Quality Of Service In Networks Research Articles

Energy-Efficient and QoS-Aware Data Routing in Node Fault Prediction Based IoT Networks

Internet of Things (IoT) enables important data collection of the sensor nodes at desired location through device-to-device communication. It helps us in various applications including tracking of physical objects, human health monitoring, climate smart agriculture, smart manufacturing, field surveillance, and intelligent transportation. Since, these applications utilize multi-hop data transmission framework, Quality-of-Service (QoS) such as data transmission delay, data throughput, and data gathering reliability degrades at the destination nodes. In addition, multi-hop data routing causes more data interference and reduced network lifetime due to non-uniform energy consumption across the network. Moreover, in an IoT network, faulty nodes further reduce the performance of data collection and network lifetime. In order to address the aforementioned challenges, various fault detection and fault tolerant-data routing methods have been proposed in the literature. However, predicting the faulty nodes a prior can result in improved network lifetime and QoS. Hence, in this work, a novel joint node fault prediction based optimal data routing method is proposed in an IoT network. The method utilizes a novel unsupervised learning based Local Outlier Factor (LOF) method for predicting forthcoming faults. The method classifies the faulty state of a sensor node as an outlier when plotted with normal state of the sensor node. Subsequently, a novel data routing method is proposed which utilizes Q-learning framework towards multi-hop data routing. Here, the Q-values decide the optimal routing path, where, data transmission path is altered based on predictions made on the faulty nodes. The performance of the proposed methods is evaluated over both, simulated IoT testbed and real-field dataset. The obtained results demonstrate that the proposed methods are able to successfully predict the faulty nodes and avoid data transmission through them, resulting in improved energy-efficiency and QoS over the network.

Load Balancing in the Combined Technology of Li-fi and Wi-Fi based on Collaborative Game

Wireless networks have become increasingly important in today's digital age. The combination of VLC and RF technologies, such as local Wi-Fi and Li-Fi networks, has emerged as a significant technology in this field. This combination enhances the coverage of weak points and strengthens the strong points of local wireless networks, resulting in higher data rates and increased coverage. However, load balancing is a crucial issue that can enhance network efficiency, especially when there are multiple access points from both networks. The selection of the appropriate access point at any given time by different nodes can significantly impact network performance. This research proposes a game theory-based method for selecting an appropriate access point, which uses multiple stages of computation and policy changes to achieve a Nash equilibrium in the game and subsequently in the network. The results show that this method can improve the quality of service in the local network by over 6% compared to previous methods such as the fuzzy method and by over 20% compared to the higher signal power selection policy.

Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN)

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.

A Resource Allocation Scheme for Packet Delay Minimization in Multi-Tier Cellular-Based IoT Networks

With advances in Internet of Things (IoT) technologies, billions of devices are becoming connected, which can result in the unprecedented sensing and control of the physical environments. IoT devices have diverse quality of service (QoS) requirements, including data rate, latency, reliability, and energy consumption. Meeting the diverse QoS requirements presents great challenges to existing fifth-generation (5G) cellular networks, especially in unprecedented scenarios in 5G networks, such as connected vehicle networks, where strict data packet latency may be required. The IoT devices with these scenarios have higher requirements on the packet latency in networking, which is essential to the utilization of 5G networks. In this paper, we propose a multi-tier cellular-based IoT network to address this challenge, with a particular focus on meeting application latency requirements. In the multi-tier network, access points (APs) can relay and forward packets from IoT devices or other APs, which can support higher data rates with multi-hops between IoT devices and cellular base stations. However, as multiple-hop relaying may cause additional delay, which is crucial to delay-sensitive applications, we develop new schemes to mitigate the adverse impact. Firstly, we design a traffic-prioritization scheduling scheme to classify packets with different priorities in each AP based on the age of information (AoI). Then, we design different channel-access protocols for the transmission of packets according to their priorities to ensure the QoS in networking and the effective utilization of the limited network resources. A queuing-theory-based theoretical model is proposed to analyze the packet delay for each type of packet at each tier of the multi-tier IoT networks. An optimal algorithm for the distribution of spectrum and power resources is developed to reduce the overall packet delay in a multi-tier way. The numerical results achieved in a two-tier cellular-based IoT network show that the target packet delay for delay-sensitive applications can be achieved without a large cost in terms of traffic fairness.

