Network Bandwidth Resources Research Articles

Event‐triggered resilient joint mobile robot localization and sensor fault estimation

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Adaptive event-triggering mechanism based Takagi-Sugeno fuzzy automatic generation controller design for offshore wind power system

Interconnected offshore wind power system (OWPS) attracts widespread attention due to its special advantage. With the development of wind power technology, higher requirements are put forward for the reliability of networked OWPS. Considering the nonlinear characteristics of OWPS and limited network bandwidth resource, an adaptive multi-event triggering mechanism based distributed Takagi-Sugeno fuzzy robust H∞ control scheme is investigated in this paper. A modeling method for OWPS integrating communication constraints is proposed. By Lyapunov stability theory, the sufficient condition for the asymptotical stability of OWPS has been derived. Compared with traditional control scheme, the proposed Takagi-Sugeno fuzzy controller provides smoother output power, smaller torque oscillation and higher wind energy utilization. Furthermore, the co-design strategy increases the utilization rate of network bandwidth. The feasibility of adaptive multi-event triggering mechanism based Takagi-Sugeno fuzzy controller is demonstrated through a simulation example.

TABDeep: A two-level action branch architecture-based deep reinforcement learning for distributed sub-tree scheduling of online multicast sessions in EON

The surge in demand for multimedia applications has brought a great pressure on network bandwidth resources. In order to improve the efficiency of bandwidth resource usage and ensure the transmission quality of multicast sessions, it is very urgent to effectively schedule online multicast sessions. To do so, in this paper, we study the distributed sub-tree scheduling problem of online multicast sessions in elastic optical network (EON). First, we model the problem as a Markov Decision Process (MDP) by defining state, action and reward. Second, a two-level action branch architecture-based deep reinforcement learning (TABDeep) algorithm is proposed to realize the destination node grouping, source node selection, route establishment and spectrum assignment of multicast sessions. Finally, to test the superiority of TABDeep, we construct a series of simulation experiments to compare TABDeep with a link-aware distributed steiner sub-tree benchmark algorithm (LA-DSST). The experimental results confirm that TABDeep performs better than LA-DSST in reducing the blocking probability of the session.

Dynamic event-triggered resilient controller design for discrete-time singular Markov jump CPSs under hybrid cyber-attacks

In this article, the hybrid cyber-attacks for singular Markov jump systems (MJSs) are mainly solved by designing the asynchronous mixed H∞ and passive controller. A kind of dynamic event-triggered (ETM) based strategy is adopted to maximize the savings of network bandwidth resources. And the hybrid cyber-attacks including two common attacks, the spoofing attack and the denial of service (DoS) attack are considered, which are assumed to be mutually independent and meet Bernoulli distribution. The singular MJSs under cyber-attacks are converted into the singular Markov jump time-delay systems. Then, the criteria of stochastically stable for the closed-loop singular MJSs meeting the mixed H∞ and passive performance are obtained by linear matrix inequalities (LMIs). And by optimizing the obtained LMIs, the gains of asynchronous resilient controller are further derived, and the weighting matrices of dynamic event-triggered based strategy are also gained. Eventually, the feasibility of the offered way is verified by an industrial example of DC motor control system.

A Routing Algorithm Based on Software-Defined Computing First Network

With the continuous development of mobile Internet technology, new services and applications appear, such as AR/VR, Internet of Vehicles, and Ultra HD Video. They not only require higher network bandwidth resources but also need more computing resources to ensure normal operation and user experience. As a new network technology, computing first network promotes the deep integration of distributed computing resources and networks to make efficient use of these resources. This paper proposes a computing power routing algorithm based on software-defined computing first network (SD-CFN) architecture. It comprehensively considers computing resources and available bandwidth resources of network links. Experimental results show that compared with the nearby scheduling strategy, the proposed computing power routing algorithm can better allocate computing resources to services. Moreover, it can improve the utilization rate of computing resources and provide optimal bandwidth resources in the process of service transmission.

Switched Observer-Based Adaptive Event-Triggered Load Frequency Control for Networked Power Systems Under Aperiodic DoS Attacks

This paper is concerned with a resilient load frequency control (LFC) problem for networked power systems against the aperiodic denial-of-service (DoS) attack. First, considering the intermittent nature of the aperiodic DoS attack and the unavailability of the partial system states simultaneously, a switched observer-based time-delay networked power model is established. Second, to save network bandwidth resources, a novel adaptive event-triggering mechanism is designed by applying a switch-adjusting threshold parameter scheme. Then, a switched observer-based PI controller is designed and a piecewise Lyapunov functional analysis approach is developed such that the exponential stability of closed-loop augmented power systems is derived with desired H∞ performance in forms of linear matrix inequality (LMI). Finally, a power system example with IEEE 39-bus is provided to illustrate the effectiveness of the designed schemes.

