This paper presents a distributed state estimation method for nonlinear systems over sensor networks with Semi-Markovian switching topologies (S-MSTs). An adaptive event-triggered quantization scheme (AETQS) is developed to reduce the communication and computation burden for bandwidth-constrained sensor networks, where the quantified measurement data is determined by the specific event triggering condition. The filtering network topology evolves over time, which is assumed to be governed by a Semi-Markov chain. Based on the Semi-Markov kernel theory and Lyapunov stability theory, sufficient conditions are obtained to guarantee that the error dynamics has $\sigma$ -error mean square stability and ${H_\infty }$ performance, which is given in the form of linear matrix inequalities. Then, the optimal disturbance attenuation level, initial triggering thresholds, and elapsed-time-dependent distributed filter gains can be determined by addressing a convex optimization problem. Finally, two numerical examples are presented to verify the effectiveness of the proposed approach.

This article deals with the distributed filtering problem for a class of discrete-time Markov jump systems over sensor networks. First, in the distributed filtering network, each local filter simultaneously fuses the estimation and measurement from itself and neighboring nodes to achieve the system state estimation. And each sensor intelligent node is embedded with an event-triggered sampling mechanism, which can reduce the computation load or saving limited network bandwidth. Then, we use Bernoulli stochastic variables to describe whether the filtering network can successfully receive the system jump modes. Next, based on the Lyapunov stability theory, we design a distributed filter dependent on partial system modes, which has the exponential stability in mean square and $H_{\infty }$ performance. Finally, all desired estimator parameters can be obtained by solving a set of linear matrix inequalities. Moreover, two numerical examples are given to illustrate the effectiveness of the distributed $H_{\infty }$ filtering design approach.

This paper develops a distributed adaptive event-triggered iterative learning controller over a filtering network for a class of tracking control systems. The proposed distributed control method is more robust than centralized control in terms of node failure and transmission constraints. In view of the limited network bandwidth, adaptive event-triggered mechanisms (AETMs) have been considered in the network communication process. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. The existence of filters and controllers is analyzed by Lyapunov stability theory and linear matrix inequality techniques, and their parameters are finally obtained. Moreover, some simulation results on a numerical example and a irrigation canal are presented to show the applicability of the obtained results.

This paper is concerned with the distributed full- and reduced-order $l_2$ - $l_{\infty }$ state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed which uses homogeneous Markov chain to reflect the change of filtering networks communication modes. Then, the sector-bound deception attacks among the communication channels are taken into consideration, which could better characterize the filtering network communication security. Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks. Furthermore, through an adjustable parameter E, we can obtain full- and reduced-order $l_2$ - $l_{\infty }$ state estimator over sensor networks, respectively. Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem. The purpose of solving the problem is to design a distributed full- and reduced-order filter such that, in the presence of deception attacks, stochastic external interference and switching topologies, the resulting filtering dynamic system is exponentially mean-square stable with prescribed $l_2$ - $l_{\infty }$ performance index. Finally, a simulation example is provided to show the effectiveness and flexibility of the designed approach.

This article focuses on a class of distributed security estimation for cyber-physical systems against denial-of-service attacks. First, an energy-constrained periodic denial-of-service jamming attack model and a Markov stochastic process-based switching topology model are developed. Then, Bernoulli random variables are applied to indicate whether measurement and estimation between nodes are successfully transmitted, where the data loss phenomenon is subject to denial-of-service attacks and inherent network properties. With the help of Lyapunov functions and linear matrix inequalities, sufficient conditions are established to guarantee that the error system satisfies exponentially mean-square stability and robust [ ] performance. Finally, two numerical simulation examples prove the effectiveness of the proposed distributed filtering method.