Search Results (22)

The launch of sixth-generation (6G) mobile networks is expected to introduce significant variability in Quality of Service (QoS), driven by environmental conditions, traffic heterogeneity, device diversity, and network slicing policies. Existing clustering-based QoS analysis methods rely primarily on using only KPI variables, such as latency, throughput, jitter and packet loss datasets, and classical geometric validity metrics, providing limited insight into the stability, predictive capability, and operational relevance of discovered clusters. To address these limitations, this study proposes a context-aware QoS modelling framework and a unified network-centric cluster evaluation protocol. A dataset comprising 2345 observations is constructed by integrating QoS indicators with contextual and operational variables, including weather conditions, time of day, geographic region, traffic type, device class, and slice identity. Four clustering paradigms, k-means, DBSCAN, spectral clustering, and Deep Embedded Clustering (DEC), are evaluated using both classical metrics and three proposed evaluation measures: Contextual Cluster Stability (CCS), QoS-Regime Predictive Consistency (QPC), and Slice-Level Reliability Separation (SLRS). The results demonstrate that classical clustering metrics alone are insufficient for assessing QoS regime quality. While DEC achieves strong structural performance in latent space, all methods exhibit near-zero predictive consistency and weak reliability separation. These findings reveal a consistent divergence between structural clustering quality and operational usefulness, indicating that unsupervised clustering alone is insufficient for QoS prediction and reliability-aware decision-making. The proposed framework provides a foundation for evaluating clustering methods in context-sensitive network environments and highlights the need for integrating temporal modelling and reliability-aware learning in future 6G network optimisation systems. Full article

In unmanned aerial vehicle (UAV)-mounted base station (MBS) networks, user equipment (UE) experiences dynamic channel variations because of the mobility of the UAV and the changing weather conditions. In order to overcome the degradation in the quality of service (QoS) of the UE due to channel variations, it is important to appropriately determine the three-dimensional (3D) position and transmission power of the base station (BS) mounted on the UAV. Moreover, it is also important to account for both geographical and meteorological factors when deploying UAV-MBSs because they service ground UE in various regions and atmospheric environments. In this paper, we propose an energy-efficient UAV-MBS deployment scheme in multi-UAV-MBS networks using a hybrid improved simulated annealing–particle swarm optimization (ISA-PSO) algorithm to find the 3D position and transmission power of each UAV-MBS. Moreover, we developed a simulator for deploying UAV-MBSs, which took the dynamic weather conditions into consideration. The proposed scheme for deploying UAV-MBSs demonstrated superior performance, where it achieved faster convergence and higher stability compared with conventional approaches, making it well suited for practical deployment. The developed simulator integrates terrain data based on geolocation and real-time weather information to produce more practical results. Full article

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article

Pregnancy is a transformative stage in a woman’s life, marked by significant physical, emotional, and social changes. This study had three main objectives: (1) to assess the quality of life (QoL) of pregnant women in Romania, (2) to identify the sociodemographic, health, and obstetric factors influencing their QoL and (3) to examine the psychometric properties of the WHOQOL-BREF questionnaire within the Romanian context, determining its effectiveness in evaluating QoL during pregnancy. Methods: A cross-sectional analytical survey was conducted between January and July 2023 among pregnant women in Romania, targeting a geographically diverse sample from urban and rural areas. Eligible participants were Romanian citizens aged 18 or older. Data were collected through an online self-administered questionnaire using Google Forms, with informed consent obtained electronically. The survey included demographic, obstetric, and health-related variables alongside the WHOQOL-BREF tool, which evaluates QoL across four dimensions: Physical, Psychological, Social Relationships, and Environment. Statistical analysis involved confirmatory factor analysis, reliability testing (Cronbach’s α and McDonald’s ω), and comparisons using Welch’s t-tests and ANOVA. Results: A total of 1550 valid responses were analyzed. The WHOQOL-BREF demonstrated excellent internal consistency (Cronbach’s α > 0.9 across all dimensions). Women with higher education and stable employment reported significantly higher QoL scores in physical and psychological dimensions. No significant differences were found based on pregnancy trimester, previous births, or participation in prenatal classes, although trends suggested slight advantages for participants in prenatal education. Women delivering in private hospitals or non-hospital settings reported better psychological and physical QoL than those delivering in public hospitals. Support from partners and urban residency positively influenced perceived QoL. Conclusions: The WHOQOL-BREF is a reliable tool for assessing QoL in pregnant women in Romania. The study highlights the role of education, employment, and delivery location in influencing QoL, emphasizing the need for targeted support for vulnerable groups during pregnancy. Full article

