Software-defined Internet Of Things Research Articles

An Effective Selection of Memory Technologies for TCAM to Improve the Search Operations: Demonstration of Memory Efficiency in SDN Recovery

Ternary Content-Addressable Memory (TCAM) is used for storing the flow tables in software-defined networking (SDN)-based OpenFlow switches. However, the TCAM can store only a certain number of flow tables (8000). Moreover, when the switch flow tables need to be updated due to the link failure in the SDN, further updates may be lost due to the flow tables limit of the TCAM space. Hence, to resolve this issue, other memories need to be used in conjunction with TCAM to enhance the memory operations of TCAM. When considering which flash memory technology is to be used in conjunction with TCAM, we need to balance several factors to ensure optimal performance, speed, endurance, reliability, integration complexity, and cost-effectiveness. Hence, it leads to a multi-criteria decision-making problem regarding the selection of other memory technologies such as 3D XPoint, Magnetoresistive RAM, Resistive RAM, and Ferroelectric RAM. In this paper, we use the analytical network process (ANP) method to select the suitable technology in conjunction with TCAM, considering the features of the memory technologies for Software-Defined Internet-of-Things (SD-IoT). We provide a comprehensive numerical model leveraging the ANP to rank the memory technologies regarding their weights. The highest weights identify the most suitable technology for TCAM. We perform simulations to show the effectiveness of the mathematical model utilizing the ANP. The results show that the suggested methodology reduces the recovery delay, improves the packets received ratio (PRR), decreases the jitter, and increases the throughput.

SecureFlow: Knowledge and data-driven ensemble for intrusion detection and dynamic rule configuration in software-defined IoT environment

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising.

Software defined internet of things in smart city: a review

The concept of smart cities has gained traction to enhance citizens’ quality of life amidst rapid urbanization. Integration of the Internet of Things (IoT) is a key component that allows for gathering real-time data to inform decision-making and drive innovation in urban planning and management. However, managing the amount of data generated and the IoT devices rapid growth poses a challenge that leads to network management, interoperability, security, and scalability issues in smart cities. To overcome such problems, integrating Software Define Networking (SDN) in IoT provides a flexible, scalable, and efficient network architecture that can better support the unique demands of IoT devices and applications. Motivated by the extensive research efforts in the Software Defined Internet of Things (SDIoT), this paper aims to review SDIoT implementation in smart cities. It first introduces the underlying technology along with various practical applications of SDIoT. The comprehension of SDIoT in smart cities focus on IoT application requirements, including interoperability, scalability, low latency requirement, handling of big data, security, and privacy, energy consumption, Quality of Service (QoS), and task offloading. The paper concludes by discussing the future research directions that need to be examined in greater depth.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

PBCLR: Prediction-based control-plane load reduction in a software-defined IoT network

The exponential growth of devices and applications in Internet of Things (IoT) networks has caused control-plane traffic to escalate. Experts have suggested Software-Defined Networking (SDN) as a solution for complicated IoT network management. Nevertheless, SDN encounters obstacles in managing the substantial control-plane traffic many IoT devices produce. Prior findings have identified the dynamic switch migration technique as a viable solution for control plane load oscillation. However, their tendency to migrate switches during instances of controller overload restricts the efficiency of traditional migration strategies. As a result, these approaches lead to inefficiencies in the switch migration process, resulting in elevated latency. The present study introduces a prediction-based novel approach for mitigating the control-plane workload by leveraging the dynamic switch migration technique. The proposed method proactively and predictively migrates the switches anticipated to generate excessive control-plane traffic to an alternative controller. We use the learning technique along with time-series analysis to predict the future workload based on the historical control-plane traffic data. The proposed methodology is evaluated through simulation and the results demonstrate that the proposed method outperforms conventional techniques in enhancing load balancing efficacy on a distributed control plane and decreasing migration cost and controller response time by 30.6% on average.

Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT

Edge computing offloading can effectively solve the problem of insufficient computing resources for terminal devices and improve the performance and efficiency of the system. When network states and tasks change rapidly, data-driven intelligent algorithms have difficulty obtaining comprehensive statistics for accurate prediction, resulting in degraded performance of computational offloading and difficulty in adaptive adjustment. It is a current challenge to improve the environment-aware, intelligent optimization so that the computational offloading algorithm can adapt to the dynamic changes in network state and task demands, thus achieving global multi-objective optimization. This paper presents optimized edge computing offloading algorithm for software-defined IoT. First, to provide global state for making decisions, a software defined edge computing (SDEC) architecture is proposed. The edge layer is integrated into the control layer of software-defined IoT, and multiple controllers share the global network state information via east–west message exchange. Moreover, an edge computing offloading algorithm in software-defined IoT (ECO-SDIoT) based on deep reinforcement learning is proposed. It enables the controllers to offload the computing task to the most appropriate edge server according to the global states, task requirements, and reward. Finally, the performance metrics for edge computing offloading were evaluated in terms of unit task processing latency, load balancing of edge servers, task processing energy consumption, and task completion rate, respectively. Simulation results show that ECO-SDIoT can effectively reduce task completion time and energy consumption compared with other strategies.

P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture

Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) attacks pose significant threats to the security of Software-Defined Internet of Things (SD-IoT) networks. The standard Software-Defined Networking (SDN) architecture faces challenges in effectively detecting, preventing, and mitigating these attacks due to its centralized control and limited intelligence. In this paper, we present P4-HLDMC, a novel collaborative secure framework that combines machine learning (ML), stateful P4, and a hierarchical logically distributed multi-controller architecture. P4-HLDMC overcomes the limitations of the standard SDN architecture, ensuring scalability, performance, and an efficient response to attacks. It comprises four modules: the multi-controller dedicated interface (MCDI) for real-time attack detection through a distributed alert channel (DAC), the MSMPF, a P4-enabled stateful multi-state matching pipeline function for analyzing IoT network traffic using nine state tables, the modified ensemble voting (MEV) algorithm with six classifiers for enhanced detection of anomalies in P4-extracted traffic patterns, and an attack mitigation process distributed among multiple controllers to effectively handle larger-scale attacks. We validate our framework using diverse test cases and real-world IoT network traffic datasets, demonstrating high detection rates, low false-alarm rates, low latency, and short detection times compared to existing methods. Our work introduces the first integrated framework combining ML, stateful P4, and SDN-based multi-controller architecture for DDoS and ARP detection in IoT networks.

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration.

Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

An efficient approach to detect  distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

AbstractThe growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

HIC: A high‐reliable in‐band control network for perception layer of software‐defined IoT

AbstractWith the rapid development of the Internet of Things (IoT) and the popularity of the software‐defined idea, the software‐defined Internet of Things (SD‐IoT) came into being. However, the transmission conflict between control flow and data flow in the perception layer with poor channel resources has become an important constraint for SD‐IoT. Thus, this paper adopts the idea of isolated forwarding control flow based on an in‐band channel, that is, selecting some sensor nodes to be responsible for forwarding control flow, and proposes high‐reliable in‐band control network (HIC), a high‐reliability in‐band control network. In order to support real‐time tasks in the IoT environment, HIC adopts a divided control area to support parallel responses of multiple partially overlapping tasks. Further, in each control area, the graph theory is used to model the control node selection problem, and an algorithm for efficiently solving the model is designed based on the community detection method. Finally, we developed the HIC experimental platform composed of HIC‐topology and HIC‐simulation and carried out simulation experiments and analysis from topology generation, maintenance, and operation. The experimental results show that HIC could better adapt to the SD‐IoT under multi‐task deployment, and has great advantages in terms of the success rate and time of topology rapid maintenance compared with similar solutions.

Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

The Internet of Things (IoT) is a complex and diverse network consisting of resource-constrained sensors/devices/things that are vulnerable to various security threats, particularly Distributed Denial of Services (DDoS) attacks. Recently, the integration of Software Defined Networking (SDN) with IoT has emerged as a promising approach for improving security and access control mechanisms. However, DDoS attacks continue to pose a significant threat to IoT networks, as they can be executed through botnet or zombie attacks. Machine learning-based security frameworks offer a viable solution to scrutinize the behavior of IoT devices and compile a profile that enables the decision-making process to maintain the integrity of the IoT environment. In this paper, we present a machine learning-based approach to detect DDoS attacks in an SDN-WISE IoT controller. We have integrated a machine learning-based detection module into the controller and set up a testbed environment to simulate DDoS attack traffic generation. The traffic is captured by a logging mechanism added to the SDN-WISE controller, which writes network logs into a log file that is pre-processed and converted into a dataset. The machine learning DDoS detection module, integrated into the SDN-WISE controller, uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to classify SDN-IoT network packets. We evaluate the performance of the proposed framework using different traffic simulation scenarios and compare the results generated by the machine learning DDoS detection module. The proposed framework achieved an accuracy rate of 97.4%, 96.1%, and 98.1% for NB, SVM, and DT, respectively. The attack detection module takes up to 30% usage of memory and CPU, and it saves about 70% memory while keeping the CPU free up to 70% to process the SD-IoT network traffic with an average throughput of 48 packets per second, achieving an accuracy of 97.2%. Our experimental results demonstrate the superiority of the proposed framework in detecting DDoS attacks in an SDN-WISE IoT environment. The proposed approach can be used to enhance the security of IoT networks and mitigate the risk of DDoS attacks.

Detecting distributed denial of service (DDoS) in SD-IoT environment with enhanced firefly algorithm and convolution neural network

The Internet of Things (IoT) devices have become part of today’s human life and the amplified use of smart phones as well as number of IoT devices in everyday life has made network security more important. These IoT devices are less secure and often abandoned making them a easy target of DDoS attacks which caused by extreme network packet flow threaten vital network services. The DoS is a single-server attack in which the DDoS is a multi-server attack and IoT devices are managed through Software-defined networks (SDN). This research is proposed to improve the Firefly method in optimizing the Convolution neural network (CNN) for detecting DDoS attacks in Software Defined-IoT (SD-IoT). The suggested SD-IoT framework has been structured into three layers, namely application layer, control layer and user infrastructure layer. The SDN-IoT architecture layer model, Security apps identify DDoS attack in Application layer C-DAD attack detection. The second layer manages the SD-IoT network with the SDNWISE controller and the IoT controller. The infrastructure layer contains SOFS (Sensor Open Flow Switch) and IoT devices. However, this proposal has considered each firefly as a single hyperparameter and by updating the position of firefly periodically results in the reduction of the search iteration. Hence, the proposed model is evaluated by Root mean square and used for measuring the training accuracy of the proposed model. DDoS attacks use a backtracking technique to pinpoint the source, which increases response time. Four CNN models by varying layers and parameters has implemented to improve DDoS detection yielded results with 98% accuracy.

An efficient centralized DDoS attack detection approach for Software Defined Internet of Things

An efficient centralized DDoS attack detection approach for Software Defined Internet of Things

Reinforcement-Learning-Based Software-Defined Edge Task Allocation Algorithm

With the rapid growth in the number of IoT devices at the edge of the network, fast, flexible and secure edge computing has emerged, but the disadvantage of the insufficient computing power of edge servers is evident when dealing with massive computing tasks. To address this situation, firstly, a software-defined edge-computing architecture (SDEC) is proposed, merging the control layer of the software-defined architecture with the edge layer of edge computing, where multiple controllers share global information about the network state through an east–west message exchange, providing global state for the collaboration of edge servers. Secondly, a reinforcement-learning-based software-defined edge task allocation algorithm (RL-SDETA) is proposed in the software-defined IoT, which enables controllers to allocate computational tasks to the most appropriate edge servers for execution based on the global network information they have obtained. Simulation results show that the RL-SDETA algorithm can effectively reduce the finding cost of the optimal edge server and reduce the task completion time and its energy consumption compared to various task allocation methods such as random and uniform.

An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem.

An intelligent energy efficient optimized approach to control the traffic flow in Software-Defined IoT networks

