Network Control Architecture Research Articles

Self-Sensing Motion Control of Dielectric Elastomer Actuator Based on NARX Neural Network and Iterative Learning Control Architecture

Self-Sensing Motion Control of Dielectric Elastomer Actuator Based on NARX Neural Network and Iterative Learning Control Architecture

Quantum drones and the future of military warfare

The advent of drones has significantly impacted military warfare, providing improved reconnaissance, surveillance, and target acquisition (RSTA), cost savings, increased convenience, safety, and flexibility. A layered network control architecture, known as the internet of drones (IoD), coordinates drone access to controlled airspace and offers navigation services. Various systems, including wireless sensor networks (WSN) and drones directed to an expanded controlling zone, integrate with IoD to improve connection performance. This paper provides an overview of the IoD and the internet of quantum drones (IoQD), highlighting key issues and potential solutions in applications and deployment. The IoQD provides primary features such as secure message exchange, fast communication processes, the viability of creating and deploying private IoQD, and enabling a new field of application, quantum well (QW). In conclusion, the advent of drone technology has significantly improved various aspects of military operations, including reconnaissance, surveillance, and target acquisition. The IoQD offers a promising solution for military networks, facilitating the safe and expedited transfer of data, ultimately benefiting the entire military network.

Multi-core shared tree based MP2MP RWA algorithms in large scale and multi-domain optical networks

The increasing demands of bandwidth-intensive parallel computing and collaborative applications, efficient service provisioning to support multipoint to multipoint (MP2MP) communications have attracted increasing attention. However, with the development of larger-scale and multi-domain optical networks, MP2MP RWA are introduced optimal domain sequence selection and core nodes belong to which domains problems which cannot be tackled by the conventional algorithms proposed aim at a signal domain. In this paper, we proposed a multi-core node shared multicast tree heuristic algorithm (MCSMT) which calculation could be parallelized, delay and minimal cost constrained for multi-domain optical networks. It could realize the accurate calculation of minimum number of cores and the hosted domains of the cores. The source and destination nodes were added in different shared trees respectively with delay constrained algorithms according to specific QoS selection strategy. At the same time, according to more complicated MP2MP algorithms we propose a PCE-cloud based control architecture for optical networks by applying the cloud computing technology (e.g. virtualization and parallel computing) to reform the control plane for improving system reliability, intelligence and maximizing resource utilization. The performances of the proposed heuristic with regard to the number of multi-core nodes, wavelength channel occupied and MP2MP setup latency are compared. In addition, the performance about path computing latency for PCE-cloud control architecture is compared with conversional control architecture.

Integrated Network Control Architecture for Terrestrial and Non-Terrestrial Network Convergence

Integrated Network Control Architecture for Terrestrial and Non-Terrestrial Network Convergence

Research on hierarchical and sub‐area network control technology of LEO giant constellation

AbstractThe low‐earth‐orbit (LEO) telecommunication constellation network is foreseen as a merging interconnection method for future sixth‐generation (6G) systems. However, how to implement constellation system composition, consider network design constraints and envision application scenarios in a low‐orbit giant constellation system, is still an open problem. In this paper, by exploring the constellation characteristics in the future LEO systems, the design of hierarchical and sub‐area network control architecture for giant constellation is first suggested. After that, a detailed research on the partition mechanism of control domain for the giant LEO constellation systems is implemented. To make the architecture design and partition mechanism feasible, the hierarchical and domain controlling flow is also discussed. Finally, a performance verification method is present with elaborate numerical results and evaluations. The results verified the correctness and effectiveness of the proposed network control architecture and partition mechanism.

Energy efficient NOMA based beamforming and power control architecture for Future communication networks

In the current and future wireless technologies beamforming techniques are one of the key features to fit in with the higher throughput demands of users for various data exhaustive applications. Simulation of energy efficient 5G network with enabled NOMA (non orthogonal multiple access) and beamforming capabilities has been done in this paper. A partially connected hybrid beamforming architecture for multiantenna NOMA based transmitter has been proposed. A test bed of fully connected phase shifters and adjustable attenuators and multiple antennas arrays with beamforming capabilities outperform the various other sub connected models. Moreover this kind of architecture makes use of hybrid beamforming to fulfill higher capacity and low power consumption requirements of network. Simulations results are presented to demonstrate the performance of proposed architecture. Hence NOMA based trans-receivers along with hybrid beamforming technique has the potential to significantly improve the performance of the network in terms of energy efficiency.

