Software-Defined Networking (SDN) has emerged as a key enabler for intelligent vehicular edge computing by providing centralized control and adaptive resource management. However, achieving reliable controller placement under Byzantine Fault Tolerance (BFT) and efficient task offloading in highly dynamic vehicular environments remains a major challenge due to frequent topology changes, communication delays, and control decision overhead. Existing works treat controller placement and task offloading as independent optimization problems and do not account for reassignment frequency, controller decision time, or BFT-compliant controller mapping. To address these limitations, this paper proposes MoTO-BFT , a novel M ulti- o bjective T ask O ffloading and B yzantine F ault- T olerant controller placement framework for SDN-enabled vehicular networks. MoTO-BFT integrates Temporal Convolutional Network (TCN)-based trajectory prediction with a novel Fast-converging Multi-objective Gradient Aggregation algorithm (FaMOGA) to jointly optimize BFT-compliant controller placement and computation task offloading. The proposed framework minimizes communication and computation delay, controller decision time, controller count, and reassignment frequency while ensuring balanced load distribution and 3 f + 1 BFT controller mapping. Extensive simulations using real vehicular mobility traces demonstrate that MoTO-BFT significantly outperforms existing controller placement and offloading baselines in terms of latency reduction, reassignment stability, and BFT robustness, making it a highly efficient and reliable solution for dynamic vehicular SDN environments.

When deploying software defined network (SDN), there is a need for optimized controller placement to ensure high network performance. Different solutions are proposed that focus on single optimization objectives, such as delay minimization or throughput maximization. This article proposes a hybrid artificial intelligence-based approach, the reliability–capacity aware joint controller placement and routing (ReCAP), which combines deep reinforcement learning and graph neural networks to identify the best placement for controllers, so there are optimal paths for communication between switches and controllers and improved network performance. Controller placement is optimized based on multiple objectives, including load balancing, path reliability and link capacity. ReCAP is validated using real network traces and evaluated using the Panopticon network. The experimental results show an improved performance for our ReCAP approach compared to existing solutions in terms of both efficiency and reliability.

Emerging megatrends like the Internet of Things (IoT), and social and mobile communication technologies are imposing new challenges on the future Internet, for which path reliability is crucial. Traditional IP networks are complex to operate and difficult to configure because they are vertically integrated: data and control plane bundled. The emerging Software-Defined Networking (SDN) architecture breaks vertical integration and separates the control logic from the data plane devices, such as switches and routers, providing a programmable infrastructure for application deployment. As a result, SDN is positioned to compute a reliable path for connected users in the event of a link failure scenario. To minimize the impact of a link failure on ongoing data flows, the prediction of a link failure requires careful attention. Machine Learning (ML) algorithms play a vital role in the link failure prediction process; however, they often have a low detection rate. To achieve a higher link failure detection rate in SDN, there is a need to design an enriched detection architecture, especially when employing ensemble ML models. This work presents LRS2, which is a meta-classification approach using base classifiers to compute a highly reliable data flow path. $\mathrm{L}\mathrm{R}\mathrm{S}^{2}$ utilizes a stacked generalization ensemble, where the base classifiers with low complexity and high diversity receive the original data as input, and each classifier predicts its subproblem. The output is a high degree of accuracy of a metaclassifier in predicting a link’s failure. Our experimental study demonstrates that the stacking ensemble has higher accuracy in predicting link failures than other ensembles or single classifiers used in the LRS2.

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose SeAC: an SDN-enabled adaptive clustering technique for SIoV. SeAC uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, SeAC improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, SeAC lowers communication and computation costs. We evaluate SeAC, and the simulation results show that for two different topologies, the adaptive approach using SeAC can produce better results in terms of a stable cluster formation.

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem calledsocial and spatial physical trade-off. This problem is also referred to as themismatchproblem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure(3-D-SIS)model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the(3-D-SIS)model, we use the real traces of social networks(INFOCOM06). The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.

The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a ${K}$ -nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.

Over the past decade, distributed hash table- (DHT-) based routing protocols have been adopted in wireless ad hoc networks (WANETs) to achieve scalability in the route discovery phase by avoiding the flooding mechanism. The security aspects of the routing protocols based on the DHT mechanism are crucial to address and have not been discussed in the existing literature. Therefore, addressing the security issues in DHT-based routing protocols would prevent the service disruption, decrease the traffic overhead, and reduce the packet loss in the network. In this paper, several security issues are identified and elaborated through an example scenario. Moreover, a novel DHT-based routing protocol is proposed that uses a digital signature and the user’s trust in order to swap securely the logical identifiers (LIDs). Trust between nodes is established by the user’s acquaintance and the first visual contact. The proposed protocol vindicates its effectiveness via simulation results in terms of computation time, normalized overhead, percent improvement, and packet delivery ratio.

