World Networks Research Articles

ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT.

In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.

Learning through Place-Based Implementation of the UNESCO MAB Program in South Africa’s Oldest Biosphere Reserve: A Case Study of the Kogelberg Biosphere Reserve

UNESCO’s Man and the Biosphere Programme (MAB) is implemented through a world network of biosphere reserves, which offer a holistic people-centered landscape-level conservation approach. When successfully implemented the program enhances social–ecological system sustainability and resilience. However, there remains a research gap in understanding and collating lessons from individual sites for the benefit of the program globally. We assess MAB implementation in South Africa’s oldest biosphere reserve, the Kogelberg Biosphere Reserve (KBR; est. 1998). Using semi-structured in-depth interviews with directors and the coordinator, complemented by document analysis, we explore the governance and implementation learnings of the KBR as it has evolved. The KBR program implementation is guided by global necessity, but driven by the local context, which for the KBR translates to a non-profit organization cooperative governance model. The site faces a perceived lack of government financial support and awareness of the ‘biosphere reserve’ concept. Despite these challenges, successes have emerged in the formation of local partnerships to fulfil critical roles in socio-economic development and biodiversity conservation. The learnings from KBR, as it strives to become a model site for sustainability, are useful for other sites similarly operationalizing an international designation for local conditions.

Correction: Berceanu, C.; Pătrașcu, M. Engineering Emergence: A Survey on Control in the World of Complex Networks. Automation 2022, 3, 176–196

In the original publication [...]

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data of observed nodes. The feature embedding is engineered to yield structural consistency: there exists an affine hyperplane that consistently partitions the set of features, separating the feature vectors corresponding to connected pairs of nodes from those corresponding to disconnected pairs. The causal inference problem is thus addressed via clustering the designed features. We demonstrate with simple baseline supervised methods the competitive performance of the proposed causal inference mechanism under broad connectivity regimes and noise correlation levels, including a real world network. Further, we devise novel technical guarantees of structural consistency for linear NDS under the considered regime.

Collective behaviors of fractional-order FithzHugh–Nagumo network

Brain connection is the mechanism of determining the brain function and cognition, and the small world network is widely investigated among these connections. In this paper, the small world complex brain network is constructed by the fractional-order neurons, and the fractional-order memristor is used to connect these neurons. Compared with the integer-order counterpart, the fractional-order memristor describes precisely the distortion of hysteresis curves, and it exhibits more abundant dynamical behaviors to function as the synapse. In addition, the neurons with different fractional orders represent the individual differences during the cell differentiation. For the two coupled neurons, the phase lock is achieved in the bursting and spiking neurons, and it denotes a time delay between the two firing process, whereas the chaotic neurons cannot synchronize. In small world network, the collective behaviors of neurons tend to synchronize with the increasing of the coupling intensity. These results promote the understanding of information coding and transformation in complex brain network.

Modeling COVID-19 spread using multi-agent simulation with small-world network approach

BackgroundThe rapid global spread of COVID-19 has seriously impacted people’s daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems.MethodsWe propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents’ social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved.ResultsExperimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different “city closure” scenarios. The results showed that implementing a ‘lockdown’ 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections.DiscussionOur methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

ASSESSMENT OF SIGNALIZED INTERSECTION: A CASE STUDY IN BAGHDAD City by USING SIDRA SOFTWARE

Over the world, signalized intersections and road networks with heavy traffic have posed severe issues. So, it is crucial to employ cutting-edge software tools to ensure the current system can be managed and assessed. Therefore; this paper aims to analyze and evaluate the traffic flow performance at AL-Masbah signalized intersections in Baghdad city during peak hours by utilizing SIDRA INTERSECTION 5. For purposes of evaluation, the Level of Service LOS, average delay time, capacity, degree of saturation, queue for Intersection, fuel consumption, and carbon dioxide emission have been considered. In this study, video count was used to collect data. The main input data included peak hour factor (PHF), numbers and width of lanes, percent of heavy vehicles for each direction and movement, median, phasing, and timing. The results of the analysis indicated that the intersection is operated at LOS ( F) with a backlog queue of 166.1 which is the queue for any lane used by movement (vehicles)and a 1262m queue for any lane used by movement (meters) m, while fuel consumption was at a rate of 849.9 liters per hour. Furthermore, the results revealed that carbon dioxide (CO2) emission was increased by 2124.8 Kg per hour. So, It can be concluded that the intersection needs to improve its level of service by changing its geometry or by implementing an Intelligent Transportation System (ITS).

Mitigating Vulnerabilities in Large-Scale Network Environments: Strategies and Best Practices

In the modern world of networks, managing multiple threats in large-scale network environments is an essential goal of organizations to protect their system and essential information. Measures to counter threats and sources of vulnerability and the chances of cyber threats and cyber incidences are the primary focuses of this paper. Vulnerability management, network segmentation, and access controls are the first pillars of a sound cybersecurity program. Vulnerability management is a repeated process of discovering, analyzing, documenting, addressing, and checking general and individual protection flaws before an attacker can. Dividing the network becomes effective in preventing threats from affecting certain highly vulnerable areas of the network, as well as improving the levels of security control. Adopting robust identity and access management solutions, including MFA and RBAC, adds another layer of protection to the network by limiting access to networks and resources to only authorized users. Furthermore, the relevance of repeating vulnerability assessments and penetration testing is underlined, as these activities help update the information on the organization’s security and identify insecure positions. However, factors like the increasing pace of vulnerabilities and the necessity to adapt instantly to novel threats show that protection is not easy. Cyber threats are still on the rise, and as a result, the adoption of the spur) advanced technologies like AI in security frameworks boost detection rates and reaction time. The paper concludes by emphasizing the need for constant monitoring and implementing preventive measures to safeguard against emerging and evolving cyber threats.

Implementing AI-Driven Intrusion Detection System with Python and Light Connect Object

The escalating number of cybersecurity threats poses significant challenges for ensuring the security of networked systems. Intrusion Detection Systems (IDS) play a vital role in detecting and preventing malicious activities. This paper focuses on the implementation of an AI-driven IDS using Python and Light Connect Object (LCO) to enhance the detection capabilities and improve the overall security of networked systems. By integrating AI techniques into the IDS framework, we aim to effectively identify both known and unknown attacks. The proposed system is evaluated using real- world network traffic data, and its performance is measured using metrics such as detection accuracy and false positive rate. The results demonstrate the effectiveness and practicality of the AI-driven IDS in enhancing network security

Assessment of Wealth Distribution in Blockchain Online Social Media

Online social networks (OSNs) revolutionized how people interact with each other, and nowadays, thanks to blockchain technology, new solutions are being considered, giving birth to blockchain online social media (BOSMs). BOSMs use the blockchain to redistribute with their users the wealth generated by the platform through a rewarding system, assigning better rewards to socially impactful users. Thus, these new systems are characterized by highly intertwined economical and social aspects and constitute a new scenario in the world of social networks. Many scenarios, economic and social alike, show a phenomenon known as “the rich-get-richer”, which states that the richest actors of a system tend to become richer over time. To the best of our knowledge, in the scenario of BOSMs, where users can acquire cryptocurrency through their social actions, this type of phenomenon was not yet studied. In this article, we propose a methodological framework composed of three hypotheses that can help study the rich-get-richer phenomenon through a set of measures and indices. In addition, we apply the proposed framework to the Steem case study, showing how unevenly wealth is distributed on its blockchain and comparing our results to other scenarios.

Quantum dynamics on tight binding small world networks and flat band spectra

We investigate spectral features of quantum walks on graphs using tight binding hamiltonians that range from nearest neighbor coupling to fully connected models. For equal strength bonds in the few bond limit we find a transition from square root DOS to a semicircular Wigner-like one while close to the mean field limit a fully degenerate band appears. When long range bonds are weaker than nearest neighbor ones we have a similar transition that however re-enters to the square root spectrum at very high long range bond fillings. These findings may have some significance in complex lattices with flat-like bands.

Factors influencing the adoption of Internet of Things (IoT) in university libraries: a systematic literature review (SLR)

PurposeFor the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.Design/methodology/approachA systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.FindingsThe findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.Originality/valueThe study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.

Back to the Future: Space Law in a Networked World

Back to the Future: Space Law in a Networked World

Disease Spreading through Complex Small World Networks

The end of the COVID-19 pandemic allows an analysis in retrospect of the spread and early containment scenarios of the disease. Today we can weigh the diversity of results imposed by highly heterogeneous urban landscapes (both geographical and social). In this research, we address scenarios of one or more communities (neighborhoods) formed by small structures with strong connectivity among themselves (families). Special attention was paid to the early containment of the epidemic. After simulating many scenarios, it was observed that early isolation of the infected individuals is more efficient than the isolation of their entire family. But we also noted that the containment of the disease loses effectiveness if the clinical tests for its detection are reported late (from 1 to 4 days). On the other hand, the existence of neighborhoods (with high population density) complicates the disease containment strategies, since (a) the disease spreads faster due to the highly dense environment, and (b) these act as “hubs of contagion”, even if the disease itself is of low contagiousness.

The Viability of Viewdata as a Mass Medium: A Case Study of Prestel, the Prototype for Computerized Information Systems, the Internet

Great Britain’s videotext Prestel was the first viewdata system in the world and is considered a prototype to the Internet. It was a planned, technological wonder for online information and communication that other countries adopted. After a whirlwind of demonstrations and promotions, it failed to achieve a mass market in its own country and its creators’ dreams of a networked world. This article analyses why and how Prestel was developed and marketed, and its public reception, using the framework of platform studies and adding systems theory.

Resource losses to Ukrainian tourism caused by full- scale Russian aggression

This article examines the concepts of urbicide and ecocide as a part of the violent policy against the state of Ukraine since the beginning of the full- scale Russian- Ukrainian war and their influence on tourism. We analyse their impact on natural recreational and tourist resources, such as land, air, river networks, and biotic and landscape diversity. Special attention is paid to protected areas of regional and national importance included in Ukraine’s ‘natural reserve fund’ and to sites of international importance that are part of the World Network of Biosphere Reserves. The article provides information about recorded crimes in different regions of Ukraine concerning historical and cultural heritage. Since 24 February 2022, at least 569 cultural heritage sites have been destroyed due to the war, including 170 monuments of national and local importance, 146 valuable historical buildings and cultural heritage sites, and 58 memorial monuments and works of art. We analyse losses in hospitality infrastructure relative to their zoning category determined by proximity to active fighting. The paper highlights the most urgent issues of the post- war restoration of Ukraine’s tourism sector, particularly facility staffing issues and ethical considerations when commemorating war events. Research methods: collection, analysis and the generalisation of information from official and research media sources concerning the loss of natural, historical, cultural and infrastructural resources suffered by tourism as a result of Russia’s military aggression in Ukraine from 24 Feb 2022 to 01 July 2023. Results: summary of the losses to tourism destination resources and infrastructure in Ukraine by region and type of impact, and a proposed framework for planning the post- war recovery of different kinds of tourism in Ukraine. This article examines the concepts of urbicide and ecocide as a part of the violent policy against the state of Ukraine since the beginning of the full- scale Russian- Ukrainian war and their influence on tourism. We analyse their impact on natural recreational and tourist resources, such as land, air, river networks, and biotic and landscape diversity. Special attention is paid to protected areas of regional and national importance included in Ukraine’s ‘natural reserve fund’ and to sites of international importance that are part of the World Network of Biosphere Reserves. The article provides information about recorded crimes in different regions of Ukraine concerning historical and cultural heritage. Since 24 February 2022, at least 569 cultural heritage sites have been destroyed due to the war, including 170 monuments of national and local importance, 146 valuable historical buildings and cultural heritage sites, and 58 memorial monuments and works of art. We analyse losses in hospitality infrastructure relative to their zoning category determined by proximity to active fighting. The paper highlights the most urgent issues of the post- war restoration of Ukraine’s tourism sector, particularly facility staffing issues and ethical considerations when commemorating war events. Research methods: collection, analysis and the generalisation of information from official and research media sources concerning the loss of natural, historical, cultural and infrastructural resources suffered by tourism as a result of Russia’s military aggression in Ukraine from 24 Feb 2022 to 01 July 2023. Results: summary of the losses to tourism destination resources and infrastructure in Ukraine by region and type of impact, and a proposed framework for planning the post- war recovery of different kinds of tourism in Ukraine.