The combination of software-defined networking (SDN) and satellite–ground integrated networks (SGINs) is gaining attention as a key infrastructure for meeting the granular quality-of-service (QoS) demands of next-generation mobile communications. However, due to the unpredictable nature of end-user requests and the limited resource capacity of low Earth orbit (LEO) satellites, improper Virtual Network Function (VNF) deployment can lead to significant increases in end-to-end (E2E) delay. To address this challenge, we propose an online algorithm that jointly deploys VNFs and forms routing paths in an event-driven manner in response to end-user requests. The proposed algorithm selectively deploys only the essential VNFs required for each Service Function Chain (SFC), focusing on minimizing E2E delay—a critical QoS parameter. By defining a minimum-hop region (MHR) based on the geographic coordinates of the routing endpoints, we reduce the search space for candidate base stations, thereby designing paths that minimize propagation delays. VNFs are then deployed along these paths to further reduce E2E delay. Simulations demonstrate that the proposed algorithm closely approximates the global optimum, achieving up to 97% similarity in both E2E delay and CPU power consumption, with an average similarity of approximately 90%. Full article

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal’s effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS. Full article

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee. Full article

Telecommunication companies collect a deluge of subscriber data without retrieving substantial information. Exploratory analysis of this type of data will facilitate the prediction of varied information that can be geographical, demographic, financial, or any other. Prediction can therefore be an asset in the decision-making process of telecommunications companies, but only if the information retrieved follows a plan with strategic actions. The exploratory analysis of subscriber data was implemented in this research to predict subscriber usage trends based on historical time-stamped data. The predictive outcome was unknown but approximated using the data at hand. We have used 730 data points selected from the Insights Data Storage (IDS). These data points were collected from the hourly statistic traffic table and subjected to exploratory data analysis to predict the growth in subscriber data usage. The Auto-Regressive Integrated Moving Average (ARIMA) model was used to forecast. In addition, we used the normal Q-Q, correlogram, and standardized residual metrics to evaluate the model. This model showed a p-value of 0.007. This result supports our hypothesis predicting an increase in subscriber data growth. The ARIMA model predicted a growth of 3 Mbps with a maximum data usage growth of 14 Gbps. In the experimentation, ARIMA was compared to the Convolutional Neural Network (CNN) and achieved the best results with the UGRansome data. The ARIMA model performed better with execution speed by a factor of 43 for more than 80,000 rows. On average, it takes 0.0016 s for the ARIMA model to execute one row, and 0.069 s for the CNN to execute the same row, thus making the ARIMA 43× ($\frac{0.069}{0.0016}$) faster than the CNN model. These results provide a road map for predicting subscriber data usage so that telecommunication companies can be more productive in improving their Quality of Experience (QoE). This study provides a better understanding of the seasonality and stationarity involved in subscriber data usage’s growth, exposing new network concerns and facilitating the development of novel predictive models. Full article

The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models. Full article

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches. Full article

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites. Full article

Reliability and security when distributing safety messages among vehicles in an extremely mobile environment are prominent issues in Vehicular Ad-Hoc Networks (VANETs). In VANET, data transfer becomes challenging because of inherent features such as excessive speed, geographically constrained topologies, unsteady communication links, diversity in the capacity of the channel, etc. A major challenge in the multi-hop framework is maintaining and building a path under such a rigid environment. With VANET, potency in the traffic safety applications has performed well because of the proper design of medium access control (MAC) protocols. In this article, a protocol is proposed pertaining to the distribution of safety messages named mobility-aware multi-hop clustering-based MAC (MAMC-MAC) to accomplish minimum communication overhead, high reliability, and delivery of safety messages in real-time environments. MAMC-MAC has the ability to establish clustering-based multi-hop sequence using the time-division multiple access (TDMA) technique. The protocol was specially developed for highway outlines to achieve network enhancement and efficient channel usage and guarantees integrity among the vehicles. The performance of the proposed protocol is evaluated using Network Simulator (NS-2), and it demonstrates its superiority over various standard protocols in terms of a number of quality-of-service (QoS)-based parameters. The criteria to select and assess these parameters are their sensitivity and importance to the safety-based applications they provide. Full article

A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks. Full article

Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users’ QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service. Full article

Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT–fog–cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability. Full article