Users In Mobile Networks Research Articles

Key Actions to Enable Automation for Mobile Network Security Operations

Over time, the landscape of Cyberspace surrounding Internet Service Providers (ISPs) has undergone enduring transformations. Notably, mobile networks, integral to contemporary societal infrastructure, consistently encounter evolving cybersecurity threats and risks. ISP processes have adapted with a persistent focus on optimizing network performance and availability, yet the challenges emerge from a laborious and protracted network change management process, hindering the practical automation of network security. Addressing the rightful demand for the highest level of security from mobile network users, our research question probes: "How can we intensify the emphasis on network security and facilitate the automation of network security operations?" To delve into this, we conducted extensive interviews with ISPs globally, affirming the inherent difficulty in automating security operations. The findings categorize challenges into three domains: Security Culture, Operational Processes, and Tools. Cultivating a security culture demands a pivotal commitment to change from top management, coupled with dedicated time and resources. Essential to this is the enhancement of security competence, extending beyond specialists to encompass network engineering staff. Robust network security not only safeguards against threats but significantly influences various business processes. Initiating a secure network requires ISPs to articulate explicit security requirements during the network procurement process, exerting pressure on vendors to fortify systems with a security-by-design approach at the factory. Critical to this is the secure deployment of networks, integrating comprehensive network hardening during the build phase. However, findings indicate a prevalent oversight where network security configuration changes are often neglected or deprioritized in favor of network performance. Achieving a harmonious balance between security and performance necessitates a predefined agreement on a network security configuration baseline. This collaborative effort involves network security specialists and competent network engineers. To effectively monitor and enforce network security configuration, ISPs require automation-enabled tools with the predefined baseline, offering capabilities for monitoring and enforcing network assets. In conclusion, our research emphasizes the imperative need for a paradigm shift in organizational culture, operational processes, and tool utilization to enhance the focus on network security and enable the critical automation of network security operations within the ever-evolving landscape of Cyberspace.

Intelligent Cellular Offloading With VLC-Enabled Unmanned Aerial Vehicles

This paper discusses a cellular network assisted by an energy-and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV’s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE-SE tradeoff. The problem posed is seen in a complex and non-convex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this non-convex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

Calculation of the mean emission power in a mobile network cell as a function of users’ density distribution

In this paper we consider two random variables in a cell in the network of mobile users. The first random variable (RV) is the distance of active mobile user from a base station. The second RV is emission power for the random active user if the control of emission power exists. RV emission power is dependent RV of the first RV. That’s why the calculations of mean values for these RVs differ one from the other. The first RV depends only on the users’ density area distribution. The second RV depends on the signal attenuation value in the considered environment. It is proved that, in real conditions, emission power to a user at the mean value of distance from a base station is always lower than mean value of emission power to a user. The difference between these two power values increases as the signal attenuation coefficient increases. These two values are calculated for three characteristic user density distributions in a cell. The results are verified by original computer simulation programs.

5G communication resource allocation strategy for mobile edge computing based on deep deterministic policy gradient

AbstractDistributed base station deployment, limited server resources and dynamically changing end users in mobile edge networks make the design of computing offloading schemes extremely challenging. Considering the advantages of deep reinforcement learning (DRL) in dealing with dynamic complex problems, this paper designs an optimal computing offloading and resource allocation strategy. Firstly, the authors consider a multi‐user mobile edge network scenario consisting of Macro‐cell Base Station (MBS), Small‐cell Base Station (SBS) and multiple terminal devices, the communication overhead and calculation overhead generated are formulated and described in detail. Besides, combined with the deterministic delay of tasks, the optimization objective of this paper is clarified to comprehensively consider system energy consumption. Then, a learning algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to minimize system energy consumption. Finally, simulation experiments show that the authors’ proposed DDPG algorithm can effectively optimize the target value, and the total system energy consumption is only 15.6 J, which is better than other compared algorithms. It is also proved that the proposed algorithm has excellent communication resource allocation ability.

