Unreliable Communication Channels Research Articles

Real-Time Status Updates With Perfect Feedback Over Erasure Channels

Real-time decision making relies on the availability of accurate data and, therefore, delivering status updates in a timely fashion is of paramount importance. The topic of real-time status updates has received much attention in recent years. This article contributes new results to this research area by studying the interplay between average timeliness and design decisions made at the physical layer, for unreliable communication channels. Specifically, this study explores the tension between the fact that more reliable transmissions with lower probabilities of decoding failure tend to improve timely delivery, unless these improvements come at the expense of significantly longer codewords. The average timeliness is adopted as an evaluation criterion, and a framework to efficiently compute the performance of various transmission schemes for the binary erasure channel is developed. We show that the average timeliness decreases as we increase the feedback rate in a hybrid ARQ scheme for a range of codeword lengths. This article also provides design guidelines for the codeword length selection for an hybrid ARQ scheme to improve the average information timeliness. Numerical examples are included to further illustrate the applicability of our findings.

Enhanced multi-instant fuzzy switching control of nonlinear system with unreliable communication channels

The problem of enhancing the adaptability against unreliable communication channels for fuzzy system is investigated by proposing a new multi-instant fuzzy switching controller with a more flexible switching manner. Compared with recent methods in the reported literature, more switching modes have been designed according to the updated information at each sampling instant and thus much more freedom can be created by introducing and assigning additional variables for all the possible switching modes. As a result, the adaptability against unreliable communication channels can be enhanced from two aspects: for the same health quality of communication channels, much larger controllable domain can be provided; or for the same system parameters, a worse health quality of communication channels can be affordable. Finally, several advantages over previous fuzzy approaches are verified by means of numerical simulations.

Stabilisation of networked control systems with remote and local controllers subject to delay and packet dropout

This study considers the stabilisation problem for networked control systems (NCSs) with remote and local controllers involving delay and packet dropout. In this NCSs model, the local controller receives the precise state and sends it to the remote controller via an unreliable communication channel where packet dropout may occur. When the remote controller obtains the observation, it delivers an acknowledgement to the local controller whether the precise state is received or not. Then the two controllers make their decisions simultaneously, which shall be sent to the plant through a delayed communication channel. The authors give the necessary and sufficient condition of the stabilisation in the mean-square sense for the system without the additive noise based on two coupled algebraic Riccati equations. For the system with the additive noise, firstly, they show the stability of the optimal estimator. Then the sufficient condition of the boundedness in the mean-square sense is presented. Numerical examples are illustrated to show the effectiveness of the proposed algorithm.

HMM‐based filtering for slow‐sampling singularly perturbed jumping systems

This study concentrates on the filtering for the slow-sampling jumping singularly perturbed systems, in which the situation that the filter mode is inconsistent with the system mode is taken into consideration. Based on the hidden-Markov model (HMM), such an asynchronous phenomenon between the system mode and the filter mode is depicted. Additionally, the unreliable communication channel resulting in packet loss is described through the assistance of a random variable. The authors' purpose is to design a filter that ensures the error system is extended stochastically dissipative. Moreover, with the aid of the Lyapunov stability theory and linear matrix inequality approach, a set of -independent conditions are derived to obtain the filter gains. Eventually, the effectiveness of the proposed method is demonstrated by a numerical example and a modified tunnel diode circuit model.

Study on Checkpoint Timer Optimal Setup of Licklider Transmission Protocol

As the main data transport protocol of delay/disruption-tolerant networking, Licklider transmission protocol (LTP) is developed to provide reliable and highly efficient data delivery over unreliable deep-space and interplanetary communication channels. For reliable data delivery, a checkpoint (CP) segment of LTP is sent, with a timer set (or, simply, CP timer) to check the arrival status of the entire data block at the receiver, and it is retransmitted upon the expiration of the CP timer prior to reception of an acknowledgment. Little work has been seen in analysis of the CP timer length for optimal performance of LTP in energy-efficient, reliable data delivery. In this article, a packet-level analysis of the CP timer length setting is presented for the reliable data delivery of LTP over unreliable deep-space communication channels based on realistic file transfer experiments conducted using a PC-based testbed. It is our intent to find an optimal CP timer setting so that the reliable file delivery in deep space is ensured with a minimum number of retransmission attempts. By this, the energy-efficient, reliable data delivery of LTP can be achieved in deep-space communications.

Recursive approximation of complex behaviours with IoT-data imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.

Analysis and Optimization of a Controlled Model for a Closed Queueing Network

We consider a closed network consisting of two queuing systems: the main system simulates a packet transmission queue over an unreliable communication channel, and the auxiliary multiserver system contains lost packets for resending. The service rate in the main system is controllable and is supposed to be optimized with the aim of minimizing the time of successful transmission, taking into account the cost of using network resources. We obtain optimality conditions in two cases: 1) in the model based on fluid approximation in the presence of heavy load; 2) in the steady state using stationary strategies.

Decentralized Dynamic Event-Triggered $\mathcal {H}_{\infty }$ Control for Nonlinear Systems With Unreliable Communication Channel and Limited Bandwidth

This article investigates the dynamic event-triggered $\mathcal {H}_{\infty }$ control problem for nonlinear networked control systems with unreliable communication channel, variable communication delays, and limited bandwidth. The nonlinear plant is represented by discrete-time polynomial fuzzy model. First, a decentralized dynamic event-triggered mechanism is proposed to determine whether the measured data are transmitted or not, and in order to exclude data collision caused by limited bandwidth, novel try-once-discard and flexible round-robin scheduling protocols are proposed to assign communication channel to certain sensor node. Then, Bernoulli distribution is employed to model the unreliable communication channel, and a new random sequence is developed to model the received data sequence under the effect of data losses and scheduling protocols. Furthermore, a discrete-time stochastic system model with both state and error delays is constructed, and sufficient conditions in the form of sum-of-squares are developed for the design of $\mathcal {H}_{\infty }$ controllers such that the closed-loop system is stochastically stable and preserves guaranteed $\mathcal {H}_{\infty }$ performance. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed results.

Dissipative-based non-fragile filtering for fuzzy networked control systems with switching communication channels

The problem of finite-time dissipative-based non-fragile filter design for a class of discrete-time nonlinear networked control systems subject to randomly occurring parameter uncertainties, external disturbances as well as packet dropouts due to unreliable communication channels and networked induced time-varying delay is discussed. The randomly occurring uncertainties are assumed to satisfy Bernoulli distribution and the considered stochastic variables are mutually independent. Multiple communication channel scenario is taken into account to enhance the data transmission performance and the channel switching phenomena is governed by a Markov chain. The intention here is to design a dissipative-based non-fragile filter for the filtering error system such that it is stochastically finite-time bounded and attains a prescribed dissipative performance attenuation level. Lyapunov stability theory and linear matrix inequality approach are exploited to develop a set of sufficient criterion for the existence of the desired filter. Finally, the filter design framework is applied to the truck-trailer model to demonstrate its effectiveness.

A New Compensator Design for Optimal Static Output Feedback Control Across a Communication Channel Subject to Random Packet Dropouts

This paper studies compensation of networked control across an unreliable communication channel subject to random packet dropouts. By posing it as a two decision-variable optimization problem, the control and compensation for static output feedback LQR are unified by the proposed design. New governing equations are derived for the controller and the compensator satisfying optimality conditions concurrently. Also presented is a convergent algorithm that solves these equations for the optimal gains. Finally, to validate the design and verify its effectiveness, a numerical example is given on which a computer simulation is conducted to compare its performance against that of three other existing schemes. The simulation results demonstrate its saliency among the four methods.

IoT-Based Improved Human Motion Estimations Method Under Cyber Attacks

Human beings and control centers are usually far away from each other, so cyber attacks on the sensor measurements can lead to loss of user privacy, information, and trust. Driven by this motivation, this letter proposes an Internet of Things (IoT)-based human motion estimations algorithm under cyber attacks. The sensing measurements are transmitted to the control center over an unreliable communication channel where cyber attack occurs. Based on the mean squared error, the optimal state estimation algorithm is derived to estimate human motions. Simulation results show that the proposed method provides significant performance improvement compared with the existing approach.

