H_∞ State Feedback Control for A Class of Singular Switched Linear Systems

研究了一类不确定切换广义系统的H∞状态反馈控制问题,以线性矩阵不等式(lMI)的形式给出了该类系统的鲁棒稳定且具有H∞扰动衰减度γ充分条件和控制器以及切换策略。将正常系统的lyAPunOV函数研究方法推广到切换广义系统。H∞ state feedback control problem is investigated for a class of singular switched linear systems.a sufficient condition on decrease de-gree of γ for the existence of subcontrollers that is expressed by linear matrix inequalit(LMI) is presented under an appropriate switching law.福建省青年科技人才创新项目资金(2005J006);厦门大学985二期信息创新平台项

Active node determination for correlated data gathering in wireless sensor networks

Cataloged from PDF version of article.In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. Given that the sensor nodes that are selected as active form a connected wireless network, the inactive sensor nodes can be turned off. Allowing some sensor nodes to be active and some sensor nodes inactive interchangably during the lifecycle of the application helps the wireless sensor network to have a longer lifetime. The problem of determining a set of active sensor nodes in a correlated data environment for a fully operational wireless sensor network can be formulated as an instance of the connected correlation-dominating set problem. In this work, our contribution is twofold; we propose an effective and runtime-efficient iterative improvement heuristic to solve the active sensor node determination problem, and a benefit function that aims to minimize the number of active sensor nodes while maximizing the residual energy levels of the selected active sensor nodes. Extensive simulations we performed show that the proposed approach achieves a good performance in terms of both network lifetime and runtime efficiency. © 2012 Elsevier B.V. All rights reserved

Routing correlated data with fusion cost in wireless sensor networks

In this paper, we propose a routing algorithm called Minimum Fusion Steiner Tree (MFST), for energy efficient data gathering with aggregation (fusion) in wireless sensor networks. Different from existing schemes, MFST not only optimizes over the data transmission cost, but also incorporates the cost for data fusion which can be significant for emerging sensor networks with vectorial data and/or security requirements. By employing a randomized algorithm that allows fusion points to be chosen according to the nodes ’ data amount, MFST achieves an approximation ratio of 5 4 log(k + 1), where k denotes the number of source nodes, to the optimal solution for extremely general system setups provided that fusion cost and data aggregation are non-decreasing against the total input data. Consequently, in contrast to algorithms that only excel in full or non-aggregation scenarios without considering fusion cost, MFST can thrive in a wide range of applications