Cognitive Routing Research Articles

Cognitive routing in software defined networks using learning models with latency and throughput constraints

<p>To address latency and throughput challenges in software defined networks (SDNs), the research investigates cognitive routing's revolutionary implications. In today's data-driven world, network performance optimisation is crucial. Cognitive routing is a dynamic and potentially disruptive network management technology. Cognitive routing, strengthened by reinforcement learning and adaptive decision-making, is crucial to network efficiency and responsiveness, according to our study. The results show that cognitive routing optimises performance by limiting delay and maximising throughput. SDN application cognitive routing engine (CRE) driving forces, design, and preliminary assessment are described in this article. The CRE finds almost optimal paths for a user's quality of service (QoS) need while minimising monitoring overhead. Instead of global monitoring to find optimal paths, local monitoring achieves this. In ad-hoc networks, finding a trustworthy path reduces latency and ensures network stability. The proposed system was simulated utilising many parameters. Compared to previous SDN-based systems, end-to-end latency and ping round-trip time were better.</p>

Experimental Evaluation of a Cognitive Routing Strategy for Efficient Energy Management in DTN

Experimental Evaluation of a Cognitive Routing Strategy for Efficient Energy Management in DTN

On Design and Implementation of Reinforcement Learning Based Cognitive Routing for Autonomous Networks

Cognitive routing is expected to be a key technology to realize intellectualized operation and administration for autonomous networks. Meanwhile, reinforcement learning (RL) excels especially in decision-making tasks by learning from trial and error, which is promising for solving various cognitive routing problems. Nevertheless, the research of RL-based cognitive routing has been severely constrained by a lack of general problem formulation and feasible network simulation frameworks. In this letter, we propose the first general problem formulation of RL-based cognitive routing. In addition, based on OpenAI Gym and ns-3, we implement the first packet-level network simulation framework, the RL4Net, that facilitates the rapid prototyping of RL-based cognitive routing algorithms. Finally, we conduct extensive experiments with the RL4Net to evaluate the effectiveness of RL-based cognitive routing. Numerical results demonstrate that our proposed RL-based cognitive routing effectively outperforms RL-based non-cognitive routing and the OSPF in terms of minimizing average end-to-end network delay.

Filtering Malicious Messages by Trust-Aware Cognitive Routing in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANET), as an inseparable part of Intelligent Transportation Systems (ITS), enable data communication between vehicles to promote road safety and traffic efficiency. But adversaries can also spread false information across these networks. Therefore, vehicles’ cognitive capabilities with respect to received data must be improved. This requires spatial intelligence and a mechanism to evaluate the trustworthiness of received data. But the dynamic nature of VANETs with intermittent connections, lack of infrastructure and real-time constraints make fulfilling this task very challenging. In this paper, we propose a trust-aware cognitive framework that exploits the redundancies in the exchanged DENM and CAM packets as well as spatial intelligence to filter malicious messages and prevent attackers from disrupting network operation. A novel supplementary mechanism is also presented that applies subjective logic on the pieces of information collected from all network vehicles to detect and isolate malicious entities. To assess the reliability of the proposed scheme, we made extensive comparisons between our model and two others. In the obtained results, our approach outperformed both of them and yielded an accuracy of over 90%, even when 50% of network participants were attackers. The supplementary malicious node detection mechanism of ours similarly yielded high accuracy and F1 scores in the simulations.

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

Simulation model of AI-assisted cognitive routing algorithm for the dynamic optical network in business

Due to the enormous bandwidth permitted in optical channels, optical networks are the foundation for high information rate/throughput systems. Furthermore, the communication fiber solved the latency issue because data is transformed into comparable light and transferred at light speed to the endpoints. Data flows at incredible speeds in the spectral fiber, up to tens of gigabits. The wavelength division multiplexer (WDM) technique controls data transmission in the spectral fiber by allocating every wavelength with its appropriate capacity at the optics fiber's intake. An artificial intelligence-based cognitive routing algorithm (AI-CRA) is designed in this article. A global topology adaptable path dependent on link cost upgrade is presented to improve the contribution of multi-layer and multi-domain data transmission routing, equalize system load during peak periods, and maximize resource efficiency. The global optimum primary and backup routing is achieved by using the routing needs and existing network resources as input data and resolving the system's multi-layer multi-sector optical line forwarding design.

