Network Users Research Articles

EFFICIENTLY UTILIZE THE SPECTRUM AND LOAD BALANCING IN COGNITIVE RADIO NETWORK USING GENETIC ALGORITHM WITH COVERSET PREDICTION

Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.

Effect of Positive Psychology Online Group Therapy on Spiritual Well-Being, Positive and Negative Affect of Working Women in COVID-19 Pandemic

Background: Positive psychology is a branch of psychology that deals with the factors shaping mental health, even in dangerous situations such as the COVID-19 pandemic, by emphasizing human capabilities. Objectives: The present study aimed to investigate the impact of positive psychology in online group meetings on the spiritual well-being and positive and negative effects of Iranian working women during the COVID-19 pandemic. Methods: The present study was a clinical trial conducted with a pre-test, post-test, 1-month follow-up, and a control group conducted on all Iranian working women aged 18 - 45 years who were users of social networks (such as Instagram, Telegram, and WhatsApp) in 2022 - 2023. The sample was selected by convenience sampling and included 52 women assigned to the experimental (26 women) and control (26 women) groups. Data were collected using the Spiritual Well-Being scale (SWBS) of Paloutzian and Ellison and the Positive and Negative Affect Schedule (PANAS). The experimental group received 6 one-hour training sessions. The data were analyzed using a multivariate analysis of covariance (MANCOVA) through SPSS software (version 22). Results: The MANCOVA results revealed that after the intervention, there was a significant difference between the groups regarding the positive affect variable (P < 0.001), while no difference was observed between the two groups in spiritual well-being and negative affect (P > 0.05). Conclusions: The findings recommend interventions based on positive psychology to increase positive affect due to its emphasis on the positive points of the current situation and strengthening them, leading to the adoption of an active stance towards life. On the other hand, the insignificant effect of the treatment on spiritual well-being and negative effects necessitates more studies and more serious and long-term interventions.

Influence of Social Networking Usage on Mental Health of Tribal Adolescents

Abstract From past two decades , Social Networking Sites beheld a sporadic enhancement in quantity, quality and utility. As the body of an individual is nourished by the intake of necessary mineral elements obtained through nutrition, likewise human mind is nurtured by the availability of nutrition for thoughts. Social networking sites have become an important part of modern life. They are generally defined as web-based platforms that allow individuals to create their own personal profile and build a network of connections with other users. From the dawn of internet and social networking sites, human resources of contemporary world have become more social virtually but less practically. The virtual life is isolating present generation from their other fellow beings there by affecting their health both physically as well as mentally and overall balance. Increased usage of social networking sites particularly among adolescents in the present scenario is a matter of concern for the parents, society and researchers as there are always two sides (positive and negative) of every innovation. The aim of the present research study was to explore the influence of social networking sites on mental health of the adolescents of three government secondary school of Koraput district, Odisha. For the present study, an ex-post facto research method was employed. The stratified random sampling technology was employed to select the sample where total of 150 adolescent boys and girls from class -X ( 50 from each school) were purposively selected. The data was collected by administering a standardized tool based on mental health scale upon the students and the collected data was analysed by using several statistical techniques like- Mean, SD and t-test. This, the findings showed that there was significant difference between the mental health conditions of social networking users and non-users. The findings also showed that there was also significant difference between social networking users' mental health in terms of gender and locality.

È un complimento?, Ist das ein Kompliment für dich?. Implicit Compliments in Italian and German Social Networks

Compliment is one of the most widely studied speech acts, as the extensive literature devoted to this topic shows. However, researches mainly focus on formulaic complimenting, probably because explicit compliments are far more frequent than the implicit ones. The present contribution aims to investigate implicit compliments, i.e., those where hearers «need to infer the corresponding implicature for their interpretation» (MAÍZ-ARÉVALO, 2012, p. 983; cf. also BOYLE, 2000), in a corpus of digital exchanges in Italian and German social networking sites, Instagram and WhatsApp. Firstly, the study examines and compares, from both a qualitative and quantitative point of view, the linguistic patterns used to express implicit compliments in the two languages; then, through a detailed analysis of the reactions of compliments’ addressees it explores how these speech acts are perceived by Italian and German social network users and what response types are mainly used by the complimented person in order to react to the implicit positive evaluation.

