Showing papers on "Asynchronous communication published in 2023"

Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences

Abstract: Perceiving human emotions from a multimodal perspective has received significant attention in knowledge engineering communities. Due to the variable receiving frequency for sequences from various modalities, multimodal streams usually have an inherent asynchronous challenge. Most previous methods performed manual sequence alignment before multimodal fusion, which ignored long-range dependencies among modalities and failed to learn reliable crossmodal element correlations. Inspired by the human perception paradigm, we propose a target and source Modality Co-Reinforcement (MCR) approach to achieve sufficient crossmodal interaction and fusion at different granularities. Specifically, MCR introduces two types of target modality reinforcement units to reinforce the multimodal representations jointly. These target units effectively enhance emotion-related knowledge exchange in fine-grained interactions and capture the crossmodal elements that are emotionally expressive in mixed-grained interactions. Moreover, a source modality update module is presented to provide meaningful features for the crossmodal fusion of target modalities. Eventually, the multimodal representations are progressively reinforced and improved via the above components. Comprehensive experiments are conducted on three multimodal emotion understanding benchmarks. Quantitative results show that MCR significantly outperforms the previous state-of-the-art methods in both word-aligned and unaligned settings. Additionally, qualitative analysis and visualization fully demonstrate the superiority of the proposed modules.

Adaptive Bipartite Output Tracking Consensus in Switching Networks of Heterogeneous Linear Multiagent Systems Based on Edge Events

Abstract: This article focuses on the problem of adaptive bipartite output tracking for a class of heterogeneous linear multiagent systems (MASs) by asynchronous edge-event-triggered communications under jointly connected signed topologies. By designing the observers to estimate the states of followers and the dynamic compensators to estimate the states of zero input and nonzero input leader, respectively, the fully distributed edge-event-triggered control protocol is presented. Moreover, it is proven that the bipartite output tracking problem is implemented, and the systems do not exhibit Zeno behavior under a fully distributed control strategy with edge-event-triggered mechanisms. Compared with the existing works, one of the highlights of this article is the design of triggering mechanisms, under which the leader avoids continuous information transmission and any pair of followers that make up the edge asynchronously transmit information through the edge. The methods greatly avoid unnecessary information transmission in the systems. Finally, several simulation examples are introduced to demonstrate the theoretical results obtained in this article.

Dynamic Event-Triggered Adaptive NN Control for Switched Uncertain Nonlinear Systems

Abstract: This article is concerned with the problem of dynamic event-triggered adaptive neural network (NN) control for a class of switched strict-feedback uncertainty nonlinear systems. A novel switched command filter-based dynamic event-triggered adaptive NN control approach is set up by exploiting the backstepping and command filter and the common Lyapunov function method. Since adaptive controllers of subsystems are event triggered, then if the switching happens between any two consecutive triggering instants, asynchronous switching will arise between candidate controllers of subsystems and subsystems. Unlike the existing literature, where maximum asynchronous time is restricted, without any strict limitations on maximum asynchronous time being needed in this article, the asynchronous switching problem is directly handled by proposing a novel switching dynamic event-triggered mechanism (DETM) and event-triggered adaptive controllers of subsystems. Moreover, a piecewise constant variable is introduced into the switching DETM, which overcomes the difficulty of switched measurement error being discontinuous. Also, a strictly positive lower bound of interevent times is obtained. Finally, a continuous stirred tank reactor system and a numerical example are presented to demonstrate the effectiveness of the developed approach.

Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

Abstract: The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.

Redefining Health Education in the Post-Pandemic World

Abstract: The COVID-19 pandemic has led to significant disruptions in health education. At the time of crisis, digital technologies were instrumental in delivering synchronous and asynchronous online classes. The maximization of digital technologies during emergency remote education ensured the training continuity of future healthcare professionals. Nevertheless, online education does not reflect all the potential that instructional technologies can offer to the education sector. There have been emerging technologies and tools that can shape learning in higher education. Moreover, digital health is increasingly becoming an integral part of healthcare services as practitioners require new knowledge and skills to serve digitally-enabled patients. Consequently, in addition to adding these topics to the health curricula, we can also benefit from the use of technology as a health intervention. In this chapter, the authors discussed how curricula can profit from technology inclusion, and which limitations need to be considered to empower learners, educators, and leaders in the digital age.