Enhanced Control Designs to Abate Frequency Oscillations in Compensated Power System

The interconnection of transmission, distribution, and generation lines has established a structure for the power system that is intricate. Uncertainties in the active power flow are caused by changes in load and a growing dependence on renewable energy sources. The study presented in this paper employs several controlling strategies to reduce frequency variations in series-compensated two-area power systems. Future power systems will require the incorporation of flexible AC transmission system (FACTS) devices, since the necessity for compensation in the power system is unavoidable. Therefore, a static synchronous series compensator (SSSC) is installed in both areas of our study to make it realistic and futuristic. This makes it easier to comprehend how series compensation works in a load–frequency model. With the integration of electrical vehicles (EVs) and solar photovoltaic (PV) systems, several control strategies are presented to reduce the frequency oscillations in this power system. Particle swarm optimization (PSO) is used to obtain the best PI control. To improve results, this work also covers the design of fuzzy logic control. In addition, the adoption of neural network control architecture is proposed for even better outcomes. The outcomes clearly show how well the proposed control techniques succeeded.

Optimization Simulation of Balanced Distribution of Multimedia Network Modular Teaching Resources

The balanced distribution of multimedia wireless network modular teaching resources is improved by optimizing and adjusting the balanced distribution of multimedia wireless network modular teaching resources. Based on block grid balanced scheduling and resource spectrum fusion, a balanced distribution method of multimedia wireless network modular teaching resources is proposed. A blockchain Internet of Things accesses control network architecture system for the balanced allocation of multimedia wireless network modular teaching resources established using the priority business scheduling method. Modular resource allocation of multimedia wireless networks and adaptive forwarding are adjusted to control the data packets. The bandwidth guarantee mechanism is used for the equitable allocation of multimedia wireless network modular teaching resources information flow. Multi-service hierarchical management of multimedia wireless network modular teaching resources spectrum is controlled by block grid balanced scheduling and resource spectrum fusion. The high-energy physical computing system structure is used to allocate the resource spectrum balance of the modular teaching resources of the multimedia wireless networks, ensuring the bandwidth requirements of various services. The classified management and balanced allocation of the multimedia wireless networks’ teaching storage, system, and teaching spectrum resources are made possible. The results of the tests show that this method effectively reduces the delay in the allocation of modular multimedia wireless network teaching resources, improves the utilization rate of modular multimedia wireless network teaching resources, and significantly improves their reading and writing performance.

Coalitional games for networked controllers with constraints on semivalues: A randomized design approach

In a networked control architecture, the semivalues of a coalitional game where the communication links are the players can be used to provide information regarding their relevance. The linear relationship between the characteristic function of the game and the semivalues can be exploited to impose constraints on the design of the corresponding networked controllers to promote or penalize the use of certain links considering their impact on the overall system performance. In previous works, this approach was restricted to small networks due to the combinatorial growth of the problem size with the number of links. This work proposes a method to mitigate this issue by performing a random sampling in the set of topologies, i.e., coalitions of links, and employing a mild bound to reflect the impact of nonsampled topologies in the calculations. The simulation results show that the proposed approach can lead to significant reductions in computation time with moderate loss of performance.

An Optimal SDN-Based Wavelength Allocation and Routing Method for 5G Network

Optical networks are changing as new advanced technologies emerge. With each passing year, their sizes and capabilities expand. The standard architecture for network control and management cannot handle all of these complexities. The proliferation of cloud services and the massive volume of traffic provided by content delivery networks are driving the present fast increase in Internet traffic. This obviously exacerbates congestion concerns in communication networks, with a focus on the core and backbone components in particular. Software-defined networking (SDN) is evolving into a consolidated network management system that comprises a variety of strategies aiming at network management that are based primarily on one basic principle: decoupling control plane decisions from data plane activities. An essential resource allocation strategy in an all-optical network is routing and wavelength assignment. A novel SDN-based approach is proposed to address the problem of old methods mixed with new architecture in optical networks. The network resources were optimized for optimal scheduling using a binary hybrid topology particle swarm optimization method. In terms of recovery time, blockage rate, and resource consumption, simulation results demonstrate that the suggested technique outperforms previous classical methods.

R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm.

