Limited Communication Resources Research Articles

Distributed fusion filtering for cyber-physical systems under Round-Robin protocol: a mixed H 2/H ∞ framework

ABSTRACT In this paper, the distributed mixed fusion filter design problem is investigated for a class of cyber-physical systems subject to non-ideal measurements under Round-Robin protocol. To save the limited communication resources, the Round-Robin protocol is employed to schedule the data transmissions from the sensors to the remote filters. The measurement is imperfect by taking into account the noises, the random saturations, nonlinearity perturbations and packet dropouts. The objective of this study is to propose a desired fusion filtering method that ensures the prescribed performance constraints on both the local and fusion filtering error dynamics. With the aid of Lyapunov function and stochastic analysis technique, a sufficient condition is established to guarantee the mixed / performance index for the local filtering error systems, and then the filter parameter matrices are derived by resorting to the solutions to some matrix inequalities. Subsequently, on the basis of the obtained local state estimates, the proper fusion parameters are acquired by solving a convex optimisation problem. Finally, a numerical example is provided to demonstrate the effectiveness of the designed fusion filter.

Reinforcement Learning-Based Device Scheduling for Renewable Energy-Powered Federated Learning

Due to its unique privacy protection advantages, emerging federated learning (FL) is regarded as a significant technique to enable Industry 4.0. However, the industrial deployment of FL encounters the primary obstacles of limited device energy and system communication resources. Nowadays, renewable energy-powered devices have been deployed in various industrial fields to tackle the challenges of unsustainable and limited energy of battery-powered devices. Inspired by this, this paper proposes a novel FL protocol to groundbreakingly improve the performance of renewable energy-powered FL systems. Specifically, with the underlying theory of FL as the guide, the proposed protocol features a reinforcement learning (RL)-based device scheduling solution to adapt to intermittent renewable energy supply. Following this device scheduling solution, an integer linear programming (ILP)-based bandwidth management scheme is introduced to optimize communication efficiency. Experimental results on two representative data distribution situations demonstrate that compared with the state-of-the-art schemes, our FL protocol can boost up to 36.63% and 50.99% accuracy, respectively.

Fully distributed event‐triggered consensus control for linear multiagent systems under DoS attacks

AbstractThis article investigates the event‐triggered consensus problem of general linear multiagent systems with denial‐of‐service (DoS) attacks. A distributed event‐triggered mechanism is presented to alleviate the limited communication resources, the designed mechanism is fully distributed owing to the introduction of some adaptive parameters. Moreover, since the DoS attacks have been considered in the event‐triggered rule design, a switching strategy for the adaptive parameters is adopted. A switching Lyapunov function is used to strictly demonstrate that secure consensus can be reached effectively under the proposed control mechanism. The quantitative relationship between the frequency of the DoS attacks and the consensus is also presented. Moreover, the Zeno behaviour can be excluded for the studied close‐looped systems. Finally, a numerical simulation example is given to demonstrate the validity of the designed control law.

Guest Editorial Communication-Efficient Distributed Learning Over Networks

Distributed machine learning is envisioned as the bedrock of future intelligent networks, where agents exchange information with each other to train models collaboratively without uploading data to a central processor. Despite its broad applicability, a downside of distributed learning is the need for iterative information exchange between agents, which may lead to high communication overhead unaffordable in many practical systems with limited communication resources. To resolve this communication bottleneck, we need to devise communication-efficient distributed learning algorithms and protocols that can reduce the communication cost and simultaneously achieve satisfactory learning/optimization performance. Accomplishing this goal necessitates synergistic techniques from a diverse set of fields, including optimization, machine learning, wireless communications, game theory, and network/graph theory. This Special Issue is dedicated to communication-efficient distributed learning from multiple perspectives, including fundamental theories, algorithm design and analysis, and practical considerations.