Fuzzy-Model-Based Lateral Control for Networked Autonomous Vehicle Systems Under Hybrid Cyber-Attacks

Abstract: This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

Asynchronous Federated Deep Reinforcement Learning-Based URLLC-Aware Computation Offloading in Space-Assisted Vehicular Networks

Abstract: Space-assisted vehicular networks (SAVN) provide seamless coverage and on-demand data processing services for user vehicles (UVs). However, ultra-reliable and low-latency communication (URLLC) demands imposed by emerging vehicular applications are hard to be satisfied in SAVN by existing computation offloading techniques. Traditional deep reinforcement learning algorithms are unsuitable for highly dynamic SAVN due to the underutilization of environment observations. An AsynchronouS federaTed deep Q-learning (DQN)-basEd and URLLC-aware cOmputatIon offloaDing algorithm (ASTEROID) is presented in this paper to achieve throughput maximization considering the long-term URLLC constraints. Specifically, we first establish an extreme value theory-based URLLC constraint model. Second, the task offloading and computation resource allocation are decomposed by employing Lyapunov optimization. Finally, an asynchronous federated DQN-based (AF-DQN) algorithm is presented to address the UV-side task offloading problem. The server-side computation resource allocation is settled by an queue backlog-aware algorithm. Simulation results verify that ASTEROID achieves superior throughput and URLLC performances.

Adaptive Asynchronous Federated Learning in Resource-Constrained Edge Computing

Abstract: Federated learning (FL) has been widely adopted to train machine learning models over massive data in edge computing. However, machine learning faces critical challenges, e.g., data imbalance, edge dynamics, and resource constraints, in edge computing. The existing FL solutions cannot well cope with data imbalance or edge dynamics, and may cause high resource cost. In this paper, we propose an adaptive asynchronous federated learning (AAFL) mechanism. To deal with edge dynamics, a certain fraction $\alpha$ of all local updates will be aggregated by their arrival order at the parameter server in each epoch. Moreover, the system can intelligently vary the number of local updated models for global model aggregation in different epochs with network situations. We then propose experience-driven algorithms based on deep reinforcement learning (DRL) to adaptively determine the optimal value of $\alpha$ in each epoch for two cases of AAFL, single learning task and multiple learning tasks, so as to achieve less completion time of training under resource constraints. Extensive experiments on the classical models and datasets show high effectiveness of the proposed algorithms. Specifically, AAFL can reduce the completion time by about 70 percent and improve the learning accuracy by about 28 percent under resource constraints, compared with the state-of-the-art solutions.

Adaptive Fuzzy Asynchronous Control for Nonhomogeneous Markov Jump Power Systems Under Hybrid Attacks

Abstract: This article investigates the adaptive fuzzy asynchronous control problem for discrete-time nonhomogeneous Markov jump power systems under hybrid attacks. A nonhomogeneous Markov process is used to describe the phenomenon of transient failures occurring in power lines and subsequent switching of associated circuit breakers. The corresponding nonhomogeneous hidden Markov model is utilized to detect the jump modes of power systems. Both deception attack and denial-of-service attack are analyzed simultaneously owing to the vulnerability of power systems. With detected modes and fuzzy logic systems, an adaptive fuzzy asynchronous control strategy is proposed. Using the mode-dependent Lyapunov function, the existence conditions of the desired controller law are obtained such that the closed-loop power systems are bounded stable in the mean-square sense. Finally, the usefulness of the developed control strategy is demonstrated by a numerical example.

Output-Feedback Control for Fuzzy Singularly Perturbed Systems: A Nonhomogeneous Stochastic Communication Protocol Approach

Abstract: In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi–Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov–Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

Asynchronous updating Boolean network encryption algorithm

Abstract: An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Abstract: Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL, the global model is periodically updated by aggregating all vehicles' local models. However, vehicles may frequently drive out of the coverage area of VEC before they finish the local model training and thus the traditional FL cannot upload all local models as expected, which would degrade the accuracy of global model. The asynchronous FL can be performed without aggregating all vehicles' local models, thus more local models can be uploaded to improve the accuracy of global model. The vehicle mobility significantly impacts the asynchronous FL. There is no published work considering the vehicle mobility to design the cooperative caching in VEC based on asynchronous FL. In addition, the caching capacity of RSU is limited and the size of the predicted popular contents usually exceeds the cache capacity of RSU. Hence, VEC should cache the predicted popular contents in different RSUs while considering content transmission delay. In this paper, we consider vehicle mobility and propose a cooperative caching scheme in the VEC based on asynchronous federated and deep reinforcement learning (CAFR) to predict popular contents and further obtain the optimal cooperative caching location for the predicted popular contents. Extensive experimental results have demonstrated that CAFR scheme outperforms other baseline caching schemes.

