Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses.

This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream.

Impact of the Dropping Function on Clustering of Packet Losses.

The dropping function mechanism is known to improve the performance of TCP/IP networks by reducing queueing delays and desynchronizing flows. In this paper, we study yet another positive effect caused by this mechanism, i.e., the reduction in the clustering of packet losses, measured by the burst ratio. The main contribution consists of two new formulas for the burst ratio in systems with and without the dropping function, respectively. These formulas enable the easy calculation of the burst ratio for a general, non-Poisson traffic, and for an arbitrary form of the dropping function. Having the formulas, we provide several numerical examples that demonstrate their usability. In particular, we test the effect of the dropping function's shape on the burst ratio. Several shapes of the dropping function proposed in the literature are compared in this context. We also demonstrate, how the optimal shape can be found in a parameter-depended class of functions. Finally, we investigate the impact of different system parameters on the burst ratio, including the load of the system and the variance of the service time. The most important conclusion drawn from these examples is that it is not only the dropping function that reduces the burst ratio by far; simultaneously, the more variable the traffic, the more beneficial the application of the dropping function.

Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks.

This paper studies the remote Kalman filtering problem for a distributed system setting with multiple sensors that are located at different physical locations. Each sensor encapsulates its own measurement data into one single packet and transmits the packet to the remote filter via a lossy distinct channel. For each communication channel, a time-homogeneous Markov chain is used to model the normal operating condition of packet delivery and losses. Based on the Markov model, a necessary and sufficient condition is obtained, which can guarantee the stability of the mean estimation error covariance. Especially, the stability condition is explicitly expressed as a simple inequality whose parameters are the spectral radius of the system state matrix and transition probabilities of the Markov chains. In contrast to the existing related results, our method imposes less restrictive conditions on systems. Finally, the results are illustrated by simulation examples.

Plant and buffer state estimation for networked predictive control over multiple erasure channels.

This paper investigates optimal estimation for networked control systems composed of linear, time-variant, and multi-input multi-output (MIMO) plants that communicate with the controller over multiple independent erasure channels. In the controller-to-plant link, we cope with data losses by considering a networked predictive control strategy in which the controller not only sends the current control inputs to the actuators, but also sequences of predicted control inputs at every time step. These are stored in buffers located at the corresponding actuator side, with the aim of being used in case of data loss. Additionally, for losses occurring in the plant-to-controller link, we assume that the controller contains a data-dropout compensation mechanism capable of replacing the missing measurements with their optimal estimates. It is assumed that acknowledgment packages are sent from the plant to the controller, to indicate if the previous sequence was received or not. For this setup, we compute optimal time-varying estimates of the plant and buffer state, and for the lost measurements as well. Finally, we provide the optimal stationary estimator, which serves as a simpler alternative to the time-varying one.

Optimal filters with multiple packet losses and its application in wireless sensor networks.

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs.

Event-triggered state estimation for networked systems with correlated noises and packet losses.

The event-triggered state estimation for the systems suffering from correlated noises and packet losses is considered. A communication mechanism that determines the measurements to be sent or not depending on a specific event-triggered condition is presented to reduce the additional data transmissions. Then a novel event-triggered state estimator related to the trigger threshold and correlation coefficient is proposed. An expected trade-off between the rate of transmission and the estimator performance can be obtained through adjusting the threshold properly, and the influence of noise correlation and packet losses is weakened effectively. The estimator performance is evaluated and certain boundedness conditions for the covariance expectation are obtained. Finally, a target tracking system is supplied to support the relevant results.

Distributed synchronization control of complex networks with communication constraints.

This paper is concerned with the distributed synchronization control of complex networks with communication constraints. In this work, the controllers communicate with each other through the wireless network, acting as a controller network. Due to the constrained transmission power, techniques such as the packet size reduction and transmission rate reduction schemes are proposed which could help reduce communication load of the controller network. The packet dropout problem is also considered in the controller design since it is often encountered in networked control systems. We show that the closed-loop system can be modeled as a switched system with uncertainties and random variables. By resorting to the switched system approach and some stochastic system analysis method, a new sufficient condition is firstly proposed such that the exponential synchronization is guaranteed in the mean-square sense. The controller gains are determined by using the well-known cone complementarity linearization (CCL) algorithm. Finally, a simulation study is performed, which demonstrates the effectiveness of the proposed design algorithm.

Quantized stabilization of wireless networked control systems with packet losses.

This paper considers stabilization of discrete-time linear systems, where wireless networks exist for transmitting the sensor and controller information. Based on Markov jump systems, we show that the coarsest quantizer that stabilizes the WNCS is logarithmic in the sense of mean square quadratic stability and the stabilization of this system can be transformed into the robust stabilization of an equivalent uncertain system. Moreover, a method of optimal quantizer/controller design in terms of linear matrix inequality is presented. Finally, a numerical example is provided to illustrate the effectiveness of the developed theoretical results.