Random Packet Losses Research Articles

Fault Detection for Nonlinear Networked Control Systems With Sensor Saturation and Random Faults

A fault detection filter is designed for a class of nonlinear networked control systems with sensor saturation, random packet loss and delay, a unified model is constructed to describe the transmission delay caused by packet loss and network induction. By constructing Lyapunov function and using the convex optimization algorithm, a parameter matrix of the $H_{\infty }$ fault detection filter is obtained by solving a series of linear matrix inequalities, which guarantees that the error system satisfies a concerned performance index. Finally, the validity of the proposed method is illustrated by a numerical example on the internet-based three-tank system.

Collaborative Multiple UAVs Navigation With GPS/INS/UWB Jammers Using Sigma Point Belief Propagation

Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS, and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably.

NexGen D-TCP: Next Generation Dynamic TCP Congestion Control Algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high-BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm over mmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32.9% gain in goodput as compared to TCP-Reno and attains 118.9% gain in throughput as compared to TCP-Cubic.

Content Retrieval Based on Prediction and Network Coding in Vehicular Named Data Networking

Named Data Networking (NDN) has the advantages of content-based, location-independent and in-network caching. These characteristics are naturally suitable for highly dynamic Vehicular Ad hoc Networks (VANETs), so VNDN has become a promising network architecture. However, achieving effective and reliable multi-hop content retrieval is still a major challenge in VNDN where network parameters change frequently and the channel reliability is poor. In this paper, we propose a protocol based on prediction and network coding for content retrieval in vehicular named data networking (PreNCCR). In order to adapt to the dynamic and random packet loss characteristics of VANETs and restrict flooding, a prediction-based opportunistic routing in which the priority of candidate forwarder is determined through prediction mechanism is proposed. On this basis, a network coding-based packet forwarding strategy is proposed in which the available network capacity are effectively utilized. The evaluation results show that under moderate network configuration parameters, compared with VNDN-geo, CCVN, and SelNC protocols, PreNCCR can not only achieve 33.8% ~ 99.8% higher request satisfaction ratio, 46.6% ~ 65.1% lower delay, but also can reduce the consumption of Interest and Data by 32.6% ~ 80.7%, 52.3% ~ 60.1%.

Event-triggered Hꝏ control for NCS with time-delay and packet losses

Abstract This paper is concerned with the discrete event-triggered dynamic output-feedback ${H}_{\infty }$ control problem for the uncertain networked control system, where the time-varying sampling, network-induced delay and packet losses are taken into account simultaneously. The random packet losses are described via the Bernoulli distribution. And then, the closed-loop system is modelled as an augmented time-delay system with interval time-varying delay. By using the Lyapunov stability theory and the augmented state space method, the sufficient conditions for the asymptotic stability of the closed-loop system are proposed in the form of linear matrix inequalities. At the same time, the design method of the ${H}_{\infty }$ controller is created. Finally, a numerical example is employed to illustrate the effectiveness of the proposed method.

Development of event‐triggered‐based minimum variance recursive estimator for the NLNS using multi‐model approach

The minimum variance-based recursive estimator is designed to estimate the unmeasurable states of a non-linear networked system (NLNS). The measurable states are transmitted from sensor to the estimator (S–E) through a communication network. An event-triggered mechanism is proposed to limit data transmission in the S–E network to improve channel utilisation. The control input is transmitted from the controller to actuator (C–A) through another communication network. The networks in the S–E and C–A channels are affected by random packet delay, loss, and uncertain observation. These effects are modelled using five Bernoulli distributed random variables. The NLNS is converted to ‘m’ local linear stochastic augmented models by piecewise linearisation technique. The ‘m’ number of local estimators are designed for every local model using projection theorem. Global estimation is obtained by blending local estimators using T–S fuzzy approach. The proposed algorithm is recursive and has the advantage of scalability and flexibility. Finally, estimator performance is demonstrated using a non-linear tunnel diode and compared with the other popular filters. Monte Carlo simulation is performed and accumulated mean-square error values are calculated. From the simulation results, the proposed filter shows satisfactory performance.