Neuromorphic computing is a recent class of brain-inspired high-performance computer platforms and algorithms involving biologically-inspired models adopting hardware implementation in integrated circuits. The neuromorphic computing applications have provoked the rise of highly connected neurons and synapses in analog circuit systems that can be used to solve today's challenging machine learning problems. In conjunction with biologically plausible learning rules, such as the Hebbian learning and memristive devices, biologically-inspired spiking neural networks are considered the next-generation neuromorphic hardware construction blocks that will enable the deployment of new analog in situ learning capable and energetic efficient brain-like devices. These features are envisioned for modern mobile robotic implementations, currently challenging to overcome the pervasive von Neumann computer architecture. This study proposes a new neural architecture using the spike-time-dependent plasticity learning method and step-forward encoding algorithm for a self tuning neural control of motion in a joint robotic arm subjected to dynamic modifications. Simulations were conducted to demonstrate the proposed neural architecture's feasibility as the network successfully compensates for changing dynamics at each simulation run.

A Single-Stage Isolated AC–DC Converter Based on the Impedance Control Network Architecture

This article introduces a new single-stage universal-input isolated ac-dc converter based on the impedance control network (ICN) architecture. This resonant converter performs both power factor correction and output voltage regulation while operating at a constant switching frequency and duty ratio, by modulating the phase-shift between its two half-bridge inverters and the phase-shift between the two legs of its full-bridge rectifier. This phase-shift control strategy along with the ICN architecture also enables the converter to achieve high efficiency by ensuring zero-voltage-switching and near-zero-current-switching of all its inverter transistors throughout the line cycle and across wide ranges of input line voltage and output power. Power density is enhanced by realizing the three ICN inductors using a single magnetic structure with two coupled windings. This coupled inductor and a transformer constitute the two magnetic components of the proposed converter. A systematic methodology to design this ICN ac-dc converter is also introduced. A universal-input (90-265 V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> ), 20-V output, 330-W, 400-kHz ICN ac-dc converter prototype is built and tested. This prototype achieves a power density of 37.9 W/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and peak efficiencies of 91.7%, 92.2%, 93.2%, and 93.4% at 90, 120, 230, and 265 V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> input, respectively.

Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence

For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelligence. We analyze operators’ network requirements (e.g., the carrying of the 5th generation mobile network (5G) service, multi-protocol label switching virtual private networks optimization, cloudification of services and the IP backbone network). Then, we propose an intelligent network control architecture based on SDN and artificial intelligence. The proposed architecture consists of three modules, including a network status collection/perception module, an AI intelligent analysis module and an SDN controller module. Moreover, this paper also analyzes the objects of traffic optimization as well as routing calculation algorithms (e.g., the greedy algorithm, the top-k-shortest paths (KSP) algorithm) and routing optimization algorithms (e.g., particle swarm optimization, simulated annealing and genetic algorithms). In addition, we also put forward three optimization algorithms for the operator’s network, namely, network congestion control and prevention algorithms, resource preemption algorithms and balance of the entire network traffic algorithms. Then, we propose optimization algorithms for the above three objectives of operator network optimization, respectively. Finally, we conduct large-scale experiments to verify the effectiveness of the control mechanism and algorithms. The experimental results demonstrate that the use of SDN and artificial intelligence in operator networks can realize network intelligent control and traffic optimization more intelligently.

In-Network Intelligence Control: Toward a Self-Driving Networking Architecture

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore exhibiting tardiness and clumsiness in responding to network dynamics. Recently, with the advancement of programmable network hardware, it is possible to implement network functions inside the network. However, current in-network schemes are largely dependent on the manual process, which presents poor scalability and robustness. Therefore, in this article, we present a new intelligent network control architecture, in-network intelligence control. We design intelligent in-network devices that can automatically adapt to network dynamics by leveraging powerful machine learning adaptive abilities. In addition, to enhance the collaboration among distributed in-network devices, a centralized management plane is introduced to ease the training process of distributed switches. To demonstrate the technical feasibility and performance advantage of our architecture, we present three use cases: in-network load balance, in-network congestion control, and in-network DDoS detection.

