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如何优化环境传感使其有助于解决网络上的信息过载

How Optimized Environmental Sensing Helps Address Information Overload on the Web
课程网址: http://videolectures.net/ijcai09_guestrin_iow/  
主讲教师: Tom Mitchell; Carlos Guestrin
开课单位: 卡内基梅隆大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
在这篇演讲中,我们解决了一个基本问题,即当我们使用传感器来监测河流和湖泊的生态状况、给水龙头供水的管道网络,或者坐在椅子上的老年人的活动时,我们应该把传感器放在哪里,以便做出有效和可靠的预测?这种传感问题通常是NP困难的,在过去,经常使用没有理论保证解质量的启发式方法。在本文中,我们提出了有效地找到大型复杂传感问题近似最优解的算法。我们的算法基于许多重要的传感问题表现出的亚模性这一关键洞察力,这是一种直观的收益递减特性:增加一个传感器有助于减少我们目前放置的传感器数量。除了确定放置传感器的信息最丰富的位置外,我们的算法还可以处理设置,在这些设置中,传感器节点需要能够通过有损链路可靠地通信,在这些设置中,移动机器人用于收集数据,或者解决方案需要能够抵御对手和传感器故障。我们介绍了将我们的算法应用于几种现实传感任务的结果,包括使用机器人传感器的环境监测、使用内置传感椅的活动识别以及传感器放置竞赛。最后,我们得出了一个有趣的结论,即在水监测传感器布置和解决网络信息过载的挑战之间建立了联系。作为这种联系的例子,我们解决了选择要阅读的博客以了解网络上讨论的最大的故事,以及个性化内容以降低博客圈中的噪音的问题。
课程简介: In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: Where should we place the sensors in order to make effective and robust predictions? Such sensing problems are typically NP-hard, and in the past, heuristics without theoretical guarantees about the solution quality have often been used. In this talk, we present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms are based on the key insight that many important sensing problems exhibit submodularity, an intuitive diminishing returns property: Adding a sensor helps more the fewer sensors we have placed so far. In addition to identifying most informative locations for placing sensors, our algorithms can handle settings, where sensor nodes need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries and sensor failures. We present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, and a sensor placement competition. We conclude with drawing an interesting connection between sensor placement for water monitoring and addressing the challenges of information overload on the web. As examples of this connection, we address the problem of selecting blogs to read in order to learn about the biggest stories discussed on the web, and personalizing content to turn down the noise in the blogosphere.
关 键 词: 生态条件; 收益递减; 传感器节点; 信息过载
课程来源: 视频讲座网
最后编审: 2019-12-12:cwx
阅读次数: 57