Scheduling and Aggregation Design for Asynchronous Federated Learning Over Wireless Networks

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents. In this work, we propose an asynchronous FL design with periodic aggregation to tackle the straggler issue in FL systems. Considering limited wireless communication resources, we investigate the effect of different scheduling policies and aggregation designs on the convergence performance. Driven by the importance of reducing the bias and variance of the aggregated model updates, we propose a scheduling policy that jointly considers the channel quality and training data representation of user devices. The effectiveness of our channel-aware data-importance-based scheduling policy, compared with state-of-the-art methods proposed for synchronous FL, is validated through simulations. Moreover, we show that an ``age-aware'' aggregation weighting design can significantly improve the learning performance in an asynchronous FL setting.

A Triggerless Backdoor Attack and Defense Mechanism for Intelligent Task Offloading in Multi-UAV Systems

In recent years, multi-unmanned aerial vehicular systems (MUAV) have become prevalent in divergent applications: agriculture, spectrum utilization, transportation, forest fire monitoring, among others, due to their flexible, robust, and autonomous operational maneuver. Battery-powered multi-UAV systems possess limited computation and communication resources, significantly reducing their functional dimension by limiting mission time and range. To address this issue, we propose a federated deep reinforcement learning (FDRL) based intelligent and decentralized task offloading scheme for resource-constrained UAVs that can enhance the operational capability of the MUAV systems. Moreover, the proposed FDRL scheme can improve offloading policy quality while preserving data privacy in MUAV. However, such intelligent systems may fall prey to backdoor attacks that can intervene in the system’s regular operation causing rapid degradation of its performance. We introduce a novel triggerless backdoor attack scheme on intelligent task offloading UAVs and analyze its impact to gauge the resiliency of the offloading policy in the presence of an adversary. Then, we propose lightweight agnostic defense mechanisms to combat such backdoors in multi-UAV settings. The extensive simulation results show that the proposed attack and defense strategies are practical and efficient.

Intelligent Offloading and Resource Allocation in Heterogeneous Aerial Access IoT Networks

Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs) and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to enhance the service experience of Internet of Things Devices (IoTD), especially in underserved areas where terrestrial base stations (TBSs) do not exist. In such scenarios, optimally orchestrating the limited computation, communication, and energy resources in both HAPs and UAVs is crucial toward for an efficient aerial networking infrastructure. Thus, in this study, we investigate and formulate the joint IoTDs association, partial offloading, and communication resource allocations (JAPORAs) decisions problem in heterogeneous Aerial Access IoT (AAIoT) networks to maximize service satisfaction for IoTDs, while minimizing their total energy consumption. In particular, the formulated problem is transformed into a multiagent Markov decision process (MAMDP) to deal with its nonconvexity and environmental dynamicity. To solve the problem, we propose a multiagent policy-gradient-based deep actor–critic algorithm, named MADDPG-JAPORA, with centralized training and decentralized execution. Our extensive numerical experiments demonstrated that MADDPG-JAPORA reliably converges and provides superior performance compared with other state-of-the-art schemes.

Discontinuous Event-Triggered Control for Local Stabilization of Memristive Neural Networks With Actuator Saturation: Discrete- and Continuous-Time Lyapunov Methods.

In this article, the local stabilization problem is investigated for a class of memristive neural networks (MNNs) with communication bandwidth constraints and actuator saturation. To overcome these challenges, a discontinuous event-trigger (DET) scheme, consisting of the rest interval and work interval, is proposed to cut down the triggering times and save the limited communication resources. Then, a novel relaxed piecewise functional is constructed for closed-loop MNNs. The main advantage of the designed functional consists in that it is positive definite only in the work intervals and the sampling instants but not necessarily inside the rest intervals. With the aid of extended reciprocally convex combination lemma, generalized sector condition, and some inequality techniques, two local stabilization criteria are established on the basis of both the discrete- and continuous-time Lyapunov methods. The proposed analysis technique fully takes advantage of the looped-functional and the event-trigger mechanism. Moreover, two optimization schemes are, respectively, established to design the control gain and enlarge the estimates of the admissible initial conditions (AICs) and the upper bound of rest intervals. Finally, some comparison results are given to validate the superiority of the proposed method.