Traffic-aware optimal routing in software defined networks by predicting traffic using neural network

Network infrastructure management has been completely transformed by Software-Defined Networking (SDN), allowing for centralized control and programmability. The significant challenge in SDN is to provide optimal routing decisions based on real-time network performance metrics. In this work, it is proposed to have Gated Recurrent Unit (GRU) based traffic prediction to dynamically adjust link weights in SDN. It facilitates real-time adaptation to traffic changes and optimal routing decisions for improved Quality of Service (QoS). This work is to provide optimal routing in SDN by leveraging the capability of GRU to predict future traffic based on network performance metrics sampled at different time sequences. It enables the network to adapt the changing traffic patterns in real-time. The predicted traffic is then used to determine the edge weights between links in the network, which are updated dynamically to reflect changes in the network. When an outbound packet is received, it can be routed optimally by selecting a path with less traffic, thereby reducing latency and improving the QoS of routing the packets. The simulation results reveals that the proposed methodology has the potential to reduce delay up to 9.16% and jitter up to 32.31%, coupled with significant throughput enhancements up to 26.81% compared to the default shortest path. These quantitative findings highlight its effective contribution towards optimizing network performance and QoS.

GTFP: Network Fault Prediction Based on Graph and Time Series

With the explosion of 5G network scale, the network structure becomes increasingly complex. During the operation of the network devices, the probability of anomalies or faults increases accordingly. Network faults may lead to the disappearance of important information and cause unpredictable losses. The prediction of network faults can enhance the quality of network services and reduce economic loss. In this paper, we propose the concept of 4D features and use the BERT algorithm to extract semantic features, the graph neural network algorithm to extract network topology information, and the Temporal Convolutional Network (TCN) algorithm to extract time series. Based on this, we propose Fault Prediction based on GraphSage and TCN (GTFP), an end-to-end solution of network fault alarm prediction, which is based on GraphSage and TCN (GTCN), a hybrid algorithm of a graph neural network and the TCN model. Our solution takes the historical alarm data as input. First, we filter out the alarm noises irrelevant to the faults through data cleaning. Then, we employ feature engineering to extract the valid alarm features, including the statistical features of the network alarm information, the semantic features of the alarm texts, the sequential features of the alarms and the network topology features of the nodes where the alarms are located. Finally, we use GTCN to predict future fault alarms based on the extracted features. Experiments on the alarm data of a real service system show that GTFP performs better than the state-of-the-art algorithms of fault alarm prediction.

Multi-service access selection algorithm in 5G heterogeneous network

As advancements occur in new wireless communication technology, varieties of network forms coexist and integrate, gradually forming a heterogeneous wireless network environment covered by various wireless networks simultaneously. Owing to disparities in wireless access technologies, network selection needs to consider various factors. Therefore, this paper proposes an access selection scheme using Multi-Attribute Decision-Making (MADM). Considering the diversity of user services, each attribute’s subjective and objective weight is respectively calculated by Analytic Hierarchy Process (AHP) and Standard Deviation (SD) for network service quality, user preference, and available load. Combined with utility functions, the synthetic assessments of evaluated networks are obtained. The results of the simulation indicate that the algorithm combines user preference and service characteristics to effectively select the appropriate access network. At the same time, load balancing is realized to some extent.

Analisis mutu pelayanan kesehatan menggunakan metode Servperf dan IPA pada pasien rawat jalan di Rumah Sakit Islam Malahayati Medan

When it comes to satisfying customers whose requirements are becoming more varied over time, quality of service in a business network is a key factor. Patients at Malahayati Islamic Hospital Medan continue to have concerns regarding the quality of care they get and the WOTJ quality indicator, which now stands at 55% instead of the target 100%. The goal of this study is to use the Servperf and IPA techniques to examine the degree of outpatient satisfaction at Malahayati Islamic Hospital Medan along the categories of tangibles, dependability, responsiveness, assurance, and emphasizing. A quantitative survey with a cross-sectional design was used for this study. This research used purposive sampling to collect data from a representative sample of 96 outpatients from Hospital in Medan. Based on the findings of this research, Malahayati Islamic Hospital Medan can enhance the quality of its outpatient treatment by focusing on three specific service aspects. The waiting area and exam rooms need to be more relaxing, drugs should be delivered quickly, and the doctors and other personnel should be more personable. Prescriptions for both outpatient and inpatient use must enter the pharmacy via different entrances to prevent a backlog.

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA)

Optimal Service Migration for Data and Reasoning Fabric

In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.