A Novel SDN-Based Architecture of Task Offloading in Mobile Ad-Hoc Cloud

As the core function of mobile Ad-hoc cloud, task offloading has always been a research hotspot of mobile cloud computing, and the construction, offloading decision, task division and scheduling strategies of mobile Ad-hoc cloud directly affect the performance of offloading. In this paper, we propose a new mobile Ad-hoc cloud architecture based on SDN solving the performance bottleneck of opportunistic task offloading. The proposed solution constructs terminal clusters in Ad-hoc cloud to provide users with offloading service, and makes offloading decision and task allocation through SDN controller to achieve the effectiveness of task allocation and the rationality of resource scheduling. Simulation results show that compared with traditional scheduling method, our method has the advantages in offloading ratio and execution efficiency, and can be more effectively used in dynamic mobile edge network environment, to improve network performance and user service satisfaction.

Evaluation of the Limitation of Operational Parameters of the IEEE 802.11 ac Network in the 20MHz Channel

IEEE 802.11 wireless network technologies are widely used to create corporate and personal local networks for data exchange and access to Internet resources. The main principle of operation of IEEE 802.11 networks is the principle of competitive access, according to which all wireless network users have the same access rights to the information transmission environment. This method of access leads to the occurrence of collisions in networks with a large number of users, which complicates the process of network functioning and leads to the degradation of quality indicators. The purpose of the study is to estimate the limit values of the operational characteristics of the IEEE 802.11 ac wireless network in the mode with the highest transmission rate (MCS8) in a frequency channel of 20 MHz with one spatial stream, provided that the network has a significant number of active stations with a saturated load. An alternative model of processes in IEEE 802.11 networks based on the concept of a virtual competitive window is used for research. According to the concept of virtual contention window (VCW), the process of data transmission in a network with competitive access is considered as a quasi-stationary process. Numerical data were obtained and graphs of channel bandwidth, transmission delay, and delay non-uniformity were given in the presence of one to sixteen active stations with a saturated load in the network, in the case of transmission of frames with a data volume of 512 or 1500 bytes. The maximum possible bandwidth of the channel with a frequency band of 20 MHz (68.387 bit/s) was determined, in the case of using frames with the maximum load (11454 bytes) provided by the standard. Estimated data on the number of collisions occurring in a network with a saturated load and the number of frames transmitted at various stages of channel access are also provided. The frame transmission delay increases almost proportionally to the number of active stations and varies from 0.605 ms to 5.293 ms, in the case of loading all data frames of 512 bytes, and from 0.785 to 6.41 ms, in the case of a load of 1500 bytes, for changes in the number of active stations in the network from 2 to 16. The unevenness of the delay exceeds the average delay and grows non-linearly, in the case of an increase in the number of active stations from 1 to 6 (CWmin=15), and linearly — with a further increase in the number of stations (over 6). The obtained results are useful for reasonable planning of wireless networks and configuration of network equipment parameters.

Fuzzy Influence Maximization in Social Networks

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user’s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user’s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.

A 930 m/180 Gbps*User Underwater Coherent Optical Code-Division Multiple-Access Network Based on Hybrid 256-Differential Pulse Position Modulation and Weighted Modified Prime Code Sequence

Currently, there are three types of optical communication networks based on the communication channel between the transmitter and receiver: the optical fiber channel, visible light channel, and optical wireless channel networks. The last type has several advantages for underwater communication, wireless sensors, and military communication networks. However, this type of optical network suffers from weather conditions in free-space communications and attenuation owing to the scattering and absorption mechanisms for underwater communication. In this study, we present a new transceiver architecture of a coherent optical code-division multiple-access (OCDMA) system based on a hybrid M-ary differential pulse position modulation scheme and a spreading code sequence called weighted modified prime code for underwater communication to minimize channel dispersion, increase the transmission rate per second, enhance the network bit error rate (BER) performance, and improve network security. Using an OCDMA system, we can simultaneously expand the network coverage area and increase the number of users sharing the network over the same channel bandwidth. The simulation results in this study proved that the proposed system can accommodate 1310 active users and a network throughput of 180 Gbps*user over a transmission distance of 930 m without any repeater at a 10−9 BER performance, compared to the 45 Gbps*user network throughput and 100 m transmission distance reported in the literature.

An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Recently, social network usage has exhibited explosive growth, leading to a huge amount of users’ private data available. The main challenge in releasing social network data publicly is the protection of the users’ privacy while preserving its utility for third parties. Accordingly, several social network privacy-preserving methods have been introduced, where anonymization is the most common approach. Structural k-anonymity is a widely used anonymization model to mask the structure of social networks by clustering the edges and nodes into super-edges and super-nodes. However, it comes at the cost of losing structural information, which is measured by a criterion called structural information loss (SIL). This study introduces an enhanced discrete particle swarm optimization (EDPSO) algorithm, which effectively minimizes the SIL within the clustering process of the structural k-anonymity model, leading to a high-utility anonymized network. In this regard, we propose a vector-based solution representation that can be efficiently exploited by the EDPSO. Moreover, a novel position updating heuristic is suggested for the EDPSO, which adaptively tunes the operators’ selection probabilities. This happens based on each operator’s performance both in the current iteration and their history regarding the number and the average amount of fitness improvements in the previous iterations. We also propose two fortified versions of the EDPSO algorithm (EDPSOVNS and EDPSOSA) by employing two new network-specific local search strategies to enhance the exploration, exploitation, and convergence rate of the process. Simulation results on the nine real-world networks demonstrate the superiority of the suggested algorithms in terms of the fitness value, reliability, and convergence rate over other analyzed approaches found in the literature.