Performance Analysis of Reed-Solomon Codes for Effective Use in Survivable Wireless Sensor Networks

In wireless sensor networks, data transmitted over wireless channels can get corrupted or lost due to channel noises and interferences. Error control coding is a technique that enables reliable delivery of digital data over unreliable communication channels. By adding redundancy to the transmitted data, error control codes allow the decoder to recover the original data based on partial data information received. The codes allow the receiver to recover errors or data loss without retransmission, thus making them suitable for applications where retransmissions are costly or impossible. Due to the stringent energy constraint of battery-powered sensor nodes, it is vital to use the energy-efficient error control scheme. In this paper, we evaluate the performance of Reed-Solomon (RS) codes with the objective to optimize their design factors, leading to optimal bit error performance over both Additive White Gaussian Noise (AWGN) channels and Rayleigh fading channels.

Quasi-Synchronization of Time Delay Markovian Jump Neural Networks With Impulsive-Driven Transmission and Fading Channels.

The problem of quasi-synchronization (QS) for the Markovian jump master-slave neural networks with time-varying delay is studied in this article, where the mismatch parameters and unreliable communication channels are considered as well. A set of stochastic variables with different expectations are used to describe the fading phenomena of parallel communication channels. An impulsive-driven transmission strategy is designed to reduce the communication load, and a corresponding impulsive controller is then designed. A synchronization error system (SES) is obtained, and a convex QS condition is established for the SES. A linear matrix inequality-based iterative algorithm is proposed to reduce the bound of the SES, and the corresponding controller gains are calculated. A numerical example is provided to illustrate the effectiveness of the developed result.

IoT-Based Electric Vehicle State Estimation and Control Algorithms Under Cyber Attacks

In order to provide intelligent services, the Internet-of-Things (IoT) facilitates millions of smart devices to be enabled with network connectivity to sense, collect, process, and exchange information. Based on the communication infrastructure, the IoT can allow cyber-physical devices, such as an electric vehicle, to sense, monitor, and control from the remote control center in real time. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks, so it is a challenging task for the IoT to explore these applications. In order to overcome the problem, this article proposes an algorithm for monitoring and controlling the electric vehicle using the IoT communication network considering false data-injection attacks. First, the driverless electric vehicle with onboard vision system is represented by a state-space framework. As the electric vehicle and monitoring control center are far away, the IoT-embedded smart sensors and actuators are used to measure and control the system states. The vehicle sensing information is transmitted to the control center over an unreliable communication channel where attacks happen. Based on the mean square error principle, the optimal state estimation algorithm is derived to know and visualize the vehicle states. In order to regulate the vehicle states, the optimal control algorithm is designed based on the semidefinite programming approach. The simulation results show that the proposed algorithms are able to properly estimate and regulate the vehicle states within a short period of time.

확률적 불완전 통신 채널에서의 강인제어

In this paper, we consider the robust (HSUB∞/SUB) control problem for linear time-invariant systems over unreliable communication channels that are subject to packet losses from sensors to the controller and from the controller to the actuators. Moreover, the acknowledgments of control packet transmissions are also sent over reverse unreliable communication channels, which is known as the quasi-TCP model. The robust control problem is formulated within a stochastic zero-sum dynamic game framework, from which we obtain an HSUB∞/SUB controller that minimizes the worst-case cost function in the presence of an adversary in the dynamical system. We characterize a set of existence conditions of the HSUB∞/SUB controller in terms of the disturbance attenuation parameter γ and packet loss rates. For the least disturbance attenuation scenario (γ →∝), we show that the HSUB∞/SUB control system converges to the corresponding LQG system. We also show that the HSUB∞/SUB controller for the TCP- and UDP-cases can be arrived at as appropriate limits of the solution for the quasi-TCP problem. Numerical examples are presented to illustrate the theoretical results.

Placement of Data Array Replicas in a Distributed System With Unreliable Communication Channels

Abstract The paper proposes an approach to solving the problem of optimal placement of data array replicas in a distributed system. It presents a formal model of a distributed system with unreliable communication channels. A system is represented as a graph with coloured arcs. The formulation of the problem of finding the optimal placement of replicas is given. As a criterion for optimization, the minimum response time to a data request is considered. The task solution heuristic algorithm is also proposed in the paper.

RFL: Robust fault localization on unreliable communication channels

The current Internet is vulnerable to various attacks, e.g., source spoofing and flow hijacking attacks, which are incurred by misconfigurations or attacks. Either users or network operators are unable to easily localize these faults. Existing fault localization mechanisms can detect such attacks under an assumption that localization is performed upon reliable communication channels. Unfortunately, the assumption does not always hold. The forwarding paths of localization are not always reliable. Packets are usually dropped for some reasons. In particular, adversaries can interfere with fault localization by maliciously dropping packets. In this paper, we relax the assumption and propose a robust data-plane fault localization protocol named RFL that can localize faults and achieve source authenticity and path compliance even if communication channels in the network are not reliable. RFL samples and verifies packets in each network entity so that the packet source can efficiently localize faults of packet forwarding by verifying the sampled packets. By leveraging packet acknowledgment, packet sampling based fault localization is not impacted by packet loss in the communication channels. In particular, RFL leverages a symmetric key distribution scheme to implement robust key distribution among different entities, which ensures that packet sources can always correctly fresh their keys to perform correct localization. Our security and theoretical analysis demonstrates the robustness of RFL protocol. We implement the RFL prototype on Click routers. The experiment results with the prototype demonstrate that RFL achieves more than 99.5% localization accuracy while incurring only 10% throughput degradation.

Push-Sum With Transmission Failures

The push-sum algorithm allows distributed computing of the average on a directed graph, and is particularly relevant when one is restricted to one-way and/or asynchronous communications. We investigate its behavior in the presence of unreliable communication channels where messages can be lost. We show that exponential convergence still holds and deduce fundamental properties that implicitly describe the distribution of the final value obtained. We analyze the error of the final common value we get for the essential case of two nodes, both theoretically and numerically. We provide performance comparison with a standard consensus algorithm.

Review of the Techniques Against the Wormhole Attacks on Wireless Sensor Networks

With the advancement of technology, the application of Wireless Sensor Networks (WSNs) has increased in various fields, such as; military, medical and business. WSNs have the ability to control geographic areas and obtaining data and processing it in different environments. Sensor nodes independently sample local information and if necessary send this information to neighboring sensors and eventually send it to the well. These networks are independent and autonomous and humans have no interference in it. In particular, all nodes are identical, and in practice they work together to meet the overall purpose of the network. Due to inherent constraints in the resources and computing power of sensor nodes, security in WSNs has different challenges in comparison with security in traditional computer networks. Low-cost sensor networks allow the expansion of sensor groups in a variety of environments which are capable of operating in military fields and in urban areas. On the other hand, unreliable communication channels in many applications make it more difficult to provide information security in these networks. One of the common attacks in WSNs is a wormhole attack. In this paper, we will review and evaluate paper related to the wormhole attack.

H∞ fault detection filter design for discrete-time nonlinear Markovian jump systems with missing measurements

Fault detection filter (FDF) design problem for a class of discrete-time nonlinear Markovian jump systems subject to unreliable communication channel is addressed in this study. The considered system nonlinearity is assumed to satisfy global Lipschitz condition and the missing measurement phenomenon is modeled by Bernoulli binary distribution. By constructing an observer-based FDF as a residual generator, the fault detection issue is cast into a stochastic H∞ filtering framework. Sufficient existence conditions of the H∞-FDF are derived via matrix inequalities. Two cases for the Markovian parameters, one is completely known transition probabilities and the other is partially known transition probabilities, have been considered in the filter design procedure, respectively. Furthermore, parameter matrices of the FDF are obtained by solving a set of linear matrix inequalities. An illustrative example is addressed to show the efficacy of the proposed filter.