Improve Network Performance Research Articles

Analisis Pengaruh Penggunaan Internet Download Manager pada Load Balancing di Mikrotik

Internet Download Manager (IDM) is one of the popular applications used by users to download various types of files from the internet. Load balancing is a technique used to increase the efficiency of using network resources by distributing data traffic evenly across several connection lines. This research aims to determine the extent to which IDM influences the effectiveness of load balancing as well as the influence of using Internet Download Manager (IDM) in the context of load balancing using the Policy-based Load Balancing (PCC), Equal Cost Multi-Path (ECMP), and Next-Hop Target methods. (NTH) on the MikroTik RB751 device. In this research, we used the PPDiOO method for systematic data collection and Quality of Services (QoS) analysis as a parameter for network data collection techniques. The research subject is the system or user who uses the device, while the research object is the impact of using IDM on load balancing performance. The main independent variable is the use of IDM, while the dependent variable is load balancing performance with different methods. Our findings show that using IDM consistently provides significant improvements in network performance compared to not using IDM.

Genetic Algorithm-Based Secure Routing Protocol for Wireless Sensor Networks

In a wireless sensor network (WSN), security threats are prevalent as a result of the network's distributed nature and resource limitations. This paper proposes a secure routing protocol for WSNs based on Genetic Algorithms (GA). The protocol utilizes GA to optimize secure route selection while considering network lifetime and energy efficiency. To showcase the efficiency of the suggested protocol, we present a case study, provide a detailed algorithmic representation, and evaluate its performance through extensive simulations. The results showcase the protocol's ability to enhance security and improve network performance, including increased packet delivery ratio, reduced energy consumption, and robustness against attacks. The proposed GA-based secure routing protocol offers a promising approach to address the unique security challenges of WSNs, contributing to the development of secure and efficient communication frameworks for various applications. Additionally, we highlight the significance of incorporating genetic algorithms into secure routing protocols as a means to optimize route selection in WSNs. By leveraging the evolutionary nature of genetic algorithms, our proposed protocol adapts to dynamic network conditions and effectively balances security requirements with energy efficiency and network lifetime considerations. The case study, algorithmic representation, and comprehensive simulation results validate the protocol's ability to enhance security while maintaining robust network performance. This study contributes to the advancement of secure routing in WSNs, offering a viable solution to mitigate security threats and ensure reliable communication in resource-constrained environments.

Mode multicasting without parasitic wavelength conversion.

Optical multicasting, which involves delivering an input signal to multiple different channels simultaneously, is a key function to improve network performance. By exploiting individual spatial modes as independent channels, mode-division-multiplexing (MDM) can solve the capacity crunch of traditional standard single-mode fiber (SSMF) transmission system. In order to realize mode multicasting with high flexibility in future hybrid wavelength-division-multiplexing (WDM) and MDM networks, we propose a mode multicasting scheme without parasitic wavelength conversion, based on the inter-modal four-wave mixing (FWM) arising in the few-mode fiber (FMF). The operation mechanism including nonlinear phase shift for efficient mode multicasting is analytically identified. Then, based on the derived operation condition, we numerically investigate the impact of the dual-pump power and the FMF length on the performance of mode multicasting. By properly setting the pump wavelength and the dual-pump power, mode multicasting performance, in terms of mode multicasting efficiency, 3-dB bandwidth, and destination wavelength, can be tuned according to various application scenarios. After the performance optimization, mode multicasting of 25-Gbaud and 100-Gbaud 16-quadratic-amplitude modulation (16-QAM) signals is numerically demonstrated. The proposed reconfigurable mode multicasting is promising for future WDM-MDM networks.

A Security Scheme for Statistical Anomaly Detection and the Mitigation of Rank Attacks in RPL Networks (IoT Environment)

A Routing Protocol for Low-power-lossy (RPL) networks builds a Destination Oriented Directed Acyclic Graph (DODAG) to provide IPv6 connectivity for resource-constrained devices over a large variety of low-power-lossy link layer technologies. Each RPL node maintains a rank value, which quantizes its relative topological distance from the DODAG root and is calculated based on the rank of its preferred parents and the objective function being employed. The RPL routing process does not impose any check to monitor the action and conduct of the parent nodes. A malicious attacking node can exploit this weakness by faking its rank value to be much lower than the original to attract more traffic to traverse through it from its neighboring and underlying child nodes. An attacking node can choose to perform selective forwarding or a sinkhole attack (Rank Attack type 1 – RA1) or exacerbate network performance parameters by causing topological instability (Rank Attack type 2 - RA2). This paper presents the Statistically-based Anomaly Detection Scheme (SARPL) to detect RA1 and RA2 and attempts to mitigate their effects. The simulations and performance evaluations show that SARPL can successfully detect RA1 attacks in all scenarios whereas it has a positive detection rate of approximately 93% for RA2 type attacks. SARPL also significantly improves network performance parameters, such as packet delivery rate and end-to-end delay, while mitigating the effects of RA1 and RA2.

Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas

This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.

Develop a new handling method for selfish nodes in mobile ad-hoc networks

Mobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods.

Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC

The steady evolution of the Open RAN concept sheds light on xApps and their potential use cases in O-RANcompliant deployments. There are several areas where xApps can be used that are being widely investigated, but the issue of mitigating conflicts between xApp decisions requires further in-depth investigation. This article defines a conflict mitigation framework (CMF) built into the existing O-RAN architecture; it enables the Conflict Mitigation component in O-RAN's Near- Real-Time RAN Intelligent Controller (Near-RT RIC) to detect and resolve all conflict types defined in the O-RAN Alliance's technical specifications. Methods for detecting each type of conflict are defined, including message flows between Near-RT RIC components. The suitability of the proposed CMF is proven with a simulation of an O-RAN network. Results of the simulation show that enabling the CMF allows balancing the network control capabilities of conflicting xApps to significantly improve network performance, with a small negative impact on its reliability. It is concluded that defining a unified CMF in Near-RT RIC is the first step towards providing a standardized method of conflict detection and resolution in O-RAN environments.

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (“▪”), 322 patterns(“▪”), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

Random Relay Selection based Heuristic Optimization Model for the Scheduling and Effective Resource Allocation in the Cognitive Radio Network

Cognitive Radio Network (CRN) provides effective capabilities for resource allocation with the valuable spectrum resources in the network. It provides the effective allocation of resources to the unlicensed users or Secondary Users (SUs) to access the spectrum those are unused by the licensed users or Primary Users (Pus). This paper develops an Optimal Relay Selection scheme with the spectrum-sharing scheme in CRN. The proposed Cross-Layer Spider Swarm Shiftingis implemented in CRN for the optimal relay selection with Spider Swarm Optimization (SSO). The shortest path is estimated with the data shifting model for the data transmission path in the CRN. This study examines a cognitive relay network (CRN) with interference restrictions imposed by a mobile end user (MU). Half-duplex communication is used in the proposed system model between a single primary user (PU) and a single secondary user (SU). Between the SU source and SU destination, an amplify and forward (AF) relaying mechanism is also used. While other nodes (SU Source, SU relays, and PU) are supposed to be immobile in this scenario, the mobile end user (SU destination) is assumed to travel at high vehicle speeds. The suggested method achieves variety by placing a selection combiner at the SU destination and dynamically selecting the optimal relay for transmission based on the greatest signal-to-noise (SNR) ratio. The performance of the proposed CrossLayer Spider Swarm Shifting model is compared with the Spectrum Sharing Optimization with QoS Guarantee (SSO-QG). The comparative analysis expressed that the proposed Cross-Layer Spider Swarm Shifting model delay is reduced by 15% compared with SSO-QG. Additionally, the proposed Cross-Layer Spider Swarm Shiftingexhibits the improved network performance of ~25% higher throughput compared with SSO-QG.

Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities

As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks.

K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks

Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved while reducing the overall communication overhead and increasing the packet delivery ratio(PDR%). In the routing phase, the proposed scheme employs inter-cluster forwarding nodes to transmit messages among different clusters. Extensive simulations demonstrate the performance of the proposed KMORP, showing a 38% better PDR compared to OLSR and over 50% less end-to-end(E2E) delay compared to typical K-Means. Furthermore, the proposed KMORP exhibited an average throughput of 955 kbps, showing a substantial improvement in network performance. The results underscore that the proposed KMORP outperforms existing techniques in terms of PDR, E2E delay, and throughput.

A trust-aware openflow switching framework for software defined networks (SDN)