Development and validation of brand strategies evaluation scale for mobile network users

Purpose: Mobile network operators (MNO) use a variety of brand strategies in the Pakistani telecommunication market to entice mobile network users (MNU), but there was no standardized measure available that provides a fixed set of criteria for their brand strategies. To fill this knowledge gap, the primary goal of the current study is to design, develop and validate the Branding Strategies Evaluation Scale (BSES). Design/methodology/approach: The mixed-methods model of scale development and validation was employed in various phases by splitting into two studies. In study 1, after piloting factorial validity of 23 items were surveyed on the sample of 150 conveniently available participants (90= male, 60= female) between ages ranging 16 to 52 years, of different mobile network users Findings: Exploratory factor analysis unfolded five sub-factors of brand strategies with a high-reliability coefficient and internal consistency, including offer and services, competitive features, advertising strategies, brand identity and facilitation with money matters. The mean gender difference was observed only on the subscale of facilitation with money matters. In Study 2, the convergent validity of BSES was found significant by weighing its positive association with the Customer-Based Brand Equity Questionnaire (CBEQ). While the Customer Switching Intention Scale (CSIS) was used to determine BSES's discriminant validity, which revealed a significant negative correlation between the two measures. However, this is the first study intended to develop a standardized instrument for brand strategy evaluation of MNO. Practical implications: The results of this research enable Mobile network operators (MNO) to understand what kind of branding strategies are useful and effective for customers. This measure will provide authentic understanding to local MNO about the effectiveness of their brand strategies from consumer perspectives. Likewise, for academics, current research provides a gateway to asses customers' choices in a broader way in different regions for brand preferences and branding strategies used by different cellular companies.

Characterization of the Mobile User Profile Based on Sentiments and Network Usage Attributes

Providing resources to meet user needs in futuristic mobile networks is still challenging since the network resources like spectrum and base stations do not increase in the same proportion as the accelerated growth of network traffic. Because of this, human/user behavior attributes can assist resource management in dealing with these challenges, which pick up aspects of how the user impacts the usage of mobile networks, such as network usage, the content of interest, urban mobility routines, social networks, and sentiment. A user profile is a combination of user/human behavior attributes. Such profiles are expected to be a knowledge for softwarization enablers to improve the management of future wireless networks fully. Nevertheless, the correlation between human sentiment and wireless and mobile network usage has not been deeply investigated in the literature about the mobile user profile. This work aims to define the user profile using a transfer learning approach for the sentiment classification of WhatsApp messages. A real-life experiment was conducted to collect users' attributes, namely the WhatsApp messages and network usage. A new data analysis methodology is proposed that consists of a frequent item-set pattern mining (FP-Growth) based on Association Rules, the Chi-squared statistical test, and descriptive statistics. This methodology assesses the correlation between sentiment and network usage in a profound way. Results show that the users participating in the experiment form three groups. The first group, with 55.6% of the users, contains users who present a strong relation between negative sentiment and low network usage and also a strong relation between positive sentiment and high network usage. The second group contains 25.9% of the users and is composed of userswho present a strong relation between positive sentiment and high network usage. The third group contains 18.5% of the users for whom the correlation between sentiment and network usage is still statistical significant, but the strength of this relation is much more weak then in the other two groups. Thus, 81.5% of the users (the first two groups) present a strong relation between user sentiment captured from WhatsApp messages and the network traffic generated by them.

Variability of mobile phone network logins in the Białowieża National Park during the 2019 and 2020 summer holiday periods in the context of the COVID-19 pandemic

Abstract This article analyses the variability of the number of mobile phone network users in and around the Białowieża National Park during the summer holidays in 2019 and 2020 based on Call Detail Records. The data collected was used to analyse the mobility of Poles in the study area during the SARS CoV 2 pandemic. The research shows that despite the area's peripheral location and the prevailing pandemic, the number of users of the mobile network in the summer of 2020 increased compared with 2019 – by 20.2% in July and 14.3% in August. The increase in users logged in results from the increased number of tourists who were looking for remote places that could afford them some protection against SARS-CoV-2 infection in the summer of 2020.

