Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks

Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Dynamic optimization of the quality of experience during mobile video watching

Mobile video consumption through streaming is becoming increasingly popular. The video parameters for an optimal quality are often automatically determined based on device and network conditions. Current mobile video services typically decide on these parameters before starting the video streaming and stick to these parameters during video playback. However in a mobile environment, conditions may change significantly during video playback. Therefore, this paper proposes a dynamic optimization of the quality taking into account real-time data regarding network, device, and user movement during video playback. The optimization method is able to change the video quality level during playback if changing conditions require this. Through a user test, the dynamic optimization is compared with a traditional, static, quality optimization method. The results showed that our optimization can improve the perceived playback and video quality, especially under varying network conditions