ESO-based adaptive event-triggered load following control design for a pressurized water reactor with samarium–promethium dynamics

Conventional control systems used in nuclear reactors rely on real-time information transmission between the controllers and the actuators, which is unnecessary and imposes a heavy communication burden. To this end, this paper proposes an extended state observer-based adaptive event-triggered control (ESO-AETC) scheme for the load following problem of a pressurized water reactor (PWR) in the presence of uncertainties and limited communication resources. Different from most of the previous controllers presented in the literature, the samarium–promethium dynamics are considered in the constructed PWR model in this paper. Based on the PWR model with the load-dependent parameters and the samarium–promethium dynamics, an ESO, which can estimate lumped uncertainties and unmeasured system states including the relative density of the delayed neutron precursor, the average fuel temperature, the xenon concentration, the iodine concentration, the samarium concentration, the promethium concentration, and the total reactivity, is designed. With the help of the ESO and the event-triggered techniques, an AETC scheme is developed to avoid the real-time communication in the controller-to-actuator channel of the PWR system. It is rigorously proved that the control error can converge to any prescribed residual set in a finite time according to Lyapunov stability theory, excluding the Zeno behavior. Finally, simulation studies involving comparisons with existing works, estimation performance verification, and sensitivity analysis are conducted to assess the proposed ESO-AETC scheme. Simulation results reveal that (1) relative to the pre-existing model-free controller (MFC) and the disturbance observer-based sliding mode controller (DO-SMC), the proposed ESO-AETC scheme is found to provide better load following performance with at least 56.12% lower maximum absolute error (MAE), at least 15.05% lower integral absolute error (IAE), and 87.58% reduced communication resources in the controller-to-actuator channel, (2) the ESO possesses strong estimation capability for system states and lumped uncertainties, and (3) the proposed ESO-AETC scheme yields a low sensitivity with respect to the change of the controller parameters.

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

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.

Non-Fragile Fuzzy Tracking Control for Nonlinear Networked Systems with Dynamic Quantization and Randomly Occurring Gain Variations

This paper investigates the observer-based non-fragile output feedback tracking control problem for nonlinear networked systems with randomly occurring gain variations. The considered nonlinear networked systems are represented by a Takagi–Sugeno (T–S) fuzzy model. The dynamical quantization methodology is employed to achieve the reasonable and efficacious utilization of the limited communication resources. The objective is to design the observer-based non-fragile output feedback tracking controller, such that the resulting system is mean-square asymptotically stable with the given H∞ tracking performance. Based on the descriptor representation strategy combined with the S-procedure, sufficient conditions for the existence of the desired dynamic quantizers and observer-based non-fragile tracking controller are proposed in the form of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed design method

Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee

With the massive deployment of the Internet of Things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing FedL schemes either take expensive computation costs (e.g., public-key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving FedL scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret-sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

Self-Triggered Coverage Control for Mobile Sensors

The deployment and coordination of mobile sensor networks for coverage control applications can present several practical challenges, including how to efficiently share limited communication resources and how to reduce the use of localization devices (e.g., radars and lidars). One potential solution to these challenges is to reduce the frequency at which agents communicate or sample each other's position. In this article, we present a distributed asynchronous self-triggered control policy for centroidal Voronoi coverage control that is shown to decrease the sampling or communication instants among agents without degrading the performance of the mobile sensor network. Each agent independently decides when to sample the position of nearby agents and uses outdated information of its neighbors until new information is required. We prove that the locational cost function describing the distribution of agents monotonically decreases everywhere outside of a bounded neighborhood around the group's optimal configuration and that the agents asymptotically converge to their Voronoi centroids if the data-sampled centroid errors approach zero. In addition, we show that the sampling intervals are always positive and lower bounded and, as illustrated by simulations and experiments, they tend to stabilize at a large value as the mobile sensor network comes to a steady state. Simulations and experiments with ground vehicles validate the control strategy and show that the proposed policy can achieve similar level of performance as a continuous or fast periodic implementation.

