Network Users Research Articles

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact

The world’s first planetary defense test mission was carried out in late 2022 by NASA’s Double Asteroid Redirection Test (DART) mission. The main DART spacecraft, which was accompanied by the ASI-provided LICIACube cubesat, intentionally impacted Dimorphos, the smaller secondary of the near-Earth object binary system (65803) Didymos, on 2022 September 26. The impact released a large amount of ejecta, which, combined with the spacecraft’s momentum, produced the observed 33 ± 1 minute period change that was subsequently observed from ground-based telescopes. The DART mission, in addition to having successfully changed the orbital period of Dimorphos, also activated the asteroid as a result of the impact but under known conditions, unlike other impacts on asteroids. We have conducted long-term monitoring over 5 months following the impact with the Las Cumbres Observatory Global Telescope (LCOGT) network and Magdalena Ridge Observatory (MRO). This was supplemented by almost 3 months of more sparsely sampled data, primarily from educational users of the LCOGT network during the period from 2022 July 5 to 2022 September 25, prior to the impact date of 2022 September 26. Here we report the observations of the Didymos system and DART impact ejecta with the telescopes of the LCOGT network from T+1.93 days to T+151.3 days after impact, and we study the evolving morphology of the ejecta cloud and evolving tail over the entire length of the data set. In addition, we combined these intensive data sets with the earlier sparse observations over the ∼90 days prior to impact to derive a new disk-integrated phase function model using the H, G 1, G 2 parameterization.

Resource Allocation and Outage Probability Optimization Method for Multi-Hop UAV Relay Network for Servicing Heterogeneous Users

Resource Allocation and Outage Probability Optimization Method for Multi-Hop UAV Relay Network for Servicing Heterogeneous Users

AQUA: Analytics-driven quantum neural network (QNN) user assistance for software validation

This paper proposes a novel analytics-driven user assistance software validation approach for quantum neural network (QNN) codes. The proposed analytics-driven QNN user assistance (AQUA) for software validation considers user interactive feedback for constructing efficient QNN software. Our proposed AQUA is based on dynamic software testing and analysis due to undetermined qubit states in QNN which is hard to be tracked via static software analysis. AQUA is for plotting gradient variances to determine whether the QNN software suffers from local minima situations, which are called barren plateaus in QNN. By utilizing AQUA software validation, the stability, feasibility, and explainability of QNN software can be tested. AQUA has been tested using real-world case study with quantum convolutional neural network software for point cloud data processing in autonomous driving applications.

Unraveling the root causes of faults in mobile communications: A comparative analysis of different model explainability techniques

The escalating demand and complexity of monitoring services handled by Network Operations Centers (NOCs) have led Mobile Network Operators (MNOs) to prioritize automated solutions for network fault detection and diagnosis. Consequently, various Machine Learning (ML)-based Root Cause Analysis (RCA) systems have been developed, however their lack of explainability poses a challenge due to the predominantly black-box nature of ML models. This paper addresses this issue by presenting a supervised clustering methodology capable of integrating both glass-box and black-box models, the latter complemented by post-hoc explainability techniques. While black-box models excel in predictive capabilities, necessitating post-hoc techniques for explainability, glass-box models prioritize transparent decision-making, fostering a clearer understanding of the model’s behavior. This work delineates a methodology for performing RCA of faults in the User Downlink (DL) Average Throughput Key Performance Indicator (KPI), simultaneously comparing the performance of black-box models (Light Gradient-Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost)) with glass-box models (Logistic Regression (LR) and Explainable Boosting Machine (EBM)). Results revealed that the LightGBM black-box algorithm coupled with the SHapley Additive exPlanations (SHAP) method demonstrated superior performance in fault detection and diagnosis, without compromising the overall explainability. Consequently, it was possible to identify faults related to radio conditions, low network usage in specific user groups, low network capacity, and mobility issues. The paper concludes with practical mitigation strategies for each identified fault cluster.

DEVELOPMENT OF ACADEMIC ADVISING INFORMATION SYSTEM APPLICATIONS AT MAKASSAR STATE UNIVERSITY USING THE ANDROID PLATFORM

Advising is the process where students communicate about issues that may arise during their study period. However, it is often hindered by the busy schedules and different timetables between students and their academic advisors. Therefore, a system has been developed to facilitate the advising process using the Android platform. This research adopts the Research and Development (R&D) methodology, specifically using the waterfall development model. Data collection techniques include interviews and questionnaires. The application is tested based on the ISO/IEC 25010 quality standards, covering four aspects: functional suitability, performance efficiency, usability, and portability. The functional suitability testing involved two expert professors, and 24 features were tested, indicating that the application is "Suitable" for use. Portability testing on the application was conducted by observing the output on three Android versions: Nougat, Red Velvet Cake, and Tiramisu. The results showed that the application can run on all three Android versions. Performance efficiency testing utilized the Android Profiler tool to assess CPU, memory, and network usage while running the application. The findings revealed that CPU usage was less than 50%, memory allocation was 122.2 MB, and network usage was less than 48 KB/s. Usability testing conducted by students resulted in a test score of 81.6%, categorized as "Very Good" feasibility.

You change the way you talk: Examining the network, toxicity and discourse of cross-platform users on Twitter and Parler during the 2020 US Presidential Election

This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.

Workload prediction based on improved error correlation logistic regression algorithm and Cross‐TRCN of spatiotemporal neural network

AbstractIn view of the randomness of user network usage behavior in data centers, which leads to a large randomness in power load, and considering that a single randomness processing method is usually difficult to fully characterize the uncertain characteristics of the system, this paper proposes a dual fusion prediction analysis model based on an improved error correlation logic regression algorithm and a novel spatiotemporal neural network structure called Cross‐TRCN. Two weight coefficients λ1 and λ2 are introduced to fuse the prediction results with different long‐term sequence prediction performance, thereby further eliminating the influence of random errors. The results show that it is feasible to predict the workload of data centers based on the improved error correlation logic regression algorithm and the innovative spatiotemporal neural network structure Cross‐TRCN.

Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks

Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.

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.