Predicting age and gender from network telemetry: Implications for privacy and impact on policy

The systematic monitoring of private communications through the use of information technology pervades the digital age. One result of this is the potential availability of vast amount of data tracking the characteristics of mobile network users. Such data is becoming increasingly accessible for commercial use, while the accessibility of such data raises questions about the degree to which personal information can be protected. Existing regulations may require the removal of personally-identifiable information (PII) from datasets before they can be processed, but research now suggests that powerful machine learning classification methods are capable of targeting individuals for personalized marketing purposes, even in the absence of PII. This study aims to demonstrate how machine learning methods can be deployed to extract demographic characteristics. Specifically, we investigate whether key demographics—gender and age—of mobile users can be accurately identified by third parties using deep learning techniques based solely on observations of the user’s interactions within the network. Using an anonymized dataset from a Latin American country, we show the relative ease by which PII in terms of the age and gender demographics can be inferred; specifically, our neural networks model generates an estimate for gender with an accuracy rate of 67%, outperforming decision tree, random forest, and gradient boosting models by a significant margin. Neural networks achieve an even higher accuracy rate of 78% in predicting the subscriber age. These results suggest the need for a more robust regulatory framework governing the collection of personal data to safeguard users from predatory practices motivated by fraudulent intentions, prejudices, or consumer manipulation. We discuss in particular how advances in machine learning have chiseled away a number of General Data Protection Regulation (GDPR) articles designed to protect consumers from the imminent threat of privacy violations.

Context-Aware Spectrum Sharing and Allocation for Multiuser-Based 5G Cellular Networks

The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.

Hyperconnectivity in action: users of mobile social networks and new technologies

PurposeThe aim of this research is to identify the characteristics that give rise to motivations to use social apps in light of behavioral concepts related to consumers’ desires and emotional values.Design/methodology/approachNetnography is used as the main methodology to analyze and categorize user profiles of online social networks. These profiles are presented through conceptual headlines, which highlight the main characteristics of each user group.FindingsThe results of the study show that many users have become dependent on the WhatsApp application, either for technological reasons or for social reasons related to fashion and status.Research limitations/implicationsFew consumers actually explained the ways they use mobile social networks in the context of the procedures and level of communication performed. However, the influence of social contexts in the consumer environment is changing perceived values focusing on prestige and status to technological elements that the majority of consumers use.Practical implicationsPractical implications are directly related to forming business connections in a less formal and more hedonic environment, improving market results while fostering user enjoyment. In addition, the ongoing updates to WhatsApp have brought new functionalities and improvements to previously weak features.Originality/valueAlthough other applications offer means by which to talk and send messages, WhatsApp continues to be (as of early 2021) the most used platform for conversation in Brazil. The sovereignty of WhatsApp is directly linked to its social value, which is related to the number of consumers who daily interact via the network.

Privacy protection scheme for mobile social network

In the recent years, popularity and number of users of mobile social networks have grown significantly. Due to different types of communication on these networks, users share a lot of information with each other. One of the most critical challenges of social networks is disclosure and unauthorized access to information, data, and communication between users that is a kind of violation of their privacy, which can be done by social network providers, especially unauthorized users. One way to protect one’s privacy is using encryption. Therefore, in the present paper, an improved secure design was presented for mobile social network using ciphertext-policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) that will encrypt users data in an end-to-end manner. In the proposed scheme by CP-ABE encryption, in addition to data privacy, the users’ desired access control is applied to the access of others to their shared data. As a result, unauthorized users will not be able to access and violate data privacy. Furthermore, it will prevent the unauthorized access of social network provider with the help of AES. This scheme provides new management to exchange encryption keys and manage network components for their information. Therefore, the keys are exchanged securely and some of them are generated outside reach of the social network server and by the user. The users' main keys are encrypted using the Rivest-Shamir-Adleman (RSA) method.Consequently, the proposed scheme will protect users privacy and prevent the unauthorized access to users’ data and information by the social network provider and unauthorized users by comparing results of the proposed scheme with other similar investigated schemes. In addition, it will be resistant to common attacks (man-in-the-middle attack and replay attack). Also, according to our findings, it is a safe and comprehensive scheme with high security and practicality while needing less time for both encrypting and decrypting the users messages.

The Effects of Internet Exposure on Sexual Risk Behavior Among Sexually Experienced Male College Students in China: Cross-sectional Study.