Software Defined Networking (in short SDN, which is also an acronym for Software Defined Network), has emerged as a response to the limitations and complexities of traditional network architectures. At the heart of SDN lies the idea to consolidate the control over network devices into a logically centralized (software) controller separated from the data plane. The separation of the control plane and the data plane is realized via an open programming interface between the data plane switches and the SDN controller. The decoupling allows the control plane to evolve independently of the data plane, which enables faster innovation since software often exceeds hardware in innovation speed. Furthermore, logical centralization has the potential to simplify network operation and management by providing a single focal point where the consequences of management actions can be assessed, and possibly rejected if they would lead to some violation of operational constraints. OpenFlow, the standard SDN protocol today, is based on a simple match-action paradigm which results in great flexibilities, e.g., in terms of traffic engineering, definition of flows, as well as in-band network control functionalities.

In the last few years, distributed hash table (DHT) has come forth as a useful addition to the design and specification of spontaneous, self-organizing networks. Researchers have exploited its advantages by implementing it at the network layer in order to design scalable routing protocols for mobile ad hoc networks. We identify two correlated issues that must be considered when designing DHT-based routing protocol, namely the mismatch problem and resilience of the logical network, which degrades the efficiency of the DHT-based routing protocols. To address these problems, we propose a DHT-based routing protocol that exploits a 3D logical space that takes into account the physical intra-neighbor relationships of a node and exploits a 3D structure to interpret that relationship. In the proposed scheme, each node runs a distributed algorithm to obtain a consecutive logical identifier that reflects its physical proximity in the 3D logical space. Moreover, the protocol utilizes the 3D-structure to maintain multi-paths to a destination node in order to address the scalability problem and gain resilience against a node/link failure. Simulation results show that the proposed approach outperforms the existing DHT-based routing protocol in terms routing overhead, end-to-end delay, path-stretch values and packet-delivery ratio.

In a traditional unstructured P2P file sharing network, each peer randomly establishes connection with certain number of other peers to ensure the connectivity of the P2P overlay. This random overly leads to redundant traffic and P2P network partition in mobile ad hoc network (MANET). This paper explains the construction of an efficient unstructured P2P overlay over MANET (E-UnP2P) using a proactive underlying routing protocol. Instead of having redundant links among the peers in the P2P network, E-UnP2P introduces a root-peer connecting all peers. Each peer maintains connection with closest peers such that it can reach the root-peer. A peer constructs a minimum-spanning tree consisting of itself, its directly connected neighbor peers and 2-hop away neighbor peers to identify far away peers and builds the overlay closer to the physical network. We can show by simulation that E-UnP2P performs better in comparison with the existing approach (XL-Gnutella).

Peer-to-peer (P2P) network is an alternative of client/server system for sharing resources, e.g. files. P2P network is a robust, distributed and fault tolerant architecture. There are basic two types of P2P networks, structured P2P network and unstructured P2P network. Each of them has its own applications and advantages. Due recent advances in wireless and mobile technology, the P2P network can be deployed over mobile ad hoc network (MANET). We consider the scenarios of P2P network over MANET where all nodes are not the members of P2P network. Due to limited radio range and the mobility of nodes in MANET, there can occur network partition and merging of networks in the physical network. This can also lead to P2P network partition and merging at overlay layer. When two physical networks merge by coming into communication range of each other then their P2P networks would not be connected at overlay layer. Because P2P network operates at application layer as an overlay network. That is their P2P networks are connected in physical network but these P2P networks are disconnected at overlay layer. To detect this situation and merge these P2P networks at overlay layer, we extend the ODACP, an address auto-configuration protocol. Then we propose an approach to efficiently merge P2P networks such that routing traffic is minimized. Considering limited radio range and mobility of nodes, the simulation results shows that CAN over MANET performs better as compared to Chord over MANET in term of routing traffic and false-negative ratio.

Peer-to-peer (P2P) network is a decentralized and self organized architecture for the sharing of resources such as files. P2P network on wired network (Internet) has been extensively studied. The advancement in the wireless and mobile technology has lead to the deployment of the P2P filesharing on mobile ad hoc networks (MANETs). The direct deployment of wired P2P network would result in poor performance for mobile ad hoc networks due to node mobility which causes link failure. In this paper we propose an approach based on route lifetime for P2P network on mobile ad hoc networks. With this approach the file is retrieved on the route of maximum lifetime, the more stable one, instead of the shortest (first acquired path) one, as used in current P2P network. This would reduce the link failure, and thus can decrease routing overhead and end-to-end delay, and increase the file delivery ratio. The route lifetime is determined by the context information, such as velocity and inter-node distance. Simulation results show that our approach in comparison with the ORION performs better in term of file delivery ratio, routing overhead and average end-to-end delay.