Design of an Iterative Method for Dynamic Resource Management in 5G Networks with IoT Integration Operations

In the realm of fifth-generation (5G) wireless networks, the escalating demands for high-speed, reliable, and efficient communication are paramount, especially with the widespread deployment of Internet of Things (IoT) devices & scenarios. Despite the advancements in 5G technologies, existing network management strategies often fall short in addressing the dynamic nature of network conditions, the heterogeneity of IoT device requirements, and the need for stringent privacy measures. These limitations underscore the necessity for innovative approaches that can adapt in real-time to varying demands while ensuring optimal network performance and user privacy. This paper introduces a suite of machine learning models designed to enhance the efficiency and reliability of 5G networks, catering specifically to the diverse needs of IoT applications. At the forefront, DynamicSlicerNet, a deep reinforcement learning-based model, dynamically slices 5G network resources tailored to IoT devices' requirements, addressing device mobility, application demands, and network congestion. This model demonstrates a substantial reduction in latency by up to 30% and improvement in reliability by up to 20%, outperforming static resource allocation methods. Further enhancing edge computing capabilities, FedEdgeAI leverages federated learning to train models directly on edge devices, a move that not only slashes latency by minimizing data transmission to centralized servers but also fortifies data privacy. Experimental evaluations highlight FedEdgeAI's efficacy in maintaining model accuracy while halving communication overhead. PredictiveNetCare, employing time-series analysis and anomaly detection, anticipates network failures, facilitating preemptive maintenance strategies. This predictive approach has shown a marked precision over 90% in identifying potential disruptions, significantly reducing maintenance-related downtime by 30% and bolstering network reliability by 15%. OptiAllocRL and AdaptiveQoSDL, both harnessing reinforcement and deep learning techniques, respectively, optimize resource allocation and manage Quality of Service (QoS) parameters adaptively. OptiAllocRL's strategy results in a 40% latency reduction and a 25% throughput increase, while AdaptiveQoSDL minimizes packet loss by up to 50% and enhances end-to-end delay by up to 35%, ensuring high QoS levels under fluctuating network conditions. This comprehensive approach sets a new benchmark for future 5G network management, paving the way for a more connected, efficient, and secure digital world.

Enhanced Feature-driven Multi-objective Learning for Optimal Cloud Resource Allocation

In cloud networks, especially those with distributed computing setups and data centers, one of the biggest obstacles is allocating resources. This is the key area, and this must be balanced between optimizing system performance on one side and affordability, stability (reliance) of operation, and energy efficiency. The importance of improving resource allocation methodologies in these complex cloud computing systems is recognized, and therefore this paper comes with an appropriate title--“Enhanced Feature-Driven Multi-Objective Learning for Optimal Cloud Resource Allocation” (OCRA), which integrates together both the latest machine learning techniques as well as traditional concepts from research into cloud computing. OCRA capably analyzes historical files on CPU, memory, disk and network usage. In addition to neatly assimilating large data sets such as that was the compliance rate with past SLAs or workload frequencies over certain time periods and resource allocations; even their patterns of service requests are an important piece of information for many busy people’s lives today the adaptive mechanism is one of the defining traits of the model. It can accurately anticipate changes in resource demand and immediately adjust supply, fully able to respond rapidly when fluctuations arise suddenly or unexpectedly. Multi-Objective Random Forests are at the very core of OCRA. Each tree for decision making is specially designed to meet a particular performance objective in mind. Combining these trees into a Random Forest ensemble increases not only the model's predictive accuracy but also its stability. Pareto optimization is wisely used to maintain a balance among performance indicators, without an excessive focus on one effect alone. OCRA is proven empirically through experimental studies where key performance indicators such as Resource Utilization Rate and Quality of Service (QoS) Adherence Rate are taken into account. OCRA is both energy-efficient, an important attribute in today's environmentally conscious world, and does not sacrifice performance. As far as speed, flexibility and overall efficiency are concerned, OCRA has always been superior to the other cloud resources allocation programs of its own day. While it's still not quite ready for users who don't have a firm background in computer science or programming skills (ocra is plotted on 0-x), with sufficient memory and dominant minutes turn into mechanical equipment without configuration services.

The Dark Side of Metaverse: A Multi-Perspective of Deviant Behaviors From PLS-SEM and fsQCA Findings

The metaverse has created a huge buzz of interest because such a phenomenon is emerging. The behavioral aspect of the metaverse includes user engagement and deviant behaviors in the metaverse. Such technology has brought various dangers to individuals and society. There are growing cases reported of sexual abuse, racism, harassment, hate speech, and bullying because of online disinhibition make us feel more relaxed. This study responded to the literature call by investigating the effect of technical and social features through mediating roles of security and privacy on deviant behaviors in the metaverse. The data collected from virtual network users reached 1121 respondents. Partial Least Squares based structural equation modeling (PLS-SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA) were used. PLS-SEM results revealed that social features such as user-to-user interaction, homophily, social ties, and social identity, and technical design such as immersive experience and invisibility significantly affect users’ deviant behavior in the metaverse. The fsQCA results provided insights into the multiple causal solutions and configurations. This study is exceptional because it provided decisive results by understanding the deviant behavior of users based on the symmetrical and asymmetrical approach to virtual networks.

Fulfilling the promise of digital tools to build rangeland resilience

The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise.

Secure Cloud Storage Solution with “Seafile” & “NextCloud”: A Resilient Efficiency Assessment

Storage and file synchronization have become increasingly important services in our everyday lives. With cloud storage providers giving users access to file space in the cloud, one might wonder how efficient these cloud storage providers are with their encryption methods. There are many different cloud storage providers and programs to set up a cloud storage solution. This paper focuses on two secure cloud storage solutions, Seafile, and NextCloud, and determines the efficiency of these cloud storage solutions’ encryption methods. By determining the efficiency of these secure cloud storage providers’ encryption methods, it can help others determine which of these providers will fit their needs. To determine efficiency, the CPU, memory, and network usage of these cloud storage solutions will be measured at idle and when files are being uploaded to them. To measure the CPU, memory, and network usage, ‘dstat’ will be used to capture their usages and record them to a CSV file. It is expected that both cloud solutions will have better performance when they have files being uploaded to them with no encryption. However, with the encryption methods of both cloud solutions being lightweight, that was not the case.

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Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies.

Malicious Social Bot Using Twitter Network Analysis in Django

Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system.

"Russian-Ukrainian conflict" through the prism of users of the Chinese social network "Weibo"

The subject of this article is the views of Chinese netizens on the "Russian-Ukrainian conflict". The object of the study is posts about this topic on the Chinese social network "Weibo". The authors analyze the importance, functions of the Weibo in the production of public opinion and its main opinion leaders. Then the article examines the number of posts in month (15.02-15.03) compared to other international events and makes a content analysis of posts to find out the main topics and the attitude of users on the social network "Weibo" to the "Russian-Ukrainian conflict". At the end of the article, authors learn the relationship of the research results with stereotypes about the image of Russia in China. The results of the study show that Chinese Weibo users began to care about the event from the beginning of a special military operation, but interest faded with the first round of negotiations between Russia and Ukraine. Chinese Weibo users pay attention to the role of the United States in the conflict and Western sanctions against Russia. They mainly reflect support for the Russian economy and opposition to Western sanctions, which also affects the Chinese government's statement on this event. The article considers that such views are closely connected with stereotypes about the image of Russia in China. The results of the study show that in future international events, the peoples of Russia and China have more opportunities to reach agreement.

Reconstructive Classification for Age and Gender Identification in Social Networks

Author profiling is a challenging task that consists of identifying a person’s relevant attributes based on the content he/she generates. In this article, we validate a classification method based on the reconstructive classification to identify two demographic attributes, age and gender, for users of social networks based on the text content they publish. For the problem, we consider balanced and unbalanced data, where a gender or age group has a larger presence than the others. The proposed method is based on using the reconstructive property of singular value decomposition (SVD), and its suitability to work with sparse data, to find a matrix with the main latent components to represent the information of specific gender or age classes. Afterward, we use such a matrix to project and reconstruct new users’ information to identify their demographic variables. We test our method with a set of datasets in several languages collected from Twitter and Pinterest users, and we use different evaluation metrics to compare their performance with the ones of several popular classifiers based on traditional machine learning, and on deep learning, and with some relevant works in the literature. The results show that the proposed classifier performs generally well in identifying the age and gender of users in social networks.