Structural network characteristics affect epidemic severity and prediction in social contact networks

Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number (R0) have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators (R0 and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.

CYBERSECURITY: RESEARCH ON METHODS FOR DETECTING DDOS ATTACKS

This article describes the problem of DDoS attacks, analyzing their nature and consequences. The paper covers common DDoS attack types, such as SYN flood, ICMP flood, UDP flood. Existing methods for detecting attacks from literature are reviewed, including machine learning approaches, including artificial neural networks, support vector machines and decision trees. The paper introduces a decision tree-based machine learning model for the detection of DDoS attacks. The model is trained and tested on a publicly available dataset. The dataset consists of 1,04,345 rows of data, where every row includes 23 features, such as source IP, destination IP, port number, number of bytes transferred from the switch port, etc. A similar set of characteristics can be obtained on a real network hardware using simple calculations, which makes it possible to approximate the model evaluation to real operating conditions. SYN flood, ICMP flood and UDP flood attack types are present in the data, as well as legitimate traffic. To avoid overfitting, only some columns were used, and columns such as IP addresses were discarded. The field “label” in each row of the dataset contains either 0 or 1 where 0 corresponds to legitimate traffic and 1 to malicious one. The problem of DDoS attack detection is therefore formally reduced to the task of binary classification of each row from the dataset. The constructed model achieves an average classification accuracy of 0.94 with a standard deviation at the level of 0.06 in detecting the above mentioned types of attacks. To objectively assess the effectiveness of the model and avoid distortion of the results, stratified 5-fold cross-validation was used. The developed model can be applied in the real world network hardware to filter malicious packets or as a tool for warning the administrator about an attack. This research advances cybersecurity by enhancing DDoS attack detection.

The atypical transport system: Natural gas pipeline system

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

Принципи як основа організації та забезпечення міжнародної екологічної безпеки

The article describes the current problems of international environmental security in the context of the systematic manifestation of the principles of its implementation, which justifies the need to consolidate the efforts of interstate environmental measures on the basis of sustainable and balanced development. The article examines prerequisites for the formation of the global environmental safety as an integral part of international relations and politics against the background of the long-term ecological crisis. Features of the global environmental safety and levels of its operation on a territorial basis are singled out and grounded. Attention is focused on transboundary character of the manifestation of most environmental threats and hazards, making it impossible for countries to overcome the consequences on their own. Emphasized are global threats and hazards that will determine areas of cooperation in the global environmental safety in the near future. Parity basis and principles of formation of global ecologically safe space are outlined. Considering the world experience of the implementation of forms and methods of economical use of natural resources and observance of safety of existence, directions of strategic planning of national environmental safety are suggested. It was determined that globalization processes on the planet increase the threat to humanity in the form of environmental hazards: destruction of the ozone layer of the atmosphere and manifestations of global climate change, pollution of the world ocean, civilizational confrontation between the Christian and Muslim world, the creation and functioning of a global terrorist network, the spread of nuclear, chemical, biological , geophysical, space, psychotropic, environmental weapons and technologies of mass destruction. The main mechanisms for the implementation of international environmental security should be considered interstate cooperation, the creation of mechanisms for effective international consulting, peaceful settlement of disputes, prevention of transboundary environmental pollution, scientific and technical cooperation, international responsibility for transboundary environmental damage, public control, and the involvement of international non-governmental and public organizations in emergency environmental situations It has been proven that the mechanisms of international environmental security during emergency situations require the consolidation of efforts of interstate environmental measures on the basis of sustainable and balanced development.