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Distributed-in/ distributed-out sensor networks : a new framework to analyze distributed phenomena

机译:分布式/分布式传感器网络:分析分布式现象的新框架

摘要

With a new way of thinking about organizing sensor networks, we demonstrate that we can more easily deploy and program these networks to solve a variety of different problems. We describe sensor networks that can analyze and actuate distributed phenomena without a central coordinator. Previous implementations of sensor networks have approached the problem from the perspective of centralized reporting of distributed events. By contrast, we create a system that allows users to infer the global state from within the sensor network itself, rather than by accessing an outside, central middleware layer. This is accomplished via dynamic creation of clusters of nodes based on application or intent, rather than proximity. The data collected and returned by these clusters is returned directly to the inquirer at his current location. By creating this Distributed-in/Distributed-out (DiDo) system that bypasses a middleware layer, our networks have the principal advantage of being easily configurable and deployable. We show that a system with this structure can solve path problems in a random graph. These graph problems are directly applicable to real-life applications such as discovering escape routes for people in a building with changing pathways. We show that the system is scalable, as reconfiguration requires only local communication.
机译:通过一种新的思维方式来组织传感器网络,我们证明了我们可以更轻松地部署和编程这些网络,以解决各种不同的问题。我们描述了无需中央协调器就可以分析和激活分布式现象的传感器网络。传感器网络的先前实现已从集中报告分布式事件的角度解决了该问题。相比之下,我们创建了一个系统,该系统允许用户从传感器网络本身内部推断全局状态,而不是通过访问外部中央中间件层进行推断。这是通过根据应用程序或意图(而不是基于邻近性)动态创建节点集群来实现的。这些聚类收集并返回的数据将直接返回到查询者当前所在的位置。通过创建绕过中间件层的分布式/分布式(DiDo)系统,我们的网络的主要优势在于易于配置和部署。我们证明具有这种结构的系统可以解决随机图中的路径问题。这些图形问题直接适用于现实生活中的应用,例如为路径变化的建筑物中的人们发现逃生路线。我们展示了该系统是可伸缩的,因为重新配置仅需要本地通信。

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