Communication Resources Research Articles

Memory-based event-triggered path-following control for a USV in the presence of DoS attack

This paper proposes a novel memory-based event-triggered path-following controller for unmanned surface vehicles (USVs) in the presence of denial of service (DoS) attacks and limited communication resources. In the guidance part, a double distance logical virtual ship (DDLVS) guidance law is developed to eliminate the accumulation error of the yaw angle velocity in the traditional LVS algorithm, and the USV will keep a smoother tracking performance for the curve reference path. In the control part, the memory-based event-triggered fusion control algorithm proposed in this paper incorporates the signal non-transmission phenomenon caused by DoS attacks into the event-triggered interval through a set of adaptive parameters. Consequently, the adverse affect caused by DoS attacks is solved in the process of designing the event-triggered control law. By combining the neural networks (NNs) and the dynamic surface control (DSC) techniques, the updating weights are significantly compressed. The proposed algorithm is with advantages of concise controller structure, low energy cost, and easy engineering implementation. All the error signals of the closed-loop system are proved to be semi-global uniform ultimate bound (SGUUB) by the Lyapunov analyses. Finally, through two numerical examples, including the comparative simulation and path following maneuver in Shuang island (an island distribute in Liaoning Dalian), are provided to verify the effectiveness of the proposed strategy.

An edge‐based event‐triggered delayed distributed algorithm for economic dispatch in smart grids

AbstractThis paper presents an edge‐based event‐triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge‐based event‐triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double‐stochastic matrix augmentation technique provide an equivalent delay‐free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time‐varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.

Event‐triggered output quantization control for asynchronously switched interval type‐2 fuzzy systems

AbstractThis article considers exponential stabilization (ES) of switched interval type‐2 (SIT2) fuzzy systems with asynchronous switching control. The SIT2 fuzzy system switches from one mode to another according to transition probabilities (TPs). A general SIT2 fuzzy quantized event‐triggered output controller (QETOC) is designed to save the communication resources. The QETOC employs distinctive membership functions from those of the IT2 fuzzy model and switches with mode‐pendent switching delay, which is more practical than asynchronously switched control techniques with a common switching delay and control schemes without TP. By designing discretized multiple Lyapunov function (DMLF) and using convex combination technique, sufficient conditions formulated by linear matrix inequalities (LMIs) are obtained to ensure that the DMLF does not ‘jump high’ at the moment of switching, and hence the conservatism of obtained results is significantly reduced since no additional dwell time is needed for the stabilization. It is discovered that the SIT2 fuzzy system is not necessary to be unstable on mismatched intervals, and it can be unstable on matched intervals. The advantages of theoretical analysis are verified by numerical simulations, and the classical assumption that each subsystem is unstable on mismatched intervals is conservative for control design.

Event‐triggered fault detector design in networked fuzzy control systems under denial‐of‐service attacks

AbstractThis article addresses the problem of designing an event‐triggered fault detector within a networked nonlinear system described by a Takagi–Sugeno (T–S) fuzzy model, accounting for time‐varying delays and denial‐of‐service (DoS) attacks characterized by a binary Markov chain. Our focus is on constructing an event‐triggered residual system, which aims to detect malfunctions in the original fuzzy system while optimizing communication resource allocation. Specifically, it is tailored to combat DoS attacks in the event generator‐to‐fault detector channel and mitigate network‐induced time‐varying delays. Utilizing the Lyapunov–Krasovskii function approach, we formulate linear matrix inequalities to establish the necessary conditions ensuring the asymptotic mean‐square stability of the residual system, meeting a predefined performance criterion. Finally, the simulation results are presented to validate the effectiveness of the proposed algorithms.

Linear convergence of event‐triggered distributed optimization with metric subregularity condition

AbstractThis paper designs a continuous‐time algorithm with event‐triggered communication (ETC) for solving a class of distributed convex optimization problems with a metric subregularity condition. First, we develop an event‐triggered continuous‐time optimization algorithm to overcome the bandwidth limitation of multi‐agent systems. Besides, with the aid of Lyapunov theory, we prove that the distributed event‐triggered algorithm converges to the optimum set with an exact linear convergence rate, without the strongly convex condition. Moreover, we provide the discrete version of the continuous‐time algorithm and show its exact linear convergence rate. Finally, we give a comparison example to validate the effectiveness of the designed algorithm in communication resource saving.

A Qualitative Insight into Pre-Departure Orientation Training for Aspiring Nepalese Migrant Workers.