Kalman filter based sensor fault detection in wireless sensor network for smart irrigation

Ensuring the reliability of data obtained from Wireless Sensor Networks (WSNs) deployed in farmland to monitor soil parameters is crucial for optimizing smart irrigation. These distributed sensor nodes encounter various challenges and are vulnerable to sensor faults that can significantly degrade the network's service quality. In this article, we present an innovative approach for detecting sensor faults within WSNs for smart irrigation by combining an autoregressive model with a Kalman filter. Integrating the Kalman filter and the autoregressive model combines their strengths in a synergistic manner. The algorithm is developed with consideration for the resource constraints of the sensor nodes and addresses the challenge of lacking ground truth information for the monitored area. The primary advantage of this proposed technique lies in its simplicity of implementation, requiring minimal computational complexity while enhancing the application's reliability. Through experimentation and validation, we demonstrate the effectiveness of this combined approach in detecting sensor fault detection in real-world WSNs scenarios.

Anomaly Detection Model of Network Dataflow Based on an Improved Grey Wolf Algorithm and CNN

With the popularization of the network and the expansion of its application scope, the problem of abnormal network traffic caused by network attacks, malicious software, traffic peaks, or network device failures is becoming increasingly prominent. This problem not only leads to a decline in network performance and service quality but also may pose a serious threat to network security. This paper proposes a hybrid data processing model based on deep learning for network anomaly detection to improve anomaly detection performance. First, the Grey Wolf optimization algorithm is improved to select high-quality data features, which are then converted to RGB images and input into an anomaly detection model. An anomaly detection model of network dataflow based on a convolutional neural network is designed to recognize network anomalies, including DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), and Probe (Probing). To verify the effectiveness of the improved Grey Wolf algorithm and the anomaly detection model, we conducted experiments on the KDD99 and UNSW-NB15 datasets. The proposed method achieves an average detection rate of 0.986, which is much higher than all the counterparts. Experimental results show that the accuracy and the detection rates of our method were improved, while the false alarm rate has been reduced, proving the effectiveness of our approach in network anomaly classification tasks.

SFC-based multi-domain service customization and deployment

In order to satisfy diversified service demands, future networks are expected to provide higher quality network services, especially in 5G scenarios. Network Function Virtualization (NFV) is a promising architecture to achieve flexible service provisioning by introducing Service Function Chain (SFC) technology. With explosive expansion of network devices and end users, the complex multi-domain network environment and personalized service demands of different users pose serious challenges to efficient service provisioning. To this end, this paper presents a novel SFC-based Multi-domain Service Customization and Deployment framework, MSCD, by blending merits of Software Defined Networking (SDN) and NFV technologies. MSCD framework can automatically customize multi-domain network services based on users’ personalized service preferences, and deploy them in multi-domain networks, to provide customizable multi-domain services for different users. Taking into account user personalized service preferences, resource allocation and VNF dependency constraints, the multi-domain service customization and deployment problem is mathematically formulated as an optimization model with the target of service satisfaction degree maximization. Furthermore, a Greedy strategy based Heuristic Service Customization and dePloyment (GHSCP) algorithm is proposed to customize and deploy the multi-domain services efficiently. Simulation results demonstrate that the proposed GHSCP algorithm is efficient and outperforms comparison algorithms in terms of service acceptance ratio and service satisfaction degree.

QoS-Dependent Event-Triggered Control for UAVs on Cognitive Radio Networks Subject to Deception Attacks

Based on cognitive radio (CR) networks, this paper considers event-triggered control for unmanned aerial vehicle (UAV) systems subject to deception attacks. Two states (ON and OFF) are switched in CR networks. For the switched model, a new Quality of Service (QoS)-dependent event-triggered scheme (ETS) is proposed over CR networks under deception attacks. For a non-ideal QoS, more sensitive trigger parameters are required against deception attacks. For an ideal QoS, less sensitive trigger parameters are adjusted to reduce data transmissions. Based on the QoS-dependent ETS, a new switched system is modeled subject to deception attacks. By constructing the Lyapunov functional, the criteria are obtained to guarantee the exponential mean-square stability of UAV systems and the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{\infty }$</tex-math></inline-formula> performance. Moreover, an algorithm is developed to adjust the parameters of QoS-dependent ETS according to the QoS of CR network. Finally, an UAV system is utilized to show the effectiveness of the proposed approach.

Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.

Simulation of the Effect of Interference and EIRP on the Qos of DVB-S2 and DVB-RCS2 Networks

The purpose of this study was to obtain the QoS value of the data sent from the user terminal to the gateway based on variations in antenna gain and to determine the effect of the level of interference caused by noise on the user's device on the quality of data reception. This research uses the NS3 software experimental method. Parameters analyzed include throughput, delay, PDR. This study uses quasi-experimental methods. The purpose of quasi-experimental research is pure experimental conditions in conditions where it is impossible to control or manipulate all relevant variables, namely estimating. The conclusion in this study has been successfully carried out simulating sending data packets using the NS3 simulator tools. Based on the simulations that have been carried out, several characteristics of the effect of antenna gain on the quality of data packet transmission are obtained. The simulations explain well that the values of throughput, delay, and PDR have increased so as to provide good service quality. Based on the simulations that have been carried out, several characteristics of the influence of the interference level that affect the user on the quality of sending data packets are obtained. The simulation explains well that the values of throughput, delay, and PDR experience stability so that the level of interference has no effect on users in sending packets. However, the values obtained are very good and have not decreased

Vehicular Networks QoS Improvement by Simultaneous Use of RSUs and Parked Vehicles in Urban Scenarios

Vehicular Networks QoS Improvement by Simultaneous Use of RSUs and Parked Vehicles in Urban Scenarios

Development of a mathematical model for assessing the quality of service on a packet switching subnet

Currently, data traffic is growing rapidly, and ensuring optimal network performance and effective data flow management have become the most important tasks. In this context, the quality of network service plays a crucial role in achieving these goals. This article suggests an approach to solving the problem of efficient service in ISDN. Namely, optimization of resource distribution between channel switching and packet switching subnets in ISDN to calculate optimal quality of service characteristics. In the process of ISDN design analysis, an optimization problem is compiled, where the evaluation of the packet-switched subnet service is used as an objective function, and the evaluation of the circuit-switched subnet service is used as one of the constraints for this task. To calculate the main characteristics of a packet-switched subnet, the subnet is considered as a service system with a delay. During the study, the methods of optimal movement of the generalized channel boundary between the subnets of channel switching and packet switching were identified, depending on the data parameters and the state of the integrated network, which made it possible to develop an optimal mathematical model of optimal control of the generalized boundary. To calculate the bandwidth for channel switching and packet switching subnets, an algorithm has been compiled to implement the resulting model and a program in C++ has been compiled. The study of the generalized boundary and the dynamic redistribution of bandwidth between subnets represents a new approach to network optimization. The results are based on the use of the classical Erlang formula for systems with service failures and on load distribution plans, which makes it possible to effectively manage the maintenance process in the network

Joint Optimization Scheme of User Association and Channel Allocation in 6G HetNets

The sixth-generation (6G) wireless cellular network integrates several wireless bands and modes with the objectives of improving quality of service (QoS) and increasing network connectivity. The 6G environment includes asymmetrical heterogeneous networks (HetNets) with the intention of making effective use of the available frequencies. However, selecting a suitable gNB and a communication channel that works for users in the network is an enormous challenge in 6G HetNets. This paper investigates a joint user association (UA) and channel allocation (CA) problem in two-tier HetNets by considering the downlink scenario to improve QoS. Our study presents an innovative scheme for user association and channel allocation, wherein the user can be connected to either the macro base station (MBS) or a possible small base station (SBS) in a direct or relay-assisted link. Furthermore, the proposed scheme identifies the optimal channel to be allocated to each user so that the overall network QoS can be maximized. A symmetric matching game-based user association is proposed to find the optimal association for users. Moreover, a modified auction game is applied to allocate the optimal channel by considering the quota of each gNB. Regarding connection probability, throughput, energy efficiency (EE), and spectrum efficiency (SE), the simulation results show that the proposed approach performs well over the state-of-the-art techniques.

Analysis of First In First Out (FIFO) Bandwidth Packet Queuing Technique with Random Early Detection (RED) on WLAN (Wireless LAN)

Implementation of bandwidth usage is generally only optimally utilized by some WLAN network users; this is often influenced by the monopoly use of bandwidth capacity for both download and upload. The aim is to determine the optimal packet queuing technique based on QoS parameters in WLAN network implementation. This research method uses QoS FIFO and RED packet queuing techniques with throughput, delay, jitter, and packet loss parameters on WLAN networks. Quality of Service (QoS) results on queuing methods using FIFO parameters (throughput 79.65%, delay 73.43 ms, jitter 82.21 ms, and packet loss 3.39%) RED technique throughput parameters: 79.29% delay (75.35 ms), jitter (71.45 ms), and packet loss (7.17%). QoS, compared to FIFO and RED queuing techniques, both perform well in maintaining the quality of network services. The FIFO queuing technique has an average index value of 3.5 with "Satisfactory" degradation, while the RED queuing technique has an average index value of 3.75 with the same degradation. Although the difference in average index values is not very significant, the RED queuing method provides slightly better results in terms of QoS.