Leaderless and Leader-Following Bipartite Consensus of Multiagent Systems With Sampled and Delayed Information

Abstract: This article proposes a hybrid systems approach to address the sampled-data leaderless and leader-following bipartite consensus problems of multiagent systems (MAS) with communication delays. First, distributed asynchronous sampled-data bipartite consensus protocols are proposed based on estimators. Then, by introducing appropriate intermediate variables and internal auxiliary variables, a unified hybrid model, consisting of flow dynamics and jump dynamics, is constructed to describe the closed-loop dynamics of both leaderless and leader-following MAS. Based on this model, the leaderless and leader-following bipartite consensus is equivalent to stability of a hybrid system, and Lyapunov-based stability results are then developed under hybrid systems framework. With the proposed method, explicit upper bounds of sampling periods and communication delays can be calculated. Finally, simulation examples are given to show the effectiveness.

Security-Based Passivity Analysis of Markov Jump Systems via Asynchronous Triggering Control

Abstract: This article considers the security-based passivity problem for a class of discrete-time Markov jump systems in the presence of deception attacks, where the deception attacks aim to change the transmitted signal. Considering the impact of deception attacks on network disruption, it causes the existence of time-varying delays in signal transmission inevitably, which makes the controlled system and the controller work asynchronously. The asynchronous control method is employed to overcome the nonsynchronous phenomenon between the system mode and controller mode. On the other hand, to reduce the frequency of data transmission, a resilient asynchronous event-triggered control scheme taking deception attacks into account is designed to save communication resources, and the proposed controller can cover some existing ones as special examples. Moreover, different triggering conditions corresponding to different jumping modes are developed to decide whether state signals should be transferred. A new stability criterion is derived to ensure the passivity of the resultant system although there exist deception attacks. Finally, a simulation example is given to verify the theoretical analysis.

Student-content interactions: Exploring behavioural engagement with self-regulated inquiry-based online learning modules

Abstract: Abstract Technological innovations and changing learning environments are influencing student engagement more than ever before. These changing learning environments are affecting the constructs of student behavioural engagement in the online environment and require scrutiny to determine how to facilitate better student learning outcomes. Specifically, recent literature is lacking in providing insights into how students engage and interact with online content in the self-regulated environment, considering the absence of direct teacher support. This paper investigates how instructional design, informed by the factors relating to behavioural engagement, can influence the student-content interaction process within the fabric of inquiry-based learning activities. Two online learning modules on introductory science topics were developed to facilitate students’ independent study in an asynchronous online environment. The study revealed that students showed a high commitment to engaging and completing the tasks that required less manipulative and pro-active effort during the learning process. The findings also revealed that instructional guidance significantly improved the behavioural engagement for student groups with prior learning experience in science simulations and technology skills. This study highlights several issues concerning student engagement in a self-regulated online learning environment and offers possible suggestions for improvement. The findings might contribute to informing the practice of teachers and educators in developing online science modules applicable to inquiry-based learning.

The Bipartite Edge-Based Event-Triggered Output Tracking of Heterogeneous Linear Multiagent Systems

Abstract: This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

Resilient Load Frequency Control of Islanded AC Microgrids Under Concurrent False Data Injection and Denial-of-Service Attacks

Abstract: Due to malicious cyber attacks, the frequency regulation of an islanded microgrid (MG) with load changes and wind/solar power fluctuations may not be guaranteed and the overall system may even be destabilized. The MG frequency control thus faces new challenges. In response to these challenges, this paper addresses a resilient load frequency control (LFC) problem for islanded AC-MGs under simultaneous false data injection (FDI) attacks and denial-of-service (DoS) attacks. Toward this aim, a new piecewise observer is constructed to provide the real-time estimates of the unavailable system state and the unknown FDI attack signal. Furthermore, a resilient $\mathcal {H}_{\infty }$ LFC scheme is developed to suppress the attack impacts. The novelty of this study lies in the development of an attack-parameter-dependent time-varying Lyapunov function approach to achieve stability analysis and resilient observer/controller design against concurrent FDI attacks and intermittent DoS attacks. Specifically, a tractable observer design criterion is first derived such that the estimation error is exponentially stable under a specified $\mathcal {H}_{\infty }$ performance level. Then a design criterion on the existence of the resilient controller is presented to guarantee the exponential stability of the resulting closed-loop system in the presence of the attacks, while preserving the anticipated $\mathcal {H}_{\infty }$ performance level. Finally, comparative simulation studies in various attack scenarios and different parameter settings are presented to verify the efficiency of the obtained theoretical results.

