Design Methods for High-Performance Sensor Networks

这项研究正在开发新的方法来设计高性能的传感器网络，这些网络在传感器网络中执行大量的计算。高带宽的信号处理应用，如分布式音频或视频处理，正在变得越来越普遍，在实现传感器网络的分布式系统中执行大量处理提出了新的设计挑战。一方面，高性能传感器网络就像分布式嵌入式系统，需要针对应用进行优化，以最大限度地降低功耗和成本，最大化性能；另一方面，它们就像自组织网络一样，需要能够在不从根本上增加网络运营成本的情况下运行在多种不同的配置中。需要新的设计方法和工具来创建具有弹性的体系结构，同时仍然利用特定于应用程序的特性。传统的分布式嵌入式系统设计算法和方法可以处理传感器网络的一些方面，即分布式计算和通信时延的影响，但它们的目的是选择一个好的设计。它们不能保证系统参数(节点数、通信、功率预算)的任何变化都会导致系统运行在任何接近最优的位置。通过正确选择节点和链路，以及高性能传感器网络中正确的拓扑和通信模式，网络的成本/功率/性能可以得到显著提高。高性能传感器网络的成功设计和部署需要设计时和运行时决策的结合，因为系统的某些特性直到运行时才能知道，而这些特性在网络运行过程中可能会发生变化。此外，在设计时可能不知道网络的配置。网络的整体硬件和软件架构必须设计为不仅在单个设计点，而且在一系列可能的配置和运营决策中都能正常运行。这项研究试图通过开发在设计空间中找到帕累托最优区域的新设计方法来平衡优化设计和在运行时调整其操作的需要。帕累托最优性已被用于设计小规模的嵌入式系统，但这项工作在以下几个方面进行了改进：处理较大的系统，低方差设计，运行时行为分析，以及纳入蒙特卡罗方法。广泛的影响：研究中开发的方法将直接使系统能够用于安全和各种工业应用。高性能的音频和视频在安全应用中尤其重要，分布式实时处理数据将有助于创建能够更快发现问题的安全系统。分布式嵌入式系统的设计工具、基准数据和讲座材料将通过网络分发。

Wolf AbstractThis research is developing new methods for the design of high-performance sensor networks that perform significant amounts of computation within the sensor network. High-bandwidth signal processing applications, such as distributed audio or video processing, are becoming increasingly common, and performing large amounts of processing in the distributed system that implements the sensor network poses new design challenges. On the one hand, high-performance sensor networks are like distributed embedded systems, which need to be optimized to the application in order to minimize power consumption and cost and maximize performance; on the other hand, they are like ad-hoc networks that need to be able to be operate in a number of different configurations without radically increasing the cost of operating the network. New design methodologies and tools are needed that can create a resilient architecture that still takes advantage of application-specific characteristics. Traditional algorithms and methodologies for designing distributed embedded systems can handle some of the aspects of sensor networks, namely distributed computation and the effects of communication delay, but their purpose is to select a single good design. They provide no guarantees that any changes in the system parameters (number of nodes, communication, power budget) will result in a system that operates anywhere close to optimality.With the proper choice of nodes and links, and the right topology and communication patterns within the high-performance sensor network, the cost/power/performance of the network can be substantially improved. A combination of design-time and run-time decisions is required for the successful design and deployment of high-performance sensor networks because certain characteristics of the system will not be known until run time, and those characteristics may change during operation of the network. Furthermore, the configuration of the network may not be known at design time. The overall hardware and software architecture of the network must be designed to operate well not just at a single design point, but across a range of possible configurations and operating decisions. This research seeks to balance the needs for optimizing the design and adapting its operation at run time by developing new design methods that find pareto-optimal regions in the design space. Pareto-optimality has been used to design small-scale embedded systems, but this work improves on previous efforts in several ways: handling larger systems, lower-variance design; analysis of run-time behavior; and inclusion of Monte-Carlo methods.Broader impacts: The methods developed in the research will directly enablesystems for security and a variety of industrial applications. High-performance audio and video are especially important in security applications and distributed real-time processing of data will help to create security systems that can identify problems more quickly. Design tools, benchmark data, and lecture materials on distributed embedded systemswill be distributed over the web.