Privacy-Preserving Distributed Localization Via Difference Transmission Under Random Data Loss

In this paper, the consensus tracking problem for nonlinear multi-agent systems subjected to the output constraint, data loss and switching topologies is considered. Firstly, a consensus term is defined based on the communication information in present of the nonlinear output saturation and time-varying switching topologies. Since the random data loss is taken into consideration, the consensus term is redesigned by an introducing stochastic variable, which obeys the Bernoulli sequence with known probability. Then, a novel distributed ILC algorithm is designed by using the incomplete communication data of agents, which is more universal than the results without considered nonlinear constraint and random factors simultaneously. Through the contraction mapping method, an obtained convergence condition can also guarantee the asymptotic convergence of the agent along the iteration axis under nonlinear saturation factor and random factors. It is verified that the proposed algorithm can handle more complex situations in the consensus control of multi-agent systems. Simulation examples are further provided to verify the effectiveness of the proposed algorithm.

This article addresses the output tracking of networked high-order fully actuated (NHOFA) systems under random denial-of-service (DoS) attacks, where an independent Bernoulli process is used to denote the possibility of launching random DoS attacks in sensor to networked controller (SNC) and networked controller to actuator (NCA) channels. A successful attack leads to random data losses and disorders in the SNC and NCA channels. A secure predictive control method is given to implement the security tracking. In this method, a high-order fully actuated (HOFA) model is applied to establish the dynamics of networked control systems, which is called the NHOFA systems. Then, an incremental HOFA (IHOFA) prediction model is constructed via a Diophantine Equation, such that multistep output predictions are obtained to achieve the optimization of tracking performance and the compensation of random data losses and disorders. A simple condition is proposed to analyze the stability and tracking performance of closed-loop NHOFA systems. A tracking control experiment of air-bearing spacecraft simulator is taken to illustrate the feasibility of secure predictive control method.

The state estimation problem is a very important and interesting issue in the field of complex dynamical networks. Yet, the constructions of the state estimators in most existing literature studies all need to measure the output information of all nodes. However, the practical situation is that not all nodes could be measured to obtain the output information. Thus, in this paper, the state estimation is realized while only measuring the output data of partial nodes for a complex dynamical network with random data loss existing on its external communication links to the observers, where the lost data are compensated by the corresponding observer data. Depending on whether the network possesses the root strongly connected components that have a perfect matching, the choice problem of measured nodes and the construction problem of specific output matrices are discussed. By applying the Lyapunov stability theory and stochastic analysis method, a sufficient condition for state estimation is given. Through simulation experiments, the effectiveness of the proposed state estimation scheme is demonstrated.

ABSTRACTThis work focuses on the fault detection for nonlinear networked systems. A fault detection filter is designed to detect the fault signal under the effect of disturbance. The communication channel of the networked system is supposed to be influenced by the limited bandwidth and random data losses. To deal with these, event-triggered scheme and Bernoulli process are employed to describe the problems, respectively. According to the above situation, we employ a formulated random series to describe the input of fuzzy filter. Differential mean value theorem based method is presented to handle the asynchronous membership functions. Synthetically, linear matrix inequality approach is employed to obtain the stability conditions and the filter design process. At last, we verify the performance of this method acting in the simulation example.

This paper studies the design of uncertain linear discrete-time Networked Control Systems over imperfect communication channels with time-varying data loss. This leakage is modeled as stochastic parameter and norm-bounded uncertainties. In addition, this paper defines the necessary conditions for the design of the dynamic output feedback controller; subsequently, an iterative LMI approach is specified to compute the controller state parameters. The simulation results show that the designed controller improves the H∞ disturbance attenuation performance of the concerned uncertain system.

The transmission of huge amounts of data generated from various sensors is the basis and prerequisite for the analysis and prediction of the marine ecosystem. The special geographical conditions of the ocean determine that its data transmission is different from the common terrestrial data transmission. In this study, a marine environmental monitoring network architecture is proposed to collect environmental data from marine ecosystems using a heterogeneous network constructed by a multi-hop network and a satellite network. Considering the characteristics of satellite networks, such as high bandwidth delay product (BDP), long latency and high bit error rate, this work proposes a transmission scheme that improves the transmission performance of Transmission Control Protocol/Internet Protocol (TCP/IP)-based Internet networks over satellite networks. The improved scheme increases the monitoring data transmission rate, distinguishes between random data loss and congested data loss, rationalizes the threshold in fast recovery, and improves the overall transmission performance of the marine environmental monitoring network. The experimental results show that the proposed scheme can greatly improve the transmission performance of the monitoring network compared with traditional transmission protocols. The network model proposed in this study makes full use of current advanced network technologies, including multi-hop networks, wireless sensor networks, and satellite networks to expand the monitoring area at sea. This study provides a useful reference for the network model and data transmission for ocean monitoring.

This study addresses an H ∞ filtering problem for distributed Markovian jump systems (DMJSs) in multi‐sensor networks with inaccessible global jumping modes, random data losses, random sensing topologies and incomplete mode transition rates (TRs). First, locally overlapped clusters are introduced to reformulate the DMJS to account for the scenario that the global modes of the DMJS are not accessible for filter design. Second, a new cluster and local mode‐dependent H ∞ filtering framework is presented to incorporate the simultaneous presence of Markovian data losses and sensing topologies as well as incomplete mode TRs. The proposed H ∞ filters depend on only available information of cluster modes and local modes within this cluster, and thus eliminating the requirement of complete global modes. Third, criteria for designing desired filters are derived to preserve the stochastic stability of the resulting filtering error system under a prescribed H ∞ performance index. The proposed results are shown to be more general by covering some existing results as special cases. Finally, an F404 aircraft engine system is employed to demonstrate the effectiveness of the proposed filter design approach.

Model Based Predictive Networked Control Systems

Networked control systems offer significant advantages in terms of reliability, commissioning and maintenance, especially for complex systems. They must be able to withstand delays and data corruption caused by the underlying communication network. Existing results in networked control systems provide varying degrees of robustness. However, cost is also an important factor for real world implementations, which further restricts the bandwidth of the underlying network and complexity of the communication protocol. In this paper we introduce a networked control system method, WMBPNCS, that we have previously proposed, and implement a control system using IEEE 802.15.4 (ZigBee) as its communication protocol, in an attempt to overcome these problems. We find through simulations that performance of our implementation is acceptable even under large amounts of random network delay and data loss.

This study discusses the external consensus problem for non-identical networked multi-agent systems with network data loss, considering both uniform and random consecutive data losses induced by long periods of transmission failure. A novel approach using a combination of prediction strategy and constant control gain based on gain error ratio (GER) is proposed for solving the problem. The main contribution of this study is a simple and practical way of selecting control gain using GER to help overcome prediction inaccuracies caused by the prediction process during a long period of consecutive data loss. The feasibility and performance of the proposed consensus protocol is demonstrated through simulation and practical experiment.

This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

This paper studies vehicle platooning with communication channels subject to random data loss. We focus on homogeneous discrete-time platoons in a predecessor-following topology with a constant time headway policy. We assume that each agent in the platoon sends its current position to the immediate follower through a lossy channel modeled as a Bernoulli process. To reduce the negative effects of data loss over the string stability and performance of the platoon, we use simple strategies that modify the measurement, error, and control signals of the feedback control loop, in each vehicle, when a dropout occurs. Such strategies are based on holding the previous value, dropping to zero, or replacing with a prediction based on a simple linear extrapolation. We performed a simulation-based comparison among a set of different strategies, and found that some strategies are favorable in terms of performance, while some others present improvements for string stabilization. These results strongly suggest that proper design of compensation schemes for the communications of interconnected multi-agent systems plays an important role in their performance and their scalability properties.

STABILITY RESULTS FOR NETWORKED CONTROL SYSTEMS SUBJECT TO PACKET DROPOUTS

The PageRank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own PageRank by communicating with linked pages. Here, we focus on the effects of unreliability in the communication and, in particular, model the random data losses as an outcome of Markov chains. By generalizing the aggregated PageRank computation previously developed, we provide a distributed scheme along with analyses on its convergence properties.

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.