Distributed Composition Services for Self-Organizing Sensor Network Applications

可以使用分布式组合服务简化自组织传感器网络应用程序的开发，分布式组合服务与分布式自适应和查找服务合作，使传感器节点能够自我感知、自配置并响应实时变化。本研究的重点是开发分布式组合服务的新概念、体系结构和机制，支持自组织应用程序的持续运行，以满足随需应变的需求，尽管存在临时部署、频繁的节点故障、动态重新配置和移动性。自组织服务的效用将在用于检测、分类和跟踪移动物体以及分布式传感器执行器控制的协同传感器融合应用中得到展示。该试验台是一个由传感器节点组成的自组织网络，配备了嵌入式处理器、射频通信、多模传感器和执行器。传感器节点被增强为可重构智能组件，具有即时联网、自组装、动态适应设备故障和退化、节点移动性以及任务和网络需求变化的能力。他们使用的创新技术包括：(1)自组织传感器应用程序的服务和机制，(2)可扩展的分层组合服务，(3)轻量级可重构通信机制，以及(4)在基于事件的以数据为中心的网络上实现分布式服务。本研究的广泛影响包括为研究生和本科生（包括少数民族学生）的教育和研究培训开发高级和入门课程课程以及创新的传感器网络测试平台和实验室。

Development of self-organizing sensor network applications can be simplified with distributed composition services that cooperate with distributed adaptation and lookup services to enable sensor nodes to be self-aware, self-configurable, and responsive to real-time changes. This research focuses on developing novel concepts, architecture and mechanisms for distributed composition services that support continuous operation of self-organizing applications to meet on-demand needs in spite of ad-hoc deployment, frequent node failures, dynamic reconfiguration, and mobility. The utility of the self-organizing services will be demonstrated in collaborative sensor fusion applications for detecting, classifying and tracking moving objects and distributed sensor-actuator control. The testbed is an ad-hoc network of sensor nodes that are equipped with embedded processors, RF communication, multi-mode sensors and actuators. Sensors nodes are augmented as reconfigurable smart components with capabilities for impromptu networking, self-assembly, dynamically adaptation to device failure and degradation, node mobility, and changes in task and network requirements. They use innovative techniques including: (1) Services and mechanisms for self-organizing sensor applications, (2) scalable hierarchical composition services, (3) lightweight reconfigurable communication mechanisms, and (4) implementation of distributed services over event-based data-centric networks. Broader impacts of this research include the development of advanced and introductory course curriculum and innovative sensor network testbeds and laboratories for education and research training of graduate and undergraduate students including minority students.