Loss Cumulant Generating Function Inference in Sensor Network

摘要：

The internal link performance inference has become an increasingly important issue in operating and evaluating network. Since it is usually impractical to directly monitor each node or link in the wireless sensor network, we consider the problem of inferring the internal link loss characteristics from passive end-to-end measurement in this paper. Specifically, the link loss performance inference during the data aggregation is considered. Under the assumptions that the link losses are mutually independent, we elaborate a bias corrected link loss cumulant generating function (CGF) algorithm. Through the simulation, we show that the internal link loss CGF can be inferred accurately, comparable to the sampled internal link loss CGF. At the end of this paper, we apply the result of internal link loss CGF inference to the lossy link identification in the sensor network.

关键词： sensor network tomography loss rate cumulant generating function data aggregation

作者: Yongjun Li Wandong Cai Guangli Tian Wei Wang

作者单位: School of Computer Science,Northwestern Polytechnical University,Xian 710072,China

会议类型: 国际会议

会议名称: 第二届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 武汉

会议语种:英文

在线出版日期: 2006-09-01（万方平台首次上网日期，不代表论文的发表时间）