Pre-departure orientation training (PDOT) can help equip aspiring migrant workers with skills and knowledge to mitigate vulnerabilities throughout their migration journey, including health. In Nepal, PDOT has been mandatory since 2004 for migrant workers awaiting labour permits. The current PDOT programme includes country-specific information as well as health and well-being advice. However, the views of trainees and trainers on PDOT are largely unknown. This qualitative study aims to explore perceptions of migrant workers and relevant stakeholders on the content and delivery of PDOT. Six focus group discussions and six in-depth interviews were conducted with migrants, and eight interviews with key stakeholders were conducted. Thematic analysis resulted in five themes: (a) PDOT structure, accessibility, and implementation; (b) role of stakeholders in labour migration process; (c) coordination and governance; (d) curriculum development and relevance; and (e) capacity of trainers and effectiveness of training. Our findings emphasise the need for a more tailored curriculum with relevant information, education, and communication resources, possibly with input from former migrant workers. Regular updates of training topics and resources, as well as continued engagement with migrants after their employment, are essential for meeting the dynamic demands of the global employment market.

Event-triggered [formula omitted]-fault-tolerant consistency control for multi-agent systems

This paper investigates the issue of active fault-tolerant control for multi-agent systems using an event-triggered mechanism, aiming to achieve state-consistency control under a leader–follower framework. The proposed control protocol consists of two main components: leader input estimation and fault tolerant controller design under an event-triggered mechanism. Firstly, a leader–follower error system is constructed, where the leader’s input is considered as a supplementary vector for the system state, resulting in an augmented error system. Then, a distributed state observer is designed for this augmented error system to transform the leader’s input estimation problem into an augmented state estimation issue. Furthermore, an event-triggered mechanism is designed to optimize network communication resources by updating the control law only when specific triggered conditions are met. Next, taking into account the errors introduced by the distributed observer and event-triggered mechanism, the problem of determining the state error feedback gain matrix is transformed into a linear matrix inequality problem using H∞ robustness design and Lyapunov stability analysis methods. Finally, the effectiveness of this theoretical framework is validated through a practical example involving several VTOLs.

Three-dimensional free-will arbitrary time cooperative guidance law against moving target

A novel three-dimensional free-will arbitrary time cooperative guidance law based on biased proportional navigation is proposed for intercepting a high-value moving target. In contrast to existing research, the presented method does not require controlling the interceptors' axial speed or prediction of the interception point. First, the engagement plane is constructed so that a three-dimensional guidance problem can be formulated as a planar problem. Second, a new accurate time-to-go estimation algorithm against the moving target is deduced based on the formulated problem model. Third, a free-will arbitrary time cooperative guidance law is designed utilizing the leader-follower architecture. Multiple missiles can intercept a moving target simultaneously and the intercept time of each missile will converge within a free-will arbitrary time. Fourth, the proposed cooperative guidance law under different communication circumstances is analyzed. When the communication resources are abundant, missiles can reach dynamic average consensus. Meanwhile, the system is robust against the communication imperfection. Finally, numerical simulations are presented to verify the effectiveness of the time-to-go estimation algorithm and proposed cooperative guidance law.

Novel Dynamic Event-Triggered Consensus Control of Multiagent Systems With Markovian Switching Topologies Under DoS Attacks.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

A joint communication and computation design for semantic wireless communication with probability graph

In this paper, the problem of joint communication and computation design for probability graph-based semantic communication over wireless networks is investigated. In the considered model, the base station (BS) extracts the compressed small-sized semantic data by removing redundant information based on the shared knowledge base between the transceivers. In particular, the knowledge base is represented as a probability graph, which summarizes the statistic relations of massive knowledge graphs. On the user side, the compressed information is accurately inferred on the basis of the same probability graph as the BS. Although this approach brings additional computation resource consumption for semantic information extraction, it effectively reduces communication resource consumption through the transmission of small-sized data. Both the communication and computation cost models are derived based on the inference process of the probability graph. Based on the formulated models, the problem of joint communication and computation resource allocation is proposed to minimize the total energy consumption of the network considering both latency and power limitations. To solve this problem, the closed-form solution of the transmission power is obtained with fixed semantic compression ratio. Then, an effective linear search-based algorithm is proposed to obtain the optimal solution of the considered problem with low complexity. Simulation results demonstrate the effectiveness of the proposed system compared with the conventional non-semantic schemes.

Fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems with dead-zone output

This paper investigates the fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems (MASs) with dead-zone output. First, by proposing a dead-zone output approximation model and utilizing Nussbaum-type functions, an adaptive compensation mechanism is developed to alleviate the effect of unknown dead-zone output. Meanwhile, the fuzzy logic systems are incorporated to approximate the unknown functions of the nonlinear MASs. Next, by adopting a time-varying scaling function, a consensus control method is recursively established to steer the consensus errors into a small range within the prescribed time. Furthermore, a self-triggered scheme is established to conserve communication resources while avoiding continuously monitoring trigger conditions. It is illustrated that boundedness of all signals and practical prescribed-time leader-following consensus can be achieved. Finally, the simulation results show the effectiveness of the proposed method.

