Limited Communication Bandwidth Research Articles

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots' end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles' limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.

Open-Source Heterogeneous Constrained Edge-Computing Platform for Smart Grid Measurements

This article presents a low-cost, open-source, heterogeneous, resource-constrained hardware platform called “Parallella” as a measurement device for edge-computing applications research in smart grid. The unique hardware architecture of the Parallella provides a multitude of edge-computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. A multifunctional intelligent electronic device (IED) design is demonstrated to showcase the capabilities of the platform. A custom I/O board has been developed for the desktop and embedded versions of Parallella, which can be interfaced with external daughter boards and peripherals for measurements. One such daughter board is an analog sensing board, which can measure voltages of all the three phases and four line currents using a 16-bit synchronous ADC set at 32 kHz. The ADC samples are synchronized to the PPS time clock of a GPS unit for providing global time reference. These captured seven-channel raw waveform data are sent to a cloud server over a bandwidth-limited communication channel using a custom anomaly-aware data compression algorithm implemented on the ARM. A phasor measurement algorithm using the Teager energy operator (TEO) is implemented on the field-programmable gate array (FPGA). A parallel power quality (PQ) measurement algorithm is implemented on the Epiphany. The obtained measurements are found to be comparable to a commercial power analyzer.

Distributed Vehicle Tracking in Wireless Sensor Network: A Fully Decentralized Multiagent Reinforcement Learning Approach

Vehicle tracking is one of the important applications of the wireless sensor network (WSN), and sensor scheduling is essential in WSN for achieving an efficient tracking process. Traditional centralized sensor scheduling frameworks cannot meet real-time requirements of vehicle tracking in WSN, because of task overloading caused by limited resource consumption and communication bandwidth. To solve this problem, this letter proposes a multiagent distributed sensor scheduling framework in WSN. This letter first proposes a preactivation-based vehicle tracking model to preactive some sensors in order to reduce unnecessary resource consumption. Then, this letter develops a fully decentralized multiagent reinforcement learning (FDMARL) algorithm to design our multiagent sensor scheduling framework. The simulation results show the convergence and the superiority of our proposed FDMARL-aided sensor scheduling algorithm.

A Heuristic Lowest Unknown-Degree Target Search Strategy Under Non-Structured Environment for Multi-Agent Systems

Multi-target searching is a hotspot and foundation topic in multi-agent systems research. However, most of the research is based on simple environment or known environment, which greatly limits the application of target search. In the non-structured environment, the searching result can be greatly affected by the complex terrain constraints and detectability of targets especially when we have no prior knowledge about the environment. In the paper, a novel search strategy combining maximum visibility and particle swarm optimization is proposed for the target search problem in a completely unknown and non-structural environment. The strategy utilizes the concept of visibility to describe how well the agent detects the map, and guides the agent to perform online path planning to complete the search task. In addition, considering the limited communication distance and communication bandwidth, the strategy introduces a cooperative mechanism for each agent to improve the search efficiency. Finally, in the experimental part, the search strategy is compared with the commonly used search strategies. Compared with the methods combining advantages, the proposed strategy can still achieve similar results, which proves the feasibility and efficiency of the strategy.

Event-based secure [formula omitted] load frequency control for delayed power systems subject to deception attacks

This paper focuses on designing an H∞ load-frequency controller for power systems suffering from deception attacks based on the event-based secure control scheme. Firstly, to alleviate the occurrence of congestion phenomenon in the bandwidth-limited communication network, an event-triggered scheme is introduced into the sensor-to-controller channel. Subsequently, a mathematical model with regard to the event-triggered load frequency control is established under considering the existence of deception attacks. Then, in terms of Lyapunov stability theory and an improved inequality technique, some sufficient conditions that can ensure the mean-square asymptotic stability with a prescribed H∞ performance index of the closed-loop system are deduced. Besides, the corresponding controller gain is obtained by solving those conditions. Finally, two numerical examples are presented to verify the effectiveness of the proposed method.

Distributed Observer-Based Consensus Over Directed Networks With Limited Communication Bandwidth Constraints

In this paper, we present a distributed observer-type consensus controller design for general linear multiagent systems over directed digital networks with limited communication data rates. Considering that each agent can only exchange binary symbolic sequence with its neighbors due to bandwidth constraints, we adopt the probabilistic quantization approach in the distributed consensus controller design. Compared to the existing works, the main contribution is that we use distributed observer-type controllers to inhibit the impact of quantization noises in agent states. First, we study the observer-type protocols with total quantized communications, where the relative-state information of each agent is quantized. The quantized consensus can be achieved, and the upper bounds of steady-state errors in the mean-square sense are also characterized, which are related to the scale of networks, the control parameters, the quantization levels, and the structures of agents. Further, we extend the controller design based on total quantization to the partial quantization case where only the neighbors’ information is quantized.

