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

Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial