Network Scenarios Research Articles

Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks

The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users’ throughput needs while reducing network imbalances and call drop rates.

Quantum steering in a star network

In this work, we will consider the star network scenario where the central party is trusted while all the edge parties (with a number of n) are untrusted. Network steering is defined with an n local hidden state model which can be viewed as a special kind of n local hidden variable model. Two different types of sufficient criteria, nonlinear steering inequality and linear steering inequality will be constructed to verify the quantum steering in a star network. Based on the linear steering inequality, how to detect the network steering with a fixed measurement will be discussed.

Delay and Fairness Analysis of C-RAN for Single and Multi Scheduling Domain Strategies

Background: Centralized Radio Access Network (C-RAN) is the most promising network architecture for next-generation communication networks. It meets the need for flexibility on fronthaul as well as large bandwidth on backhaul of the network. All along, scheduling is very important for the transmission of information in an organized manner. C-RAN has not been studied with the scheduling domain strategies yet in the literature. Objective: In this work, packet transmission duration, overall transmission time, wait time, and fairness index parameters have been calculated and analysed for C-RAN architecture for two different scheduling domains. The total transmission cycle time parameter is calculated for the three upper functional split options of C-RAN. The overall transmission time is a parameter calculated for the entire uplink channel. Methods: To implement the network scenario, extensive scripting is done on MATLAB Editor for single scheduling domain (SSD) and multi-scheduling domain (MSD) for three higher functional split options of C-RAN. The data traffic generated in the network is considered random. Results: A closer examination of results reveals the advantages and disadvantages of both algorithms, as well as trade-offs between them. Conclusion: For quicker data transmission, SSD should be preferred whereas MSD should be preferred if multiple users want to access resources simultaneously. Lower functional split options of C-RAN require less transmission cycle time. The MSD technique is fairer than SSD.

Privacy-preserving authentication approach for vehicular networks

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.

ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model

As the application of networks permeates various aspects of daily life, maintaining network security has become a crucial challenge. A network intrusion detection system (NIDS) functions as a critical technique for securing cyberspace and has gained considerable attention. Although researchers have made significant progress in developing NIDSs, challenges still exist in high-speed networks with overwhelming network traffic. Existing methods largely focus on improving model detection accuracy and often overlook speed and computational efficiency. This oversight renders most current methods impractical for real-world high-speed network scenarios. To address this issue, we propose an innovative and efficient network intrusion detection algorithm, namely, the Bayesian gamma mixture model (GaMM) classifier. With the recently proposed extended stochastic variational inference (ESVI) framework, we introduce lower-bound approximations to the evidence lower bound (ELBO), namely, the original variational object function. An analytically tractable Bayesian estimation algorithm for a GaMM is derived through stochastic optimization of the obtained lower bound and we validate its performance and computational efficiency on three publicly available datasets (CICMalmem2022, OPCUA, and CICIDS2018). The experimental results indicate that the proposed classifier not only achieves a detection performance comparable to that of other benchmark models but also significantly reduces both the training and detection times.

A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies. The pre-processing of data has been performed effectively and a host of ML methods have been applied to classify the outputs into normal, regular node or untrustable /malicious node. We have evaluated the analysis results in presence of attacks as well as without presence of attacks, supervised machine learning techniques basic measurements like accuracy, True positive, False positive etc. Our proposed trust model shows better accuracy, nearby 98% and we have shown that our proposed machine learning based security model performs better to get rid of DDoS attacks on integrated LEO satellite-terrestrial networks without compromising on the packet routing efficiency. We are able to improve routing speed and improve network security against distributed denial of service (DDoS) attacks by integrating an ensemble-based trust model trained on NSL-KDD+STIN+Exata Simulated resultant dataset with ACO for routing decisions. In dynamic network scenarios, as trustworthiness is an essential criterion in route decision-making, this proposed approach signifies resilient and adaptable routing.

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

BLPCA-ledger: A lightweight plenum consensus protocols for consortium blockchain based on the hyperledger indy

An examination of different distributed real-time applications operating on the blockchain platform is conducted. These applications can be broadly classified into three types: permissionless public, permissioned private, and consortium chains. In order for a decentralized network to function independently, consensus mechanisms are needed to facilitate the delivery of transactions and keep track of them in a ledger. But the fundamental idea behind Blockchain technology is the use of several consensus protocols, like Proof of Stake, Proof of Elapsed Time, etc., which requires greater processing power. In order to arrange transactions, it increases the demand for buying more computing units. Furthermore, present consortia blockchain consensus mechanisms lack a policy to collect socio-economic financial levies, including monies for charitable donations, education, and social activities. To collect socio-economic taxes, this study suggests a lightweight Plenum consensus algorithm called "BLPCA" for consortium blockchains built on Hyperledger Indy. The Byzantine Fault Tolerance concept combined with optimization is used in the suggested BLPCA systems to manage large-scale decentralized traffic of socioeconomic hierarchy. Because there are no transaction costs, BLPCA encourages government analysts to review funds using fewer resources. By including a multithreading heterogeneous technique, the BLPCA can effectively handle multi-transaction needs and execute this protocol in an industrial setting that operates in real-time. It is observed from the simulations that even in the worst-case network scenario—such as a fork—the suggested consortium blockchain does not crash a single transaction. In order to guarantee node scalability, a high propagation speed is determined. Furthermore, the BLPCA shows an excellent average time while constructing socioeconomic transaction blocks.

Economic Alternatives for the Provision of URLLC and eMBB Services Over a 5G Network

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit.

Machine-Learning and Physics-Based Tool for Anomaly Identification in Propulsion Systems

Launch anomalies occur frequently during the early phase of a program, with many of the anomalies attributed to propulsion systems. Approaches for identifying and mitigating potential propulsion failures can aid development programs and accelerate the resolution of root cause investigations. In reusable systems, anomaly detection methods can be employed to detect latent system health issues that could become problematic as the system ages. Modern launch support relies on human judgement for redline limit generation and visual family data comparison for many operational aspects, which makes it challenging to identify failure modes and to diagnose an anomaly. Additionally, family data comparison is unavailable for the first few launches of a new vehicle. Automated tools to quickly identify system failures of new and reusable systems can bridge these gaps. Physics-based modeling and machine learning (PBMML) offers methods that can improve the reliability of new or reusable launch vehicles by identifying propulsion anomalies or issues before they jeopardize future space missions. PBMML can then be used to inform corrective actions. This paper describes an anomaly data generation module which automates the process of simulating anomalous scenarios in launch vehicle and fluid networks, while a long-term short-term memory network is used to provide real-time anomaly classification on a simplified stage test case.

Replica fault-tolerant scheduling with time guarantee under energy constraint in fog computing

Fog computing offers stronger local computing power and reduces data transmission load, making it an ideal solution to meet the energy-saving and efficient requirements of intelligent connected vehicle applications. As intelligent networked vehicles and vehicle–road collaboration technologies advance rapidly, optimizing scheduling under fog computing architecture has become a prominent research area. However, existing studies primarily concentrate on parallel task scheduling with low energy consumption or high real-time performance, failing to address the requirement for high reliability in intelligent networked vehicle scenarios. To achieve time and reliability optimization for vehicular applications in fog computing architecture, this paper proposes a fog computing task scheduling algorithm and explores its extension using replication techniques. Subsequently, the algorithm underwent evaluation utilizing randomly generated directed acyclic graph models as well as real-life automotive application instances. The experimental findings indicate that in comparison to existing methods, the proposed algorithm exhibits a notable improvement in reliability while ensuring time optimization, thereby demonstrating a distinct level of advancement and practicality.

DEVELOPMENT OF NETWORK SIMULATION MODEL FOR EVALUATING THE EFFICIENCY OF DISTRIBUTED CONSENSUS TAKING INTO ACCOUNT THE INSTABILITY OF NETWORK CONNECTIONS

The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems. Our proposed model represents a significant advancement in the simulation of distributed networks. It employs a sophisticated network emulation layer capable of generating a wide spectrum of unstable network conditions. The core of the model is a highly configurable consensus mechanism simulator that allows for the adjustment of key parameters such as heartbeat intervals, election timeouts, and message loss rates. This level of configurability enables a comprehensive analysis of consensus behaviors under different network scenarios. The article focuses on the methodology behind the development of the model, detailing the theoretical underpinnings and the implementation strategies used to ensure a realistic representation of network instability. We also discuss the potential applications of the model, which extend beyond academic research into practical domains where distributed ledger technologies and distributed databases are prevalent. Through the deployment of this model, researchers and system architects can gain deeper insights into the resilience and adaptability of consensus algorithms. The model serves as a tool for preemptively identifying and addressing potential issues in distributed systems, facilitating the development of more robust and reliable technologies. In summary, the article showcases the design and capabilities of a new model that enables an in-depth understanding of the delicate interplay between network instability and consensus efficiency. By focusing on the model itself, the article aims to lay a foundation for future studies and improvements in the field of distributed systems.

Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey

Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.

