Scalable scheduling and transmission for sensor systems

传感器网络的一个很好的原型是一种由大量独立设备组成的技术，这些设备通过通信，可以对它们监测的区域内发生的事件达成一致，与它们的大小和通信范围相比，这是一个很大的区域。这个项目有三个主要的研究重点，将有助于建立这样的基础设施：1)传感器传输的调度；2)同步3)物理层传输体系结构。我们提出的想法是分布式的，合作的和数据驱动的，并挑战了一些传统的智慧，用于建设通信网络今天忽视了问题的基本方面，如：1)传感器不是最终用户；2)传感器在传递关键信息时可以相互合作，而不是争夺媒介；3)数据在空间和时间上的总熵/复杂度较低。事实上，传感器网络的部署是为了记录意外的分散事件、突然但局部的变化、不一致或不连续性，这些事件很少发生。目前用于解决这一问题的网络技术在一个基本方面失败了：可扩展性。节点之间的交互在许多分层功能中被大量结构化。可扩展性将源于大量设备之间的交互简化，并将其操作和消息交换限制为通过广播，嘈杂，有限范围的接口。本研究最原始的贡献是找到了一类调度策略，这些策略在这些限制范围内运行，并由传感器的数据驱动，实现分布式源的有效传输，而不需要它们之间的任何事先交换数据。

A good archetype for a sensor network is that of a technology that consists of large set of independent devices that, through communications, can reach consensus on the fly on events that occur in an area they monitor, a region, which is large, compared to their size and communication range. This project has three main research thrusts that will contribute to build such infrastructure: 1) the scheduling of the sensor transmissions; 2) the synchronization 3) the physical layer transmission architecture. The ideas we propose are distributed, cooperative and data driven and challenge some of the conventional wisdom used in building communication networks today which neglect fundamental aspects of the problem, such as: 1) the sensors are not end users; 2) the sensors can cooperate rather than contend for the medium to deliver critical information; 3) the aggregate entropy/complexity of the data in space and time is low. In fact, sensor networks are deployed to record unexpected dispersed events, sudden but localized changes, inconsistencies or discontinuities that occur rarely. Current network technology applied to this problem fails in one fundamental aspect: the scalability. The interactions among nodes are heavily structured in a number of layered functionalities.Scalability will result from un-complicating the interactions between thelarge population of devices and constraining their operations and message exchange to be through a broadcast, noisy, limited range interface. The most original contribution of this research is in finding a class of scheduling strategies that operate within these restrictions and is driven by the sensors' data, achieving efficient transmission from the distributed sources without requiring any prior exchange of data among them.