BackgroundAs a young subgroup, college students have become the main users of mobile social networks. Considering that people can indiscriminately access explicit sexual content on the internet, coupled with the increase of HIV infections in male college students, the role of the internet in meeting sexual partners and its correlation to risky sexual behavior has become an important topic.ObjectiveThe aim of this study is to explore the effects of internet exposure on sexual partners and sexual risk behavior among sexually experienced male college students.MethodsAn institution-based cross-sectional study design was used to collect data through a paper-based questionnaire administered to male college students recruited from colleges and gay organizations in Hangzhou, Zhejiang Province, China. A total of 1045 sexually experienced male students were incorporated in our analysis, with the following information collected: sociodemographic characteristics, sexual intercourse–related behaviors, and sexually transmitted disease (STD) knowledge. Mann-Whitney U and Kruskal-Wallis tests were used to examine differences regarding basic characteristics and sexual risk behaviors between male college students who meet sexual partners via the internet and those who do not. Sequential logistic regression models were employed to examine the influence of meeting sexual partners via the internet on risky sexual behaviors after controlling for other factors.ResultsThe mean age of the sexually experienced male students was 21.6 (SD 2.0) years. The likelihood of risky sexual behavior was varied, yet it was the highest for those who aim to meet paid sexual partners (145/192, 75.5% to 19/22, 86.4%), followed by those seeking partners for love or romance (258/435, 59.3%). Compared to non-internet partner seekers, internet partner seekers tended to have more casual intercourse (292/542, 53.9% versus 51/503, 10.1%), paid intercourse (32/542, 5.9% versus 12/503, 2.4%), and intercourse with same-sex partners (349/542, 64.4% versus 41/503, 8.2%); they were also more likely to use psychoactive drugs (125/349, 35.8% versus 5/41, 12.2%) and have more than 2 partners. With the increase of HIV and STD knowledge, the probability of having unprotected intercourse decreased for non-internet partner seekers. However, it increased for internet partner seekers with a rising HIV knowledge score. Sequential logistic regression showed that meeting sexual partners on the internet was statistically associated with sexual risk behaviors with multiple sexual partners (odds ratio 4.434; P<.001).ConclusionsMeeting sexual partners via the internet is a common behavior among sexually experienced male college students, and those who meet partners on the internet exhibited higher levels of risky sexual behaviors although they had sufficient HIV and STD knowledge; this is especially true for students who aimed to find partners for sexual intercourse. Thus, more attention should be paid to young adults to address the risky sexual behaviors that may contribute to STD spread among this population.

A novel clustering based algorithm to mitigate the demand of forecasting errors for newly deployed LTE cells with insufficient historical data

This study presents a novel clustering-based algorithm to mitigate the demand of forecasting errors of newly deployed LTE (Long-Term Evolution) cells with insufficient historical data. The numbers and the usage of mobile networks are growing day by day. So, new base stations are set every day, and the newly deployed cells do not have enough historical data to forecast. We developed a clustering-based algorithm to overcome this problem. We compared our approach with different forecasting methods such as classical time series methods, time series decomposition-based methods, and deep NN (Neural Network) methods. We tested our clustering-based solution compared with other approaches using seventy LTE cells’ daily historical performance data for two years. We collected this data from a Tier-1 Mobile Network Operator (MNO). We also analyzed the clustering features and benchmarked them for their contribution to the solution, and we measured the error rate by MAPE (Mean Absolute Percentage Error). As a result, we decreased the previous forecasting error rate from 133% to approximately 35%, showing that our novel algorithm is an efficient tool for this process.

An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

Matching Subcarrier Resource Allocation and Offloading Decision

A heterogeneous cellular network can define as a network that is composed of different cell sizes (macrocell (MeNB), small cell (SeNB), femtocell). Such heterogeneity of network is the backbone of the 5G networks where new applications on mobile devices need extensive computing power consumption and ultra-low latency constraints. Using a heterogeneous network will provide multiple paths in which the users’ data can flow through the network depending on the users’ available resources, remaining energy, etc. we study the heterogeneous network model, which contains MeNB, SeNB and femtocells. Also, we propose a matching subcarrier resource allocation and offloading decision (MSRAOD) algorithm, that depends on recourse allocation optimization. The main optimization goal has been set to minimize the total energy consumption of mobile users’ devices with acceptable latency requirements of the applications. Our proposed algorithm results show that the proposed algorithm enhances the average energy consumption of mobile users in the heterogeneous network.

