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SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions.

Recent advancements in AI have significantly propelled machine learning (ML) as a cornerstone of modern technological infrastructure, especially in enhancing machine-to-machine (M2M) communication. This necessitates the development of custom, advanced passive optical network (PON) devices tailored to sophisticated functionalities, heralding a shift towards decentralized data processing that aligns with global data protection and privacy standards. This paper introduces a novel software-defined architecture for TWDM-PON, designed to support Federated Learning (FL) traffic in a peer-to-peer (P2P) manner. Our proposed architecture integrates an advanced P2P-FL-OLT with an enhanced P2P-FL-DBA and P2P-FL-ONU with cache storage and a dedicated P2P transceiver, optimizing network resources and enhancing performance. Extensive simulations demonstrated that this architecture effectively manages increased P2PFL traffic, substantially improving system performance metrics such as throughput, packet delay, jitter, and packet loss ratio. The results affirm that our design not only meets but exceeds Quality of Service (QoS) requirements, making it a viable solution for future fiber optic networks facilitating distributed M2M machine learning applications.

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

Optical networks have undergone a remarkable transformation with the adoption of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL). The Next Generation (NG)-EPON is one such technology that is essential for supporting high-bandwidth applications like 4K video streaming, ultra-high-definition (UHD) CCTV, and other emerging video-type applications that have strict Quality-of-Service (QoS) requirements. In this paper, we present a ground-breaking Temporal Dynamic Wavelength Bandwidth Allocation (T-DWBA) mechanism based on the Long-Short-Term-Memory (LSTM) architecture. The T-DWBA uses past experiences to learn data as knowledge and predict time series with time lags of unknown size. Our proposed mechanism reduces upstream control message overheads, eliminates idle periods, and significantly improves bandwidth utilization, ensuring superior QoS specifically for video-type applications. The simulation results show that the T-DWBA significantly enhances system performance, reducing packet delay, and jitter, and improving bandwidth utilization. The use of AI techniques like ML and DL coupled with the recent advancements in SDN-Enabled Broadband Access (SEBA), hardware/software, and cloud technologies provide the perfect platform for deploying our proposed T-DWBA mechanism. Overall, our research proposes a promising solution for boosting EPON performance, revolutionizing optical networks, and providing seamless access to high-quality video streaming for a next-generation audience.

Over the past decade, network operators worldwide have regarded passive optical networks (PONs) as a successful access network system and the preferred technology for fiber-to-the-home/building/curb/distribution-point (FTTx) architecture. However, implementing the state-of-the-art PONs requires a high initial capital expenditure (CapEx) to deploy fiber in the field and acquires the necessary active devices. Thus, new architectures need to be developed to share the physical architecture and enable the multi-tenancy capability in a flexible way to satisfy different requirements from the operators and users. This research proposes to integrate the next-generation TWDM-PON with software-defined network (SDN) and the network function virtualization (NFV) to provide highly enhanced form of virtual unbundling of broadband access networks, enabling cost sharing and the dynamics of a competitive landscape. The simulation results show that the proposed architecture and mechanism can provide better performance in multi-tenancy condition, while guaranteeing the quality-of-service requirements in terms of the mean packet delay, jitter, throughput, and packet loss ratio.

The purpose of this study is to increase the number of access users without having to install a new optical cable. With the proposed solution, the cost of installation work can be reduced and the number of users can be increased. Several parameters were observed to ensure that the modified network not only improved the scalability but also met the standard parameters. Among the parameters observed are the Q factor, bit error rate (BER), eye diagram and number of users. The study was continued using the spectrum conversion, slicing and duplication technique, where the signals would be duplicated in arrayed waveguide grating (AWG) and sliced using a demultiplexer WDM (WDM Demux). Simulations were performed using the latest-version Optisystem 18.0 software by setting the transmitter frequency value of 1491 nm, transmitter power of 0 dBm and loss of 0 dB. The result shows the total user access achieved is 196,608 users. Meanwhile, the common FTTH network is allowed 256 users only. The criterion is set based on the calculation of the Q factor, which is greater than 6, while the BER is less than 1 × 10−9. The Q factor for 196,608 users is 6.43617 and the BER is 4.52 × 10−11. The number of users is increased without compromising the quality of data offered to the customer. Our solution is the first reported to date.

