Messages In Network Research Articles

Implementation of A Delay-Tolerant Routing Protocol in the Network Simulator NS-3

The paper explores the implementation of the Epidemic Routing Protocol using the NS-3 simulator, focusing on its application in intermittently connected networks like Mobile Adhoc Networks (MANETs). The Epidemic Protocol maximizes message delivery by flooding the network with multiple copies, ensuring reliability even in sparse or highly mobile environments. Through NS-3 simulations, the study examines the protocol’s performance, considering factors such as network size, node mobility, and message Time-To-Live (TTL). The simulation, which models an ad-hoc wireless network using IEEE 802.11b, demonstrates that the protocol achieves a 100-percentage packet delivery ratio with minimal latency. Tools like NetAnim provide detailed insights into node interactions and message dissemination, highlighting the protocol’s effectiveness in maintaining communication under challenging conditions. The paper concludes that while the Epidemic Protocol is resource- intensive, it is highly effective for scenarios where traditional routing methods fail, offering a reliable solution for dynamic and partitioned network.

Hybrid vehicular access protocol and message prioritization for real-time safety messaging

Real-time safety services rely on the exchange of messages to enhance the operations of connected and automated vehicles (CAVs). These safety messages convey vital information about traffic conditions, enabling drivers to take necessary measures to prevent accidents. The timely and reliable delivery of these messages is essential, necessitating efficient channel access. Vehicular Deterministic Access (VDA) is employed as a channel access scheme with distinct priorities and stringent timing guidelines, particularly for urgent safety warnings. In this paper, we propose a hybrid approach that combines VDA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols, along with a message prioritization algorithm, to ensure efficient and reliable communication of safety messages in vehicular networks. Our approach leverages the strengths of both VDA and CSMA/CA to avoid message collisions; VDA is more efficient under high traffic loads, while CSMA/CA is better suited for low traffic loads. Additionally, the incorporation of the message prioritization algorithm ensures strict deadline guarantees for high-priority messages, such as Decentralized Environmental Notification Messages (DENMs). We evaluate our proposed solution using the Artery simulation framework. Our results show over a 93% delivery rate for DENM exchanges while maintaining low collision probability across various traffic loads. This research provides practical guidance for the development of efficient and reliable communication systems for CAVs. It also offers a detailed analysis of the trade-offs among different access protocols and message prioritization strategies in vehicular networks.

AnxietyNet: social media trends with fuzzy logic trees

In the digital age, social media platforms have become integral parts of daily life, facilitating communication, information sharing, and community building on a global scale. However, alongside the benefits of connectivity, concerns regarding public anxiety within these virtual communities have emerged. This research paper delves into the phenomenon of public anxiety on social media and proposes a novel approach utilizing fuzzy tree logic to analyze and address this issue. Through a comprehensive review of literature, theoretical frameworks, and practical applications, this paper aims to provide insights into the complex dynamics of public anxiety and offer potential solutions leveraging machine learning techniques. The proposed methodology involves the development of a cascading model to dynamically compute individual anxiety scores within social media networks. By analyzing structural influences and user interactions, this model aims to provide a nuanced understanding of anxiety dynamics within online communities. Additionally, a probabilistic model is designed to measure anxiety scores of social network messages, utilizing a generalized user profile. The integration of fuzzy tree logic enables the construction of a tree structure to effectively compute anxiety scores, offering a more flexible and human-like decision-making process. Preliminary experiments demonstrate the effectiveness of the proposed approach in capturing and analyzing public anxiety on social media platforms. The fuzzy tree model shows promise in accurately assessing anxiety levels and identifying key factors contributing to social anxiety within online communities. Moreover, the integration of machine learning techniques enhances the scalability and adaptability of the model, allowing for real-time analysis and proactive intervention strategies. This research underscores the importance of addressing public anxiety on social media through innovative analytical frameworks. By leveraging fuzzy tree logic and machine learning techniques, we can gain deeper insights into the dynamics of anxiety within online communities and develop targeted interventions to support individuals experiencing distress. Collaborative efforts in this endeavor hold the potential to foster societal well-being and resilience in the digital age.

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

Operations, financing and performance of paraguayan MSMEs in times of crisis: an analysis in the context of the Covid-19 pandemic

In addition to the health impact, as well as the impact on the economic aspects and everything related to sales and jobs, the Covid-19 pandemic has had a major influence on the operational aspects of businesses, largely due to issues related to access to financing, which resulted in variations in overall business performance. The objective of this research was to analyze how the pandemic impacted the operations and access to finance of MSMEs and its influence on their performance. For this study, the case of Paraguayan MSMEs was taken, and a total of 360 managers and/or owners of this type of companies, belonging to the industrial, commercial and service sectors, participated in the study. The quantitative method was used through the application of a digital survey sent via email and messaging networks, during the first half of 2021. Quantitative data analysis was performed by applying tests such as Pearson's Chi-square and analysis of variance. The most relevant results (on a rating scale from 1 to 5) related to operations, indicate that companies were forced to modify their product and service offerings (3.47), in addition to lowering sales prices (3.31) and implementing other adaptation activities (3.21). Regarding access to financing, it was observed that 40.3% of the MSMEs participating in the study that have requested financing obtained it under the same conditions as the previous year, while 19.9% indicated that they obtained financing, but under worse conditions, and 36.4% have not been able to access credit. With regard to performance, significant differences were found among the MSMEs, taking into account the sector to which they belong. Differences in performance were observed among those offering higher quality products (industrial sector with 3.81), those with more efficient internal processes (services sector with 3.55), and those with more satisfied clients (industrial sector with 3.96). The results indicate that Paraguayan MSMEs were able to adapt and keep their businesses operating with a certain level of success during the crisis, despite the fact that many of them were unable to access sources of financing or did so under unfavorable conditions.

“I Don’t Understand How These Two Things Go Together”: Toward a Theory of Risk Ecologies

ABSTRACT Rhetorical ecological approaches, which posit that messages exist not as isolated entities but instead in the relations of a dynamic network, offer a valuable lens for examining the discursive construction of health-related risk. Drawing from interview data (n = 50) exploring maternal vaccine refusal, this article expands upon these frameworks to trace how intertextual, synchronous messages – particularly those associated with prenatal and infant nutrition – influence mothers’ understanding about early childhood vaccine-related risk. Ultimately, I propose the notion of a risk ecology – a network of intertextual messages which shape and constrain sense-making about risk as it emerges within a particular moment and biosocial community.

A Keypad-Based Mobile App Security Alert System with Virtual Private Network and Short Message Service Integration

In the digital age, mobile devices are integral to daily life, making robust security measures essential for protecting sensitive data. This paper presents the development of a mobile application aimed at enhancing personal security through a keypad-based authentication system, integrated with Short Message Service (SMS) and Virtual Private Network (VPN) functionalities. The intuitive interface of keypads allows users to swiftly send predefined emergency messages with a single keystroke, including their current location and urgency details. The application ensures encrypted and secure communication via a VPN and leverages SMS for reliable real-time notifications, even in areas with limited connectivity. A user-centered design approach, utilizing React JS for frontend development and Bootstrap for a responsive UI, ensures accessibility and ease of use for individuals with varying levels of technological proficiency. Rigorous testing validates the application's functionality and performance, demonstrating its potential to streamline emergency communication and enhance personal security in critical situations.

Organisational antecedents of employee ambassadorship on social network sites

AbstractOrganisations seeking to enhance their reputation increasingly depend on social network messages—for instance, from employees—instead of being able to control this through their formal communications. The present research aimed to examine how employees’ willingness to share positive messages about their organisation on their social network sites (SNS) relates to organisational features and activities. Specifically, we examined whether employee ambassadorship on SNS relates to two key dimensions of social evaluation: perceived organisational morality and competence. Results of three studies suggest that organisational features can motivate employees to support their organisation online. Across different samples and measures, employee evaluations of organisational morality were a stronger statistical predictor of online ambassadorship than their evaluations of organisational competence. Organisational identification, not external prestige, mediated the effect of organisational morality on online ambassadorship. This suggests that perceived organisational morality relates to intrinsic motives of employees to support their organisation on SNS.