Finite-Horizon $H_{\infty }$ Consensus Control of Discrete Time-Varying Multiagent Systems: A Dynamic Event-Based Learning Approach

This paper addresses the finite-horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> consensus problem for general discrete time-varying multi-agent systems with energy-bounded external disturbances and limited network bandwidth resource. A dynamic event-based learning approach is proposed to produce a dynamic discrete-time event-triggered scheme and a dynamic distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> consensus protocol. Based on the local state information, the dynamic distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> consensus protocol is developed to guarantee the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> consensus of the general discrete time-varying multi-agent systems with external disturbances. Besides, a novel criterion is established by employing the Lyapunov theory and considering the special dynamic triggering constraint, and the corresponding recursive learning algorithm is developed. In contrast with the previous works, the proposed dynamic event-based approach results in less network bandwidth utilization, and the novel recursive algorithm brings less conservatism of the designed controller. Finally, the effectiveness of the corresponding algorithm and the advantages of the proposed dynamic event-based approach are testified by a numerical simulation.

Switching ETM-based neural adaptive output feedback control for nonaffine stochastic MIMO nonlinear systems subject to deferred constraint

This article focuses on the neural adaptive output feedback control study related to nonaffine stochastic multiple-input, multiple-output nonlinear plants. First, a K-filter state observer based on a radial basis function neural network is designed to estimate the remaining unavailable states. Then, a novel adaptive command-filtered backstepping output feedback control framework is established, where an improved command filter with a fractional-order parameter is applied to conquer the calculation size problem. Specifically, the highlight of this work is that it designs a modified error compensation signal and incorporates the concept of deferred constraint to eradicate the negative effect caused by the filter errors. In addition, the network bandwidth resources, control impulse, and control accuracy are synthesized using an amended switching event-triggered mechanism. The theoretical analysis proved that the proposed control approach guarantees that the tracking error can converge to a preassigned region within a user-defined time while the violation of the deferred output constraint can be excluded. Two illustrative studies are provided to demonstrate the validity and superiority of the developed control method.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

Event-based adaptive tracking control for robotic systems with deferred position constraints and unknown backlash-like hysteresis

This paper proposes an event-based adaptive tracking control scheme for the n-link robotic systems in the presence of unknown backlash-like hysteresis (BLH) and deferred position constraints. By combining a transformation error with an asymmetric Lyapunov function, the devised control tactic achieves that the position constraints of robotic systems are not violated after user pre-specified time. In contrast to the results of robotic systems with position constraints, this paper removes a common assumption condition generated by the conventional barrier Lyapunov function method. Then, the adverse effect of unknown BLH can be offset by the Nussbaum function. Meanwhile, an event-triggered mechanism is designed to economize on the network bandwidth resources. Finally, based on the Lyapunov theory, an event-based adaptive tracking control tactic is proposed to ensure that all the signals of robotic systems are bounded under unknown BLH and deferred position constraints. Some simulation results proof that the devised control scheme is valid.

Stable Service Caching in MECs of Hierarchical Service Markets With Uncertain Request Rates

A 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing the computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network of a hierarchical service market. We first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with an approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed game to measure its efficiency loss due to selfishness of players. When the request rates of services are not given in advance, we study the service caching problem with request rate uncertainty, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

A Cloud Computing Based Deep Compression Framework for UHD Video Delivery

Ultra-high-definition (UHD) videos has increased popularity. However, the data size of UHD videos is 4-16 times larger of HD videos. This brings many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this paper, we propose a cloud computing based deep compression framework named Pearl, which utilizes the power of deep learning and cloud computing to compress UHD videos. Pearl compresses UHD videos from two respects: the frame resolution and the colorful information. In pearl, an optimal compact representation of the original UHD video is learned with two deep convolutional neural networks (DCNNs): super resolution CNN (SR-CNN) and colorization CNN (CL-CNN). SR-CNN is used to reconstruct a high-resolution video from a low-resolution video, while CL-CNN is adopted to preserve the color information of the video. Pearl focuses on video content compression in two new directions. Thus, it can be integrated with any existing video compression system. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.