Integration of multiple distributed solar PV (DSP) into the grid: The case of the distribution network in Freetown, Sierra Leone

The optimal integration of distributed solar photovoltaic (DSP) is a transformative engineering method that is key in contributing to a sustainable and resilient energy future, especially when countries seek to increase the proportion of renewables in their energy mix to reduce carbon emissions. This paper presents a power flow-based approach that makes use of Newton Raphson's method in the ETAP tool to integrate multiple DSPs into both the existing and expanded Freetown distribution networks. The study determined the network's hosting capacities and optimal points of injection for the reduction of active power loss and improvement of bus voltage profiles. The study showed that the existing Freetown distribution network had a hosting capacity of 34.6 MW with an active power loss reduction of 0.967 MW while the expanded Freetown distribution network had a hosting capacity of 59.57 MW with an active power loss reduction of 5.12 MW. Before the injection of the DSPs into both networks, most of the bus voltages were not within acceptable limits. However, with the intervention of the injected DSPs, bus voltages considerably improve. The study showed that the expanded Freetown distribution network is better for DSPs integration compared to the existing Freetown distribution network. To evaluate the impacts of the injected DSPs and to validate the model used, four network scenarios were considered. The study used an analytical approach, considering future load growth and an evolving grid to integrate DSPs for long-term planning. The study will inform policymakers, utilities, etc., about the potential of integrating DSPs.

Ambiguous Devices as an example of a networked relational encounter

ABSTRACT This article uses the author’s collaborative environment for networked music performance, Ambiguous Devices (developed with Paul Stapleton), as a case study for illustrating a shift in thinking from a transactional to a relational understanding of networked encounters. It draws on Gill’s notions of relational interfaces and Buber’s understanding of dialogic relations as a way of understanding ways that people might communicate with others at a distance. It looks at an embodied theory of interaction that relates to intersubjective and intercorporeal ways of knowing the other and then looks at ways that these relationships can be facilitated over spatially separate sites of interaction such as those found in networked performance scenarios. Finally, it puts forward a notion of relational encounters as a way thinking about these kinds of affective interactions at a distance and discusses the designed properties of Ambiguous Devices that support and facilitate this kind of interaction.

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection.

Motivated by potential financial gain, companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. Such groups are considerably more successful in misleading customers, as people are more likely to be influenced by the opinion of a large group. To detect such groups, a common model is to represent fraudster groups' static networks, consequently overlooking the longitudinal behavior of a reviewer, thus, the dynamics of coreview relations among reviewers in a group. Hence, these approaches are incapable of excluding outlier reviewers, which are fraudsters intentionally camouflaging themselves in a group and genuine reviewers happen to coreview in fraudster groups. To address this issue, we propose "FGDT," a framework for "fraudster group detection through temporal relations." FGDT first capitalizes on the effectiveness of the HIN-recurrent neural network (RNN) in both reviewers' representation learning while capturing the collaboration between reviewers. The HIN-RNN models the coreview relations of reviewers in a group in a fixed time window of 28 days. We refer to this as spatial relation learning representation to signify the generalizability of this work to other networked scenarios. Then, we use an RNN on the spatial relations to predict the spatio-temporal relations of reviewers in the group. In the third step, a graph convolution network (GCN) refines the reviewers' vector representations using these predicted relations. These refined representations are then used to remove outlier reviewers. The average of the remaining reviewers' representation is then fed to a simple fully connected layer to predict if the group is a fraudster group or not. Exhaustive experiments of FGDT showed a 5% (4%), 12% (5%), and 12% (5%) improvement over three of the most recent approaches on precision, recall, and F1-value over the Yelp (Amazon) dataset, respectively.

A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

Multicast Radar Data Transmission Using the Tunnel in Tunnel Method and IGMP (Internet Group Management Protocol) Filtering by Utilizing the Internet Network

This research proposes the implementation of multicast radar data transmission using the tunnel in tunnel method by utilizing the installed IP VPN network. The background of this study is the failure in radar data transmission resulting in a change of service from surveillance to procedural service, as well as the impact of the Covid-19 pandemic on aviation organizations. The purpose of this implementation is to enhance flight safety while achieving cost efficiency for the company. Objectives include changing transmission methods, budget efficiency, improving operational safety, compliance with international standards, and support for the concept of flight information management. Advantages of multicast radar data transmission include centralized IP base, ease of monitoring, and the use of FO converters for surge protection. Implementation is based on aviation regulations, international standards, and partnership contracts. Contingency plans cover network and device failure scenarios. Cost benefit analysis shows significant financial gains from this implementation. It is hoped that this research will make a positive contribution to improving efficiency and operational safety in aviation.