Lossy Communication Research Articles

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

Bit-Flipping Helper-Assisted Lossy Communications: Performance Analyses Over Fading Multiple Access Channels

Bit-Flipping Helper-Assisted Lossy Communications: Performance Analyses Over Fading Multiple Access Channels

StegaStyleGAN: Towards Generic and Practical Generative Image Steganography

The recent advances in generative image steganography have drawn increasing attention due to their potential for provable security and bulk embedding capacity. However, existing generative steganographic schemes are usually tailored for specific tasks and are hardly applied to applications with practical constraints. To address this issue, this paper proposes a generic generative image steganography scheme called Steganography StyleGAN (StegaStyleGAN) that meets the practical objectives of security, capacity, and robustness within the same framework. In StegaStyleGAN, a novel Distribution-Preserving Secret Data Modulator (DP-SDM) is used to achieve provably secure generative image steganography by preserving the data distribution of the model inputs. Additionally, a generic and efficient Secret Data Extractor (SDE) is invented for accurate secret data extraction. By choosing whether to incorporate the Image Attack Simulator (IAS) during the training process, one can obtain two models with different parameters but the same structure (both generator and extractor) for lossless and lossy channel covert communication, namely StegaStyleGAN-Ls and StegaStyleGAN-Ly. Furthermore, by mating with GAN inversion, conditional generative steganography can be achieved as well. Experimental results demonstrate that, whether for lossless or lossy communication channels, the proposed StegaStyleGAN can significantly outperform the corresponding state-of-the-art schemes.

Regression-Based Self-Triggered Control of Distributed Economic Dispatch over Lossy Communication Channels

An essential building block of today’s smart grids is the integration of various renewable power generators into the system. Renewable distributed generators (DGs) provide environment friendly energy at a low cost. In this work, a distributed economic dispatch (ED) system is considered for renewable DGs to address the scalability challenge and reduce single points of failure compared to a centralised ED system. A consensus control mechanism is devised to achieve the incremental cost (IC) consensus among the DGs and utilise a self-triggered control (STC) mechanism to reduce the communication of the distributed DG system. A lossy communication channel between local DGs is considered in this work, which can arise from power lines or congestion on noisy wireless communication channels. A regression-based estimation approach is proposed to account for lost or corrupted data. The proposed method is compared with a centralised ED system and periodic communication. The proposed STC approach achieves the same level of performance as periodic communication while using half the communication resources.

Joint Optimization of Fairness and Energy Efficiency in Zero-Trust Federated Learning for Consumer Internet of Things: A Lossy Communication Perspective

Joint Optimization of Fairness and Energy Efficiency in Zero-Trust Federated Learning for Consumer Internet of Things: A Lossy Communication Perspective

Two-Stage Successive Wyner-Ziv Lossy Forward Relaying for Lossy Communications: Rate-Distortion and Outage Probability Analyses

Two-Stage Successive Wyner-Ziv Lossy Forward Relaying for Lossy Communications: Rate-Distortion and Outage Probability Analyses

Engineering Traffic Prediction With Online Data Imputation: A Graph-Theoretic Perspective

Accurate and timely prediction about the current and near-term future traffic conditions is one of the effective ways to relieve traffic congestion and improve the operation efficiency of road networks. However, the missing data problem is inevitable when collecting real-time traffic flow information due to lossy communication and detector malfunction, which significantly affects the accuracy of prediction. To address this problem, in this article, we propose an approach to exploit online traffic prediction considering missing data effects. Unlike most existing methods imputing all the missing data before traffic prediction, the proposed strategy takes the spatio-temporal relationships between missing data and observed data into account to optimize data imputation patterns, thereby improving the efficiency of online traffic prediction. Specifically, we first propose an analytical framework for online traffic prediction with missing data by extending the space-time autoregressive integrated moving average model to incorporate missing data effects. Then, based on the combined use of optimal cut and data imputation optimization, a graph-theoretic technique is presented to determine the imputed data with consideration of road network topology and missing data pattern. Finally, experiments are conducted based on two real-world datasets. Experimental results indicate the superiority of the proposed approach in accuracy and efficiency compared with existing strategies of traffic flow prediction with missing data, particularly under the circumstance of high data missing ratios in urban road networks.

