the spread of the virus and commuters are turning even more to private cars to perform their daily trips. Carpooling under a set of precaution rules is a potential solution to help commuters perform their daily trips while respecting COVID-19 safety recommendations. This research aimed to develop an analysis of the road traffic and emission impacts of implementing carpooling, with social distancing measures, in three university campus networks through microscopic traffic simulation modeling and microscopic vehicular exhaust emissions estimation. Results indicate that employing carpooling for groups of up to three people to safely commute from their residence area to the university campus has the potential to significantly reduce pollutant emissions (reductions of 5% and 7% in carbon dioxide and nitrogen oxides can be obtained, respectively) within the network while significantly improving road traffic performance (average speed increased by 7% and travel time reduced by 8%).

Research on the Reform of Ideological and Political Theory Courses in Colleges and Universities in the Big Data Era

The continuous progress of information technology provides favourable conditions for colleges and universities to innovate teaching methods and develop new teaching systems. Colleges and universities are important places for the generation of network information, and many of the contents contained in these network information are of great value to the teaching of ideological and political courses and campus management in universities. How to collect college campus network information, analyse and manage this information, and find hot topics from it, has a profound impact on the reform of college ideological and political courses. Based on this, this article attempts to design a system program that can be used to analyse hot topics in colleges and universities, and introduces the design from several aspects such as design goals, demand analysis, functional architecture, and module design. It can provide a certain reference for college ideological and political courses.

ARANAC: A Bring-Your-Own-Permissions Network Access Control Methodology for Android Devices

In this paper, we introduce a new methodology for network access control for Android devices based on app risk assessment. Named ARANAC (which stands for Application Risk Assessment based Network Access Control), this methodology is specially tailored for scenarios using the Bring-Your-Own-Device (BYOD) policy, where the adoption of some solutions can lead to problems in security and privacy for both the employees and the business organization. ARANAC mainly relies on the analysis of an aggregate of permissions declared in the manifests of installed applications on users' devices. The access control scheme combines three operational modules: i) a device monitoring tool, ii) a novel permission-based risk model, and iii) an anomaly-based detection machine learning module based on a methodology (called MSNM, from Multivariate Statistical Network Monitoring) that provides both detection and diagnostic capabilities. ARANAC's novelty is in the combination of four features. Firstly, it is privacy-aware, and thus, it does not require detailed information about installed applications but only an aggregate of permissions. Secondly, it builds a normality model by combining expert knowledge with data, capturing the behavior of a complete population of mobile devices. Thirdly, it is dynamic, as permissions are updated in real time, allowing the network to re-assess access control on a continuous basis. Finally, its diagnostic capabilities allow for giving recommendations to final users so that they are capable of mitigating their risks when accessing networks. We evaluated the approach with more than 80 Android devices at a university campus network and obtained interesting results regarding security risks in the usual deployment of device apps.

Flow length and size distributions in campus Internet traffic

The efficiency of flow-based networking mechanisms strongly depends on traffic characteristics and should thus be assessed using accurate flow models. For example, in the case of algorithms based on the distinction between elephant and mice flows, it is extremely important to ensure realistic flows’ length and size distributions. Credible models or data are not available in literature. Numerous works contain only plots roughly presenting empirical distribution of selected flow parameters, without providing distribution mixture models or any reusable numerical data. This paper aims to fill that gap and provide reusable models of flow length and size derived from real traffic traces. Traces were collected at the Internet-facing interface of the university campus network and comprise four billion layer-4 flow (275 TB). These models can be used to assess a variety of flow-oriented solutions under the assumption of realistic conditions. Additionally, this paper provides a tutorial on constructing network flow models from traffic traces. The proposed methodology is universal and can be applied to traffic traces gathered in any network. We also provide an open source software framework to analyze flow traces and fit general mixture models to them.

The Safety Management of University Campus Network

The development of the Internet in higher education, teaching, scientific research, management and service plays an increasingly important role, but the safety management of university campus network problems also increasingly protruding comes out now. Therefore, we must strengthen the campus network security management consciousness, improve the campus network security management system, create a campus network security work environment, to ensure the safe operation of campus network.

Identification of Information Security Threats Using Data Mining Approach in Campus Network

Comprehensive risk assessment implementation in an organization is crucial in order to safeguard valuable organization assets and to minimize information security threats. Thus, inadequate information security risk assessment may result in compromised confidentiality, integrity, and availability of the information system due to unauthorized access particularly in the education domain. Therefore, the objective of this paper is to identify several information security threat risks related to the University Information System. Hence, data from intrusion prevention system (IPS) has been collected from the selected university campus network. Moreover, under Python language, Anaconda is used as a machine learning environment to do the data analysis of the collected data. Basically, the analysis of the university campus network data identified various types of information security threats such as database-related attacks. The contribution of this research is to guide the network administrator to develop an appropriate incident response plan based on the identified threats from the risk assessment activity.