Are Active Users the Most Central to Hacker Social Networks? A Comparative Analysis of Public and Private Online Network Structures Among Hackers

ABSTRACT The last decade witnessed significant research of user behavior and social networks within online hacker forums and illicit markets. Researchers have used the quantifiable nature of forum posting behaviors to assess network ties between actors and identify key participants based on their connections. These studies are based on the publicly identifiable associations between actors in public posts. Participants within forums can also connect with users through private messaging systems linking users directly through a sort of e-mail-based exchange. Few have considered how the public and private message networks of actors differ and what can be gleaned from their analysis. This study employed a quantitative analysis of a highly popular web forum used by hackers involved in both legal and criminal hacks. First, the networks were built of the relationships between 1,313 users across 12,531 threads and 19,862 public posts, and between those users’ 1,738 private messages. Then the centrality on hidden networks was estimated from the centrality on the visible networks. The implications of this study for policy and our understanding of online network structures are discussed.

Forecasting the molecular interactions: A hypergraph-based neural network for molecular relational learning

Molecular relational learning refers to forecasting the interaction performance between pairs of molecules. Graph neural networks (GNNs) enables the effective modeling of chemical molecules by transferring them as abstract structures of graph, thus considering node-level interactions instead of atom’s. However, the abilities of modeling and predicting of classical GNN methods is restricted by edges’ redundancy and insufficient process of interaction modeling in a pair of molecular graphs. Therefore, based on the novel hypergraph convolutional neural network and bi-view cross interaction message passing and updating, we propose a hypergraph-based neural network method named IE-HGNN (Internal-External Bi-view Hypergraph Neural Network). We evaluate our method on two real-world molecular datasets, i.e., LEP and ZhangDDI, demonstrating IE-HGNN achieves excellent performance for both macro and micro molecules, while maintaining high training efficiency. We believe that this study introduces a novel approach for exploring the field of molecular relational learning and interaction prediction.

Multi-order feature interaction-aware intrusion detection scheme for ensuring cyber security of intelligent connected vehicles

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99.99%, and increase the highest F1-score to 97.18%, demonstrating the model’s ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs.

Mobile Forensic Investigation on iOS & Android Smartphones: Case Study Investigation on WhatsApp

Following the exponential growth of information and communication technologies, the smartphone market, as well as advances in wireless data networks (3G and 4G), has accelerated. Mobile apps for social networking and instant messaging have been created by these firms. Other instant messaging (IM) smartphone programs like WhatsApp (WA), Viber, and IMO have also been created. WA is the most widely used instant messaging program. With WA, you can send and receive messages in a variety of formats, including text, voice, video, and documents. Various cybercrime incidents were committed through WA's. WA use leaves several artifacts that may be examined to detect the digital evidence. In addition, iOS and Android are two of the most popular smartphone operating systems. Because of this, the inquiry will involve the use of forensic investigative techniques and methodologies. Forensics on both iOS and Android cellphones were utilized to investigate a digital crime that was believed to have been perpetrated in WA. To conclude the investigation, we analyzed chat logs, phone records, and other media to gather proof. Legal framework and established processes were used to guarantee that evidence was preserved from change or destruction and that the witness's account was acceptable in court throughout the investigative process. It was finally stated that the inquiry and evidence had been presented. As a result, WA forensic artifacts might be evaluated and found effectively utilizing the mobile forensic procedure.

Beyond thanatopsis: the sublime experience of visitors at natural disaster sites

ABSTRACT Understanding positive emotions associated with dark tourism experiences is critical in highlighting their social significance. The sublime experience provides a new perspective for exploring the association. This research aimed to uncover the characteristics and generation of sublime experiences at natural disaster sites based on two classic Chinese earthquake memorials. Using thematic analysis of messages in guest books and travel-based social networks, a model was developed of the sublime experiences of dark tourism at natural disaster sites. The model suggests that the visitor experience at natural disaster sites can be seen as a sublime experience. Safe experiences, diverse experience objects, and meaningful experiences foster positive emotions of dark tourism experiences. This research provides new findings on sublime experiences and has theoretical contributions for interpreting positive emotions in dark tourism encounters.

Efficient Optimization approach in WSN using Multicast Forwarding Scheme in 6LowPAN Environment

The rapidly growing network of devices that are connected known as the Internet of Things, also referred to as IoT, is designed to collect and exchange data. These devices are integrated with sensors, software, and other technologies. An IPv6-based routing system called 6LowPAN was created especially for Internet of Things devices with little resources. However, because multicast packets must be replicated to every multicast group member, the conventional multicast forwarding technique in 6LowPAN may be wasteful. This can result in excessive energy usage and network traffic, particularly in large-scale Internet of Things implementations. An optimization process is presented here using a multicast forwarding scheme. In the proposed forwarding scheme, nodes can send multicast packets up and down at Low Power and Lossy networks over the 6LowPAN environment. Should the sink node be on the root node, it should immediately request that other nodes join the multicast messaging network of things. If otherwise, an ICMPv6 packet will be sent towards the root node in the Destination-oriented Directed-Acyclic Graphs (DODAG) tree, which expands the multicast messages in the first source's interest. In multi-bounce forward straight-line topology, when the DODAG network root is the source of multicast congestion, it exhibits a similar ratio of packet delivery and delay from end to end with a minimal memory overhead. When traffic via multicast originates from the most important rank node in a backward straight-line topology, this paper focuses on the efficiency of the multicast network at 6LowPAN.

Students' experience of interpersonal interactions quality in e-Learning: A qualitative research.

Online Interaction is a critical characteristic of distance learning, and effective online communication models empower students. This research aimed to explain students' experiences on the quality of interpersonal interactions in e-learning. This study was conducted from November 2021 to October 2022. The qualitative descriptive design via conventional content analysis was utilized. Purposeful and maximum variation methods recruited sixteen participants from three medical science universities in Iran. The data were collected through semi-structured, in-depth, face-to-face, or online interviews. Interviews were recorded through a digital recorder, and analysis was achieved simultaneously with data collection using Graneheim and Lundman (2004). The Lincoln and Guba criteria, including credibility, dependability, transferability, and confirmability, were used to improve the trustworthiness of the findings. The results indicated the importance of different dimensions related to teaching-learning. It seems crucial to develop a comfortable and safe environment to improve interpersonal interactions. Educators should be provided with pedagogical skills to support interactions. In addition, focusing on some learners' soft skills is also vital. In addition to the significance of the teacher's inclusive role, the educational content must have critical standards. Constructive feedback and the proper use of simultaneous and non-simultaneous communication tools and social networks are other important issues in strengthening interpersonal relationships. Ultimately, comprehensive and ongoing support of learners improves the quality of interpersonal interactions. The results indicated the significance of different dimensions of teaching-learning as facilitating factors of interpersonal interactions. The proper use of simultaneous and non-simultaneous communication tools and social networks are other important issues in strengthening interpersonal relationships. Ultimately, comprehensive and ongoing support of learners improves the quality of interpersonal interactions. The results of this study give teachers the insight to keep essential issues in mind when developing their online courses and students to be aware of their roles in the online learning process. Also, the characteristics of simultaneous and non-synchronous platforms, social messaging networks, and learner support are crucial.

OMNeT++ Simulation Framework for Avionics Full-Duplex Switched Ethernet

Avionics Full-Duplex Switched Ethernet (AFDX) is a popular network technology for integrated modular avionics (IMA). When designing an AFDX network, it is important to fulfill the real-time communication requirements of avionic systems. To this end, simulation tools offer a low-cost solution for the performance evaluation of a given AFDX network and message set configuration at the design stage. In this paper, we develop an improved AFDX model for the widely used OMNeT++ (extensible, modular, component-based C++ simulation library and framework) network simulator. Our model exhibits high fidelity to the standard, adds detailed parameter configuration and data logging capabilities, and is compatible with the most recent OMNeT++ version. Our model is accepted by the OMNeT++ community and published as the current AFDX simulation model. Moreover, we propose a complete AFDX simulation framework by augmenting our improved OMNeT++ models with a new network configuration and analysis software tool that we call ANCAT. ANCAT enables users who lack familiarity with the OMNeT++ interface and specific file formats to provide simulation parameters in generic file formats such as.xlsx. Subsequently, ANCAT runs the simulation and generates reports for the results. We first systematically verify the correctness of our AFDX simulation framework by custom-designed experiments. Then, we investigate the practicability of our proposed framework with the performance analysis of a realistic AFDX network topology and message set. In particular, we demonstrate that our proposed framework can be efficiently used to explore the real-time capabilities and relevant features of AFDX networks, as well as provide basic guidelines for network configuration.