A novel saturation degree‐dependent adaptive event‐triggered control for systems with asymmetric saturation

AbstractThis paper investigates the control problem of systems with asymmetric saturation by the formulated saturation degree‐dependent event‐triggered mechanism. The subregion partition method is adopted to represent the asymmetric saturation as the symmetric saturation in each subregion. In order to further reduce the triggering number when the system is saturated and save the communication resources of the system, this paper establishes the saturation degree‐dependent adaptive event‐triggered condition. Then, the subregion‐related control law under the presented event‐triggered scheme is designed, and the asymptotic stabilization of the underlying system is realized. Moreover, an optimization algorithm is proposed to estimate the maximal domain of attraction. Finally, simulation examples are used to validate the theoretical results.

Event-Based Distributed Set-Membership Estimation for Complex Networks Under Deception Attacks

This paper addresses the problem of event-based distributed set-membership estimation for complex networks with unknown but bounded (UBB) disturbances. To reflect the compromised data transmissions in cyber security, deception attacks are taken into consideration. Meanwhile, a novel estimation model is proposed against UBB disturbances. In order to schedule the signal transmissions between nodes and remote estimators, a novel decentralized dynamic periodic event-triggered mechanism (DPETM) with a time-varying threshold is developed for each node of the complex networks, which reduces the waste of communication resources and the complexity of computation. Thereafter, a series of distributed set-membership estimators are designed, whose parameters are explicitly determined in terms of the resolution of a particular linear matrix inequality (LMI) related to the information of the communication topology. An optimized ellipsoid estimation set is obtained by applying a recursive optimization algorithm. Finally, the simulation results are shown to demonstrate the viability of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by set-membership state estimation problem of complex networks in practical missions, such as military, environment, industry, etc. The set-membership estimation of complex networks provides a reliable confidence region for each system node. Event-triggered control is an effective method for the design of set-membership estimator. But the common results require the systems to monitor the measurements point-to-point, which leads to the huge consumption of calculation and communication resources. For this reason, this paper originally extends the DPETM to the discrete-time version from the field of continuous-time systems. Meanwhile, this paper considers the deception attacks in communication channels, and the generic framework established earlier can tackle simultaneously sector-bounded nonlinearity, UBB disturbances, and deception attacks. The main difficulty of this paper lies in the analysis for the sawtooth constraint of periodic samplings. For this difficulty, we introduce a piecewise auxiliary function, which is similar with the loop-function in the field of continuous-time systems. Together with recursive optimization algorithm, the detailed analysis method is proposed for the reliable confidence regions of each set-membership estimator.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Event-Triggered Adaptive Bipartite Secure Consensus Asymptotic Tracking Control for Nonlinear MASs Subject to DoS Attacks

This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. First, by incorporating the concept of shortest path into the hierarchical algorithm, the bipartite consensus control problem in the presence of unbalanced communication topology is successfully addressed. Further, an anti-attack bipartite control strategy by designing the improved forms of the tracking errors and virtual controllers is proposed under DoS attacks. Then, a modified event-triggered mechanism based on the relative threshold strategy is proposed, which can ensure that the bipartite consensus tracking errors converge to zero asymptotically by utilizing the Barbalat’s Lemma. Under the Lyapunov stability theory, it is guaranteed that the outputs of all agents can reach agreement with an identical magnitude but opposite signs and all the signals of the closed-loop MASs are bounded. Finally, the simulation results on the model of damping pendulums are given to verify the efficiency of the proposed secure controller. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper, the adaptive bipartite secure consensus asymptotic tracking control problem is considered for nonlinear MASs subject to DoS attacks, whose models can describe many critical applications, such as bidirectional formation of unmanned vehicles and robots. The research on the secure tracking control problem will be rather complicated yet challenging if the DoS attacks of the communication channels are taken into account in the insecure complex network environment. In addition, the event-triggered mechanism is used to reduce the usage of communication resources, which makes the proposed control scheme more easier to implement and more friendly for control engineers.

Quasi-Synchronization of Discrete-Time-Delayed Heterogeneous-Coupled Neural Networks via Hybrid Impulsive Control.

This article explores the quasi-synchronization of discrete-time-delayed heterogeneous-coupled neural networks (CNNs) via hybrid impulsive control. By introducing an exponential decay function, two non-negative regions are introduced that are named time-triggering and event-triggering regions, respectively. The hybrid impulsive control is modeled by the dynamical location of Lyapunov functional in two regions. When the Lyapunov functional locates in the time-triggering region, the isolated neuron node releases impulses to corresponding nodes in a periodical manner. Whereas, when the trajectory locates in the event-triggering region, the event-triggered mechanism (ETM) is activated, and there are no impulses. Under the proposed hybrid impulsive control algorithm, sufficient conditions are derived for quasi-synchronization with a definite error convergence level. Compared with pure time-triggered impulsive control (TTIC), the proposed hybrid impulsive control method can effectively reduce the times of impulses and save communication resources on the premise of ensuring performance. Finally, an illustrative example is given to verify the validity of the proposed method.