An optimal hybrid learning approach for attack detection in linear networked control systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems &#x0028 NCS &#x0029 , wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.

Distributed cooperative secondary networked optimal control with packet loss for islanded microgrid

The hierarchical networked control has attracted many attentions in the field of the islanded microgrid (MG). However, how to reduce the effects caused by random packet loss of communication is still need to be studied. In this study, the authors proposed a fully distributed cooperative secondary networked optimal control for both frequency and voltage restoration. A distributed consensus Kalman filter based estimator (DCKF-estimator) is introduced to obtain the estimation of the output state with the communication packet loss. Then, by incorporating the DCKF-estimator into the output feedback-like controller, the optimal output feedback-like gain is obtained based on the linear quadratic Gaussian-like control scheme. In addition, they also deduced the stability condition of the islanded MG closed-loop system. The simulations in different scenarios were carried out to evaluate the control performance. The results show that the proposed approach achieves accurate regulations (on frequency, voltage) and fast transient response, and has better stability and robust performance over communication packet loss.

Adaptive event-triggered control of discrete-time networked systems against randomly occurring infinite distributed delays, random packet losses and sensor saturation

The saturation input control problem of discrete-time networked systems via adaptive event-triggered communication scheme is discussed in this paper. The criteria are derived by utilising a new Lyapunov functional to guarantee that the considered networked system with randomly occurring infinite distributed delays, random packet losses and sensor saturation is exponentially stable in mean square sense. A novel adaptive event-triggered law is proposed, which is dependent on the exponentially stable index α. The effectiveness of our proposed method is illustrated by both theoretical analysis and numerical simulations.

Finite-time event-triggered non-fragile control and fault detection for switched networked systems with random packet losses

This paper investigates the problem of event-triggered observer-based fault detection for a class of uncertain switched nonlinear network control systems (SNNCSs) with random communication packet losses and infinite distributed delay. The main objective of this work is to design an event-triggered state feedback non-fragile H∞ controller such that the resulting closed-loop form of the considered SNNCSs is robustly finite-time bounded and satisfies a prescribed H∞ performance constraint in the finite-time interval. By employing average dwell time approach, the problem of finite-time boundedness and residual H∞ performance analysis is discussed. Specifically, a new set of conditions in the form of LMIs are derived to ensure the finite-time boundedness with the prescribed residual H∞ performance for the relevant closed-loop system. Moreover, the desired observer-based state feedback controller gain matrices and residual weighting matrices can be expressed in an explicit form. Finally, the applicability and effectiveness of the developed results are examined via numerical examples with simulation results.

Fault Diagnosis and Model Predictive Fault Tolerant Control of Networked Control Systems with Random Packet Loss

In this paper, a fault diagnosis and fault tolerant control algorithm is performed for the networked control system with packet loss problem. Firstly, the random packet loss phenomenon of the system is viewed as a binary switching sequence which obeys a Bernoulli distributed white sequence taking on values of zero and one with certain probability. When a failure occurs in the system, fault diagnosis is performed by designing a residual signal and an adaptive adjustment rate for the faulty system, and then based on the fault diagnosis information, model predictive control is used for fault tolerant control for the system. By optimizing the solution of the model predictive control system, a suitable fault-tolerant controller is designed to complete the active fault-tolerant control of the system. Then the closed-loop stability of the system is analyzed. Simulation results are provided to show the effectiveness of the proposed method.

Robust Consensus-Based Distributed Energy Management for Microgrids With Packet Losses Tolerance

The well-developed consensus algorithm provides an elegant distributed way for solving the energy management problem. The convergence of the consensus-based distributed approach depends on the reliable exchange of the information with neighbors. This assumption might be violated in practice due to inevitable and random packet losses. In this paper, the impact of packet losses on the consensus-based distributed approach is analyzed. We show the iterative calculation converges to an incorrect solution in the presence of packet losses, where the power supply could not meet with the demand. More importantly, we find the error in the final result is accumulated over every packet loss event, and we identify the critical information that cause the error. Based on the analysis, a corrective method is proposed to assure convergence to the right schedule. The corrective method has two features: 1) it uses the original consensus network, and no communication reconfiguration is needed; and 2) no retransmissions for the lost packets are needed, instead, the errors are compensated by a new variable introduced in the update rule. We show that the proposed algorithm could achieve the optimal solution in the presence of packet losses. Numerical simulation results are used to validate the proposed algorithm.