File sharing is one of the most common uses in Peer-to-peer (P2P) networks and structured P2P systems, such as BitTorrent (BT), which may overcome the limitations of features such as improved scalability, efficiency, and deterministic data location. Recent research has attempted to reduce inter-ISP P2P traffic by including locality awareness into neighbor-selection strategies of popular P2P apps, which prioritize adjacent peers over distant peers when transferring data, reducing server bandwidth burden, and inter-ISP traffic expense. However, the locality awareness P2P exchanged, handled and coordinated by the network infrastructure to turn around traffic as nearest as possible to the end users was proposed for the passive optical network (PON) because of the large amount of bandwidth in only one single fiber. The PON offers many system architectures; in particular, the NG-PON2 with colorless ONUs, adopting time and wavelength division multiple access technology is often recognized as the best access network solution for simplifying network operation, lowering installation costs, and maintenance costs under control. Software-defined networking (SDN) has sparked interest in various fields as a viable research topic, promising better agility, improved automation, security, and lower capital and operating costs. Taking advantage of the SDN’s OpenFlow protocol in NG-PON2, centralized control renders more flexible control over the BT traffic (P2P intra-traffic) file sharing application provided by different ISPs. Finally, we evaluated our proposed scheme in real-time traffic share collected by Sandvine’s report on 2020 despite the Covid-19 pandemic for different geographic regions, for example; Global, APAC, EMEA, and AMERICA.

The IEEE 802.3ca Task Force group has recently approved the standardization of the next-generation Ethernet passive optical network (NG-EPON) to satisfy future bandwidth demands. NG-EPON architecture classifies the optical network unit (ONU) transmission to allow simultaneous Wavelength-Agile (WA-PON) transmission, which increases the network capacity (50 Gb/s) and flexibility. The recently launched 5G services and related functional standards, new capabilities, and quality of service (QoS) imposed on the optical transport network. The Peer-to-peer (P2P) based live streaming application is the most popular multimedia application in access networks, i.e., Facebook Live, YouTube Live, Twitch, and other streaming and video conferencing services are increasing, especially in global epidemics which requires a higher QoS. Hence, media-based applications require adequate infrastructure resources to meet customer demands such as high bandwidth, low latency, high quality audiovisual, live streaming, and services. Therefore, this paper proposes an SDN enhanced P2P virtual reality (VR) live video streaming application NG-EPON multicast architecture based on 5G-enabled Tactile Internet (TI) and aims to improve the QoS, QoE and provide flexible services to the end-users. Finally, we proposed an SDN-based TI-dynamic wavelength and bandwidth allocation (SD-TI-DWBA) mechanism. Simulation results show that our proposed architecture can improve the system performance and QoS metrics regarding packet delay, jitter, system throughput, and packet loss.

Historically, the optical access network (OAN) plays a crucial role of supporting emerging new services such as 4 k, 8 k multimedia streaming, telesurgery, augmented reality (AR), and virtual reality (VR) applications in the context of Tactile Internet (TI). In order to prevent losing connectivity to the current mobile network and Tactile Internet, the OAN must expand capacity and improve the quality of Services (QoS) mainly for the low latency of 1 ms. The optical network has adopted artificial intelligence (AI) technology, such as deep learning (DL), in order to classify and predict complex data. This trend mainly focuses on bandwidth prediction. The software-defined network (SDN) and cloud technologies provide all the essential capabilities for deploying deep learning to enhance the performance of next-generation ethernet passive optical networks (NG-EPONs). Therefore, in this paper, we propose a deep learning long-short-term-memory model-based predictive dynamic wavelength bandwidth allocation (DWBA) mechanism, termed LSTM-DWBA in NG-EPON. Future bandwidth for the end-user is predicted based on NG-EPON MPCP control messages exchanged between the OLT and ONUs and cycle times. This proposed LSTM-DWBA addresses the uplink control message overhead and QoS bottleneck of such networks. Finally, the extensive simulation results show the packet delay, jitter, packet drop, and utilization.

