Software-Defined Networking (SDN) is an innovative concept that segments the network into three planes: a control plane comprising of one or multiple controllers; a data plane responsible for data transmission; and an application plane which enables the reconfiguration of network functionalities. Nevertheless, this approach has exposed the controller as a prime target for malicious elements to attack it, such as Distributed Denial of Service (DDoS) attacks. Current DDoS defense schemes often increased the controller load and resource consumption. These schemes are typically tailored for single-controller architectures, a significant limitation when considering the scalability requirements of large-scale SDN. To address these limitations, we introduce an efficient Federated Learning approach, named “FedLAD,” designed to counter DDoS attacks in SDN-based large-scale networks, particularly in multi-controller architectures. Federated learning is a decentralized approach to machine learning where models are trained across multiple devices as controllers store local data samples, without exchanging them. The evaluation of the proposed scheme's performance, using InSDN, CICDDoS2019, and CICDoS2017 datasets, shows an accuracy exceeding 98%, a significant improvement compared to related works. Furthermore, the evaluation of the FedLAD protocol with real-time traffic in an SDN context demonstrates its ability to detect DDoS attacks with high accuracy and minimal resource consumption. To the best of our knowledge, this work introduces a new technique in applying FL for DDoS attack detection in large-scale SDN.

The Internet of Things (IoT) has emerged as a promising paradigm which facilitates the seamless integration of physical devices and digital systems, thereby transforming multiple sectors such as healthcare, transportation, and urban planning. This paradigm is also known as ad-hoc networks. IoT is characterized by several pieces of equipment called objects. These objects have different and limited capacities such as battery, memory, and computing power. These limited capabilities make it difficult to design routing protocols for IoT networks because of the high number of objects in a network. In IoT, objects often have data which does not belong to them and which should be sent to other objects, then leading to a problem known as permutation routing problems. The solution to that problem is found when each object receives its items. In this paper, we propose a new approach to addressing the permutation routing problem in single-hop IoT networks. To this end, we start by representing an IoT network as an oriented graph, and then, based on a reservation channel protocol, we first define a permutation routing protocol for an IoT in a single channel. Secondly, we generalize the previous protocol to make it work in multiple channels. Routing is done using graph theory approaches. The obtained results show that the wake-up times and activities of IoT objects are greatly reduced, thus optimizing network lifetime. This is an effective solution for the permutation routing problem in IoT networks. The proposed approach considerably reduces energy consumption and computation time. It saves 5.2 to 32.04% residual energy depending on the number of items and channels used. Low energy and low computational cost demonstrate that the performance of circuit-based and oriented graph theory is better than the state-of-the-art protocol and therefore is a better candidate for the resolution of the permutation routing problem in single-hop environment.

Internet of Things (IoT) consists of a variety of heterogeneous interconnected devices called objects or things. These objects are generally equipped with sensing, processing and wireless communication capabilities. Unfortunately, these capabilities are not enough compared to those of devices in traditional networks. For example, objects have low battery power, limited memory storage, and less processing power. There are some cases where objects have to communicate with each other. In an Autonomous Vehicular Network for example, when a vehicle needs to change its direction, it has to alert other vehicles in the same network. This is known as the permutation routing problem. More precisely, the permutation routing problem in IoT occurs when some things of the network possess items that belong to others. The goal is to send items to their respective owners. A number of solutions to the problem have been proposed in literature which focus mainly on Wireless Sensor Networks (where the memory size of objects are the same). In this paper, we propose an efficient permutation routing scheme for a single-hop IoT network (the memory size differs from one object to another). The proposed NESEPRIN protocol consists of two phases: In the first phase, we solve the permutation routing problem in a single-hop environment with a single channel, and secondly we generalize the previous solution to a network with multiple channels. Our solution makes use of the wake and sleep technique to improve the energy conservation of objects. The simulation results show that our protocol outperforms the existing protocols designed to solve the permutation routing problem in terms of energy saving when the volume of data to route is large. NESEPRIN is the better candidate to solve the permutation routing problem in a single-hop multi-channels IoT environment where the volume of data to route is huge.

Designing 5G networks and related applications such as the Internet of Things (IoT), Cellular and autonomous vehicular networks (AVNET) is a challenge. Indeed, these networks are subjected to a number of constraints that play a crucial role in the network’s quality of service (QoS). Among these constraints, we have the management of computing, storage, bandwidth resources and low latency requirements. If in the past cloud computing has been used to avoid networks being affected by some of the previous constraints, that technology negatively affects the low latency requirements required by AVNETs for example. Multi-access Edge Computing (MEC) has recently emerged as a palliative solution to cloud computing. MEC aims to bring computing and storage resources from Cloud Data Center to Edge Data Center nearer to the User Equipment (UEs) in order to reduce the UEs’ requests latency for an improvement of the network’s QoS. Many MEC architectures have been proposed for AVNET. These solutions make use of Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC) or Network Slicing (NS) technologies. Some of these techniques combine partially these technologies while others do not. But to the best of our knowledge, none of them combines all these technologies to obtain a better QoS. In this paper, we combine the SDN, NFV, SFC and NS technologies to efficiently manage the MEC server resources for guaranteeing the QoS requirements in AVNETs. The QoS parameters we consider are latency, computing, storage and bandwidth resources. In that way, we first present a MEC server mathematical resource management model, then, we propose a new MEC architecture adapted to AVNETs that uses the aforementioned technologies also as the mathematical model we first present to manage the bandwidth, computing, and storage resources. The simulation results show that these resources are well managed, resulting to a low end to end latency for autonomous vehicles (AVs) requests executing on edge/cloud servers when the direct communication of AVs with cloud server takes long end to end delays, ensuring a better QoS for the AVNET.

The Wireless Mesh Networks (WMNs) technology has recently emerged as a promising high‐speed wireless technology, which provides the last mile broadband Internet access and delivers integrated wireless communication solutions. Integrating the traditional wireless with new wireless technologies such as cognitive radio (CR) technology creates a platform for high‐speed broadband communication. In a multihop ad hoc cognitive radio network (CRN) environment, the performance of the network is degraded by the routing protocols, which are adapted from the traditional wireless networks. In an endeavor to optimize the performance of the CRNs, existing routing protocols can be adapted and optimized. Secondly, new dynamic routing protocols can be designed to meet the requirements of CRNs. This paper investigates the existing routing protocols in WMNs and proposes a new routing protocol called extended Weighted Cumulative Expected Transmission Time (xWCETT). The xWCETT routing protocol was evaluated through network simulations using the NS 2. Its performance was evaluated with respect to the end‐to‐end average latency, the throughput, jitter, packet delivery ratio, and the normalized routing load. The comparative evaluation results show that the xWCETT achieves superior results in terms of average throughput, latency, and the normalized routing load.