Software Defined Networks (SDN) and Network Function Virtualisation (NFV) are prime driving technologies behind 5G and Beyond 5G (B5G) communications. The network control intelligence segregation in the SDN infrastructure enables dynamic network features (such as dynamic end-to-end management of security and quality of service (QoS)) offering significantly improved network performance. Even if one assumes that the centralised SDN controller can be security hardened and hence can be trusted, a fundamental challenge in such networks is that the data plane and switching devices are susceptible to cyberattacks. A malicious adversary can compromise them during run-time making them unreliable for secure and trusted communications. Furthermore, the controller communicating with OpenFlow switching devices is unable to accurately assess the state of the switching devices, which serves as the communication base for NFVs in 5G networks. Vulnerable switching devices can put the whole 5G network infrastructure at risk. Hence, there is a clear need for the controller and the management layer to determine the trustworthiness of the switching devices at run-time. The current trend is for many such devices to deploy trusted computing functionality such as Trusted Platform Module (TPM) or Software Guard Extension (SGx) to achieve local as well as remote attestation. In this paper, we present a dynamic trust management framework for evaluating the trustworthiness of the OpenFlow switching devices deployed in the SDN based networks. We formulate device properties that need to be assessed to determine the trust status of the device. We develop a trust enhanced security architecture which can be used to evaluate the trustworthiness of devices and determine their deployment in the provision of network services. The proposed framework uses subjective logic based techniques to derive trust levels of the switching devices at run-time, which are then used by the architecture to make trust enhanced decisions on the provision of network services. A prototype implementation of the proposed architecture is described, which demonstrates how the trustworthiness of the OpenFlow devices are assessed at run-time. The paper concludes with the performance and security analysis of the implemented trust enhanced architecture services.

Traffic Classification Model in Software-Defined Networks with Artificial Intelligence Elements

Application classification is essential to improve network performance. However, with the constant growth in the number of users and applications, as well as the scaling of networks, traditional classification methods cannot fully cope with the identification and classification of network applications with the required level of delay. The use of deep learning technology together with the architecture features of software-defined networks (SDN) will allow the implementation of a new hybrid deep neural network for application classification, which can provide high classification accuracy without manual selection and feature extraction. The proposed structure proposes a classification of applications, taking into account the logical centralized management on the SDN controller. The processed data is used to train a hybrid deep neural network consisting of stacked autoencoder with a high dimensionality of the hidden layer and an output layer based on softmax regression. The necessary network flow parameters can be obtained by processing traffic with a stacked auto-encoder instead of manual processing. The softmax regression layer is used as the final application classifier. The article presents simulation results that demonstrate the advantages of the proposed classification method in comparison with the support vector machine.

Optimal Energy-efficient Resource Allocation and Fault Tolerance scheme for task offloading in IoT-FoG Computing Networks

Due to technology development in the recent past, it is observed that an exponential growth in the usage of high speed intelligent devices which includes smart objects, smart home, smart vehicles etc. Therefore, for the efficient transmission between different smart objects/devices situated across different regions, an effective communication is required and the corresponding technology is called as ‘Internet of Things (IoT)’. This leads to an issues in various aspects like, increase in complexity, low data rate & latency and security may reduce the performance of the existing technologies. Therefore, there is a huge requirement of advanced technology which may support ultra-low latency transmission for the task received from the devices. Fog computing (FC) is one of an emerging technology that can improve network performance and provide resource-constrained applications for the IoT devices. In this paper, the Optimal Energy-efficient Resource Allocation (OEeRA) algorithm is proposed based on Minimal Cost Resource Allocation (MCRA) and Fault Identification and Rectification (FIR) algorithms for effective task offloading of IoT-FoG computing networks. The MCRA algorithm is proposed to assign at least one FN and Resource Block (RB) for each device, and also it ensures that each FN is connected with one or more RBs and devices. The leftover RBs are collected and stored in the buffer to replace the faulty RBs, as proposed in the FIR algorithm, which achieves better processing and response time with higher fault detection accuracy. The energy efficiency (EE) of the proposed OEeRA algorithm is computed through MCRA and FIR algorithms by varying FN, RB, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 5.69 × 1010 bit/J, and 3.019 × 1010 (bit/J) for varying RBs, IoTs, and FNs, respectively.

Integer Programming Based Optimization of Power Consumption for Data Center Networks

With the quickly developing data centers in smart cities, reducing energy consumption and improving network performance, as well as economic benefits, are essential research topics. In particular, Data Center Networks do not always run at full capacity, which leads to significant energy consumption. This paper experiments with a range of optimization tools to find the optimal solutions for the Integer Linear Programming (ILP) model of network power consumption. The study reports on experiments under three communication patterns (near, long, and random), measuring runtime and memory consumption in order to evaluate the performance of different ILP solvers.While the results show that, for near traffic pattern, most of the tools rapidly converge to the optimal solution, CP-SAT provides the most stable performance and outperforms the other solvers for the long traffic pattern. On the other hand, for random traffic pattern, Gurobi can be considered to be the best choice, since it is able to solve all the benchmark instances under the time limit and finds solutions faster by 1 or 2 orders of magnitude than the other solvers do.

