CPS: Small: Programming Environment and Architecture for Situational Awareness and Response

CPS：小型：用于态势感知和响应的编程环境和体系结构本研究的目的是调查和实现一种软件体系结构，以提高快速部署、健壮、实时态势感知和响应应用程序开发的生产力。该方法基于模块化的跨层体系结构，该体系结构将以数据为中心的描述性编程模型与基于覆盖的通信模型相结合。跨层架构将促进高效的实现。同时，以数据为中心的编程模型和基于覆盖的通信模型将促进健壮的实现，该实现可以利用异构资源并响应不同的故障。目前还没有能够满足许多态势感知和响应应用程序严格要求的高级软件体系结构。提议的项目将通过开发一种新颖的以数据为中心的编程模型来填补这一空白，该模型跨越具有不同计算和通信能力的设备。类似地，覆盖通信模型将通过将网络资源与编程模型集成来扩展现有的工作。这种跨层设计将促进高效和健壮的应用程序的实现。这项研究将通过为应急人员提供及时呈现关键信息的软件工具而造福社会。这反过来又将增加安全，减少紧急情况下的经济和人员损失。部署传感器和移动设备的生产力提高将有利于其他领域，例如使用传感器网络的实地研究。软件将在开源许可下发布，以促进政府机构、研究机构和个人的使用。这项研究的产品，包括软件，将在北卡罗来纳大学的课程中使用。

CPS: Small: Programming Environment and Architecture for Situational Awareness and ResponseThe objective of this research is to investigate and implement a software architecture to improve productivity in the development of rapidly deployable, robust, real-time situational awareness and response applications. The approach is based on a modular cross-layered architecture that combines a data-centric descriptive programming model with an overlay-based communication model. The cross-layer architecture will promote an efficient implementation. Simultaneously, the data-centric programming model and overlay-based communication model will promote a robust implementation that can take advantage of heterogeneous resources and respond to different failures.There is currently no high-level software architecture that meets the stringent requirements of many situational awareness and response applications. The proposed project will fill this void by developing a novel data-centric programming model that spans devices with varying computational and communication capabilities. Similarly, the overlay communication model will extend existing work by integrating network resources with the programming model. This cross-layer design will promote the implementation of efficient and robust applications. This research will benefit society by providing emergency responders with software tools that present key information in a timely fashion. This, in turn, will increase safety and reduce economic and human loss during emergencies. The productivity gains in deploying sensors and mobile devices will benefit other domains, such as field research using sensor networks. Software will be released under an open-source license to promote the use by government agencies, research institutions, and individuals. Products of this research, including the software, will be used in courses at the Universityof North Carolina.