An ECC-Based Authentication Protocol for Dynamic Charging System of Electric Vehicles

Dynamic wireless charging emerges as a promising technology, effectively alleviating range anxiety for electric vehicles in transit. However, the communication between the system’s various components, conducted over public channels, raises concerns about vulnerability to network attacks and message manipulation. Addressing data security and privacy protection in dynamic charging systems thus becomes a critical challenge. In this article, we present an authentication protocol tailored for dynamic charging systems. This protocol ensures secure and efficient authentication between vehicles and roadside devices without the help of a trusted center. We utilize a physical unclonable function (PUF) to resist physical capture attacks and employ the elliptic curve discrete logarithm problem (ECDLP) to provide forward security protection for session keys. We validated the security of our proposed scheme through comprehensive informal analyses, and formal security analysis using the ROR model and formal analysis tool ProVerif. Furthermore, comparative assessments reveal that our scheme outperforms other relevant protocols in terms of efficiency and security.

Analysis of translation teaching skills in colleges and universities based on deep learning

With the progress of the times and the improvement of science and technique, network message technique has occupied a vital position in people's lives. At the same time, society has been implementing university English education reform in recent years, and the “internet plus” wisdom education model is the product of the improvement of the times. This new education model has gradually integrated into the education of various subjects. Introducing the concept of message technique and wisdom education into university translation education can innovate education mode, optimize education content, and integrate high-quality education resources. Cultivating applied translators has become the trend of educational reform. Based on deep learning, this paper studies translation education skills in universities. In-depth education enables learners to acquire systematic knowledge, critical spirit, creative thinking, etc. This kind of learning fully taps individual potential to cultivate a complete personality. According to the research in this paper, wisdom education is 12% better than traditional education, and it is suitable to be widely put into practice.

Tensor Network Message Passing.

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops, while tensor network methods can have exponential computational complexity in large graphs due to long loops. In this Letter, we propose a new method called "tensor network message passing." This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small subgraphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally treelike and locally dense-connected when the dense local graphs have a limited tree width. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, and have demonstrated the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.

Digital Footprint: Assessing Student Satisfaction with Education Quality

The COVID-19 pandemic has changed the way learning is organized around the world. Russian universities have also been faced with the need to quickly transfer all teaching to an online format. The importance of student satisfaction with the education quality in online learning is increasing, since it is an important condition for motivation. The paper shows that based on the analysis of students’ messages in the social network, it is possible to observe and analyze the overall dynamics and trends in student community satisfaction with the quality of the learning / the efficiency of universities and conduct a comparative analysis of the identified characteristic data groups with their totality. It is shown that the data on the reaction of students of a particular university may have significant deviations from the totality of data, which reflects the characteristics of the reaction of students of a particular university to events occurring at the same time. This may indicate the internal differences of the university, which form an appropriate response to external events.To understand the satisfaction of students in the transition to a new implementation format of the learning. The digital traces of students from the social network VKontakte were analyzed using individual Big Data tools on the PolyAnalyst software platform. This made it possible to trace changes in the mood of students and, on the example of a single university, to identify and explain deviations in the attitude of students to the implementation of the learning, as well as to verify the methodology. The methodology developed by authors makes it possible to detect problematic issues in the university, including the moment of their occurrence, relevance, degree of concern of students. Such content analysis can be used not only to assess students’ satisfaction with the quality of the learning, but also to monitor the emergence of any problems that cause concern and strong reactions on the part of the student community, as well as other communities and individual groups.