Communication Infrastructure Research Articles

Design and Implementation of a Smart Wireless Access Point for A Gas Station (SWAP-GS)

The gas distribution sector is a good fit for wireless communications, especially considering how often distribution facilities are located in distant areas. The underlying communications infrastructure needs to be extremely dependable, with a resilient system that links sensors and controls throughout a vast field area network, when working with accumulated and real-time data. The resulting detail assists managers in creating and modifying business processes, managing the station site fully, and increasing operational efficiency. A wireless gas station offers an architecture based on Wi-Fi standards for applications that track and collect data all the way down sto the sensor level. The goal of the research was to install a wireless infrastructure network at a service station. When used in a gas station, plant, enable users to access real-time information, exchange and share sensor readings from the access point, and develop a smart wireless access point system with higher secure encryption than the generic wireless network. The NodeMCU microcontroller, which broadcasts the real-time data to the clients, receives inputs from a temperature, pressure, and humidity sensor. This allows the system to do such activities. It was believed that the built wireless infrastructure will enhance the existing LAN infrastructures. A few basic prerequisites are taken into account. First and foremost, a microcontroller, a sensor unit, software design, and compliance with IEEE 802.11 standards were required.

Cellular Internet of Things: Principles, Potentials and Use Cases

Internet of things (IoT) can either be deployed over existing cellular networks or their own custom-built standalone networks. Based on the infrastructure, IoT can be classified into two types: cellular and non-cellular categories. In the cellular form, IoT network needs the support of cellular infrastructure of the mobile service providers. Currently, three forms of cellular IoT are being deployed across the world. They are: narrowband Internet of things (NBIoT), extended coverage GSM (EC-GSM) and long term evolution for machines (LTE-M). Out of these three, NBIoT and EC-GSM are low energy and low resource consuming versions of cellular IoT. They need narrow bandwidths for their operations. Their energy consumption is also very low and thus suitable for low energy applications. Both NBIoT and EC-GSM are compatible with all types of cellular communication infrastructure such as 2G, 3G, 4G and 5G. They can cover a large area with a very small amount of power. Both these forms are popular low power wide area (LPWA) technologies. Due to their LPWA features, they are popular for the connected living applications at home and workplace surroundings. Their LPWA features make them popular green technology for digital transformation. LTE-M uses comparatively larger bandwidth and higher power. It is suitable for higher bandwidth and higher data rate applications. We survey the recent literature on cellular IoT and present their key principles, potentials and applications. We provide their main characteristics, deployment options, standards, and some specific applications in different sectors.

Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era

5G Mobile Adhoc Networks (5G-MANETs) are a popular and agile solution for data transmission in local contexts while maintaining communication with remote entities via 5G. These characteristics have established 5G-MANETs as versatile communication infrastructures for deploying contextual applications, leveraging physical proximity while exploiting the possibilities of the Internet. As a result, there is growing interest in exploring the potential of these networks and their performance in real-world scenarios. However, the management and monitoring of 5G-MANETs are challenging due to their inherent characteristics, such as highly variable topology, unstable connections, energy consumption of individual devices, message routing, and occasional inability to connect to 5G. Considering these challenges, the proposed work aims to address real-time monitoring of 5G-MANETs using a connection-aware Digital Twin (DT). The approach provides two main functions: offering a live virtual representation of the network, even in scenarios where multiple nodes lack 5G connectivity, and estimating the performance of the infrastructure, enabling the specification of customized conditions. To achieve this, a communication architecture is proposed, analyzing its components and defining the involved processes. The DT is implemented and evaluated in a laboratory setting, assessing its accuracy in representing the physical network under varying conditions of topology and Internet availability. The results show 100% accuracy for the DT in fully connected topologies, with ultra-low latency averaging under 80 ms, and suitable performance in partially connected contexts, with latency averages below 3000 ms.

Policy-driven innovations in fraud prevention: Developing an ARFLGB-XGBoost early warning model to mitigate online romance scams in telecommunication networks

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

A Strategy for Predicting the Performance of Supervised and Unsupervised Tabular Data Classifiers

AbstractMachine Learning algorithms that perform classification are increasingly been adopted in Information and Communication Technology (ICT) systems and infrastructures due to their capability to profile their expected behavior and detect anomalies due to ongoing errors or intrusions. Deploying a classifier for a given system requires conducting comparison and sensitivity analyses that are time-consuming, require domain expertise, and may even not achieve satisfactory classification performance, resulting in a waste of money and time for practitioners and stakeholders. This paper predicts the expected performance of classifiers without needing to select, craft, exercise, or compare them, requiring minimal expertise and machinery. Should classification performance be predicted worse than expectations, the users could focus on improving data quality and monitoring systems instead of wasting time in exercising classifiers, saving key time and money. The prediction strategy uses scores of feature rankers, which are processed by regressors to predict metrics such as Matthews Correlation Coefficient (MCC) and Area Under ROC-Curve (AUC) for quantifying classification performance. We validate our prediction strategy through a massive experimental analysis using up to 12 feature rankers that process features from 23 public datasets, creating additional variants in the process and exercising supervised and unsupervised classifiers. Our findings show that it is possible to predict the value of performance metrics for supervised or unsupervised classifiers with a mean average error (MAE) of residuals lower than 0.1 for many classification tasks. The predictors are publicly available in a Python library whose usage is straightforward and does not require domain-specific skill or expertise.

The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building

Climate change has recently induced an increase in natural disasters worldwide, which has affected agriculture, a sector highly sensitive to climatic alterations. The paper measures agricultural disaster vulnerability of China in the period 2000–2020 by establishing vulnerability assessment system, and examines how agricultural infrastructure correlates with agricultural disaster vulnerability by constructing grey relational analysis model. Research results indicate that China's agricultural disaster vulnerability presents an overall decreasing trend during the research period. This is mainly attributed to the decreased disaster exposure and increased adaptability to disaster. Spatially, the agricultural disaster vulnerability in the northeastern, central and southwestern regions of China is higher than other regions. Agricultural transportation and communication infrastructure are found to be more influential to agricultural disaster vulnerability followed by irrigation and power facilities. Implications are proposed to enhance agricultural resilience to climate disasters in China.

Enhancing distance protection in transmission grids with high penetration of renewable energy sources through cooperative protection

AbstractThis article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter‐based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high‐speed operation is ensured through non‐iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real‐time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high‐RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39‐bus test system validate the effectiveness of the proposed method.

The Impact of Deployment of National Police Reservists on the Overall Security Incidents in Elgeyo Marakwet County, Kenya

The use of police reservists has been recognized as a key strategy in enhancing security, especially in conflict-prone regions like Elgeyo Marakwet County, Kenya. Their unique advantage lies in their proximity to the local community, quick response capabilities, and familiarity with both the terrain and the people they serve. These traits, combined with their role in community policing, have proven vital in addressing resource-based conflicts and cattle rustling. Despite challenges such as limited personnel and the presence of firearms, reservists have significantly contributed to reducing violence and fostering better relations between law enforcement and local communities. The data used in this paper was part of a study that examined the relationship between the deployment of National Police Reservists and security incidents in Elgeyo Marakwet County, Kenya. Through a cross-sectional survey, data was collected from community members through a questionnaire (n = 362), interviews (the County Commander, Subcounty Commanders, Officer Commanding Police Stations and administrative Chiefs; n = 7) and FGDs (n = 7 police reservists). The Friedman Test was conducted to evaluate whether the deployment of NPRs led to reduction in the frequency of security incidents. Qualitative data, was analysed thematically. The study established that the deployment of National Police Reservists (NPRs) had a significant impact on crime reduction (χ²(4) = 11.800, p = .019). The community considered the police reservists as effective in maintaining peace and security, with residents reporting increased safety and expressing strong trust in their abilities. There was a high level of trust in the reservists' ability to protect the community. The potential challenges in the deployment of National Police Reservists include logistical constraints, inadequate communication infrastructure, limited training and resource shortages. The study recommends the continued deployment of NPRs while monitoring and evaluating their impact regularly. In addition, there is need to build on the strong community trust in NPRs by enhancing community engagement and communication. There is need to invest to increase resource allocation to the NPRs as well as address the logistical constraints faced by reservists.

Undersea cable security in the Indo‐Pacific: Enhancing the Quad’s collaborative approach

Undersea communication cables carry the bulk of global communications, intelligence, and financial transactions daily. They are increasingly threatened, and Australia, Japan, India, and the US have begun coordinating cable security activities in the Indo-Pacific as the Quad. This article investigates the Quad’s role in undersea cable communication security, and analyzes its motivations, strengths, and the challenges it faces thereby illuminating the Quad’s evolving position in international security and its role in global governance. In doing so, it evaluates the Quad’s significance in addressing emerging security threats in the Indo-Pacific, attempts to fill critical gaps in policy formulation, and contributes to theories of informality and global governance. It begins by outlining threats to undersea communication cables. Second, it details the Quad Partnership for Cable Connectivity and Resilience within the context of an informal intergovernmental organization literature. Third, it provides recommendations for policy formulation in undersea cable security and explores the Quad's potential for improving connectivity, resilience, and security. Finally, the article emphasizes the need to tailor policies to India’s unique position within the Quad and explores proactive measures like floating Quad-supported cable repair ships. These recommendations offer insights for policymakers to bolster global communication infrastructure, resilience, and provide public goods amidst multipolarity and great power rivalry.

Miniaturized tri-band integrated microwave and millimeter-wave MIMO antenna loaded with metamaterial for 5G IoT applications

This study presents a revolutionary tri-band combined microwave (MW) and millimeter wave (MMW) multiple-input-multiple-output (MIMO) antenna. The antenna is built on a Rogers RT-5880 substrate with dimensions of 32 × 32 × 1.6 mm³. This integration increased the isolation from 18 dB to 27 dB in the MW band and 28 dB–40 dB in the MMW band. The effective bandwidths for the MW and MMW bands were initially 3.3–3.7 GHz, 5.45–9.2 GHz, and 24–28 GHz, respectively. The MTM increased these bandwidths to 2.6–4.1 GHz, 5.3–11.3 GHz for the MW band, and 22.5–29.3 GHz for the MMW band. In addition, the realized gain increased significantly, from 1.5 dBi, 3.5 dBi, and 6.5 dBi to 4.5 dBi, 6.1 dBi, and 11 dBi at 3.5 GHz, 6.9 GHz, and 28 GHz. The antenna's overall efficiency increased by 10–12 % across all operational bandwidths. The suggested antenna has excellent diversity performance, with an envelope correlation coefficient of less than 0.0002, a diversity gain greater than 9.998, and a channel capacity loss of less than 0.12 bits/s/Hz. The proposed antenna addresses the increasing demand for IoT devices by efficiently supporting both MW and MMW frequency bands, offering high performance in wireless communication and modern infrastructures. The proposed design not only improves connectivity and efficiency in IoT devices but also supports the Sustainable Development Goals (SDGs) by contributing to the development of smart city infrastructure (SDG 11) and fostering innovation and sustainable industrial practices (SDG 9), thereby promoting sustainable development and industrial innovation.

VLC performance in underground vehicular tunnels

In recent years, the integration of intelligent transportation systems (ITS) has gained popularity as a means of enhancing the safety of roadways and underground tunnels and reducing traffic congestion. Given the fact that conventional radio frequency (RF) communication systems are vulnerable to significant limitations as a result of a variety of factors, including signal attenuation and interference, which affect their application, the emerging visible light communication (VLC) technology is an exciting potential candidate for facilitating wireless access in such environments. This study investigates the deployment of VLC systems in underground vehicular tunnels involving a handover strategy based on the software-defined network (SDN) approach, with the objective of addressing the fundamental challenges faced by communication systems in such scenarios. The Optisystem software is used to simulate and investigate the performance of the proposed system, which is based on orthogonal frequency division multiplexing (OFDM) technology in both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The simulated scenario is capable of achieving a data rate of 10 Gbps within a link range of 3 meters in the LOS approach. In the NLOS propagation model, a data rate of 2 Gbps can be attained without any error. The simulation results reveal a particular perspective on the viability of VLC systems in improving the communication infrastructure in underground vehicular tunnels and promoting efficient tunnel operations. The evaluation of the simulated system is conducted based on bit error rate (BER), signal-to-noise ratio (SNR), and the constellation diagram.

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

Design and analysis of a high-efficiency bi-directional DAB converter for EV charging

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies.

On mitigating reactive jamming with dynamic resource allocation in optical IRS and UAV-assisted FSO-based networks

Free space optics (FSO) offers a promising opportunity to enhance next-generation network’s capacity with its unlicensed spectrum and wide bandwidth. However, jamming attacks, coupled with inherent anomalies in the FSO-based channel, threaten the performance of these networks. This is especially problematic for security-sensitive applications that demand a resilient communication infrastructure. To address this issue, optical intelligent reflecting surfaces (IRS) and unmanned aerial vehicles (UAVs) can provide promising solutions. This work introduces an efficient approach for mirror element assignment in UAV-assisted FSO-based networks, aimed at mitigating reactive jamming attacks while satisfying users’ quality-of-service (QoS) requirements. To ensure network reliability, we formulate an optimization problem that enhances overall network performance by simultaneously allocating resources such as mirror elements with awareness of jamming attacks. The formulated optimization problem is a binary linear programming problem, which is generally NP-hard. To address this, we introduce a batch-based sequential fixing linear programming procedure called the Reactive Jamming-Aware Mirror Element Allocation (RJA-MEA) scheme. This scheme optimally assigns mirror elements to satisfy the users’ rate demands. In this paper, the performance of the RJA-MEA scheme is compared with reference schemes such as Reactive Jamming Unaware-Mirror Element Allocation (RJU-MEA), Reactive Jamming-Aware Equal Mirror Element Allocation (RJA-EMEA), and Reactive Jamming Unaware-Equal Mirror Element Allocation (RJU-EMEA) schemes. The simulation results reveal that the proposed RJA-MEA scheme surpasses existing reference schemes, thereby significantly improving the overall network sumrate performance.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

The Share of Data in Maritime Communications is Increasing

Communication is of great importance for the safe and delay-free navigation of ships. Especially life/property safety, navigational warnings, weather forecast reports, storm warnings, navigation maps, etc. Timely transmission of information to ships is only possible with effective maritime communications. In addition, communication is of great importance in making appropriate navigation plans based on the information received from the ships. In recent years, the share of data communication in individual and corporate communications has been increasing. In order to benefit from the great conveniences and opportunities provided by data communication in maritime communication systems, very radical decisions have recently been taken within IMO. These regulations made at the international level paved the way for data communication in close-range maritime communication systems. In line with these regulations, broadband data communication between the ship and the land will begin to be made via VHF devices in the next few years. Another development that will improve broadband data communication in maritime communication systems has been in Near Orbit Satellite systems. The technical features of these systems, the number of which has been increasing in recent years, are extremely suitable for data communication between ship and land. Among these, the Starlink system completes its network in space faster than other satellite systems. It is inevitable that the Starlink system, whose global satellite structure will be completed to a large extent by the end of 2025, will be used to a large extent in data communication between ships and land and between ships with each other. With the use of broadband data feature in the near future, it will be possible for ships to navigate much more safely, efficiently and with a reduced risk of accidents. For this purpose, maritime enterprises, ship personnel and land units must be informed about these developments and their communication infrastructure must be established by taking these systems into consideration.

Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.

Optimizing Key Value Indicators in Intent-Based Networks through Digital Twins aided service orchestration mechanisms

For many years, the orchestration of network resources and services has been addressed by considering homogeneous communication infrastructures and simple Service Level Agreements (SLAs), generally defined through a list of traditional Key Performance Indicators (KPIs). Unfortunately, state-of-the-art solutions risk being quite ineffective for future telecommunication systems. Beyond 5G networks, for instance, are emerging as complex and heterogeneous ecosystems where resources belonging to diverse network domains with evolving capabilities can be dynamically exposed to support much more complex and cross-domain services and applications. At the same time, SLAs will be defined by also considering novel performance demands, including security, economic, and environmental needs. Based on these premises, this work proposes a novel orchestration strategy designed to fulfill service requirements expressed through Key Value Indicators (KVIs), while combining the potentials of both Network Digital Twins and Intent-Based Networking. Leveraging insights from Network Digital Twins, multiple service orchestration options are explored to optimize resource utilization. Simultaneously, Intent-Based Networking is adopted to streamline network management via intents, specifying Beyond 5G requirements through KPIs and KVIs. An optimal orchestration scheme has been conceived through a multi-criteria decision-making algorithm and a many-to-many matching game between domains and service requests mapped into intents, aiming to minimize SLA violations over time. The performance of the conceived solution has been investigated through computer simulations in realistic scenarios. The obtained results clearly highlight its effectiveness and demonstrate that it is able to reduce SLA violations (related to latency, throughput, costs, and cyber risk requirements) by up to 22.44% compared to other baseline techniques.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.