Securing Collaborative Environment Monitoring in Smart Cities Using Blockchain Enabled Software-Defined Internet of Drones

The Internet of Drones (IoD) is a layered network control architecture, which is having a revolutionary impact on the monitoring and preservationvof the environment. Large-scale drone-assisted environmental monitoring can provide a better perspective and high-quality data by monitoring the operation of critical components of smart cities. However, with the continuous expansion of IoD scale and the increase of multi-drone collaboration tasks, the large-scale drone-assisted service in smart cities monitoring will inevitably encounter the problem of relay and transfer of drone control. Lack of a trust collaboration paradigm between drone controllers will bring huge security challenges to real-time monitoring the environment, collaboration of tasks, data, location privacy of drones, and so on. To address this important issue in IoD, this article proposes a paradigm that uses smart contracts and blockchain to ensure trusted collaboration between controllers of software defined IoD (SD-IoD). First, we propose a novel SD-IoD architecture to enhance the support for heterogeneity and flexibility of IoD for monitoring of the environment. Second, we propose a controller consortium blockchain for secure and efficient cooperation and interoperability of drone controllers, which includes a new cryptographic currency cooperation coin and a new consensus mechanism proof of security guarantee (PoSG). Third, we have designed a novel incentive mechanism to encourage controllers to maintain their security and provide safer services to other controllers. The security analysis and performance simulation results indicate the effectiveness of the proposed mechanism.

A safety preserving control architecture for cyber‐physical systems

AbstractIn this article, we propose a networked control architecture to ensure the plant's safety in the presence of cyber‐attacks on the communication channels. In particular, we consider systems subject to both state and input constraints that must be preserved for safety reasons despite any admissible attack scenario. To this end, first, two different detectors are proposed to detect attacks on the setpoint signal as well as on the control inputs and sensor measurements. Then, an emergency controller (EC), local to the plant, is designed to replace the networked controller whenever an attack is detected. Finally, the concept of robust N‐step attack‐safe region is introduced to ensure that the EC is activated, regardless of the detector performance, at least one‐step before the safety constraints are violated. It is formally proved that the plant trajectory is uniformly ultimately bounded in an admissible region regardless of the attacker's actions and duration. Finally, by considering a continuous‐stirred tank reactor system, numerical simulations are presented to show the proposed solution's capabilities.

Design and Analysis of Lightweight Authentication Protocol for Securing IoD

The Internet-of-drones (IoD) environment is a layered network control architecture designed to maintain, coordinate, access, and control drones (or Unmanned Aerial vehicles UAVs) and facilitate drones’ navigation services. The main entities in IoD are drones, ground station, and external user. Before operationalizing a drone in IoD, a control infrastructure is mandatory for securing its open network channel (Flying Ad Hoc Networks FANETs). An attacker can easily capture data from the available network channel and use it for their own purpose. Its protection is challenging, as it guarantees message integrity, non-repudiation, authenticity, and authorization amongst all the participants. Incredibly, without a robust authentication protocol, the task is sensitive and challenging one to solve. This research focus on the security of the communication path between drone and ground station and solving the noted vulnerabilities like stolen-verifier, privileged-insider attacks, and outdated-data-transmission/design flaws often reported in the current authentication protocols for IoD. We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. Its security has been verified formally using Random Oracle Model (ROM), ProVerif2.02 and informally using assumptions and pragmatic illustration. The performance evaluation proved that the proposed protocol is lightweight compared to prior protocols and recommended for implementation in the real-world IoD environment.

Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems.

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

Extendable NFV-Integrated Control Method Using Reinforcement Learning

Network functions virtualization (NFV) enables telecommunications service providers to realize various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services, an NFV control method should optimally allocate such VNFs into physical networks and servers by taking account of the combination(s) of objective functions and constraints for each metric defined for each VNF type, e.g., VNF placements and routes between the VNFs. The NFV control method should also be extendable for adding new metrics or changing the combination of metrics. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this approach is not extendable because the problem needs to be reformulated every time a new metric is added or a combination of metrics is changed. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed that can optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.

IP Traffic Modelling in Scalable Drone Network for Carrying Scalable Logistic Operations

This study presents a self modelling method for control system of multi-coupled non-linear scalable drones based logistic services with the help of second-order dynamics of communicating membrane system by realizing consensus. This usually requires a layered multiple network control architecture designed mainly for coordinating access of various unmanned aerial vehicles to control airspace, package delivery, rescue, traffic surveillance and more. Here, the communicating membrane system is modelled based on the theory of fractional differential equations. At every interval of the sampling time, all the subsystems are optimally synchronized in P population system by multiset-rewriting rules including the effect of symport/antiport systems. Model of the minimal symport or antiport is also derived to ensure the universality for P systems with multiset-rewriting rules. Further research in this domain will enable the development of evolving architecture of a non-linear cyber physical system. The close loop stability and the recursive feasibility of the evolved architecture are also studied. Comparison are also drawn predictive control to prove the effectiveness of the proposed system.