Fiber-to-the-home (FTTH) networks are seen as the most future-proof technology to offer increasing bandwidth to customers. By utilizing passive optical network (PON) technology, they provide flexibility and capability to carry higher bandwidths as compared to the legacy copper-based access network. Optical performance monitoring could potentially enable higher stability, reconfigurability, and flexibility in a self-managed optical network. This paper will describe the specific fiber impairments that affect the quality of service for fiber-to-the-home networks. The impairment needs to be monitored and restored. The proposed solution will utilize a photodiode and optical switch as the main components, can easily be integrated with the ONU through the optical interface, and will be referred to as ‘PROMO’. With this scheme, the protection and restoration mechanisms are archived through the detection and availability of the downstream signal from the OLT. The results show that the received power, BER values, and maximum Q factor are acceptable for both simulation and experimental conditions in the case of normal and protection conditions.

An Ethernet passive optical network (EPON) is an optical access network that allows a higher data rate with low power consumption. To improve energy savings for an EPON, the sleep and doze modes for the optical network units (ONUs) play a pivotal role. Many prediction schemes have been proposed to control these modes. To increase the prediction accuracy, this study proposes an energy-efficient approach that uses a support vector regression (SVR) model. A dynamic bandwidth allocation (DBA) scheme called SVR-DBA is designed to allocate bandwidth to ONUs more efficiently and fairly. To determine the effectiveness of the proposed scheme, simulations are performed. The simulation results show that the proposed scheme decreases energy consumption for ONUs by up to 47% and fulfills the quality-of-service (QoS) requirements in terms of delay, jitter, and packet loss.

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

Emerging research trends in smart healthcare, smart manufacturing, and Industrial Internet-of-Things (IIoT) applications are based on 5G services, which can achieve ultra-reliable and low-latency communication networks. In such fields of application, haptic applications have gained importance. The invention of 5G wireless communication networks and advances in Tactile Internet (TI) technology, which provides controlled communications through the transmission of touch and actuation in real-time, have been envisioned as promising enablers of TI services. This study introduces TI-based smart hospital healthcare applications to enhance the alignment of services provided to patients. The existing telesurgery system has high communication delay and overhead, which limit its applicability. To alleviate these problems, we analyze and provide insights into the communication architecture for 5G-enabled low-latency telesurgery in a smart hospital. We then propose a new TI-software-defined optical access networking (TI-SDOANs) framework in Next-Generation Passive Optical Network 2, which includes cloud-based human-to-machine steering servers and supports multiple cloud-based applications. We further propose the implementation of an effective TI-dynamic wavelength and bandwidth allocation (TI-DWBA) resource provisioning scheme that meets the quality of service requirements of TI services. Simulation results show that the proposed scheduling schemes can significantly improve the Quality of Service (QoS) performance in terms of the packet delay, jitter, throughput and packet drop.

Localization is an important research issue in Wireless Sensor Networks (WSNs). Though Global Positioning System (GPS) can be used to locate the position of the sensors, unfortunately it is limited to outdoor applications and is costly and power consuming. In order to find location of sensor nodes without help of GPS, collaboration among nodes is highly essential so that localization can be accomplished efficiently. In this paper, novel localization algorithms are proposed to find out possible location information of the normal nodes in a collaborative manner for an outdoor environment with help of few beacons and anchor nodes. In our localization scheme, at most three beacon nodes should be collaborated to find out the accurate location information of any normal node. Besides, analytical methods are designed to calculate and reduce the localization error using probability distribution function. Performance evaluation of our algorithm shows that there is a trade off betweendeployed number of beacon nodes and localization error, and average localization time of the network can be increased with increase in the number of normal nodes deployed over a region.

The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized.