Dynamic Decoupled Event-Triggered Nonlinear State Estimation for Sensor Networks with Incomplete Measurements

The article presents the development of algorithms for data transmission in wireless sensor networks based on fully homomorphic encryption (FHE) using a symmetric encryption algorithm Kuznyechik. During the study, the main points related to the use of modern sensor networks were considered: data transfer protocols, the most common vulnerabilities and various emulators of sensor networks. Approaches to the use of homomorphic encryption for data exchange are considered. An algorithm for the interaction of the base station and sensor nodes of the sensor network for data exchange based on FHE using the Kuznyechik algorithm has been developed. To use the encryption algorithm Kuznyechik as part of the FHE, an algorithm for constructing Zhegalkin polynomials for nonlinear cipher elements has been developed and implemented. For the encryption algorithm Kuznyechik, for the first time, Zhegalkin polynomials are obtained, which describe the operation of the replacement S-block. The use of the obtained Zhegalkin polynomials makes it possible to decrypt the data encrypted by the Kuznyechik algorithm and at the same time leave them in the encrypted form of the FHE.

Low energy assumption is one of design challenges for data transmission for sensor network. In this paper, we identify the drawbacks of direct transmission protocol and hop- by-hop transmission protocol in terms of evenness of energy con- sumption. We present an Energy-Balanced Transmission (EBT) approach for even energy consumption transmission in sensor networks. Simulations show that our flexible approach can be adaptive to various data transmission situations, and make a sensor network achieve a longer and more evenly distributed overall life.

We investigate the characteristic features of data transmission in sensor networks. According to these features, a three-layer model appropriate for sensor networks was obtained by simplifying the OSI seven-layer model. Using this simplified model, a high-speed reliable transmission protocol was presented. Transmission efficiency is improved by optimizing the hardware-driver and simplifying the API functions and reliable transmission is realized through the serial number and request-response mechanism. This protocol provides higher transmission efficiency under the precondition of transmission reliability comparing with TCP/IP protocol. Our experiments showed that the optimized protocol has greatly improved the network utilization than TCP/IP and achieved a reliable transfer speed up to 920Mbps.

This study addresses the state estimation problem for the preceding vehicle in vehicle-to-vehicle (V2V) cooperative perception, with the objective of balancing estimation accuracy against communication load under constraints of limited bandwidth and non-Gaussian noise interference. To this end, an extended Kalman filtering algorithm is proposed that integrates an event-triggered (ET) mechanism with the maximum correntropy criterion (MCC). The event-triggered mechanism adaptively regulates communication frequency, significantly reducing network load while preserving estimation performance. Meanwhile, the maximum correntropy criterion enhances the filter’s robustness against non-Gaussian noise, enabling high-precision state estimation under limited communication resources. Simulation results under double lane-change and continuous sinusoidal steering scenarios show that, compared to conventional extended Kalman filtering and existing event-triggered robust filtering methods, the proposed algorithm achieves better estimation accuracy and stability in non-Gaussian noise environments. Moreover, it reduces the average communication load by approximately 68% without compromising estimation performance. This work provides an effective solution for state estimation in V2V cooperative perception that combines high robustness with high communication efficiency.

Meng, X. and Li, X., 2020. Research on optimization of port logistics distribution path planning based on intelligent group classification algorithm. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 205-207. Coconut Creek (Florida), ISSN 0749-0208.At present, the logistics industry has become an important part of social and economic development. The success of modern logistics enterprises depends on efficient cooperation with all upstream and downstream enterprises in the supply chain, and information technology is the prerequisite for such cooperation. Artificial Fish Swarm Algorithm (AFSA) is applied to robot path planning in intelligent bionic way. In order to improve the solving speed of the planning and reduce the length of the planning path, the convergence speed of the algorithm in the later stage is reduced and the local optimal solution is easily trapped. Location information is of great significance to the application of artificial fish swarm algorithm. The location of events or information sources is an important part of data transmission in sensor networks. The operation of the distribution system plays a vital role in the whole logistics system, and its analysis and design are the premise for its good operation.

In this paper are presented the results regarding the simulation and the implementation of an enhanced communication interface based on pulse position modulation technique. The proposed module is aimed for integration in the conditioning circuits of the sensors that are connected to data acquisition systems which require transmission of information signals over short to moderate distances. Because the circuit allows that the optical driver of the transmitter to be operated at high power levels results an increased signal-to-noise ratio and an extended lifetime for the optical source. These advantages make this design particularly suited for the free space laser or optical fiber based data transmission in sensor networks. The novel approach based on variable width carrier pulses allows the control of the frequency spectrum for the transmitted signal and an improved signal-to-noise ratio, these characteristics leading to increased performances for the proposed module. Compared with other circuits based on cable or radio transmission, the optical communication interface presented in this paper represent a very suitable solution for transferring the signals from sensors, because it ensures high immunity to interferences and superior data transfer rates.

This article investigates the adaptive fuzzy distributed optimal event-triggered (ET) control problem for high-order nonlinear multiagent (NMA) system under weight-unbalanced directed graph. An optimal signal generator with ET mechanism is first established to estimate optimization point and optimal network channel utilization between agents and neighbors. Second, since the ET mechanism is introduced in optimal signal generator, the first-order derivative of optimal virtual signals is not continuous at the trigger instant, a high-order filter is established to estimate high-order derivatives of optimal virtual signals. Based on the optimal signal generator with ET mechanism and high-order filter, an adaptive fuzzy distributed optimal ET control scheme is proposed by backstepping control design technology. It is shown that controlled NMA system is asymptotically stable and the global cost function is minimized. Finally, we apply the proposed distributed optimal ET control approach to marine surface vehicle systems, the simulation and comparison results verify its effectiveness.

In this work, the flexible performance-based control scheme is presented for the networked uncertain mechanical system under the quasi-periodic denial-of-service (DoS) attack and the appointed event-triggering (ET) mechanism. This scheme features the capability of releasing and recovering the prescribed performance in light of the activation and sleeping of the DoS attack. These functionalities attribute to the introduction of a self-regulation auxiliary system that is developed based on the theories of the positive system and the finite-time stability. Furthermore, the neural network (NN) is utilized to approximate the system uncertainty, and the noncertainty-equivalent structure is adopted to construct the update law of the NN. Then, the influence of the quasi-periodic DoS attack and the appointed ET mechanism on uncertainty approximation are totally isolated. Based on the Lyapunov function method and the invariant set theory, the boundedness of the closed-loop system is theoretically proved. Finally, both the numerical and experimental illustrations are presented to demonstrate the effectiveness and the benefits of the developed control scheme.

In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.

In recent years, intelligent manufacturing has developed rapidly. Industrial IoT technologies are applied to different industrial scenarios. OPC UA is a service-oriented architecture, which has good interoperability. At the same time, it is a cross-platform communication standard that can realize the interconnection between heterogeneous network devices and solve the problem of information silos in the industrial IoT field. However, with the gradual increase of industrial equipment, the amount of data transmission in the network is increasing. The traditional OPC UA communication method based on client/server mode has defects such as tight coupling and performance bottleneck. It cannot meet the high throughput demand of the network. Therefore, these phenomena lead to longer time delays and lower transmission efficiency of network communication systems. To solve the above problems, this paper proposes a publish/subscribe communication model based on OPC UA. We design the overall architecture of the OPC UA publish/subscribe model and adopt a message agent mechanism to realize distributed communication of OPC UA. This model has functions such as message modeling, address space construction, and publish/subscribe. The architecture of this communication model is compatible with the C/S model, which can ensure compatibility and coexistence with the traditional OPC UA communication system. Meanwhile, in order to improve the distinguished service capability of the OPC UA system, a multi-priority data scheduling algorithm is proposed and integrated into the publish/subscribe communication model to improve the efficiency of real-time data transmission in industrial networks. The experimental results show that the communication model can accomplish distributed communication in industrial networks and be better applied in wireless sensor networks. The scheduling algorithm included in the model significantly improves the efficiency of real-time data transmission and reduces the time delay.

To fully adapt to the distributed access of renewable energy, microgrid technology has been developed rapidly. Aiming at the coordination and efficient regulation of distributed resources in microgrid, this paper proposes a distributed autonomous economic control strategy for microgrid considering event triggering mechanism. First, a distributed autonomous economic control architecture is built to provide a distributed operation architecture for optimal regulation of the microgrid. Secondly, a distributed secondary control strategy based on the consensus control theory is established to realize the economic allocation of active power as well as safe and stable operation of the microgrid. On this basis, an event trigger protocol based on the consensus error of the control variables is constructed, which is conductive to reduce redundant communication. The stability of the event trigger protocol is deduced by means of Lyapunov function analysis. The simulation analysis based on the equivalent microgrid verifies that the proposed control strategy can reduce redundant communication and acquire fair distribution of reactive power and active power among DGs, realizing distributed, economical and safe operation of microgrid.

This paper investigates the distributed event-triggered (ET) ${H} _{\boldsymbol \infty }$ filtering problem for discrete-time T–S fuzzy systems over sensor networks. In order to mitigate the effect of channel noise on the filtering error systems, dynamic saturation levels are considered in this paper. Different from the existing linear distributed ET filters design results, new piecewise fuzzy distributed filters are established to address the challenges caused by the distributed ET mechanisms. By constructing piecewise differentiable Lyapunov functionals, sufficient conditions in terms of linear matrix inequalities are obtained to guarantee the exponential stability and desired performance of the filtering error system. Finally, two examples are employed to verify the effectiveness of the proposed method.

Aiming at the limited network bandwidth and various cyber attacks, the H∞ neural network controller triggered by discrete events is studied. In order to further improve the utilization of network bandwidth, the event triggering mechanism is improved by taking advantage of the advantages of event triggering in network transmission. At the same time, due to various uncertain and deliberate network attacks, data tampering and packet loss problems are caused. A new neural network control system model is established based on the attack mode obeying Bernoulli probability and dynamic integration. By using Lyapunov function, sufficient conditions are obtained to ensure the asymptotic stability of the closed-loop system, which meets the requirements of H∞. Finally, a numerical example is given to verify the validity of the results.

Event triggered prescribed time convergence sliding mode control of DFIG with disturbance rejection capability

In this article, a novel hybrid event-triggered (ET) control framework is constructed based on the adaptive critic technique, aiming to address the optimal tracking issue of discrete-time nonlinear networked control systems. First, an augmented plant is created by combining the system state with the reference trajectory, transforming the optimal tracking control design into the optimal regulation problem of the reconstructed nonlinear error system. Subsequently, to conserve communication network resources and ensure the stability of the error system, a hybrid ET mechanism is developed to determine a constant interval for event silence. This approach not only alleviates the limited network bandwidth but also eliminates the need for continuous evaluation of triggering conditions, as seen in traditional event-based methods. Regarding algorithm implementation, the model, critic, and action networks are established to execute the online adaptive critic algorithm, which allows the tracking control policy to be adjusted in real-time to reach the optimal level. Finally, an experimental plant with nonlinear characteristics is presented to illustrate the overall performance of the proposed online tracking control method with the hybrid ET mechanism.