A Resilient Adaptive Event-triggered H\(\infty\) Tracking Control of T-S Fuzzy Systems with Markovian Jump Parameters under DOS Attacks

In this paper, the problem of H\(\infty\) output-tracking control for T-S fuzzy markov jump systems(MJSs) with imperfect premise matching is investigated. The conditional probability information between the system and the controller is imperfectly known. Firstly, due to the in influence of network-induce delay, the system mode information can not be transmitted to the controller synchronously, thus, a discrete-time hidden markov model(HMM) is established to describe the asynchronous phenomenon between the system and the controller; Secondly, form the prospectively of practical applications, packet loss in two channels is taken into consideration, which is caused by denial-of-service(DOS) attacks; Thirdly, in order to improve the data transmission efficiency and save network bandwidth resources, a novel resilient adaptive event-triggered(AET) mechanism is proposed, in which the event-based threshold parameter is dynamically regulated over the processing. Additionally, resorted the compensation principle and the lyapunov-krasovski(L-K) functions methods, some sufficient conditions in form of liner matrix inequality(LMI) are achieved to co-guarantee the stability and H\(\infty\) tracking property of the closed-loop system; Finally, two examples are given to verify the effectiveness of our design mentality.

Column Generation Based Service Function Chaining Embedding in Multi-Domain Networks

Network function virtualization achieves cost-effective network service provisioning through exploitation of virtualization and automation by decoupling network functions (software) from dedicated hardware. The software of the devices can then be hosted by low-cost computation devices rather than by more expensive dedicated devices. To obtain a specific network service, the traffic flow is steered to go through a specific order of network functions that are hosted by cloud computing, and this network function sequence is known as a service function chaining (SFC). To allocate computation resources for network functions and bandwidth resources between network functions in a physical network is the SFC embedding problem. In this paper, we consider the SFC embedding problem in multi-domain networks, where no domain information, like domain topology and network resource, is disclosed among domains. We propose a new optimization algorithm based on column generation method to solve this problem, which is distributedly computed in domains. To further improve the scalability, we provide two heuristic algorithms. We selected two networks one large (158 nodes) and one small (18 nodes) to numerically validate the proposed algorithms and demonstrate that the acceptance ratio obtained by the heuristic algorithms is close (within 5.6%) to that of the optimal algorithm.

Event-based sliding mode control for fuzzy singular systems with semi-Markovian switching parameters

This paper addresses the observer-based dynamic event-triggered (DET) sliding mode control (SMC) problem for fuzzy singular semi-Markovian jump systems (FSS-MJSs) subject to generalized dissipative performance, in which a novel double-quantized structure is reasonably merged into a unified model. The main aim of this paper is to develop a mode-dependent adaptive sliding mode control (ASMC) law through the DET rule, which not only makes the closed-loop systems mean-square admissibility and generalized dissipative, but also the finite-time reachability around the predefined sliding mode surface (SMS) can be achieved. Firstly, in order to improve the data transmission efficiency and save network bandwidth resources, DET and doubled-quantized-based control protocol are introduced, in which the event-based threshold function is dynamically regulated and the data of input and output are both quantized; Secondly, due to the sensor information constraints, system state information is not always obtained in practice, hence, a suitable observer design can make up for this defect. Meantime, in terms of elegant linearization technique and implicit function theorem, the uniqueness of the solution for FSS-MJSs is also established; Additionally, by making use of the Lyapunov functional and linear matrix inequality (LMI) technique, both the desired SMC gains, observer gains and triggering parameter matrices are co-designed, more than that the derivative singular matrix is also integrated into the whole design process such that the derived conditions are much more easily to be checked; Finally, a numerical example and a practical application example are co-given to verify the effectiveness of our design mentality.

Event-triggered dynamic output quantized control for 2-D switched systems

It is well known that designing mode-dependent event-triggered control (MDETC) brings challenging difficulties to theoretical analysis, especially for two-dimensional (2-D) switched systems. Therefore, for 2-D switched Fornasini–Marchesini local state-space (FMLSS) systems, this paper designs a MDETC to investigate global exponential stabilization almost surely (GES a.s.). A MDETC based on dynamic output quantization control scheme is designed, which not only has a wide range of practicability, but also greatly saves network bandwidth resources. By constructing mode-dependent Lyapunov functions that include two time directions, some novel sufficient conditions are provided such that the switched FMLSS system achieves GES a.s. Unlike most previous results, our results do not require each mode to be stable, not even after adding control. Finally, numerical experiments are provided to verify the validity of our main theoretical results.

Dynamic Event-Triggered H∞ Load Frequency Control for Multi-Area Power Systems Subject to Hybrid Cyber Attacks

This article aims at designing a dynamic event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> load frequency controller for multi-area power systems affected by false data-injection attacks and denial-of-service attacks. A dynamic event-triggered scheme, whose threshold parameter varies with objective system states, is employed to make rational use of limited network bandwidth resources and improve the efficiency of the data utilization. Then, taking the impacts of the aforementioned hybrid cyber attacks into consideration, an attractive system model is established. Whereafter, several sufficient conditions, which can guarantee the exponential mean-square stability with a preset <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H} _{\infty }$ </tex-math></inline-formula> performance index of the studied system, are obtained through utilizing Lyapunov stability theory. Additionally, the desired controller is designed via handling convex optimization problems. Finally, a simulation example is displayed to explain the validity of the proposed method.

Adaptive Event-Triggered [formula omitted] Control of Semi-Markov Jump Distributed Parameter Systems

This paper focuses on the problem of adaptive event-triggered L2−L∞ control for distributed parameter systems with semi-Markov jump parameters. An event-triggered transmission scheme, whose threshold function can be adaptively adjusted, is established to decrease the frequency of data transmission. Simultaneously, the utilization of limited network bandwidth resources is also improved due to plenty of “unnecessary” packets being discarded before accessing networks. Furthermore, the jumping of stochastic parameters in networks can be described by a semi-Markov jump model, in which the sojourn-time is considered subject to the Weibull distribution instead of the exponential distribution. Based on the aforesaid discussions, a mode-dependent controller is designed under an adaptive event-triggered scheme. Whereafter, some criteria are constructed based on the Lyapunov stability theory to guarantee stochastic stability and a prescribed L2−L∞ performance of the system. Ultimately, two simulation examples are provided to verify the feasibility of the proposed design approach.

Robust Fault-Tolerant Control of Continuous Lurie Networked Control Systems Based on the Observer with the Event-Triggered Mechanism

For a class of uncertain continuously networked Lurie control systems with both sensor-to-controller time-delay and controller-to-actuator time-delay, the problem of codesigning its observer and fault-tolerant controller based on an event-triggered mechanism under actuator failure is investigated. Firstly, considering that the state of the system cannot be measured directly, an observer is constructed on the controller node. Secondly, to reduce the waste of network bandwidth resources and improve the performance of the network control system, a network control system approach based on event-triggered mechanism is proposed. By introducing the event-triggered mechanism and the actuator fault indication matrix, the Lurie networked control system is modeled as a Lurie system with time-delay using the state augmentation technique to obtain a model of the closed-loop system. Finally, based on Lyapunov stability theory, sufficient condition for the stability of the closed-loop system is obtained, and the design method of the fault-tolerant controller and the observer is given. The obtained results are given in the form of linear matrix inequalities, which are easy to be solved by using the linear matrix inequality toolbox. Finally, the feasibility and effectiveness of the method are illustrated by a simulation example.

A Path Load-Aware Based Caching Strategy for Information-Centric Networking

Ubiquitous in-network caching plays an important role in improving the efficiency of content access and distribution in Information-Centric Networks (ICN). Content placement strategies, which determine the location distribution of content replicas in the network, have a decisive impact on the performance of the cache system. Existing strategies primarily focus on pushing popular content to the network edge, aiming to improve the overall cache hit ratio while neglecting to effectively balance the traffic load between network links; this leads to insufficient utilization of network bandwidth resources and further excessive content delivery time and user QoE degradation. In this paper, a Path Load-Aware Based Caching strategy (PLABC) is proposed, in which content-related information and dynamic network-related information are comprehensively considered to make cache decisions. Specifically, the utility of caching the content at each on-path node is calculated according to the bandwidth consumption savings and the load level of the transmission path, and the node with the greatest utility value is selected as the caching node. Extensive simulations are conducted to compare the performance of PLABC with other state-of-the-art schemes by quantitative analysis. Simulation results validate the PLABC strategy’s effectiveness, especially in balancing link load and reducing content delivery time.