Analysis of Stochastic Switched Systems With Application to Networked Control Under Jamming Attacks

We investigate the stability problem for discrete-time stochastic switched linear systems under the specific scenarios where information about the switching patterns and the probability of switches are not available. Our analysis focuses on the average number of times each mode becomes active in the long run and, in particular, utilizes their lower and upper bounds. This setup is motivated by cyber security issues for networked control systems in the presence of packet losses due to malicious jamming attacks where the attacker's strategy is not known a priori. We derive a sufficient condition for almost sure asymptotic stability of the switched systems that can be examined by solving a linear programming problem. Our approach exploits the dynamics of an equivalent system that describes the evolution of the switched system's state at every few steps; the stability analysis may become less conservative by increasing the step size. The computational efficiency is further enhanced by exploiting the structure in the stability analysis problem, and we introduce an alternative linear programming problem that has fewer variables. We demonstrate the efficacy of our results by analyzing networked control problems where communication channels face random packet losses as well as jamming attacks.

PM-LPDR: a prediction model for lost packets based on data reconstruction on lossy links in sensor networks

During the data gathering process in sensor networks, lots of transmitted data packets can be lost due to the limiting node energy and the influence of data redundancy. In order to solve this problem, a prediction model is proposed for lost packets based on data reconstruction on lossy links in sensor networks. In this model, retransmission is adopted for data recovery when a random packet loss is predicted while prediction algorithms based on time sequences can be employed for data recovery when a random packet loss cannot be predicted. The operation of the whole system can still be guaranteed with this model when the packet loss probability of the network is lower than 15% while the relative error for data reconstruction remains between 0.17%~0.22% when the packet loss probability is higher than 22%. It is thus proven that this prediction model exhibits a strong stability and flexibility.

PM-LPDR: a prediction model for lost packets based on data reconstruction on lossy links in sensor networks

During the data gathering process in sensor networks, lots of transmitted data packets can be lost due to the limiting node energy and the influence of data redundancy. In order to solve this problem, a prediction model is proposed for lost packets based on data reconstruction on lossy links in sensor networks. In this model, retransmission is adopted for data recovery when a random packet loss is predicted while prediction algorithms based on time sequences can be employed for data recovery when a random packet loss cannot be predicted. The operation of the whole system can still be guaranteed with this model when the packet loss probability of the network is lower than 15% while the relative error for data reconstruction remains between 0.17%~0.22% when the packet loss probability is higher than 22%. It is thus proven that this prediction model exhibits a strong stability and flexibility.

H-Infinity Fault Detection for Delta Operator Systems With Random Two-Channels Packet Losses and Limited Communication

This paper is concerned with the H-infinity fault detection for time-delays delta operator systems with random two-channels packet losses and limited communication. The considered networked control systems (NCSs) are modeled as the time-delays delta operator systems with random packet losses and limited communication, where the packet losses exist in sensor-to-controller (S-C link) or controller-to-actuator (C-A link), and the limited communication occurs in the controller-to-actuator link. The random two-channels packet losses are described by the Markov chain process and limited communication is transformed into system state by feedback control in time-delays delta operator systems. The above delta operator systems are modeled as the Markovian jump systems, and the designed method of H-infinity fault detection filter is proposed under arbitrary transition probabilities matrix. The sufficient conditions for the asymptotical stability of the residual systems with H-infinity performance for the considered delta operator systems are presented by Lyapunov-Krasovskii functional in delta domain, and the gain matrices of the designed fault detection filter can be easily determined by linear matrix inequalities (LMIs). Finally, comparative examples are provided to illustrate the effectiveness of the proposed method.