Spectrum Handoff Aware AODV Routing Protocol for Cognitive Radio Vehicular Ad Hoc Networks

End-to-end application performance and throughput of vehicular cognitive transport control protocol depend on how efficiently the segments (TCP header) are being transmitted from source to destination. One way to enhance the performance of vehicular cognitive TCP protocol is by reducing the packet drops in between the source and destination. In general, packet drops occur in between the source and destination of Cognitive Radio Vehicular Ad Hoc Network (CR-VANET) because of spectrum handoff or cognitive node handoff, or network congestion. In this paper, we focus on enhancing the performance of cognitive TCP protocol through “cognitive AODV routing protocol with the spectrum handoff mechanism.” In the proposed work, channel-route control messages of cognitive AODV routing protocol are updated with the support of spectrum handoff which helps to provide the backup opportunistic channel during PU active and helps to reduce the end-to-end spectrum handoff packet drops. Simulation results reveal that the overall performance of the vehicular cognitive TCP protocol with the proposed spectrum handoff aware cognitive AODV routing protocol is enhanced as compared to the existing cognitive TCP protocol.

Intelligent Software-Defined Network for Cognitive Routing Optimization using Deep Extreme Learning Machine Approach

In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor analysis, and dynamic updating of forwarding rules) make it simpler to apply machine learning strategies. Various perspectives of fiber-optic communications including fiber nonlinearity coverage, optical performance checking, cognitive shortcoming detection/anticipation, and arranging and improvement of software-defined networks are examined in Machine Learning (ML) applications. This research paper has presented an imaginative framework concept called Intelligent Software Defined Network (ISDN) for Cognitive Routing Optimization (CRO) using Deep Extreme Learning Machine (DELM) approach (ISDN-CRO-DELM) in light of the new challenges in the development and operation of communication systems, and capturing motivation from how living creatures deal with difficulty and usability. The proposed methodology develops around the planned applications of progressive DELM methods and, specifically, probabilistic generative models for framework wide learning, demonstrating, improvement, and information description. Furthermore, ISDN-CRO-DELM, suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level. MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.

Adaptive DTN Routing: A Neuromorphic Networking Perspective

Routing is one of the main drivers of the end-to-end performance of bundle transmissions over a disruption tolerant network given the potentially large impact of the temporary but long-term partitioning that can occur at different sections of the network. A neuromorphic networking approach that defines an adaptive bundle routing for disruption-tolerant networks (DTN) is proposed where spiking neuronal networks (SNN) are used to determine the routing decisions of autonomous agents. The event-driven information encoding of spiking neurons involves very low energy consumption, which makes this approach attractive for challenging DTN applications with limited access to energy sources. The SNNs are continually updated within an autonomic loop, which produces synapse strength updates that are proportional to the expected communication costs of the routing decisions. A reward shaping procedure and a delay-tolerant mechanism for finding the local link-state is proposed, which allows determining instantaneous learning rewards for the agents. The method was tested on an emulated space communications network with scheduled disruptions. The results show that the proposed cognitive routing approach offers improved bundle delivery performance under network congestion compared to the standard Contact Graph Routing.

SC-RPL: A Social Cognitive Routing for Communications in Industrial Internet of Things

Industrial Internet of Things (IIoT) is regarded as the basis of the future industrial system. The interaction between nodes in IIoT shows strong regularity or sociality, which has the potential to improve the efficiency of IIoT. This article focuses on the investigation of the relationship of social activity and data transmission, and proposes a novel routing protocol to enhance QoS of social and cognitive IIoT networks. In accordance with practical requirements of an application, the routing protocol owns two purposes: one is to meet the requirement of delay-sensitive information transmission, and the other focuses on the load balance of the social and cognitive IIoT network. As a special feature of social and cognitive IIoT, the mechanism for protection of social activities is proposed while data transmission is adopted in the proposed protocol. System-level evaluation shows that the proposed protocol can achieve better performances compared with existing routing protocols for social and cognitive IIoT networks.