Event-Based Fuzzy Tracking Control for Nonlinear Networked Systems Subject to Dynamic Quantization

Abstract: This paper investigates the problem of event-based output feedback tracking control for discrete-time nonlinear networked systems with dynamic quantization. The Takagi-Sugeno (T-S) fuzzy systems theory is utilized to approximate the investigated nonlinear systems. Three general dynamic quantizers and an improved asynchronous event-triggering communication scheme are carried out to comprehensively decrease the amount of data in the communication of network and realize the rational utilization of limited communication resources. The objective is to design an event-based static output feedback tracking controller such that, in the presence of dynamic quantization, the closed-loop system can be asymptotically stabilized, and the tracking error achieves the predefined tracking performance. Moreover, the parameters for the desired dynamic quantizers and tracking controller can be obtained simultaneously by solving a set of linear matrix inequalities. Finally, the simulation responses are provided to demonstrate the validity of the proposed tracking control strategy.

Chimeras on a ring of oscillator populations.

Abstract: Chimeras occur in networks of coupled oscillators and are characterized by coexisting groups of synchronous oscillators and asynchronous oscillators. We consider a network formed from N equal-sized populations at equally spaced points around a ring. We use the Ott/Antonsen ansatz to derive coupled ordinary differential equations governing the level of synchrony within each population and describe chimeras using a self-consistency argument. For N=2 and 3, our results are compared with previously known ones. We obtain new results for the cases of 4,5,…,12 populations and a numerically based conjecture resulting from the behavior of larger numbers of populations. We find macroscopic chaos when more than five populations are considered, but conjecture that this behavior vanishes as the number of populations is increased.

Distributed Bayesian Inference Over Sensor Networks

Abstract: In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback–Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.

Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Abstract: Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

Distributed and Expensive Evolutionary Constrained Optimization With On-Demand Evaluation

Abstract: Expensive optimization problems (EOPs) are common in industry and surrogate-assisted evolutionary algorithms (SAEAs) have been developed for solving them. However, many EOPs have not only expensive objective but also expensive constraints, which are evaluated through distributed ways. We define this kind of EOPs as distributed expensive constrained optimization problems (DECOPs). The distributed characteristic of DECOPs leads to the asynchronous evaluation of both objective and constraints. Though some researchers have studied the asynchronous evaluation of objectives, the asynchronous evaluation of constraints has not gained much attention. Therefore, this article gives a formal formulation of DECOPs and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE can adaptively evolve different constraints in an asynchronous way through the on-demand evaluation strategy. The on-demand evaluation works from two aspects to improve the population convergence and diversity. From the aspect of individual selection, a joint sample selection strategy is adopted to determine which candidates are promising. From the aspect of constraint selection, an infeasible-first evaluation strategy is devised to judge which constraints need to be further evolved. Extensive experiments and analyses on benchmark functions and engineering problems demonstrate that DEAOE has better performance and higher efficiency compared to centralized state-of-the-art SAEAs.

Investigation of energy indicators with asymmetry of the voltage of the power source of mainline electric locomotives of alternating current

Abstract: The article discusses the issues of improving the energy performance of mainline locomotives with asynchronous motors through the use of a method for controlling a traction induction motor according to the criterion of minimum electrical power losses with asymmetry of the source voltage. This ensures a decrease in electrical losses in the traction asynchronous drive in the entire operating power range.

Almost-Surely Terminating Asynchronous Byzantine Agreement Against General Adversaries with Optimal Resilience ✱

Abstract: In this work, we present an almost-surely terminating asynchronous Byzantine agreement (ABA) protocol for n parties. Our protocol requires expected time and is secure against a computationally-unbounded malicious (Byzantine) adversary, characterized by a non-threshold adversary structure , which enumerates all possible subsets of potentially corrupt parties. Our protocol has optimal resilience where satisfies the condition; i.e. union of no three subsets from covers all the n parties. To the best of our knowledge, this is the first almost-surely terminating ABA protocol with condition. Previously, almost-surely terminating ABA protocol is known with non-optimal resilience where satisfies the condition; i.e. union of no four subsets from covers all the n parties. To design our protocol, we present a shunning asynchronous verifiable secret-sharing (SAVSS) scheme with condition, which is of independent interest.