Fengzeng Zhu

1. Wireless sensor networks are decentralised systems composed of numerous fixed or mobile sensors that function in a self-organised, multi-hop manner to collaborate in detecting, collecting, proce...

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [ ] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [ ] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.

This paper concerns the adaptive smooth event-triggered consistent estimation problem for discrete nonlinear time-varying systems over sensor networks with uncertain communication topologies. The topology switching law obeys a Markovian stochastic process, where the transition probability is not totally known. To save limited communication resources, a novel adaptive smoothing event-triggered mechanism (ASETM) is proposed to manage data transmission in sensor networks that contain smooth-processed historical data and a dynamic triggering threshold. The developed ASETM benefits from smoothing the transmitted data to avoid certain unexpected triggering events, such as system jitter and large random noise. The sufficient conditions for the existence of the estimator are given in terms of recursive matrix inequalities, which guarantee that the filter error system meets both ${H_\infty }$ performance and envelope constraints. Finally, two simulation examples are provided to demonstrate the effectiveness of the developed estimation 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.

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 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.