Cognitive Routing in Software-Defined Maritime Networks

Due to the constantly changing sea surface, there is a high risk of link fragility caused by sea waves when different marine users are intended to establish stable links for communication. To ensure stability with less delay, finding a stable route is one of the crucial aspects of maritime networks. In order to achieve this aim, we propose a routing protocol for cognitive maritime networks based on software-defined networking (SDN). This SDN-based cognitive routing protocol provides stable routes among different marine users. To provide the global view of the whole network, a main controller is placed close to the seashore, whereas the localized views are provided by the cluster heads. Autonomous surface vehicles are used as gateways under sparse network conditions to collect and transport data among clusters, and to and from the main controller. This is an SDN-based ship-to-ship communication scheme where two ships can only establish a link when they not only have consensus about a common idle channel but are also within the communication range of each other. We perform extensive simulations to test the proposed scheme with different parameters and find better performance in comparison with both SDN-based and non-SDN-based schemes in terms of end-to-end delay, packet delivery ratio, and routing overhead ratio.

VLA-CR: A Variable Action-set Learning Automata-based Cognitive Routing Protocol for IoT

Internet of Things (IoT) is a heterogeneous, mixed, and uncertain ubiquitous network, which has significantly affected the concept of wireless networking. A large number of wireless devices have connected through the IoT and shared large amounts of data. So, efficient routing and forwarding data packets from the wireless devices toward gateways, which have connected to the Internet, is one of the most important issues in IoT. This paper has focused on routing and forwarding data packets in IoT. Firstly, a learning automata-based cognitive framework has been applied to integrate cognition into IoT; because current IoT lacks intelligence and cannot satisfy the increasing application performance requirements, and also adding cognition into IoT equips it with a brain and high level intelligence. Then, a new routing and forwarding protocol, which benefits from cross-layer optimization between routing and Media Access Control (MAC) layer protocols, has been proposed. In the proposed protocol a network of variable action-set learning automata establishes a route between source nodes and a corresponding gateway, by making a directed acyclic graph toward the gateway. Then, using a set of learning automata, MAC layer protocol parameters are configured to properly forward data packets hop by hop. The proposed protocol has been named VLA-CR (Variable Action-set Learning Automata-based Cognitive Routing Protocol). Based on Martingale theorem, the convergence of VLA-CR has been proved. Finally, extensive simulation experiments have been conducted to show the performance of the proposed protocol. Simulation results show the superiority of VLA-CR over several existing routing protocol in terms of end-to-end reliability, end-to-end delay, power consumption, and routing time.

Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

SummaryOpportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.

Link optimization of the new generation instant messaging network based on artificial intelligence technology

The current network environment is dynamic, open and extensible. In order to better ensure the needs of users, higher requirements are placed on link resource allocation. Based on the research and analysis of the instant communication protocol, this paper studies an intelligent routing evolution algorithm and related fault recovery strategy for the instant communication network. Research on instant messaging intelligent algorithms for routing evolution is mainly based on routing algorithms and artificial intelligence intelligent algorithms. When a link failure occurs in the communication network, the routing algorithm performs route reconstruction and optimization on the entire instant communication network. Considering that there may be evolutionary needs of large-scale routing networks in practical applications, this paper introduces artificial intelligence intelligent algorithms to optimize intelligent algorithms to improve efficiency. A cognitive routing protocol based on MIMO (Multiple Input Multiple Output) technology is proposed. By using MIMO technology, a lot of gain is brought to the communication link under multiple antennas. These gains correspond to different link types. The protocol realizes cognition through intelligent routing evolution algorithm and predicts the state of the network. Setting the routing life and hello period according to the perceived network status can optimize the performance of the network.