Low-Profile and Low-Visual Impact Smart Electromagnetic Curved Passive Skins for Enhancing Connectivity in Urban Scenarios

This paper explores the possibility of integrating a smart electromagnetic skin into a street light or traffic light pole to enhance the coverage of a 5G base station operating in the millimeter-waves frequency band with the consequent improvement of the wireless network performance. Specifically, the electromagnetic characteristics of a curved smart electromagnetic skin, which can be easily integrated into a cylindrical radome enclosing the pole, have been analyzed and compared with those of a planar solution. Additionally, the coverage provided by a curved smart electromagnetic skin in a real urban scenario has been considered. The obtained results confirm that using a curved smart electromagnetic skin is both feasible and convenient from several points of view: it guarantees better performance than a planar configuration occupying the same volume, it is simpler to mount and it has a lower visual impact.

Communication-Aware Flight Algorithms for UAV-Based Delay-Tolerant Networks

In this paper, a wireless communication network that uses unmanned aerial vehicles (UAVs) in the sky to transmit information between ground users is considered. We highlight a delay-tolerant network, where information is relayed in a store-and-forward fashion by establishing two types of intermittent communication links: between a UAV and a user (UAV-to-user) and between UAVs (UAV-to-UAV). Thus, a flight algorithm that controls the movement of the UAVs is crucial in achieving rapid information transmission. Our study proposes new flight algorithms that simultaneously consider the two types of communication links. In UAV-to-UAV links, the direct information transmission between two UAVs and the indirect transmission through other UAVs are considered separately. The movement of the UAVs is controlled by solving an optimization problem at certain time intervals, with a variable consideration ratio of the two types of links. In addition, we investigate not only the case where all UAVs move cooperatively but also the case where each UAV moves autonomously. Simulation results show that the proposed algorithms are effective. Moreover, they indicate the existence of an optimal consideration ratio of the two types of communication and demonstrate that our approach enables the control of frequencies of establishing the communication links. We conclude that increasing the frequency of indirect communication between UAVs improves network performance.

Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learning

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite–terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial–satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial–satellite networks, paving the way for enhanced connectivity and improved network performance in various applications.

Study of Guard Interval Duration on the Operation Impact of IEEE 802.11 Network

Purpose. One of the new additions in the IEEE 802.11n standard amendment was the introduction of the short inter-symbol guard interval, which makes it possible to operate at slightly higher data rates. However, using a short guard interval is not always recommended. The fact is that on some types of sites where radio coverage is provided, the peculiarities of signal propagation with a short guard interval may give worse frame retry rate. In modern networks, this aspect remains more relevant than ever, and the task of correct guard interval configuration remains relevant for both the designer and the administrator of a wireless network. In order to conduct a comparative analysis of the measurement results with a short and long guard inter-symbol interval, it is necessary to conduct a full-scale radio site-survey of industrial WLAN site. As part of the present experiment, a WLAN was investigated at a site in one of the industrial zones of St. Petersburg. The main goal of the work is to experimentally determine the benefits that can be obtained from choosing a modulation and coding scheme with a long inter-symbol guard interval using the example of a distributed wireless network of the IEEE 802.11n/ac/ax standard. A related goal is to experimentally test the potential for reducing the radiation power of access points radios in order to improve network performance. Methods. Conducting a radio survey of a wireless network at a typical industrial site with subsequent analysis of its results. Novelty. The novelty of the presented solution includes a full-scale examination of an actually operated industrial network with simultaneous monitoring of characteristics at various levels of the OSI model: physical (spectrum analysis), channel (protocol analysis), application (control of application operation). Results. Analysis of the collected data shows a practically significant difference between the values of the frame retry rate for different values of the duration of the inter-symbol guard interval and the radiation power of the radio modules of the access points. Practical relevance. In is quantitatively shown that there is a reduction in frame retry rate by 8-10% in an production wireless network, achievable through correct control of the inter-symbol interval length for an industrial site.

Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques

Routing and security are the two main prerequisites for ensuring the correct operation of wireless networks. The importance of these cases doubles in wide networks such as IoT. This paper presents an algorithm to improve Secure Routing in IoT called SRAIOT. This algorithm uses a hierarchical structure to determine the connections between network components and data transfer routing. In SRAIOT, the network structure is managed hierarchically and through SDN. For this purpose, the IoT network is first divided into a set of subnets using the SDN solution, communication control and authentication are managed using the controller nodes of each subnet. The communication between two objects (located in different subnets) will be possible if their identity is confirmed through the controller nodes related to them. On the other hand, in order to identify the sources of attacks and network security threats, the controller nodes in each subnet monitor the network traffic pattern using an ensemble learning model and identify possible attacks in their subnet. The performance of SRAIOT was tested in the simulation, and the results were compared with previous methods. The results of these tests show that SRAIOT improves network performance regarding routing and detecting attacks.