A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Future mobile networks are envisioned to have critical limitations in terms of latency, energy usage, capacity and network resources since these networks are expected to become extremely dense and complex. The rapid enormous advances in recent technologies such as Internet of Things (IoT) highlights the urgent need for network performance enhancement as well. To this end, self-organizing networks are a promising solution to push the network performance to the next level. These scalable networks can dynamically adapt to possible changes in the network. Smart mobility management, in particular mobility prediction, is a subsection of self-organizing functions which are mainly based on the machine learning techniques. In this paper, we propose to estimate user’s future trajectory using machine learning approaches for a better network management. We propose a novel bidirectional trajectory prediction model called BTPM to model the user mobility behavior. The proposed method exploits the potential benefits of bidirectional gated recurrent unit (GRU) for having an accurate prediction. Moreover, we introduce a data preprocessing phase to obtain better results with significantly lower execution time. The proposed approach takes full advantage of data analysis in both directions (backward and forward) in order to provide a long-term prediction and model user’s mobility even with complex patterns. Experimental results show that the proposed bidirectional approach significantly improves the performance of the mobility predictor in terms of model accuracy, robustness and execution time. It achieves a model error of 0.014 and decreases the execution time up to 97%.

QoE estimation for different adaptive streaming techniques in mobile networks

Video services are becoming more and more popular for mobile network users and require greater and greater resources and provisions from telecommunications service providers. But operators suffer from problems of interoperability between the different adaptive transmissions techniques they employ in an attempt to satisfy the quality of experience (QoE) of the service provided to users and improve network performance. This article presents a comparison of four such streaming techniques - DASH (dynamic adaptive streaming over HTTP), HDS (HTTP dynamic streaming), HLS (HTTP2 live streaming) and HSS (HTTP smooth streaming) - used in a live video playback by a user in different test scenarios on an emulated long-term evolution (LTE) network. Comparison of performance was carried out using the mean opinion score (MOS) metric calculated based on ITU-T Recommendation P.1203. The streaming techniques that performed best in each of the different test scenarios are revealed.

Enhancement for Content Caching and Delivery with Energy Consumption Awareness in Wireless Networks

The advanced capabilities of smart mobile devices for mobile network users have led to an explosive increase in the loads of networks, mainly driven by the increasing demand for multimedia services. Content caching in users’ devices can potentially enhance both the quality of service for mobile users and network performance. Upon users’ requests and their mobility models, a network can perform proactive caching to reduce network congestion. Furthermore, implementing an optimal caching method can increase the offloading probability, leading to an overall enhanced performance of the network. In this paper, we present an enhanced energy-based content caching and delivery approach for D2D assisted wireless networks. A cost problem is formulated for optimal caching by considering two important constraints establishing D2D links: Signal-to-Noise Ratio (SNR) and Energy Consumption (EC). An effective optimization algorithm, the Frequency Searching Adaptive Bat Algorithm (FSABA), is used to enhance the cached and delivered content, minimizing the total network cost. The system performance is analyzed and evaluated in terms of total network gain, offloading probability and average transmission delay. To validate the proposed enhancement caching approach, extended simulations are provided.

Detection of mobile network abnormality using deep learning models on massive network measurement data

In recent years, with the continuous growth of mobile network users, the traditional methods based on expert experiences and threshold analysis find it hard to meet the need of network maintenance. These methods expose a lot of weakness, such as subjectivity, differences, and inconsistency. This paper proposes a deep learning method to make use of massive mobile data and replace the expert system. Network measurement data and pseudo labels are leveraged to train the supervised learning model, which assists in the feature selection for the deep learning model. Through extensive experiments and analysis, our method is proved to be effective in abnormality detection and achieves better performance than the traditional expert system.

Matching Subcarrier Resource Allocation and Offloading Decision

A heterogeneous cellular network can define as a network that is composed of different cell sizes (macrocell (MeNB), small cell (SeNB), femtocell). Such heterogeneity of network is the backbone of the 5G networks where new applications on mobile devices need extensive computing power consumption and ultra-low latency constraints. Using a heterogeneous network will provide multiple paths in which the users’ data can flow through the network depending on the users’ available resources, remaining energy, etc. we study the heterogeneous network model, which contains MeNB, SeNB and femtocells. Also, we propose a matching subcarrier resource allocation and offloading decision (MSRAOD) algorithm, that depends on recourse allocation optimization. The main optimization goal has been set to minimize the total energy consumption of mobile users’ devices with acceptable latency requirements of the applications. Our proposed algorithm results show that the proposed algorithm enhances the average energy consumption of mobile users in the heterogeneous network.