CCPAV: Centralized cooperative perception for autonomous vehicles using CV2X

Cooperative perception improves awareness for various traffic situations by connecting autonomous vehicles to each other as well as to the surrounding environment. Overcoming the line-of-sight challenge due to the limitations of vehicle's local sensors is of great importance. However, inefficient solutions can quickly lead to depletion of the limited communication resources. This is critical, especially that the dominant CV2X (Cellular Vehicle to Everything) technology witnesses unprecedented growth in the number and density of connected smart devices. To this end, this paper provides a new centralized cooperative perception approach using CV2X. The system uses vehicle trajectories to prioritize messages communicated messages. Through the basestation (BS), the system prioritizes message requests while being constrained by the available network resources. This system is then implemented and evaluated using 1000 different traffic scenarios. These scenarios are generated using both SUMO (Simulation of Urban MObility) traffic simulator and a new implemented camera simulator used to represent the vehicle's sensors’ abilities to perceive the surrounding environment. Results show that the system, on average, can execute at least 95% of the total message values using only 100 physical resource blocks for CV2X regardless of the autonomous vehicle densities. This is very robust especially for congested networks as the number of messages requests executed varies between 65% to 95% given the different autonomous vehicle densities. To the best of our knowledge, this work is the first to use vehicle trajectories to jointly select messages for transmission and allocate RBs (Resource Blocks).

Event-based bipartite consensus of second-order discrete-time multi-agent systems with matrix-weighted signed networks

This article researches the bipartite consensus for discrete-time second-order multi-agent systems on matrix-weighted signed networks, which can describe the inter-dependencies of multidimensional states among states. So as to save limited communication resources, based on the matrix-weighted combined measurements of the position and velocity states, a matrix-weighted event-triggered control algorithm is designed. With the help of the stability theory, variable transformation and the inequality technique, the bipartite consensus conditions which are based on coupling gains, discrete interval, the parameters in the event-triggered rule and communication topology are obtained. Furthermore, the conditions to avoid the controller updating in each discrete-time are supplied. At last, a simulation example is offered to demonstrate the theoretical results.

Emergent Dynamics of Asynchronous Multiagent Systems Under Signed Networks and Limited Communication Resources

Signed networks, usually used to describe cooperative and competitive coordination between agents, have potential applications in fields such as multi-robot formation and opinion dynamics, et al. Existing work on multi-agent dynamics on signed networks assumes that all agents communicate with their neighbors synchronously, which usually requires sufficient communication resources. However, due to the shortage of communication resources in practical applications, the synchronous communication manner is often not easy to achieve, especially on large scale networks. With that in mind, this paper proposes an asynchronous communication manner to investigate the dynamic behavior of multiagent systems on signed networks without structural restrictions, where only part of the communication links are allowed to transmit information each time instant. By designing a distributed feedback control law, the related properties of sub-stochastic matrices can be used to analyze the dynamic behavior of the agents. It turns out that the whole multi-agent system will evolve a variety of dynamic behaviors, including consensus, bipartite consensus and bipartite containment, which is influenced by the locations and roles of agents in the network. At last, computer simulations are provided to demonstrate the theoretical discovery.

Dynamic Event-Triggered Communication-Based Adaptive Consensus Tracking of Uncertain Nonlinear Multiagent Systems in Pure-Feedback Form

This study is aimed at investigating a dynamic event-triggered communication-based adaptive distributed consensus control problem for a class of uncertain pure-feedback nonlinear multiagent systems with limited communication resources. The nonaffine nonlinear functions of multiagent systems are assumed to be unknown and heterogeneous, and intermittent inter-agent communication is considered to occur within a directed network. A novel adaptive distributed dynamic surface control strategy using neural networks is developed to manage non-differentiable virtual control laws associated with the intermittent communication between agents with uncertain nonaffine nonlinear parts. The key contribution of this research is the derivation of dynamic event-triggering conditions using distributed tracking errors to efficiently adjust inter-event times, while ensuring the consensus tracking performance and robustness against unexpected external disturbances. Compared with the existing static event-triggered communication approach, the proposed controller can alleviate the communication burden among agents. Using technical lemmas, it is shown that all signals of the considered system are semiglobally uniformly ultimately bounded, and Zeno behavior is strictly excluded. Comparative simulation results illustrate the effectiveness of the proposed dynamic event-triggered control approach.