New Results on the Observer-Based <inline-formula> <tex-math notation="LaTeX">$H_{\infty}$ </tex-math> </inline-formula> Control for Uncertain Nonlinear Networked Control Systems With Random Packet Losses

This paper presents the new results on the H ∞ control of a class of completely uncertain nonlinear networked control systems with random communication packet dropouts, which is partially considered in this paper. The uncertainties in the plant are assumed to be real and time-varying, as well as norm, bounded. The random packet losses are modeled as a Bernoulli distributed white sequence with known conditions on their probability distribution. The controller was designed as an observer-based H ∞ dynamic, such that the closed-loop system is exponentially mean square stable and the effect of the disturbance input on the controlled output is less than a minimum level γ for all admissible uncertainties. New sufficient conditions for the existence of such a controller are presented and proved based on the linear matrix inequalities' approach. The theory presented is illustrated by a numerical example to show the effectiveness of the developed techniques.

Dynamic sliding mode controller design for networked control systems with random packet loss and event driven quantisation

This study addresses the sliding mode controller design for a networked control system with quantisation and random packet loss. A logarithmic quantiser is adopted in order to guarantee the coarsest density and decrease the possibility of unsuccessful transmission. The quantisation process is designed as an event driven to reduce the amount of data to be transmitted over the channel. A sliding mode controller is adopted to suppress the measurement noise and error caused by the features of a wireless communication channel. The existence of quasi-sliding mode in the sense of mean-square and closed loop stability in the sense of mean square are claimed. Simulations are carried out to support the algorithms proposed.

Robust [formula omitted] control for networked control systems with randomly occurring uncertainties: Observer-based case

This paper investigates the problem of observer-based robust H∞ output feedback control for networked control systems (NCSs) with randomly occurring uncertainties, dynamic quantization, and packet loss. It is assumed that the system measurement output will be quantized by a dynamic quantizer and the random packet loss is represented by Bernoulli random binary distribution. In the presence of randomly occurring uncertainties, dynamic quantization, and packet loss, the attention of this research is focused on the design of observer-based robust H∞ output feedback controller such that the resulting system is stochastically stable with the prescribed H∞ noise attenuation level. The sufficient condition for the existence of such output feedback controller is expressed in the form of linear matrix inequality (LMI). Simulation results of two illustrative examples are proposed to demonstrate the effectiveness of the developed design method.

ARFC: Advance response function of TCP CUBIC for IoT‐based applications using big data

SummaryThe throughput of a TCP flow depends on the average size of Congestion Window (cwnd) of a congestion control mechanism being used during the communication. The size of cwnd depends upon the usage of available link bandwidth during communication. The response function is a measure of average throughput of a single TCP flow of congestion control mechanism as the level of random packet loss is varied. Nowadays, many organizations are deploying IoT based applications for the analytics of their Big Data. TCP CUBIC and TCP Compound are the default congestion control mechanisms in Linux and Microsoft Windows Operating Systems respectively. Whereas, TCP Reno which is also known as Standard TCP congestion control mechanism is act as a trademark congestion control mechanism. During communication among different IoT applications, TCP CUBIC flows use more available link bandwidth as compared to TCP Reno flows, thus, the average cwnd size of TCP CUBIC flows are greater than the TCP Reno flows. It implies that these both kinds of flows are not sharing available link fairly, which refers as low TCP friendliness of TCP CUBIC. It means that the throughput of TCP CUBIC flows is higher than the trademark congestion control mechanism, i.e., TCP Reno flows. As a result, friendliness behavior or fair share of available link bandwidth among TCP CUBIC flows and TCP Reno flows is decreased. In other words, TCP friendliness of TCP CUBIC flows is reduced. Now, it is important to decrease the average cwnd size of TCP CUBIC flows, such that available link bandwidth can be shared fairly among flows of TCP CUBIC and TCP Reno. The aim of this research is to enhance the TCP friendliness behavior of TCP CUBIC congestion control mechanism for IoT based applications using Big Data. In this paper, an Advance Response Function of TCP CUBIC (ARFC) is designed to share fairly available link bandwidth among flows of TCP CUBIC and TCP Reno. Results show that TCP friendliness behavior of TCP CUBIC is increased by using ARFC. Overall, 18.6% performance is increased by using ARFC.