Distributed Dynamic Event-Triggered Control for Voltage Recovery in Islanded Microgrids by Using Artificial Delays.

This article tackles secondary voltage recovery problem in islanded microgrids with the aim of reducing communication frequency among distributed generation (DG) units, while maintaining desired performance and saving communication network workload. To pursue this objective, a distributed proportional-integral-derivative controller is first introduced, whose sampled-data implementation is enabled by leveraging the finite-difference approximation for the derivative action, which leads to a distributed proportional-integral-retarded (PIR) controller with a small enough sampling period . Then, the resulting fully distributed PIR control law is combined with a dynamic event-triggered mechanism (DETM), which embeds Zeno-freeness property and avoids the requirement of continuous transmission in triggering process. Thus, the communication burden is significantly mitigated and the waste of communication resources is avoided. By exploiting Lyapunov-Krasovkii method, we derive exponential stability conditions expressed as linear matrix inequalities (LMIs), whose solution allows evaluating the maximum sampling period and DETM parameters preserving the stability of the microgrid. A thorough numerical analysis, carried out on the standard IEEE 14-bus test system, confirms the theoretical derivation.

Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

SummaryThis article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

Fully Distributed Hierarchical ET Intrusion- and Fault-Tolerant Group Control for MASs With Application to Robotic Manipulators

This paper studies group synchronization tracking problem for a class of high-order multi-agent systems (MASs) with nonidentical and unknown direction faults (NUDFs) under multiple cyber attacks (i.e., denial-of-service (DoS) attacks and false data injection attacks (FDIAs)). Be motivated by this, a fully distributed hierarchical (cyber layer and physical layer) Zeno-free event-triggered (ET) intrusion-and fault-tolerant controller is presented based on Nussbaum-type gain technique, where a positive inter-event time exists in the state-dependent asynchronous ET mechanism (ETM). The constructed virtual cyber layer realizes the interaction among different agents so as to avoid the interaction in physical processes and thus reduce the error propagation. This two layer controller greatly increase the flexibility of the controller design compared with single-layer control strategies. In addition, a novel Nussbaum function stability lemma (Lemma lem4lem4) is developed for the first time. Based on this lemma, the fully distributed adaptive controller can adjust the direction of the input to match the fault direction with the help of Nussbaum function. Finally, a robotic manipulator example demonstrates the effectiveness and merit of the proposed control scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In industrial processes, NUDFs and cyber attacks often occur in many different systems, which usually lead to performance degradation or even serious accidents. Typically, NUDFs affect the performance of chemical and industrial processes, circuits, and sensors. Meanwhile, in driverless vehicle platoon, attackers change the control signal through the wireless network, and then issue emergency braking commands, etc. Therefore, this paper designs a fully distributed hierarchical ET intrusion-and fault-tolerant scheme for high-order MASs that are usually used to model ship dynamics, robotic manipulators, and quarter-car active suspension. The control scheme gives solutions to the problem of NUDFs, multiple cyber attacks, and network bandwidth limitations at different layers, and effectively integrates the physical and cyber layers to achieve group synchronization. Meanwhile, a new event-triggered mechanism under the framework of group synchronization is developed to save communication resources. Robotic manipulator simulation studies verify the validity of the proposed scheme.

Analysis of direct traffic at the transport protocol level in the WiMax-1/2 cluster oriented to offload the smart city's wireless ecosystem.

Rerouting of direct information traffic under the WiMax-1/2 technology control in the case of licensed frequency spectrum overload ensures communication continuity in the smart city's critical infrastructure. The support of such a process in the WiMax-1/2 cluster has its specificity, worthy of analytical formalization. The article presents a mathematical apparatus that allows the average service duration of an information message during its transfer from the terminal to the WiMax-1/2 base stationto be estimated. Unlike analogues, the presented concept adequately describes the investigated process for any number of terminals, taking into account both the queuing effect on their side and the functioning of the cumulative query transmission mechanism inherent in WiMax-1/2 technology. Therefore, the proposed mathematical apparatus, describing the process of servicing an information message, takes into account both the average duration accompanied by potential collisions in the process of sending a request for the allocation of communication resources for its transmission to the base station, and the average duration of the information message's stay in the terminal queue. Experimental studies demonstrated the adequacy of the proposed mathematical apparatus for describing the investigated process. The experimental section also formulates the optimization problem of the investigated process resulting from the management of competitive access parameters.