Prototype of a Computer Vision-Based CubeSat Detection System for Laser Communications

Up to now, CubeSat nano-satellites have strong limitations in communication data rates ( $$\sim \hbox {100}$$ kbps) and bandwidth due to the strictness of CubeSat standard. However, if they could be endowed with optical communications (data rates up to 1 Gbps in optimal state), CubeSat applications would exponentially increase. Nonetheless, laser communications face some important drawbacks as the development of a very strict and accurate tracking mechanism. This work proposes an on-board fine pointing system to locate an optical ground station beacon using an embedded system complying with the restrictive CubeSat standard. Such on-board fine pointing system works based on computer vision. The experimental prototype is implemented in Matlab/Simulink, within a Raspberry Pi 3B. The main outcome is the usage of off-the-shelf components (COTS), obtaining an efficient tracking with low power consumption in very noisy and reflective environments. The developed system proves to be fast, stable and strong. It also satisfies the strict size and power consumption restrictions of CubeSat standard.

Communication and Computing Resource Optimization for Connected Autonomous Driving

Transportation system is facing a sharp disruption since the Connected Autonomous Vehicles (CAVs) can free people from driving and provide good driving experience with the aid of Vehicle-to-Vehicle (V2V) communications. Although CAVs bring benefits in terms of driving safety, vehicle string stability, and road traffic throughput, most existing work aims at improving only one of these performance metrics. However, these metrics may be mutually competitive, as they share the same communication and computing resource in a road segment. From the perspective of joint optimizing driving safety, vehicle string stability, and road traffic throughput, there is a big research gap to be filled on the resource management for connected autonomous driving. In this paper, we first explore the joint optimization on driving safety, vehicle string stability, and road traffic throughput by leveraging on the consensus Alternating Directions Method of Multipliers algorithm (ADMM). However, the limited communication bandwidth and on-board processing capacity incur the resource competition in CAVs. We next analyze the multiple tasks competition in the contention based medium access to attain the upper bound delay of V2V-related application offloading. An efficient sleeping multi-armed bandit tree-based algorithm is proposed to address the resource assignment problem. A series of simulation experiments are carried out to validate the performance of the proposed algorithms.

Event-based disturbance compensation control for discrete-time SPMSM with mismatched disturbances

This paper investigates the problem of event-based disturbance compensation control for discrete-time surface-mounted permanent magnet synchronous motor (SPMSM) subject to both mismatched external disturbances and the limited communication bandwidth. The mismatched external disturbances make it impossible to transform the original model into the multiple-step-ahead predictor model by using the existing predictor technology. To tackle such a challenge, a novel backstepping-based disturbance compensation control framework is firstly proposed for the discrete-time SPMSM with known system dynamics. In the proposed framework, the disturbance observer is designed to compensate for the external disturbances, and the novel variable substitution/decoupling technology is proposed to design the event-based controller for the original controlled system. The proposed controller is composed of the event-based feedback control signal and the feedforward disturbance compensation signal, thereby improving the system disturbance rejection ability and mitigating the communication resource. Subsequently, an event-based adaptive neural control scheme is proposed by combining a modified neural disturbance observer. The proposed scheme ensures that the tracking error converges to a small neighbourhood of the origin, all the signals in the closed-loop system are bounded and meanwhile the communication resources are greatly reduced. The effectiveness of the proposed controller is illustrated through the simulation results.

Finite-Time $\mathcal {L}_{2}$-$\mathcal {L}_{\infty }$ Synchronization for Semi-Markov Jump Inertial Neural Networks Using Sampled Data

This paper investigates the finite-time synchronization issue for semi-Markov jump inertial neural networks, in which the sampled-data control is employed to alleviate the burden of the limited communication bandwidth. Due to the existence of inertial item, the semi-Markov jump inertial neural networks as hybrid neural systems, are depicted with second-order derivatives for the first time, which can be turned to first-order derivatives by the variable transformation. Furthermore, by applying appropriate integral inequalities and constructing the proper Lyapunov functional, some sufficient conditions, which not only guarantee the finite-time synchronization of the resulting error system but also ensure a specified level of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance, are acquired based on the optimization of integral inequality technique. A numerical example is, eventually, proposed to substantiate the validity of the developed method.

Finite-TimeH∞State Estimation for Two-Time-Scale Complex Networks Under Stochastic Communication Protocol

The issue of finite-time H∞ state estimation is studied for a class of discrete-time nonlinear two-time-scale complex networks (TTSCNs) whose measurement outputs are transmitted to a remote estimator via a bandwidth-limited communication network under the stochastic communication protocol (SCP). To reflect different time scales of state evolutions, a new discrete-time TTSCN model is devised by introducing a singular perturbation parameter (SPP). For the sake of avoiding/alleviating the undesirable data collisions, the SCP is adopted to schedule the data transmissions, where the transition probabilities involved are assumed to be partially unknown. By constructing a new Lyapunov function dependent on the information of the SCP and SPP, a sufficient condition is derived which ensures that the resulting error dynamics is stochastically finite-time bounded and satisfies a prescribed H∞ performance index. By resorting to the solutions of several matrix inequalities, the gain matrices of the state estimator are given and the admissible upper bound of the SPP can be evaluated simultaneously. The performance of the designed state estimator is demonstrated by two examples.

Event-Triggered Quantized Communication-Based Consensus in Multiagent Systems via Sliding Mode

To handle the common existing constraints, that is, limited energy supplies and limited communication bandwidth in multiagent systems (MASs), this article investigates the consensus problem in MASs with event-triggered communication (ETC) and state quantization. In order to compensate for the effect brought by mismatched disturbances, we also propose a novel multiple discontinuous sliding-mode surface, and the corresponding sliding-mode control law is constructed by considering the event-triggered and dynamic quantized mechanisms jointly. Under such a scheme, it is shown that the state trajectories of all the agents will be regulated to achieve consensus asymptotically and the Zeno behavior can be avoided completely. We further extend this work to self-triggered and periodic event-triggered cases. Particularly, in a periodic event-triggered approach, the new form of triggering conditions and upper bound of the sampling periods are provided explicitly. As a result, all agents can reach bounded consensus. Moreover, the upper bound of the consensus error can be arbitrarily adjusted by appropriately selecting parameters, and the periodic event-triggered case will be reduced to the event-triggered case when the bound approaches 0 (sampling periods approach 0 at the same time). A numerical example is illustrated to verify the effectiveness of the proposed algorithms.

Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

Intelligent Internet of Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence." This shall unleash the full potential of intelligent IoT in a plethora of exciting applications, such as self-driving cars, unmanned aerial vehicles, healthcare, robotics, and supply chain finance. These applications drive the need to develop revolutionary computation, communication, and artificial intelligence technologies that can make low-latency decisions with massive realtime data. To this end, federated machine learning, as a disruptive technology, has emerged to distill intelligence from the data at the network edge, while guaranteeing device privacy and data security. However, the limited communication bandwidth is a key bottleneck of model aggregation for federated machine learning over radio channels. In this article, we shall develop an overthe- air computation-based communication-efficient federated machine learning framework for intelligent IoT networks via exploiting the waveform superposition property of a multi-access channel. Reconfigurable intelligent surface is further leveraged to reduce the model aggregation error via enhancing the signal strength by reconfiguring the wireless propagation environments.

Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO

Machine learning for hybrid beamforming has been extensively studied by using centralized machine learning (CML) techniques, which require the training of a global model with a large dataset collected from the users. However, the transmission of the whole dataset between the users and the base station (BS) is computationally prohibitive due to limited communication bandwidth and privacy concerns. In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users. We design a convolutional neural network, in which the input is the channel data, yielding the analog beamformers at the output. Via numerical simulations, FL is demonstrated to be more tolerant to the imperfections and corruptions in the channel data as well as having less transmission overhead than CML.

Sensor tasking for unmanned aerial vehicles in disaster management missions with limited communications bandwidth

This research considers the problem of maximizing information collection and exchange between unmanned resources and a control station in a bandwidth constrained environment. Unmanned aerial vehicles (UAVs) are utilized in disaster response to gather data and aid in intelligence, surveillance, and reconnaissance (ISR). They are regularly equipped with multiple gimbal-mounted heterogeneous sensors, generating large amounts of data which are to be routed through a communications network back to mission managers. A mixed integer linear program (MILP) is formulated for this problem. However, due to the complexity of this problem, the MILP is only able to solve trivially-sized problems. A three-phase heuristic approach is proposed which, through extensive testing, is proven to efficiently solve large-sized problems. Our analysis also gives insight into the features of this problem and their impact on mission success.

Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

&lt;span&gt;Wireless sensor networks comprise of a large number of lightweight and relatively low-cost computational nodes which their main task is to sense the surrounding environment and collect the information to send it wirelessly to a central point to take the appropriate actions. Although these networks had been used in various applications, achieving this task is challenging due to the many constraints of sensor nodes including their limited processing power, communication bandwidth, and power supply. Therefore, an energy efficient routing protocols had to be developed specifically for sensor networks to insure longer lifetime and reasonable performance of the network. In this work, we propose an energy efficient hierarchical routing protocol using chain-based clustering. &lt;span&gt;By simulation on MATLAB, the proposed protocol proved to enhance the performance as it prolongs the lifetime of the network and decreases the energy consumption, the transmission delay, and the overhead compared to other existing protocols as it depends on some advanced methods including dynamic selection of number of chains method, k-means clustering method, advanced greedy chain construction method, and multi-factor based leader selection method.&lt;/span&gt;&lt;/span&gt;

Event-triggered output consensus disturbance rejection algorithm for multi-agent systems with time-varying disturbances

This study investigates the problem of event-triggered output consensus disturbance rejection for multi-agent systems subject to time-varying disturbances. By virtue of the reduced-order generalized proportional-integral observer (GPIO) technique, a new output consensus disturbance rejection protocol is developed based on the measurement outputs. Taking the limited communication bandwidth into account, the multi-agent system closes the loop under the proposed consensus protocol only when a distributed event-triggering mechanism decides to transmit agent’s current output to its neighbors. The proposed output consensus protocol can effectively enhance the robustness against the time-varying disturbances and save the communication resource, since the time-varying disturbances are accurately estimated and compensated. Furthermore, the proposed consensus algorithm does not require continuous communication among the neighboring agents and can successfully avoid the Zeno phenomenon. Finally, the numerical simulation results are presented to verify the effectiveness of the proposed output consensus protocol.

Quantized feedback particle filter for unmanned aerial vehicles tracking with quantized measurements

Many existing state estimation approaches assume that the measurement noise of sensors is Gaussian. However, in unmanned aerial vehicles tracking applications with distributed passive radar array, the measurements suffer from quantization noise due to limited communication bandwidth. In this paper, a novel state estimation algorithm referred to as the quantized feedback particle filter is proposed to solve unmanned aerial vehicles tracking with quantized measurements, which is an improvement of the feedback particle filter (FPF) for the case of quantization noise. First, a bearing-only quantized measurement model is presented based on the midriser quantizer. The relationship between quantized measurements and original measurements is analyzed. By assuming that the quantization satisfies [Formula: see text], Sheppard’s correction is used for calculating the variances of the measurement noise. Then, a set of controlled particles is used to approximate the posterior distribution. To cope with the quantization noise of passive radars, a new formula of the gain matrix is derived by modifying the measurement noise covariance. Finally, a typical two-passive radar unmanned aerial vehicles tracking scenario is performed by QFPF and compared with the three other algorithms. Simulation results verify the superiority of the proposed algorithm.

Event-based fault detection for interval type-2 fuzzy systems with measurement outliers

<p style='text-indent:20px;'>This paper investigates the event-based fault detection (FD) problem for a category of discrete-time interval type-2 fuzzy systems with measurement outliers. For the sake of decreasing the utilization of limited communication bandwidth, an event-based mechanism is introduced. Based on the saturation function technique, a novel event-based FD observer is first designed to reduce the influence of outliers in the dynamic systems. Then, on the basis of Lyapunov stability theory, sufficient conditions are provided to ensure that the error system satisfies the <inline-formula><tex-math id="M1">\begin{document}$ \mathcal{H}_{\infty} $\end{document}</tex-math></inline-formula> performance and the <inline-formula><tex-math id="M2">\begin{document}$ \mathcal{H}_{\infty} $\end{document}</tex-math></inline-formula> fault performance in different cases, respectively. In contrast to the existing event-based FD results, the false alarm, which is induced by measurement outliers, can be effectively avoided by the designed FD observer with saturation function. Lastly, some simulation results are given to verify the effectiveness of the method presented in this paper.

Sliding Mode Control of Markovian Jump Fuzzy Systems: A Dynamic Event-Triggered Method

In this article, the sliding mode control (SMC) problem is addressed for a class of Markovian jump systems via the T–S fuzzy model. First, in order to reduce the frequency of state transmission for alleviating congestion phenomenon in the bandwidth-limited communication network, a dynamic event-triggered (DET) strategy is introduced into the sensor-to-controller channel, in which an additional internal dynamical variable is employed to adjust the event-triggered condition adaptively. A fundamental issue resulting from the event-triggered strategy is that the controller cannot obtain the information about system mode during the triggering interval. Aiming at the phenomenon, this work utilizes a mode detector to estimate the unavailable system mode. Then, this article proposes a detected-mode-dependent event-triggered sliding mode controller whose membership grades are determined only via the transmitted state at the triggering instant. By constructing a relation on the membership functions (MFs) between the fuzzy model and the controllers for MF-dependent analysis, the conditions on the reachability and stability conditions are relaxed. Furthermore, an optimization algorithm is provided for the minimum control power via a high-dimensional grid searching for the coefficients of the internal dynamic variables, which, together with the designed detected-mode-dependent sliding mode controller, constitutes the novel SMC scheme under the DET strategy. Finally, the simulation results via the single-link arm system are provided to illustrate the efficiency of the proposed method.