In modern society numerous digital devices play a very significant role in day-to-day life. Digital devices are well connected and easily accessible through multiple sensors and Internet of Things (IoT) devices. Due to the rapid growth of digital devices, large amount of data traffics are being generated, which induces network congestion. To deal with large amount of data traffic a programmable Software Defined IoT (SD-IoT) infrastructure is utilized. For efficient and sustainable network, the data must be transmitted through optimal path in such a way to as to minimize energy consumption. Here, the network is partitioned into clusters to find an optimal path. Finding an optimal path from a set of possible paths is an NP-complete problem. To solve this problem, we propose to find a set of optimal border nodes of each cluster with other clusters in the network, so as to reduce the number of possible paths between clusters. The set of optimal border nodes will be selected in such a way so that they have maximum energy and minimum distances. This paper proposes an intelligent approach to find the set of optimal border nodes using Lion Swarm Optimization algorithm (LSOA). Once a set of optimal border nodes are obtained, an optimal path can be generated using a routing mechanism. The performance of the proposed work is analyzed in terms of packet delivery ratio, average latency, network lifetime and energy consumptions. The results show that the border nodes selected using LSOA finds better routes as compared to the border nodes selected using other state-of-the-art metaheuristics algorithm thereby, increases suitability of the network by energy conservation.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network

The deployment of multiple controllers in Software-Defined Internet of Things (SD-IoT) networks enhance the distributed control plane scalability and reliability. With the rapid advancement of IoT, millions of smart devices generate enormous amounts of IoT traffic, causing significant challenges for the network to acquire the better Quality-of-Service (QoS) to the user. The control plane load management is crucial due to the dynamic nature of IoT networks and the heterogeneity of IoT devices in terms of flow generation rate. The load distribution among SD-IoT controllers relies on their available capacity to ensure optimal load balancing performance. The dynamic switch migration method was proposed in prior findings to handle load balancing distribution among controllers. However, the migration efficiency of conventional approaches is limited due to inefficient load balancing performance of the control plane after the migration of switch, leading to additional migration overhead on the network. Taking this into account, we propose a dynamic switch migration-based load balancing approach (DSMLB) to prevent control plane overhead and distribute traffic efficiently while considering heterogeneous IoT devices. The proposed DSMLB scheme measures the real-time load ratio to the mean load ratio of each controller by describing various load metrics and selecting the target controller by optimizing the migration efficiency with residual controller resource utilization. When choosing the candidate switches from the associated overloaded controller, DSMLB considers the migration efficiency and load balancing degree simultaneously for the fastest load reduction and enhances the load balancing performance. The simulation results show that DSMLB outperforms conventional approaches for efficient load balancing performance on a distributed control plane by reducing the controller response time up to 38.6% and migration cost about 45.5% on average. Moreover, DSMLB also improves controller load balancing rate and reduces communication overhead.

ReLFA: Resist link flooding attacks via renyi entropy and deep reinforcement learning in SDN-IoT

Link flooding attack (LFA) is a fresh distributed denial of service attack (DDoS). Attackers can cut off the critical links, making the services in the target area unavailable. LFA manipulates legal low-speed flow to flood critical links, so traditional technologies are difficult to resist such attack. Meanwhile, LFA is also one of the most important threats to Internet of things (IoT) devices. The introduction of software defined network (SDN) effectively solves the security problem of the IoT. Aiming at the LFA in the software defined Internet of things (SDN-IoT), this paper proposes a new LFA mitigation scheme ReLFA. Renyi entropy is to locate the congested link in the data plane in our scheme, and determines the target links according to the alarm threshold. When LFA is detected on the target links, the control plane uses the method based on deep reinforcement learning (DRL) to carry out traffic engineering. Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT. In addition, the rerouting time of ReLFA is superior to other latest schemes.

A Deep Learning Based Method for Network Application Classification in Software-Defined IoT

Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising.

Proactive Network Fault Management for Reliable Subscribed Network Slicing in Software-Defined Mobile Data IoT Services

Proactive network solutions (PNS) become the precise management and orchestration (MANO) in the applied artificial intelligence (AI) era. The PNS proposed to invent future mobile edge communications by predicting the fault networks for reliable slicing configurations. Furthermore, federated learning (FL) systems have been appealed to apply for critical mobile data privacy of the Internet of Things (IoT) services. Therefore, FL-based IoT communications need a precise PNS to pretend the network failures to maximize the model inference and improve end-to-end (E2E) quality of services (QoS). This paper proposed an adopted software-defined network slicing (NS) for IoT communications based on network failure prediction and resource allocations by utilizing a deep-Q-network approach (DQN). The proposed proactive reliable subscribed network slicing was based on software-defined DQN-based proactive dynamic resource allocations (SDQN-PDRA) for adaptive communication configurations. The experiment showed that the proposed approach enhanced the significant outcomes of stability, reliability, convergence time, and other communication QoS.