Enhanced Hybrid Hierarchical Federated Edge Learning Over Heterogeneous Networks

In this work, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hybrid Hierarchical Federated Edge Learning</i> (HHFEL) architecture that consists of a device layer, an edge layer, and a cloud layer over heterogeneous networks, is investigated for large-scale model training. In such systems, learning efficiency is severely degraded by limited communication resources and device heterogeneity in terms of local data distribution and computation capability, especially for synchronous FL mechanisms where the training of each round should wait for the slowest device. To tackle this issue, asynchronous FL is proposed, which allows the devices with powerful computation and communication capabilities exchanging information with the server more frequently. However, this asynchronous FL framework faces a new challenge of low accuracy caused by the imbalanced local model updating. To overcome the shortage of both synchronous and asynchronous FLs, we propose an enhanced online semi-asynchronous FL mechanism between the edge-device layers, where each device trains its local model with the newly generated data and each edge server aggregates a number of local models based on their arrival order in each round. Particularly, devices with faster training speeds would fully utilize the idle time by training their local models repetitively. Meanwhile, synchronous FL with an edge elastic update strategy is adopted to the cloud-edge layers for personalized information exchange. Considering the continuous data generation feature, we formulate the objective problem as an online <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Markov Decision Process</i> (MDP) to realize efficient communication-and-computing HHFEL via joint device selection and resource allocation. Due to the non-convex and combinatorial problem structure, we develop a hybrid <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Q-Network</i> (DQN) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Deterministic Policy Gradient</i> (DDPG) approach with low computational complexity to adapt the device selection and resource allocation strategies. Numerical results show the effectiveness of the proposed mechanism compared with existing benchmarks.

A Totally Asynchronous Algorithm for Time-Varying Convex Optimization Problems

This paper presents a decentralized algorithm for a team of agents to track time-varying fixed points that are the solutions to time-varying convex optimization problems. The algorithm is first-order, and it allows for total asynchrony, i.e., all communications and computations can occur with arbitrary timing and arbitrary (finite) delays. Convergence rates are computed in terms of the communications and computations that agents execute, without specifying when they must occur. These rates apply to convergence to the minimum of each individual objective function, as well as agents’ long-run behavior as their objective functions change. Then, to improve the usage of limited communication and computation resources, we optimize the timing of agents’ operations relative to changes in their objective functions to minimize fixed point tracking error over time. Simulation results illustrate these developments.

Location-Dependent Augmented Reality Services in Wireless Edge-Enabled Metaverse Systems

Metaverse is envisioned as the next-generation paradigm that links a virtual world to the physical world, providing immersive experience for human activities in the hypothetical environment with augmented reality (AR) and virtual reality (VR) technology. Recent development of wireless multiaccess edge computing (MEC) has accelerated the realization of this vision. In this paper, we consider location-dependent AR services in MEC-enabled Metaverse systems. In such a system, each user performs simultaneous localization and communication first to estimate its position and request AR contents from the MEC server. Due to limited computation and communication resources, the MEC server adaptively adjusts the resolution of AR contents. However, two challenges arise in such a system. First, the localization error will degrade the user’s quality-of-experience (QoE); and second, the coupling of communication and computation complicates the resource management. To address these issues, we first model the QoE taking the localization errors into consideration, and then propose a minimum QoE maximization scheme to achieve fairness among users by jointly optimizing the waveform at users, the transmit power at the base station (BS), and the resolution of transmitted videos. Simulation results verify the effectiveness of the proposed scheme in comparison with benchmark schemes.