Multiagent Distributed Secondary Control for Energy Storage Systems With Lossy Communication Networks in DC Microgrid

For the energy storage system (ESS) with lossy communication networks of packet loss in DC microgrid, the multiagent distributed secondary control strategy is proposed to manage the ESS. The multiagent dynamic-tracking consensus protocol (DCP) based on the random packet loss model is constructed to estimate the global information for the dynamic network with lossy communication. Then the adaptive current sharing algorithm (ACSA) is designed to balance the state of charge (SoC) for ESSs with different capacities. Furthermore, the data-driven model-free adaptive control (MFAC) scheme is proposed for the single-input multiple-output (SIMO) nonlinear ESS to provide accurate voltage tracking and current sharing. Finally, the real-time simulation tests of different case studies are carried out through the OPAL-RT platform to verify the effectiveness of the designed control strategy. Simulation results can prove that the favorable capabilities of current sharing and voltage regulation as well as robustness to communication packet loss and communication faults can be achieved.

Optimal networked state estimation in the presence of multi-channel correlated packet losses

This paper studies the optimal estimation problem for linear time-invariant systems over lossy communication networks subject to multi-channel packet losses. In its full generality, we assume that the random packet losses are spatially correlated across different channels. The problem is addressed by employing a modified Kalman filter to estimate the system’s states, and the primary issue under study is the convergence of the state estimate under a mean-square criterion. This convergence is shown to be equivalent to the existence of a stabilizing solution to a modified algebraic Riccati equation. We provide a necessary and sufficient condition to characterize the existence of the stabilizing solution, and show that the solution can be computed by solving a set of linear matrix inequalities. Additionally, from a robust convergence perspective, we consider further packet losses whose probabilities are unknown and uncertain. We develop a matrix measure referred to as the critical matrix to characterize the maximal tolerance for the modified Kalman filter to converge. Specifically, we show that the mean-square estimation error is bounded if and only if the critical matrix satisfies a positive semi-definiteness condition.

Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

Deep learning has been widely used in intelligent vehicle driving perception systems, such as 3D object detection. One promising technique is Cooperative Perception, which leverages Vehicle-to-Vehicle (V2V) communication to share deep learning-based features among vehicles. However, most cooperative perception algorithms assume ideal communication and do not consider the impact of Lossy Communication (LC), which is very common in the real world, on feature sharing. In this paper, we explore the effects of LC on Cooperative Perception and propose a novel approach to mitigate these effects. Our approach includes an LC-aware Repair Network (LCRN) and a V2V Attention Module (V2VAM) with intra-vehicle attention and uncertainty-aware inter-vehicle attention. We demonstrate the effectiveness of our approach on the public OPV2V dataset (a digital-twin simulated dataset) using point cloud-based 3D object detection. Our results show that our approach improves detection performance under lossy V2V communication. Specifically, our proposed method achieves a significant improvement in Average Precision compared to the state-of-the-art cooperative perception algorithms, which proves the capability of our approach to effectively mitigate the negative impact of LC and enhance the interaction between the ego vehicle and other vehicles. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jinlong17/V2VLC</uri>

A Kalman-Based Compensation Strategy for Platoons Subject to Data Loss: Numerical and Empirical Study

This article considers a homogeneous platoon with vehicles that communicate through channels prone to data loss. The vehicles use a predecessor-following topology, where each vehicle sends relevant data to the next, and data loss is modeled through a Bernoulli process. To address the lossy communication, we propose a strategy to estimate the missing data based on the Kalman filter with intermittent observations combined with a linear extrapolation stage. This strategy enables the followers to better deal with data dropouts. We compare this approach to one purely based on the linear extrapolation of previous data. The performance of both strategies is analyzed through Monte Carlo simulations and experiments in an ad hoc testbed, considering various data loss and transmission loss probabilities depending on the inter-vehicle distance. The results show that for the considered cases, the proposed strategy outperforms the linear extrapolation approach in terms of tracking and estimation error variances. Our results also show that the proposed strategy can achieve string stability for the mean and variance for both the tracking and estimation errors in scenarios where the basic extrapolation strategy cannot.

Distributed consensus-based multi-agent temporal-difference learning

In this paper we propose two new distributed consensus-based algorithms for temporal-difference learning in multi-agent Markov decision processes. The algorithms are of off-policy type and are aimed at linear approximation of the value function. Restricting agents’ observations to local data and communications to their small neighborhoods, the algorithms consist of: (a) local updates of the parameter estimates based on either the standard TD(λ) or the emphatic ETD(λ) algorithm, and (b) dynamic consensus scheme implemented over a time-varying lossy communication network. The algorithms are completely decentralized, allowing efficient parallelization and applications where the agents may have different behavior policies and different initial state distributions while evaluating a common target policy. It is proved under nonrestrictive assumptions that the proposed algorithms weakly converge to the solutions of the mean ordinary differential equation (ODE) common for all the agents. It is also proved that the whole system may be stabilized by a proper choice of the network and that the parameter estimates weakly converge to consensus. Discussion is given on the asymptotic bias and variance of the estimates, on the projected forms of the proposed algorithms, as well as on restrictiveness of the adopted assumptions. Simulation results illustrate the main properties of the algorithms and provide comparisons with similar schemes.

On Model Transmission Strategies in Federated Learning With Lossy Communications

Recently, federated learning (FL) has received tremendous attention in both academia and industry, in which decentralized clients collaboratively complete model training by exchanging model updates with a parameter server through the Internet. Its distributed nature well utilizes the localized data and preserves clients' privacy, but also incurs heavy communication overhead. Existing studies on model update have mostly focused on the bandwidth constraint of the communication channels. Today's Internet however is highly unreliable. Simply using Transmission Control Protocol (TCP) would lead to low network utilization under frequent losses. In this paper, we closely examine the optimal transmission strategies in FL over the realistic lossy Internet. We systematically integrate model compression, forward error correction (FEC) and retransmission towards Federated Learning with Lossy Communications (FedLC). We derive the convergence rate of FedLC under non-convex loss with the optimal transmission. We then decompose this non-convex problem and present effective practical solutions. Public datasets are exploited for performance evaluation by varying the packet loss rate from 10% to 50%. In a fixed training time budget, FedLC can improve model accuracy by 3.91% on average or reduce the communication traffic by 34.27%-47.57% in comparison with state-of-the-art baselines.

Learning to Control Under Communication Constraints

How to effectively communicate over wireless networks characterized by link failures is central to understanding the fundamental limits in the performance of a networked control system. In this paper, we study the online remote control of linear-quadratic Gaussian systems over unreliable wireless channels (with random packet drops), where the controller is a priori oblivious to the cost parameters. We first reformulate the problem using a semi-definite program and consequently compute a stabilizing policy from its solution. We then derive a O(T) regret bound (against a best offline policy in hindsight) for a projected online gradient algorithm, where T is the length of the horizon of interest. In the process, we introduce finite-time notions of the classical mean-square stability, which may be of independent interest. Finally, we provide a numerical example to validate the theoretical results, demonstrating the limitations induced by lossy communication on the control performance.

Improved methodology for assessment of communication protocols for distributed road traffic simulation

A computer simulation of road traffic is a commonly used tool, which can help to manage constantly intensifying road traffic. It can help to analyze behavior of existing road traffic networks or to predict the behavior of new designed road traffic structures. There are several existing simulators designed or adapted to run in a distributed computing environment in order to achieve a faster execution. In this environment, the inter-process communication ensured by a high-level communication protocol is one of the main bottlenecks limiting the speed of the entire computation. Various high-level communication protocols can have various efficiency, applicability, and scalability. This paper describes an improved methodology for testing and assessment of high-level communication protocols for micro-scale (or microscopic) distributed road traffic simulations. The methodology investigates the dependencies of the communication protocols’ performance on various features of the simulation and enables to easily calculate score for each of the tested protocols. Using the scores, the tested protocols can be directly compared. This can be useful when designing or improving a distributed road traffic simulation as the best protocol can be used in this simulation to improve its performance (e.g., its speedup or communication time). The improved version of the methodology is an evolution of its original version. It newly incorporates the assessment of the error introduced into the simulation by lossy communication protocols and reduces overall number of performed tests. The improved methodology was tested using a case study assessing several communication protocols of our own design.

Timeliness optimization of unmanned aerial vehicle lossy communications for internet-of-things

Information freshness is a key factor for Internet-of-Things (IoT) to make appropriate decisions and operations. This paper proposes an analytical framework for evaluating the timeliness performance of the IoT system based on Unmanned Aerial Vehicle (UAV) lossy communications. The performance analysis consists of the outage probability analysis and the Age-of-Information (AoI) analysis with outages. To begin with, we solve a lossy coding problem formulated from the UAV communication system, and derive a closed-form expression of the outage probability based on Shannon’s lossy source-channel separation theorem. Then, we characterize the Peak AoI (PAoI) for the considered system, and further minimize the PAoI by deriving the optimal rate for generating information. Moreover, we analyze the system performance through theoretical calculations and simulations. The results indicate that the optimal server utilization ratio is always no larger than 0.5. In practical applications, we can utilize the proposed analytical framework to determine the system parameters which guarantee the timeliness performance of UAV lossy communications.

Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of Things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two subproblems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables, including power variables, bandwidth variables, and transmission indicators. Then, a linear-search-based power and bandwidth allocation method is developed. Given appropriate hyperparameters, we show that the proposed communication-efficient FL (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

Stochastic String Stability of Vehicle Platoons via Cooperative Adaptive Cruise Control With Lossy Communication

This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which effectively mitigates the effects of the losses. Our design explicitly takes into account the stochastic nature of the losses by considering both the average evolution of the system and the stochastic variations around it. The control design promotes both plant stability and string stability of the average error dynamics by an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathscr {H}_\infty $ </tex-math></inline-formula> approach, while minimizing the variance of the trajectories around their average. We show by simulations that the proposed controller is able to compensate losses even for high loss probabilities.

Networked supervisor synthesis against lossy channels with bounded network delays as non-networked synthesis

In this work, we study the problem of supervisory control of networked discrete event systems. We consider lossy communication channels with bounded network delays, for both the control channel and the observation channel. By a model transformation, we transform the networked supervisor synthesis problem into the classical (non-networked) supervisor synthesis problem (for non-deterministic plants), such that the existing supervisor synthesis tools can be used for synthesizing networked supervisors. In particular, we can use the (state-based) normality property for the synthesis of the supremal networked supervisors, whose existence is guaranteed by construction due to our consideration of command non-deterministic supervisors. The effectiveness of our approach is illustrated on a mini-guideway example that is adapted from the literature, for which the supremal networked supervisor has been synthesized in the synthesis tools SuSyNA and TCT.

Drive-by-Wi-Fi: Model-Based Control Over Wireless at 1 kHz

In this work, we address the problem of remotely controlling possibly unstable systems using wireless channels with communication and control rates on the order of 1 kHz. While remote control over wireless has been used for applications in the kHz control rate range via low-bitrate communication protocols such as IEEE 802.15.4 (Zigbee) and IEEE 802.15.1 (Bluetooth), this represents the first successful demonstration of remotely controlling an unstable system, namely a balancing robot, using the high-bitrate IEEE 802.11 standard (Wi-Fi) with a control/communication rate in the kHz range, thus being suitable for time-critical industrial applications. This is achieved through a suitable design of both the communication and the control layer; the Wi-Fi protocol parameters are modified to reduce communication latency while still abiding to the standard and a model-based controller is implemented using a time-varying buffered Kalman filter to compensate for the sensor-to-controller lossy communication and using a packetized predictive controller to compensate for the controller-to-actuator lossy communication. The validity of this architecture is experimentally tested on a balancing robot, which adopts off-the-shelf embedded hardware (Rasperry Pi) and software (Arduino) within an industrial-like environment where substantial channel interference is present due to coexisting Wi-Fi networks, showing major improvements compared to more traditional emulation-based architectures.