SDQIM: Software-Defined Quadcopter Interdomain Management Protocol With Decentralized Inductive Power Transfer (DIPT) Platform

Due to the flexible deployment, low-cost and management, unmanned aerial vehicles, quadcopters have attracted significant interest lately in expanding wireless communications to areas that lack to cellular coverage network. Currently, software-defined network (SDN) is considered a viable alternative that improves network scalability and management. Earlier in 2018, we designed a novel software-defined quadcopter (SDQ) with a mobile Internet of Things vehicle to measure the best efficient wind power location for effective future wind turbine placement. However, large wind farms require complex and scalable wind measurement platform that consists of multiple quadcopters and ground mobile nodes that operate under a decision-making softwarized protocol. To this end, this paper proposes a novel cognitive routing protocol called SDQ interdomain management (SDQIM) Protocol. The SDQIM structure consists of multiple SDN controllers with failover capability. Our proposed protocol operate with multiple phases as each phase is used to cover a specific scenario that could occur in the interdomain network. The SDQ-main will have a total overview of the network topology and the SDQ-slave will be responsible for communication management with all other local subdomains. However, for large-scale farmland, quadcopter power level is essential for the continuity of the system services. Thus, we additionally propose a decentralized inductive power transfer (DIPT) platform that operates autonomously using software defined controlling approach. In essence, The DIPT platform is considered to be automated and low-cost approach that encompasses a custom-designed SDN controller for all interdomain quadcopters. Our system has been implemented by using an experimental testbed. The experimental results showed that scalability of the SDQ system can be increased to cover large wind farms with uninterrupted power management platform.

Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks.

The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations.

SDCoR: Software Defined Cognitive Routing for Internet of Vehicles

The Internet of Vehicles (IoV) is a subapplication of the Internet of Things in the automotive field. Large amounts of sensor data require to be transferred in real-time. Most of the routing protocols are specifically targeted to specific situations in IoV. But communication environment of IoV usually changes in the space-time dimension. Unfortunately, the traditional vehicular networks cannot select the optimal routing policy when facing the dynamic environment, due to the lack of abilities of sensing the environment and learning the best strategy. Sensing and learning constitute two key steps of the cognition procedure. Thus, in this paper, we present a software defined cognitive network for IoV (SDCIV), in which reinforcement learning and software defined network technology are considered for IoV to achieve cognitive capability. To the best of our knowledge, this paper is the first one that can give the optimal routing policy adaptively through sensing and learning from the environment of IoV. We perform experiments on a real vehicular dataset to validate the effectiveness and feasibility of the proposed algorithm. Results show that our algorithm achieves better performance than several typical protocols in IoV. We also show the feasibility and effectiveness of our proposed SDCIV.

Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths

In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/ interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength light- path. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ~95% when using 1200 data points.

MO detector (MOD): a dual-function optical modulator-detector for on-chip communication.

While augmenting network on chips (NoC) with photonic links enables high-bandwidth communication, the overhead for photonics is rather large, mainly driven by bulky footprints and the multi-functionality of transceivers. The latter requires, in addition to a photon source, signal modulation and detection. If the NoC were photonically augmented at every network point to enable all-to-all connectivity, the resulting photonic overhead would be excessive. Besides, the high bandwidth of a single optical bus may be sufficient to supply the data-sharing demand of a network. Spatial signal routing is a necessary function of data communication in NoCs. However, if photonic links are used to augment electronics, an energy-costly optical-electrical-optical (OEO) conversion is required since routing is currently executed in the electronic domain. Here we show a novel integrated broadband hybrid photonic-plasmonic device termed an MO detector featuring dual light modulation and detection. With 10 dB extinction ratio and 0.8 dB insertion loss at the modulation state and 0.7 A/W responsivity at the detection state based on the finite-different time-domain simulation, this transceiver-like device (i) eliminates the OEO conversion, (ii) reduces optical losses from photodetectors via bypassing the photodetector when not needed, and (iii) enables cognitive routing strategies for network-on-chips. As such, the MO detector acts as a micrometer-compact transceiver for next-generation NoCs.